Boucard - Neuroimaging of visual field defects › research › portal › files › 14548089 › 06_thesis.pdf · Visual field defects provide a unique opportunity to examine how

University of Groningen

Neuro-imaging of visual field defectsBoucard, Christine

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.

Document VersionPublisher's PDF, also known as Version of record

Publication date:2006

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):Boucard, C. (2006). Neuro-imaging of visual field defects. s.n.

CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.

Download date: 16-06-2020

https://www.rug.nl/research/portal/en/publications/neuroimaging-of-visual-field-defects(ff139874-609a-4c4e-8481-7acc43bee007).html

https://www.rug.nl/research/portal/en/publications/neuroimaging-of-visual-field-defects(ff139874-609a-4c4e-8481-7acc43bee007).html

Neuro-Imaging of

Visual Field Defects

C.C. Boucard

Paranimfen: Sonja Tomašković

Janja Plazar

Illustrations: Frenk Plazar, Janja Plazar, Alex Sierra, Primož Pirih, and Joyce!

Cover illustration: Alex Sierra Hernández ([email protected])

Financial support for the publication of this thesis was provided by:

- Prof. Mulder Stichting (NL)

- University of Groningen (RUG) and the Faculty of Medicine of the RUG

- Graduate School of Behavioural and Cognitive Neuroscience (BCN)

Printed and bound in Italy by Printer Trento, srl

© 2006 C.C. Boucard

Publisher: Bibliotheek der RUG

ISBN number: 90-367-2621-2

ISBN electronic version: 90-367-2620-4

RIJKSUNIVERSITEIT GRONINGEN

Neuro-Imaging of Visual Field Defects

Proefschrift

ter verkrijging van het doctoraat in de

Medische Wetenschappen

aan de Rijksuniversiteit Groningen

op gezag van de

Rector Magnificus, dr. F. Zwarts,

in het openbaar te verdedigen op

maandag 19 juni 2006

om 16.15 uur

door

Christine Boucard

geboren op 10 februari 1972

te Barcelona

Promotores: Prof. Dr. J.M.M. Hooymans

Prof. Dr. A.C. Kooijman

Copromotor: Dr. F.W. Cornelissen

Beoordelingscommissie: Prof. Dr. P. van Dijk

Prof. Dr. R.J.W. de Keizer

Prof. A. B. Safran, MD

ISBN: 90-367-2621-2

“If these be vague words, then seek not to clear them. Vague and nebulous is

the beginning of all things, but not their end, and I fain would have you

remember me as a beginning. Life, and all that lives, is conceived in the mist

and not in the crystal. And who knows but a crystal is mist in decay?”

Kahlil Gibran, the prophet.

A la Liberté d’interprétation

Pour Mamie

Per la Nonna

1

Table of contents

Table of contents ................................................................................................ 1

List of abbreviations ............................................................................................ 5

Author affiliations ................................................................................................ 7

SECTION 1 - INTRODUCTION

1.1. Background ....................................................................................... 13

1.2. Outline of the thesis .......................................................................... 14

1.3. Visual field defects ............................................................................ 15

1.3.1. The normal visual field

1.3.2. Measuring the visual field

1.3.3. Age-related macular degeneration

1.3.4. Glaucoma

1.4. The visual brain ................................................................................ 21

1.4.1. Retina and visual pathways

1.4.2. Functional areas in visual cortex

1.4.3. Primary visual cortex organisation

1.5. Visual Field Defects and the brain .................................................... 29

1.5.1. Plasticity in the brain

1.5.2. The filling-in phenomenon

1.6. Neuro-imaging .................................................................................. 32

1.6.1. Anatomical Magnetic Resonance Imagining

1.6.2. Functional Magnetic Resonance Imagining

1.6.3. Magnetic Resonance Spectroscopy

1.7. References ........................................................................................ 38

2

SECTION 2 – EXPERIMENTAL RESEARCH

Chapter 1 ............................................................................................................ 45

Visual field defects and the structural brain – part I

Occipital grey matter changes in retinal visual field defects in humans

(submitted)

Chapter 2 ............................................................................................................ 59

Visual field defects and the structural brain – part II

Cortical thickness and visual field defects (submitted)

Chapter 3 ............................................................................................................ 77

Visual field defects and the functional brain

Reorganisation in visual cortex associated with visual field defects?

Chapter 4 ............................................................................................................ 95

Visual field defects and the metabolic brain – part I

Occipital 1H-MRS reveals normal metabolite concentrations in retinal visual

field defects (submitted)

Chapter 5 ............................................................................................................ 107

Visual field defects and the metabolic brain – part II

Visual stimulation, 1H-MR Spectroscopy and fMRI of the human visual pathways

(published in: European Radiology 2005 Jan;15(1):47-52)

Chapter 6 ............................................................................................................ 121

Exploring activity in the visual brain under no physical stimulation

fMRI of brightness induction in human visual cortex

(published in: Neuroreport 2005 Aug 22;16(12):1335-8)

3

SECTION 3: CONCLUSION

3.1. Summary of results ........................................................................ 137

3.2. General discussion ........................................................................ 139

3.3. Future perspective ......................................................................... 141

References .......................................................................................................... 142

Samenvatting ...................................................................................................... 145

Acknowledgements ............................................................................................. 148

4

5

List of abbreviations

1H-MRS Proton Magnetic Resonance Spectroscopy

AMD Age-related Macular Degeneration

aMRI Anatomical Magnetic Resonance Imaging

BOLD Blood Oxygen Level-Dependent

CBS Charles Bonnet Syndrome

CHESS Chemical Shift Selective Excitation

Cho Choline

Cr Creatine

CSF Cerebral Spinal Fluid

CSI Chemical Shift Imaging

EEG Electroencephalography

EPI Echo Planar Imaging

FDR False Discovery Rate

fMRI Functional Magnetic Resonance Imaging

FOV Field of View

FWHM Full Width at Half Maximum

Glu Glutamate

GM Grey Matter

HFA Humphrey Field Analyzer

IOP Intraocular Pressure

IT Inferotemporal

Lac Lactate

LGN Lateral Geniculate Nucleus

MAP Multiple Angle Projection

MAR Minimum Angle of Resolution

MD Mean Deviation

MEG Magnetoencephalography

6

MNI Montreal Neurological Institute

MRI Magnetic Resonance Imaging

MRS Magnetic Resonance Spectroscopy

MT Medial Temporal

NAA N-Acetyl Aspartate

PET Positron emission tomography

POAG Primary Open Angle Glaucoma

PPC Posterior Parietal Cortex

PRL Preferred Retinal Locus

RGC Retinal Ganglion Cell

ROI Regio Of Interest

RPE Retinal Pigmented Epithelium

SPM Statistical Parametric Mapping

TR Repetition Time

VBM Voxel-Based Morphometry

VISTA Vision Science and Technology Activities

VOI Volume Of Interest

WM White Matter

7

Author affiliations

Christine C. Boucard Laboratory for Experimental Ophthalmology

University Medical Center Groningen, The Netherlands

BCN Neuro-imaging Centre

University of Groningen, The Netherlands

Frans W. Cornelissen Laboratory for Experimental Ophthalmology




Bruce Fischl Department of Radiology MGH

Athinoula A Martinos Center

Harvard Medical School

Charlestown, MA, United States

Johannes M. Hoogduin BCN Neuro-imaging Centre


8

Johanna M.M. Hooymans Department of Ophthalmology


Nomdo M. Jansonius Department of Ophthalmology

University Medical Center Groningen,, The Netherlands

Jacques H.A. de Keyser Department of Neurology


R. Paul Maguire BCN Neuro-imaging Centre


Jop P. Mostert Department of Neurology


Matthijs Oudkerk Department of Radiology


9

Brian T. Quinn Department of Radiology MGH

Athinoula A Martinos Center

Harvard Medical School

Charlestown, MA, United States

Jos B.T.M. Roerdink Institute for Mathematics and Computing Science


Paul E. Sijens Department of Radiology


Jeroen van der Grond Department of Radiology

University Medical Center Leiden, The Netherlands

Just J. van Es Laboratory for Experimental Ophthalmology




SECTION 1:

INTRODUCTION

12 Introduction

13

1.1. Background

The two leading causes of visual impairment in the developed world, age-related

macular degeneration (AMD) and glaucoma, are associated with acquired retinal visual

field defects [1]. Such defects are regions of the retina that are blind, or have reduced

visual acuity and a reduced sensitivity to light. If these field defects occur in both eyes

and overlap, a section of the visual cortex no longer receives stimulation. The main

question we ask in this thesis is this: when such a visual field defect occurs, what

happens to the part of the visual cortex representing the damaged area of the retina? It

is known that non-working cortical tissue degenerates or reorganises. What will be the

fate of the grey matter lacking direct retinal stimulation?

The aim of the work in this thesis is to learn more about the consequences of these

retinal visual field defects on the visual cortex. Using neuro-imaging techniques, we

investigate their structural, metabolic and functional consequences in the brain. We

hypothesise that, as a consequence of the acquired visual field defects, the cortex

would either degenerate or reorganise. We further hypothesise that the occurrence of

either of these processes is related to the extent of retinal ganglion cell (RGC) and optic

nerve damage. The type of visual field defects studied in this thesis can test this. While

glaucoma involves RGCs and optic nerve damage, in AMD the RGCs and optic nerve

remain intact. Our hypothesis is that RGC damage may induce cortical degeneration,

while reorganisation may occur with intact RGCs.

In addition to gaining a better understanding of the consequences of retinal visual field

defects on the visual cortex, the present work also aims to contribute to basic

neuroscience. Visual field defects provide a unique opportunity to examine how human

visual cortex responds to abnormal visual experience. Moreover, in normal subjects, we

study the neural basis of filling-in, a phenomenon also often associated with visual field

defects.

14 Introduction

1.2. Outline of the thesis

In the remainder of this introduction, an overview of important concepts and background

knowledge for understanding the work presented in the experimental research chapters

will be described. In Section 1.3, visual fields and field defects are described. In Section

1.4, an introduction to the visual pathways and retinotopic organisation is provided.

Section 1.5 introduces the theoretical background for the hypothesis regarding the

influence of retinal field defects on the visual cortex. Finally, Section 1.6 describes the

neuro-imaging techniques used in the experiments.

The experimental work of the thesis is presented in detail in Section 2.

Within the conclusion, Section 3.1 provides a brief summary of the results presented in

the experimental chapters. A general discussion follows in Section 3.2, leading to the

final Section, 3.3, where an outlook for future research is presented.

15

1.3. Visual field defects

Two leading causes of visual impairment in the developed world, AMD and glaucoma

[1], are associated with the occurrence of retinal visual field defects.

A visual field defect is an area or island of loss of visual acuity, surrounded by a field of

normal or relatively well-preserved vision. Visual field defects may be due to a wide

range of disease processes affecting the retina, visual pathways or visual cortex. In this

thesis, we investigate visual field defects that originate in the retina.

1.3.1. The normal visual field

One definition of visual field is “the extent of space in which objects are visible to an eye

in a given position” [2].

The binocular visual field extends horizontally for 180º, while monocular vision extends

120º.

Eccentricity has a dramatic influence on visual acuity. Retinal sensitivity, and therefore

visual acuity, decreases proportionally in relation to the distance of a photoreceptor from

the fovea. The visual scene falling in the peripheral field of view lacks detailed

information. Instead, the highest resolution is contained in the central part, where details

are easy to perceive. Therefore, when exploring an environment, the eyes constantly

move towards the object of interest, in order to get the maximal information.

Everybody has a scotoma. Within the visual field of each

eye, there is an area of complete blindness. The so-

called “blind spot” is due to a lack of photoreceptors in

the location where the axons of the RGCs leave the retina to form the optic nerve. It is

typically located 15º temporally and 1-2º inferiorly to the fovea, and its size is

approximately 5º horizontally and 7º vertically. The fact that a large central portion of the

16 Introduction

field of view is common to both eyes, in addition to the contribution of the brain in filling-

in the gap, ensure that the “blind spot” remains unnoticed.

1.3.2. Measuring the visual field

The determination of the extent of the visual field is called perimetry. It is a quantitative

examination of the visual acuity along the visual field. It usually has the purpose of

detecting anomalies in the visual system.

Many methods have been developed to assess the extent of the visual field. In this

thesis, we used the Humphrey Field Analyzer (HFA; Carl Zeiss Meditec, Dublin,

California, USA; Fig. 1), an instrument for the automated evaluation of the visual field.

Fig. 1. Humphrey Field Analyzer. (source http://www.augenchirurgie.at)

During the examination, the subject covers one eye, and with the other eye fixates a

central light located in the middle of the inside of a sphere (Fig. 1). Small flashes of light

of varying intensities are presented at various locations throughout the sphere and thus,

the visual field. The subject is requested to react to each flash that (s)he perceives by

pushing a button. The intensity of the flash is adapted according to the response of the

subject. This allows the device to map the visual sensitivity within the entire visual field.

17

In this manner, an area showing sensitivity significantly below the corresponding

estimated normal value can prove the existence of a visual field defect (Fig. 2). In the

measurements, sensitivity is expressed as a deviation in sensitivity from the norm. The

values are thus compared to the average sensitivity in the age group of the subject. One

final value is the Mean Deviation (MD). This is a number expressing the average visual

field sensitivity of the subject in decibels (dB). In chapters 1 and 2 of this thesis, this

number was correlated with changes in cortical structure.

Fig. 2. Example of a perimetry analysis performed by HFA. Dark areas indicate low sensitivity.

1.3.3. Age-related macular degeneration

Age-related macular degeneration (AMD) occurs when the photoreceptors of the central

part of the retina, called macula, deteriorate as a result of the degeneration occurring

within the macular retinal pigmented epithelium (RPE). AMD is divided into "dry" or non-

18 Introduction

exudative (with no subretinal choriodal neovascularisation), and "wet" or exudative (with

subretinal choriodal neovascularisation) forms [3,4] (Fig. 3).

Fig. 3. Illustration of ocular damage in AMD. (source: www.ahaf.org)

In the "dry" type, (85%-90% of the cases), abnormal waste material, known as drusen,

accumulates underneath the macula between the RPE and Bruch’s membrane, which

supports the retina. This interferes with the normal metabolism of the retina, and causes

its atrophy [5,6]. This form of AMD is less severe, producing gradual loss of central

vision. Until recently, there had been no effective treatment for this, except for nutritional

supplements, low vision training, or aid devices to improve quality of life.

The “wet” type (10%-15% of the cases) involves abnormal growth of blood vessels

inward from the choroids (the layer containing blood vessels that nourish the retina),

which penetrates the Bruch’s membrane. Consequent leakage of sero-sanguinous fluid

provokes detachment of the RPE. The consequence is a severe and rapidly progressive

loss of central vision. Treatment includes the use of laser photocoagulation to control

new blood vessel formation. However, the effect is often only temporary, and requires

repeated therapy.

The majority of the subjects in the AMD group who participated in our studies were

afflicted with the ”dry” type of AMD.

19

Since the highest visual acuity is located in the centre of the visual field, the eye affected

by AMD loses its ability to see details such as facial features or small objects, which

provokes important visual impairment (Fig. 4). The peripheral field of view is usually

spared, but can also be affected in advanced stages of the disease.

Fig. 4. Example of vision with AMD. (source: www.nih.gov)

After cataract and glaucoma, AMD is the third leading cause of visual impairment in the

developed world [1]. According to the Bulletin of the World Health Organization 2004,

8.7% of the cases of blindness in the world are caused by AMD. The prevalence in

developed countries is approximately 1.7% to 1.9%. This increases significantly with

age, affecting 7.8% of persons 75 years or older [7].

For this thesis, an important characteristic of AMD is that while the photoreceptor layer

degenerates, the RGCs remain intact.

1.3.4. Glaucoma

Glaucoma is a frequently inherited disease of the RGCs, which is accompanied by

degeneration of the optic nerve. Damage in glaucoma is related to, but not exclusively

caused by, an elevated or unstable intraocular pressure (IOP).

Aqueous humour is produced by the ciliary body and flows through the pupil into the

anterior chamber. The humour is further drained by the trabecular meshwork into the

venous system through a canal (Schlemm's canal). In case of obstruction of the

20 Introduction

drainage canals, the elevated pressure may not be sustained without damaging RCGs,

and consequently the optic nerve (Fig. 5). However, other factors, such as blood flow

malfunction in the head of the optic nerve, can interact with IOP to affect the optic nerve.

Fig. 5. Illustration of ocular damage in glaucoma. (source: www.ahaf.org)

The most common type is primary open angle glaucoma (POAG). Approximately 1 in

200 individuals over the age of 40 is affected by POAG. Due to the gradual loss of

vision, the disease may not be diagnosed for some time. In the beginning, the vision

loss is paracentral, then nasal, and after that, peripheral, presenting an arc-shaped

defect (Fig. 6). In most cases, only one hemifield is affected, allowing for relatively good

visual acuity. If untreated, glaucoma may eventually affect central vision, and in turn,

visual acuity. The disease may progress to blindness. Visual loss is irreversible, but can

be prevented or decelerated by treatment. Although IOP is only one of the possible

causes of glaucoma, decreasing it via pharmaceuticals or surgery (trabeculectomy) is

currently the only available treatment.

21

Fig. 6. Example of vision with glaucoma. (source: www.nih.gov)

The more unusual type of the disease is primary acute angle-closure glaucoma.

Approximately 1 in 1000 individuals over the age of 40 develops primary acute angle-

closure glaucoma. This form is characterised by an acute rise of the IOP. In susceptible

eyes, the peripheral iris may block the trabecular meshwork during pupil dilatation,

preventing the flow of fluid. Subsequent visual loss will occur within a very short time.

Acute angle-closure glaucoma can be painful.

The subjects in the glaucoma group who participated in our studies were afflicted with

POAG. The field defect was binocular and with at least 10º homonymous scotoma

located centrally in at least one quadrant.

After cataract, glaucoma is the second leading cause of visual impairment in the

developed world [1]. According to the Bulletin of the World Health Organization 2004,

12.3% of the cases of blindness in the world are caused by glaucoma.

For this thesis, an important characteristic of glaucoma is that both the photoreceptor

and RGC layers, as well as optic nerve fibers, degenerate.

1.4. The visual brain

The visual system is the part of the nervous system that allows organisms to see. In the

human, the visual pathways originate in the retina of the eye, and project to the visual

22 Introduction

cortex via the optic nerve, optic chiasm, optic tract, lateral geniculate nucleus (LGN),

and optic radiations (Fig. 7).

Fig. 7. Visual pathways: from retina to visual cortex.

A number of retinal projections travel to subcortical structures, such as the

suprachiasmatic nucleus in the hypothalamus, which contributes to the generation of

circadian rhythms; the pretectal area, which is responsible for the pupillary reflex; and

the superior colliculus, which controls saccadic eye movements and hand-eye

coordination. The focus of this thesis is the pathway to early, and in particular primary,

visual cortex.

1.4.1. Retina and visual pathways

Visual information enters the eye through the cornea, passes through the pupil, and

reaches the lens where it is inverted and projected onto the retina (Fig. 8).

23

Fig. 8. Schematic anatomy of the eye. (source: http://en.wikipedia.org/wiki/Eye)

When light reaches the retina, it is absorbed by the photopigments of the photoreceptors

(rods and cones), and transformed into electrical signals through a process known as

phototransduction.

Rods and cones are located in the outermost layer of the retina (the one farthest from

the incoming light) (Fig. 9). In the middle layer, bipolar interneurons propagate impulses

received from the photoreceptors to the RGCs. The inner layer (the one closest to the

incoming light) contains the RGCs whose axons constitute the optic nerve. In addition,

horizontal and amacrine cells transmit information from a neuron in one layer to

adjacent neurons in the same layer. This intricate organisation results in complex

receptive fields.

24 Introduction

Fig. 9 Simple diagram of the organisation of the retina. (source: webvision.med.utah.edu)

The three types of cones react to different wavelength sensitivity, which are long (red

light), medium (green light), or short (blue light). Consequently, they constitute the basis

of colour perception. Unlike rods, cones work best in lighted conditions. On the other

hand, rods are highly sensitive photoreceptors, enabling vision at low levels of light, at,

for example, nighttime.

Cones are primarily found in the centre of the retina, while rods occupy the periphery.

Rods and cones are not equally distributed throughout the retina (Fig. 10).

Fig. 10. Cones and rods distribution in the retina. (source: Osterberg, 1935)

25

The highest density of cones is situated in the fovea, which makes it the point of highest

acuity in the eye. Away from the fovea, cone density decreases sharply. The density of

rods increases, and finally declines in the most peripheral part of the retina. In addition

to this, approximately 15º temporally and 1-2º inferiorly to the fovea, there is a region of

about 5º horizontally and 7º vertically, where there is a complete lack of photoreceptors.

This corresponds to the location of the optic disc (also called the “blind spot”), where the

axons of the ganglion cells leave the retina.

There are approximately 105 million photoreceptors and 1.2 to 1.5 million RGCs in the

human retina. This means that on average, each RGC receives input from

approximately 100 photoreceptors. However, as mentioned previously, the number of

photoreceptors varies with retinal eccentricity. As a result of this, in the fovea, a single

photoreceptor can communicate with even five RGCs, while in the extreme peripheral

retina, a unique ganglion cell can receive information from numerous photoreceptors.

RGCs can be classified into three main types, based on their projections and functions

[8,9].

Parvocellular RGCs (80% of the RGCs) receive input from relatively few photoreceptors,

and project to the parvocellular layers of the LGN. With small cell bodies, they operate

slowly and with detail. They belong to the “what” perceptual system or “ventral stream”,

involved in processes such as object recognition and form representation.

Magnocellular (M) RGCs (10% of the RGCs) receive input from numerous

photoreceptors, and project to the magnocellular layers of the LGN. With large cell

bodies, they operate quickly, but lack detail. They are part of the “where” perceptual

system, or “dorsal stream”, which deals with the processing of object localisation and

motion.

Koniocellular (K) RGCs (10% of the RGCs) receive input from a moderate quantity of

photoreceptors, and project to the koniocellular layers of the LGN. They have moderate

spatial resolution, and perform at a moderate rate. Relatively little is known about these

minuscule cells. They pertain to the “ventral stream” system, and may play a role in

colour vision.

26 Introduction

For this thesis, no distinguishment was made between contributions of the different

types of ganglions cells.

The axons of the different types of RGCs leave the retina at the optic disc and form the

optic nerve. At the optic chiasm, the axons from both eyes are sorted according to the

field of view. Information from the right visual field travels through the left optic tract, and

information from the left visual field travels through the right optic tract. Each optic tract

terminates in the lateral geniculate nucleus (LGN), in the thalamus.

The LGN is considered a relay nucleus. However, it is assumed that it plays a

considerable role in visual processing. Each of its 6 layers receives input from a

particular type of RGC. P cells synapse in layers 3, 4, 5, and 6, while M cells synapse in

layers 1 and 2. Between each P and M cell layer lies a zone where K cells synapse. As

explained before, each of these cells is related to particular perceptual functions.

The separation of visual field information is conserved. Input from the ipsilateral eye

terminates in layers 2, 3, and 5, and information from the contralateral eye in layers 1, 4,

and 6. The LGN presents a retinotopic reorganisation.

Information leaving the LGN travels further through the optic radiations to reach the

primary visual cortex (also known as striate cortex, Brodmann area 17 or "V1"), in the

occipital lobe. Here, information is relayed to different areas of the visual cortex, where

the information coming from the different types of receptors and modalities is integrated

and processed to create an image to be perceived.

1.4.2. Functional areas in visual cortex

The primary visual cortex is the first functional cortical area of the visual system. Its

small receptive fields contain low-level information about orientation, spatial-frequency

and colour. The primary area projects to and from other functional areas. Although

visual processing is to some degree distributed into separate areas, there appears to be

considerable integration across functional regions.

27

The visual cortex can be divided into the ventral and dorsal stream [10]. Each visual

area contains a complete representation of the visual field. However, along the streams,

size, latency, and complexity of the receptive fields increase. The ventral stream is

associated with object and form recognition. Visual areas V1, V2, V4, and

inferotemporal cortex (IT) constitute part of it. In contrast, the dorsal stream runs

dorsally into the posterior parietal cortex (PPC), and is involved in the processing of

object localisation, spatial awareness, guidance of actions, and movement detection.

Visual areas V1, V2, V5, and the PPC belong to it. Both dorsal and ventral streams are

heavily interconnected. There are also important connections between the visual areas

and the medial temporal lobe, which stores long-term memories, and the limbic system,

which processes emotions. Due to these connections, the ventral and dorsal streams do

not simply provide a description of the visual world but also play an essential role in

judging the significance of it.

Thirty-two different cortical areas involved in visual processing have been identified in

the macaque monkey [11]. Using neuro-imaging and retinotopic mapping techniques,

over 10 functional areas have been recognised in the human [12].

In summary, the following are the main visual extrastriate functional areas. Similarly to

V1, area V2 responds to simple properties such as orientation, spatial frequency, and

colour, but is also modulated by attention, and moderately more complex patterns. Area

V4 is particularly important in colour processing [13,14]. Area V5 (MT) is specialised in

motion detection [15-17]. Area V3 may be important for stereoscopic vision, and for

processing global motion [18]. Area IT responds to highly specific shapes and complex

stimulus patterns [19-23]. Finally, the PPC area plays an important role in spatial

representation, control of eye movements, and planning and execution of object-centred

movements [24].

28 Introduction

1.4.3. Primary visual cortex organisation

Early Observations and Discovery of the Primary Visual Cortex In the 19th century, several efforts to correlate specific mental processes with particular

brain regions by means of empirical observation lead to a number of successful

functional localisations.

It was in 1876, when David Ferrier, a Scottish neurologist and physiologist, first

attempted to assign visual function to a specific region of the cerebral cortex [25]. He

observed that monkeys with damage to the posterior region were blind in the opposite

eye, and he allocated primate vision to the angular gyrus of the posterior parietal lobe. It

was the German physiologist Hermann Munk who first properly located the visual area

[26]. In 1878, he found that unilateral removal of the occipital lobe caused partial

blindness, and bilateral removal produced total blindness.

Retinotopic Organization in Primary Visual Cortex Subsequent work was directed towards determining how the visual field was mapped

onto the cortex. Tatsuji Inouye, a Japanese ophthalmologist, studied brain-damaged

soldiers wounded during the Russo-Japanese War of 1904-1905 [27,28]. Carefully

relating the location of their specific visual field defects to the location of the cortical

damage, he discovered the retinotopic arrangement of the visual cortex. The resulting

maps included the phenomenon of cortical magnification, by which central vision is

disproportionately represented in a larger area in comparison with peripheral vision. In

1916, his findings were confirmed by Holmes and Lister [29] who, assisted by the use of

X-rays, studied a larger number of wounded after World War I that led to the design of

their widespread accepted map. Later, technical progress, specifically the use of neuro-

imaging has provided new insight into the human brain structure and function [30]. With

the aid of MRI, Holmes' map was modified in 1991 by Horton and Hoyt [31].

One of the ways in which sensory information is coded is spatial organization. As Inouye

first observed, the primate visual cortex holds a one-by-one retinotopic organisation,

where each point of the retina has a corresponding cortical representation. The spatial

relationships existing between RGCs are preserved between cortical neurons in such a

29

way that neighbouring points on the retina project to neighbouring points on the cortical

surface. In addition, in the cortex, receptive fields eventually overlap. This provides the

basis for the coding of stimulus position in visual space, or numerous kinds of

perceptual processes requiring the detection of continuity in the visual scene. On the

other hand, as mentioned previously, the cortical map does not exactly match the shape

of the retina, as there is more cortical tissue devoted to the processing of input from the

fovea than from the peripheral regions. This disproportion is due to the fact that the

highest density of cones is situated in the fovea.

1.5. Visual field defects and the brain

Due to the retinotopic cortical organisation, when field defects occur in both eyes and

overlap, a section of visual cortex no longer receives stimulation. Possible

consequences of the absence of input are degeneration or reorganisation of the

corresponding cortical areas. Both can be considered forms of brain plasticity.

1.5.1. Plasticity in the brain

Plasticity in the brain helps the organism to adapt to the environment [32,33], both

during development, as well as later in life.

Developmental plasticity has been studied in great detail in animals and humans. For

example, from animal studies, it is known that the visual pathways encoding binocular

depth, orientation, motion and colour develop abnormally without appropriate stimulation

during early life [34]. In the human, cross modal plasticity has been demonstrated in

several functional neuro-imaging studies [35,36]. For example, in the early-onset blind,

primary visual cortical areas are actively involved during Braille reading. This

remarkable plasticity permits tactile information to be processed in visual cortical areas

lacking visual input [37,38].

30 Introduction

Recent neuro-anatomical studies have demonstrated that developmental visual

disorders, such as strabismus [39], amblyopia [40] and albinism [41], affect the structure

of human occipital cortex.

The adult brain also retains an important degree of plasticity. For example, it has been

demonstrated that string players have larger digit representation [42], which indicates

that the representation of parts of the body can be use-dependent. Learning processes

are mediated by the rearrangement of cortical connections [33,43,44], and can therefore

be considered a form of plasticity as well.

In the case of deactivation or altered pattern of activation, the brain responds by

adapting to the new condition. A visual field defect with associated optic nerve damage

will cause deafferentation of the brain from the retina. This neuronal deafferentation may

cause either degeneration, reorganisation, or have no consequences at all. In case of

degeneration, cortical neurons may suffer atrophic changes and die. If this occurs,

shrinkage of the silenced cortical region may be expected. In chapters 1 and 2 of this

thesis, this possibility is studied in more detail. One of the possible mechanisms behind

cortical cell death following deafferentation is transneuronal degeneration. By

transneuronal degeneration, the damage of one neuron is propagated to the next

neuron via its axon. In animal studies, it has been found that due to anterograde

transneuronal degeneration, atrophy from damaged parts of the retina, caused by

induced high IOP, can propagate towards the cortex, to provoke cortical atrophy [45,46].

On the other hand, when sufficient input from neighbouring sources is still available,

cortical neurons may survive and establish new connections. For example, in the

somato-sensory cortex, the cortical representation of the digits of adult monkeys

undergoes significant translocation after amputation [47]. However, the presence or

absence of reorganisation following visual field defects is the topic of current debate.

Some early animal studies have shown reorganisation of the receptive fields with the

consequent cortical remapping [48-56]. More recent work [57,58] has cast doubt on the

immediate occurrence of such reorganisation. In addition to the animal studies,

31

functional cortical reorganisation has been shown in a neuro-imaging study on human

adults with visual field defects [59-61]. In chapter 3 of this thesis, one case of functional

reorganisation following AMD is presented. The possibility that the presence or absence

of reorganisation is related to the RGC layer being damaged or intact is discussed.

1.5.2. The filling-in phenomenon

Defect Unawareness Brain plasticity can be present after damage of the visual system. As an attempt to

compensate for gaps in perception, subsequent cortical remapping is very likely to occur

and will presumably imply perceptual filling-in [62] of visual field defects.

Comparably to the completion of the physiological “blind spot”, patients can remain

unaware of their defects, as a consequence of the filling-in phenomenon [63,64]. One of

the negative consequences in the ophthalmologic clinical practice is the subsequent

difficulty to evaluate the deficits using routine visual field testing procedures [63,64].

Cortical plasticity should therefore defenitely be kept in mind in rehabilitation

procedures.

Image Distortion and Visual Hallucinations in the Blind In the visual impaired, as a result of filling-in, perceived images are distorted and lose

their correspondence to reality. For example, in the case of perceptual completion and

shape distortion, intermittent patterns appear continuous, which can lead to such

distortions whereby, for example, people seem thinner than they actually are [63].

On the other hand, visual hallucinations appear to be a frequent, although not a well-

recognized, side effect of visual field defects. Charles Bonnet Syndrome (CBS) was first

introduced in medical terminology in 1760. The Swiss philosopher Charles Bonnet

described the condition disturbing his grandfather who, blinded by cataracts, still

reported seeing birds and buildings that were not there. The syndrome is characterized

by vivid complex visual hallucinations in psychologically normal people [65].

Predominantly present in the visually handicapped elderly, its prevalence among low-

vision patients is 11% [66]. Little is known about its etiology. It has been proposed that

32 Introduction

constant seeing prevents the brain from creating its own pictures. However, when the

brain no longer receives visual input, spontaneous images can arise from fantasy. The

motif of these fictive visual percepts varies widely from person to person. They can vary

from simple patterns of straight lines, to complicated designs such as brickwork, mosaic

or tiles, to detailed pictures of real or unreal people, buildings, or landscapes. Whatever

the type, they are always experienced with full insight about their unreal nature. There is

no proven treatment, but many patients benefit from learning that their hallucinations are

not related to mental illness [67].

Perceptual experience is a representation of the outer world. However, perception is

more the result of a subjective interpretation, than a fair reproduction of the physical

world. Perceptual experience can indeed easily be dissociated from physical reality.

Hallucinations and visual illusions provide significant proof of this. In chapter 6 of this

thesis, iIn order to acquire more insight about this issue, we explore visual brain activity

related to brightness induction and visual filling-in in the absence of actual physical

stimulation by means of a perceptual illusion in normal subjects.

1.6. Neuro-imaging

In the previous sections, human visual fields defects and the brain have been briefly

explored. The subsequent question that logically arises is how to measure their

consequences in the brain.

Prior to the development of modern neuro-imaging techniques, the exploration of the

structure and function of the human brain was available only via accidental lesions.

Nowadays, neuronal activity can be measured using different methods. With single cell

recordings, the activity is measured directly, but in humans, it is only practicable during

neurosurgery. Therefore, only non-invasive or indirect measurements are used in the

human. Electroencephalography (EEG) and magnetoencephalography (MEG) measure

the electromagnetic signals induced by neuronal firing. Positron emission tomography

(PET), magnetic resonance spectroscopy (MRS), and functional magnetic imaging

33

(fMRI) measure physiologic or metabolic aspects of the brain. These techniques differ in

their spatial and temporal resolution. Functional MRI has a poorer temporal resolution

than of MEG or EEG, but has the highest spatial resolution.

During the last decades, by taking advantage of the great technological and

methodological improvement in neuro-imaging, the understanding of the functional

organization of visual brain areas in the human has greatly improved [30]. Detailed and

non-invasive MR images of the brain are available to both clinicians and researchers on

a routine basis. These developments have profound implications on the study of visual

dysfunction.

Note: In the experiments carried out during this PhD project, neuro-imaging was the tool that provided me with data. It is beyond the scope of this thesis to provide in depth explanation of the complex physical principles behind MR. For more detailed information, you can refer to: Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance imaging, physical principles and sequence design. New York: John Wiley and Sons, Inc. 2002 [68] or http://www.cis.rit.edu/htbooks/mri/.

Fig. 11. 3.0 Tesla Philips Intera scanner at the BCN-Neuro Imaging Centre (Groningen, NL).

1.6.1. Anatomical Magnetic Resonance Imaging

Felix Bloch and Edward Purcell, both awarded with the Nobel Prize in 1952, discovered

the magnetic resonance phenomenon independently in 1946. Nuclear magnetic

resonance was further developed and used for chemical and physical molecular

34 Introduction

analysis. In 1971, when Raymond Damadian showed that the nuclear magnetic

relaxation times of tissues and tumours differed [69], magnetic resonance imaging (MRI)

began to be considered for disease detection. In 1973, Paul Lauterbur performed the

first MRI measurement on small test tube samples [70], and in 1977, Raymond

Damadian implemented MRI of the entire body. The science of MRI has since then

known great technological and methodological progress.

Nowadays, anatomical MRI (aMRI) is a common tool used to visualize the anatomy of

living organisms. Anatomical MRI relies on the relaxation properties of the excited

hydrogen nuclei in water. In all atoms, nuclear particles spin around their atomic axis.

During an aMRI session, the large magnet (Fig. 11) creates a strong uniform magnetic

field around the head of the subject. Consequently, the spinning of the atomic nuclei

align either parallel or antiparallel to the static magnetic field. By means of brief

electromagnetic energy pulses, the nuclei adopt a temporary high-energy state. When

the high-energy state ceases, the nuclei relax and realign, returning to equilibrium.

During this relaxation, the nuclei emit energy at specific rates, providing information

about their nature. The intensities on the obtained images depend on the acquisition

parameters. In T1-weighted images, white matter appears white, grey matter grey, and

cerebrospinal fluid (CSF), black. T1 provides high contrast 3D information about the

brain structure, permitting cross-sectional images in any direction. The image resolution

is approximately 1 mm3.

Anatomical MRI is commonly used as a form of medical imaging, to investigate

physiological alterations and pathological cases. However, its application in brain

research is increasing, and becoming more essential. As an example, the technique

permits comparison of the size of a specific brain structure between a group of patient

and controls, as presented in chapters 1 and 2 of this thesis. Furthermore, aMRI also

provides high-resolution brain images, on which the functional data can be

superimposed.

35

1.6.2. Functional Magnetic Resonance Imaging

In order to support neuronal metabolism, a local increased neural activity results in an

increased demand for oxygen in that particular region. The vascular system

overcompensates for this, and increases the blood flow and the amount of oxygenated

haemoglobin relative to deoxygenated haemoglobin [71. The mismatch between oxygen

demand, and the increase in oxygenated blood flow produces the blood oxygen level-

dependent (BOLD) signal [72,73]. Oxygenated and deoxygenated haemoglobin have

different magnetic properties, which result in slightly different MR signals. Using an

appropriate MR pulse sequence, called echo planar imaging (EPI), the local changes in

oxygen level can be detected with functional magnetic resonance imaging (fMRI), and

produce a BOLD signal. Functional MRI must be considered as an indirect measure of

brain activity. The exact link between neural activity and the BOLD signal remains an

active research topic [74,76].

Whole brain volume images are usually acquired every 2-4 seconds, and the image

resolution is approximately 3 mm3. When brain activity is measured during a particular

experimental task, the corresponding localised activation can be attributed to the

specific function being studied. Brain activation will induce a slight local intensity

increase in the image. After a series of pre-processing steps (for example, realignment,

co-registration, normalisation, smoothing), the images will be ready to be analysed. It is

when contrasted with other acquisition moments that the slight increase in intensities

can generate detectable differences. A predefined experimental design will specify how

the activation corresponding to the different conditions will be contrasted with each

other. A typical procedure is to include a baseline condition (wherebby the subject is in a

rest state). Because the BOLD signal is very subtle (3-5%), the use of statistics is

essential to refine observations, as well as to avoid false-positive results. In addition to

this, a sufficient amount of subjects and repeated acquisitions contribute to the reliability

of the results. In order to attain good synchronisation between the experimental task and

the measurement of the corresponding brain activity, it is also important to consider the

time delay associated with the BOLD effect, whose peak appears 5-6 seconds after

stimulus onset. Finally, the activation maps resulting from the statistical analysis can be

36 Introduction

superimposed on high-resolution anatomical MR images, which permit the precise

localisation of the brain activity.

The first measurements of fMRI signals from a human cortex were reported in 1992

[77,78]. Since then, remarkable technical and methodological progress has been made

in the field. Due to its relative spatial and temporal high resolution, the technique has

provided interesting findings in terms of mapping brain functions. In visual science, the

signal-to-noise ratio of fMRI providing relatively good spatial resolution, and the

development of advanced software, has permitted fruitful contributions in functional

localisation [12,79-83]. Visual areas responding to specific visual features such as

motion, colour, face or object recognition have been identified, and

their properties measured [13,17,23,84,85]. The retinotopic

organisation and columnar architecture of the visual system has

also been successfully revealed with fMRI [86-90]. In order to

study retinotopic organization in the human visual cortex,

dartboard wedges and expanding rings are used to periodically

stimulate retinal regions at different eccentricities and polar

angles [79,80,91,92]. In this phase-encoding method, periodic

visual field stimulation leads to periodic activation in the

retinotopic organised visual cortex. Nowadays, the software-based visualisation of these

activation patterns on a flattened cortical surface is the standard fMRI retinotopic

mapping method. This particular method of retinotopic mapping has been used in the

work presented in chapter 3.

In this thesis, experiments using fMRI are described in chapters 3, 5 and 6.

1.6.3. Magnetic Resonance Spectroscopy

After 25 years of nuclear magnetic resonance spectroscopy (MRS) being used as a

major tool by chemists, in 1981, the intact mammalian brain was first studied in vivo by

Thulborn [93]. That same year, the first in vivo MRS of human muscle was performed by

37

Ross [94], and in 1983, the human brain was first scanned by Bottomley [95,96]. Since

then, MRS has proven to be an essential tool in the evaluation of the biochemistry of the

human body, especially the brain.

MRS takes advantage of the magnetic resonance phenomenon to provide access to

living chemistry [97,98]. Proton MRS (1H-MRS) acquires resonance signals from tissue

nuclei, such as hydrogen (1H) [95]. It follows the same principles as aMRI, but also

measures the magnetic behaviour of molecules other than water. In 1H-MRS, each

proton located in a particular environment gives rise to a distinct peak in the spectrum.

The resulting data is presented as a biochemical spectrum, which provides quantitative

information about each metabolite being studied.

The important metabolite peaks routinely quantified on a standard 1H-MRS analysis of

the human brain, and the ones that we will focus on in chapters 4 and 5 of this thesis,

are N-acetyl aspartate (NAA), Creatine (Cr), Choline (Cho), and Lactate (Lac). Each of

these compounds can be used as a specific marker. In short, NAA is the most frequently

and easily studied metabolite. It is considered to be a reliable indicator of brain

pathology and disease progression [99-101]. Creatine is known to play an important role

in energy metabolism, and has been reported to be constant throughout the brain, and

resistant to change in several degenerative brain diseases [102-104]. Choline is

considered a marker for cell turnover [104]. Lactate offers information on bioenergetic

metabolism. Increased energy demand, coupled with local anoxia, elevates lactate

levels in the brain [104].

The non-invasive acquisition of biochemical information has provided important

knowledge about both the normal and pathological brain. MRS is useful for the

investigation of disorders of metabolism, tumours and certain inflammatory and ischemic

diseases, and can also be used for diagnostic purposes [105,106]. Furthermore,

functional MRS offers the possibility to measure metabolite changes in the brain during

neuronal activation [107]. In chapter 5, 1H-MRS was used to investigate the effect of

visual stimulation in the visual pathway and visual brain areas of some metabolites,

particularly lactate, whose increase has been controversially related to brain activity.

In this thesis, experiments using 1H-MRS are described in chapters 4 and 5.

38 Introduction

References

1. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, et al. Global data on visual impairment in the year 2002. Bull World Health Organ 2004; 82:844-851.

2. Millodot M, Laby DM. Dictionary of ophthalmology. Reed Educational and Professional Publishing Ltd, 2002.

3. Ambati J, Ambati BK, Yoo SH, Ianchulev S, Adamis AP. Age-related macular degeneration: etiology, pathogenesis, and therapeutic strategies. Surv Ophthalmol 2003; 48:257-293.

4. Chopdar A, Chakravarthy U, Verma D. Age related macular degeneration. Bmj 2003; 326:485-488.

5. Holz FG, Pauleikhoff D, Klein R, Bird AC. Pathogenesis of lesions in late age-related macular disease. Am J Ophthalmol 2004; 137:504-510.

6. Zarbin MA. Current concepts in the pathogenesis of age-related macular degeneration. Arch Ophthalmol 2004; 122:598-614.

7. Gottlieb JL. Age-related macular degeneration. Jama 2002; 288:2233-2236.

8. Kandel ER, Schwartz JH, Jessell TM. Principles of neural science. McGraw-Hill, 2000.

9. Callaway EM. Structure and function of parallel pathways in the primate early visual system. J Physiol 2005; 566:13-19.

10. Goodale MA, Milner AD. Separate visual pathways for perception and action. Trends Neurosci 1992; 15:20-25.

11. Van Essen DC, Anderson CH, Felleman DJ. Information processing in the primate visual system: an integrated systems perspective. Science 1992; 255:419-423.

12. Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 1995; 268:889-893.

13. Zeki S. A century of cerebral achromatopsia. Brain 1990; 113 ( Pt 6):1721-1777.

14. Zeki S, Watson JD, Lueck CJ, Friston KJ, Kennard C, Frackowiak RS. A direct

demonstration of functional specialization in human visual cortex. J Neurosci 1991; 11:641-649.

15. Zihl J, von Cramon D, Mai N. Selective disturbance of movement vision after bilateral brain damage. Brain 1983; 106 (Pt 2):313-340.

16. Zihl J, von Cramon D, Mai N, Schmid C. Disturbance of movement vision after bilateral posterior brain damage. Further evidence and follow up observations. Brain 1991; 114 ( Pt 5):2235-2252.

17. Zeki S. Cerebral akinetopsia (visual motion blindness). A review. Brain 1991; 114 ( Pt 2):811-824.

18. Braddick OJ, O'Brien JM, Wattam-Bell J, Atkinson J, Hartley T, Turner R. Brain areas sensitive to coherent visual motion. Perception 2001; 30:61-72.

19. Chow KL. Further studies on selective ablation of associative cortex in relation to visually mediated behavior. J Comp Physiol Psychol 1952; 45:109-118.

20. Mishkin M. Visual discrimination performance following partial ablations of the temporal lobe. II. Ventral surface vs. hippocampus. J Comp Physiol Psychol 1954; 47:187-193.

21. Mishkin M, Pribram KH. Visual discrimination performance following partial ablations of the temporal lobe. I. Ventral vs. lateral. J Comp Physiol Psychol 1954; 47:14-20.

22. Grill-Spector K, Kushnir T, Hendler T, Edelman S, Itzchak Y, Malach R. A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Hum Brain Mapp 1998; 6:316-328.

23. Tanaka K. Representation of Visual Features of Objects in the Inferotemporal Cortex. Neural Netw 1996; 9:1459-1475.

24. Grefkes C, Fink GR. The functional organization of the intraparietal sulcus in humans and monkeys. J Anat 2005; 207:3-17.

25. Ferrier D. The Functions of the Brain. Smith, Elder: London, 1876.

26. Munk H. Weitere Mittheilungen zur Physiologie der Grosshirnrinde. Verhandlungen der Physiologischen Gesellschaft zu Berlin 1878:162-178.

39

27. Inouye T. Die Sehstorungen bei Schussverletzungen der kortikalen Sehsphare nach Beobachtungen an Verwundeten der letzten japanischen Kriege. W Engelmann: Leipzig, 1909.

28. Glickstein M. The discovery of the visual cortex. Sci Am 1988; 259:118-127.

29. Holmes G, Lister WT. Disturbances of vision from cerebral lesions with special reference to the macula. Brain 1916; 39:34-73.

30. Kollias SS. Investigations of the human visual system using functional magnetic resonance imaging (FMRI). Eur J Radiol 2004; 49:64-75.

31. Horton JC, Hoyt WF. The representation of the visual field in human striate cortex. A revision of the classic Holmes map. Arch Ophthalmol 1991; 109:816-824.

32. Buonomano DV, Merzenich MM. Cortical plasticity: from synapses to maps. Annu Rev Neurosci 1998; 21:149-186.

33. Pascual-Leone A, Amedi A, Fregni F, Merabet LB. The plastic human brain cortex. Annu Rev Neurosci 2005; 28:377-401.

34. Brenner E, Cornelissen FW. A way of selectively degrading colour constancy demonstrates the experience dependence of colour vision. Curr Biol 2005; 15:R864-866.

35. Goldreich D, Kanics IM. Tactile acuity is enhanced in blindness. J Neurosci 2003; 23:3439-3445.

36. Burton H. Visual cortex activity in early and late blind people. J Neurosci 2003; 23:4005-4011.

37. Celesia GG. Visual plasticity and its clinical applications. J Physiol Anthropol Appl Human Sci 2005; 24:23-27.

38. Sadato N. How the blind "see" Braille: lessons from functional magnetic resonance imaging. Neuroscientist 2005; 11:577-582.

39. Chan ST, Tang KW, Lam KC, Chan LK, Mendola JD, Kwong KK. Neuroanatomy of adult strabismus: a voxel-based morphometric analysis of magnetic resonance structural scans. Neuroimage 2004; 22:986-994.

40. Mendola JD, Conner IP, Roy A, Chan ST, Schwartz TL, Odom JV, et al. Voxel-based analysis of MRI detects abnormal visual cortex in children and adults with amblyopia. Hum Brain Mapp 2005; 25:222-236.

41. von dem Hagen EA, Houston GC, Hoffmann MB, Jeffery G, Morland AB. Retinal abnormalities in human albinism translate into a reduction of grey matter in the occipital cortex. Eur J Neurosci 2005; 22:2475-2480.

42. Elbert T, Pantev C, Wienbruch C, Rockstroh B, Taub E. Increased cortical representation of the fingers of the left hand in string players. Science 1995; 270:305-307.

43. Gilbert CD. Learning. Neuronal dynamics and perceptual learning. Curr Biol 1994; 4:627-629.

44. Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol 2005; 15:161-167.

45. Chen X, Sun C, Huang L, Shou T. Selective loss of orientation column maps in visual cortex during brief elevation of intraocular pressure. Invest Ophthalmol Vis Sci 2003; 44:435-441.

46. Gupta N, Yucel YH. Brain changes in glaucoma. Eur J Ophthalmol 2003; 13 Suppl 3:S32-35.

47. Merzenich MM, Nelson RJ, Stryker MP, Cynader MS, Schoppmann A, Zook JM. Somatosensory cortical map changes following digit amputation in adult monkeys. J Comp Neurol 1984; 224:591-605.

48. Gilbert CD, Wiesel TN. Receptive field dynamics in adult primary visual cortex. Nature 1992; 356:150-152.

49. Gilbert CD. Adult cortical dynamics. Physiol Rev 1998; 78:467-485.

50. Kaas JH. Neurobiology. How cortex reorganizes. Nature 1995; 375:735-736.

51. Kaas JH. Plasticity of sensory and motor maps in adult mammals. Annu Rev Neurosci 1991; 14:137-167.

52. Kaas JH. Sensory loss and cortical reorganization in mature primates. Prog Brain Res 2002; 138:167-176.

53. Chino YM, Kaas JH, Smith EL, 3rd, Langston AL, Cheng H. Rapid reorganization of cortical maps in adult cats following restricted deafferentation in retina. Vision Res 1992; 32:789-796.

54. Chino YM. Adult plasticity in the visual system. Can J Physiol Pharmacol 1995; 73:1323-1338.

55. Darian-Smith C, Gilbert CD. Axonal sprouting accompanies functional reorganization in adult cat striate cortex. Nature 1994; 368:737-740.

56. Calford MB, Wang C, Taglianetti V, Waleszczyk WJ, Burke W, Dreher B. Plasticity in adult cat visual cortex (area 17) following circumscribed monocular lesions of all retinal layers. J Physiol 2000; 524 Pt 2:587-602.

57. Murakami I, Komatsu H, Kinoshita M. Perceptual filling-in at the scotoma following a

40 Introduction

monocular retinal lesion in the monkey. Vis Neurosci 1997; 14:89-101.

58. Smirnakis SM, Brewer AA, Schmid MC, Tolias AS, Schuz A, Augath M, et al. Lack of long-term cortical reorganization after macaque retinal lesions. Nature 2005; 435:300-307.

59. Baker CI, Peli E, Knouf N, Kanwisher NG. Reorganization of visual processing in macular degeneration. J Neurosci 2005; 25:614-618.

60. Baseler HA, Brewer AA, Sharpe LT, Morland AB, Jagle H, Wandell BA. Reorganization of human cortical maps caused by inherited photoreceptor abnormalities. Nat Neurosci 2002; 5:364-370.

61. Morland AB, Baseler HA, Hoffmann MB, Sharpe LT, Wandell BA. Abnormal retinotopic representations in human visual cortex revealed by fMRI. Acta Psychol (Amst) 2001; 107:229-247.

62. Walls GL. The filling-in process. Am J Optom Arch Am Acad Optom 1954; 31:329-341.

63. Safran AB, Achard O, Duret F, Landis T. The "thin man" phenomenon: a sign of cortical plasticity following inferior homonymous paracentral scotomas. Br J Ophthalmol 1999; 83:137-142.

64. Safran AB, Landis T. From cortical plasticity to unawareness of visual field defects. J Neuroophthalmol 1999; 19:84-88.

65. Teunisse RJ, Cruysberg JR, Hoefnagels WH, Verbeek AL, Zitman FG. Visual hallucinations in psychologically normal people: Charles Bonnet's syndrome. Lancet 1996; 347:794-797.

66. Teunisse RJ, Cruysberg JR, Verbeek A, Zitman FG. The Charles Bonnet syndrome: a large prospective study in The Netherlands. A study of the prevalence of the Charles Bonnet syndrome and associated factors in 500 patients attending the University Department of Ophthalmology at Nijmegen. Br J Psychiatry 1995; 166:254-257.

67. Cohen SY, Safran AB, Tadayoni R, Quentel G, Guiberteau B, Delahaye-Mazza C. Visual hallucinations immediately after macular photocoagulation. Am J Ophthalmol 2000; 129:815-816.

68. Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance imaging, physical principles and sequence design. John Wiley and Sons, Inc: New York, 2002.

69. Damadian RV. Tumor Detection by Nuclear Magnetic Resonance. Science 1971; 171.

70. Lauterbur PC. Image formation by induced local interactions: examples employing nuclear

magnetic resonance. Nature 1973; 242:190-191.

71. Roy C, Sherrington C. On the regulation of the blood supply of the brain. Journal of Physiology 1890; 11:85-100

72. Fox PT, Raichle ME. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc Natl Acad Sci U S A 1986; 83:1140-1144.

73. Fox PT, Raichle ME, Mintun MA, Dence C. Nonoxidative glucose consumption during focal physiologic neural activity. Science 1988; 241:462-464.

74. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 2001; 412:150-157.

75. Logothetis NK. The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philos Trans R Soc Lond B Biol Sci 2002; 357:1003-1037.

76. Logothetis NK, Wandell BA. Interpreting the BOLD signal. Annu Rev Physiol 2004; 66:735-769.

77. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 1992; 89:5675-5679.

78. Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A 1992; 89:5951-5955.

79. DeYoe EA, Carman GJ, Bandettini P, Glickman S, Wieser J, Cox R, et al. Mapping striate and extrastriate visual areas in human cerebral cortex. Proc Natl Acad Sci U S A 1996; 93:2382-2386.

80. Engel SA, Glover GH, Wandell BA. Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb Cortex 1997; 7:181-192.

81. Tootell RB, Mendola JD, Hadjikhani NK, Liu AK, Dale AM. The representation of the ipsilateral visual field in human cerebral cortex. Proc Natl Acad Sci U S A 1998; 95:818-824.

82. Warnking J, Dojat M, Guerin-Dugue A, Delon-Martin C, Olympieff S, Richard N, et al. fMRI retinotopic mapping--step by step. Neuroimage 2002; 17:1665-1683.

41

83. Slotnick SD, Yantis S. Efficient acquisition of human retinotopic maps. Hum Brain Mapp 2003; 18:22-29.

84. Tootell RB, Reppas JB, Kwong KK, Malach R, Born RT, Brady TJ, et al. Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J Neurosci 1995; 15:3215-3230.

85. Kanwisher N. Domain specificity in face perception. Nat Neurosci 2000; 3:759-763.

86. Cheng K, Waggoner RA, Tanaka K. Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron 2001; 32:359-374.

87. Courtney SM, Ungerleider LG. What fMRI has taught us about human vision. Curr Opin Neurobiol 1997; 7:554-561.

88. Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA. Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J Vis 2003; 3:586-598.

89. Kim SG, Duong TQ. Mapping cortical columnar structures using fMRI. Physiol Behav 2002; 77:641-644.

90. Wandell BA. Computational neuroimaging of human visual cortex. Annu Rev Neurosci 1999; 22:145-173.

91. Engel SA, Rumelhart DE, Wandell BA, Lee AT, Glover GH, Chichilnisky EJ, et al. fMRI of human visual cortex. Nature 1994; 369:525.

92. DeYoe EA, Bandettini P, Neitz J, Miller D, Winans P. Functional magnetic resonance imaging (FMRI) of the human brain. J Neurosci Methods 1994; 54:171-187.

93. Thulborn KR, Waterton JC, Styles P, Radda GK. Rapid measurement of blood oxygenation and flow by high-field 1H N.M.R. . Biochem Soc Trans 1981; 9:233−234.

94. Ross B, Kreis R, Ernst T. Clinical tools for the 90s: magnetic resonance spectroscopy and metabolite imaging. Eur J Radiol 1992; 14:128-140.

95. Bottomley PA, Hart HR, Edelstein WA, Schenck JF, Smith LS, Leue WM, et al. NMR imaging/spectroscopy system to study both anatomy and metabolism. Lancet 1983; 2:273-274.

96. Ross B, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec 2001; 265:54-84.

97. Passe TJ, Charles HC, Rajagopalan P, Krishnan KR. Nuclear magnetic resonance spectroscopy: a review of neuropsychiatric applications. Prog Neuropsychopharmacol Biol Psychiatry 1995; 19:541-563.

98. Caramanos Z, Narayanan S, Arnold DL. 1H-MRS quantification of tNA and tCr in patients with multiple sclerosis: a meta-analytic review. Brain 2005; 128:2483-2506.

99. Arnold DL, De Stefano N. Magnetic resonance spectroscopy in vivo: applications in neurological disorders. Ital J Neurol Sci 1997; 18:321-329.

100. Matthews PM, Taivassalo T. Applications of magnetic resonance spectroscopy to diagnosis and monitoring of mitochondrial disease. Ital J Neurol Sci 1997; 18:341-351.

101. De Stefano N, Bartolozzi ML, Guidi L, Stromillo ML, Federico A. Magnetic resonance spectroscopy as a measure of brain damage in multiple sclerosis. J Neurol Sci 2005; 233:203-208.

102. Schuff N, Amend D, Ezekiel F, Steinman SK, Tanabe J, Norman D, et al. Changes of hippocampal N-acetyl aspartate and volume in Alzheimer's disease. A proton MR spectroscopic imaging and MRI study. Neurology 1997; 49:1513-1521.

103. Chan YL, Yeung DK, Leung SF, Cao G. Proton magnetic resonance spectroscopy of late delayed radiation-induced injury of the brain. J Magn Reson Imaging 1999; 10:130-137.

104. Gujar SK, Maheshwari S, Bjorkman-Burtscher I, Sundgren PC. Magnetic resonance spectroscopy. J Neuroophthalmol 2005; 25:217-226.

105. Ettl A, Fischer-Klein C, Chemelli A, Daxer A, Felber S. Nuclear magnetic resonance spectroscopy. Principles and applications in neuroophthalmology. Int Ophthalmol 1994; 18:171-181.

106. Hashimoto M, Ohtsuka K, Harada K. N-acetylaspartate concentration in the chiasm measured by in vivo proton magnetic resonance spectroscopy. Jpn J Ophthalmol 2004; 48:353-357.

107. Richards TL, Dager SR, Posse S. Functional MR spectroscopy of the brain. Neuroimaging Clin N Am 1998; 8:823-834.

SECTION 2:

EXPERIMENTAL RESEARCH

44 Chapter 1

Occipital grey matter changes in retinal visual field defects in humans 45

CHAPTER 1 Visual field defects and the structural brain

Part 1

Occipital grey matter changes

in retinal visual field defects in humans

Authors:

Christine C. Boucard R. Paul Maguire

Jos B.T.M. Roerdink Nomdo M. Jansonius

Johanna M.M. Hooymans Frans W. Cornelissen

Submitted

46 Chapter 1

Abstract

While developmental ocular disorders are known to affect the structure of visual cortex,

surprisingly little is known about the effects of acquired retinal disorders. We

investigated whether prolonged cortical deprivation, due to retinal field defects acquired

later in life, leads to structural changes in the adult brain. Magnetic resonance images

were obtained in subjects with glaucoma, age-related macular degeneration and

controls. Grey matter density was compared using voxel-based morphometry. In

glaucoma, we find a reduced grey matter density in anterior visual cortex, in accordance

with the primarily peripheral location of the field defects in this group. On the contrary, in

age-related macular degeneration, we do not find a comparable decrease. Our results

indicate that an eye disease with an associated retinal disorder can lead to structural

changes in visual cortex but only in the presence of nerve damage, as in the case of

glaucoma. We discuss the presumable implication of transneuronal degeneration.


Introduction


macular degeneration (AMD) and glaucoma (Resnikoff et al., 2004), are associated with

the occurrence of retinal visual field defects. If these field defects occur in both eyes and

overlap, a section of visual cortex no longer receives stimulation, due to the retinotopic

cortical organisation. Prolonged absence of stimulation may result in changes in cortical

tissue (Johansson, 2004; Merzenich et al., 1984). The question we address here is:

does cortical degeneration occur when retinal field defects prevent the stimulation of

visual cortex? This question is highly relevant from a clinical point of view as cortical

degeneration might affect functional recovery after treatment of retinal disease.

While there is evidence that developmental visual disorders such as strabismus (Chan

et al., 2004), amblyopia (Mendola et al., 2005) and albinism (dem Hagen et al., 2005)

affect the structure of human occipital cortex, surprisingly little is known about the

consequence of visual deprivation later in life. To our knowledge, only one study

indicates a possible link between visual field defects and cortical degeneration. Kitajima

et al. (1997) reported wider calcarine sulci in a small group of patients with retinal

pathology (Kitajima et al., 1997). The heterogeneous pathological background of their

subjects, the lack of control for the potential confounding effect of general atrophy, and

the coarse way in which atrophy was measured make that this issue is not conclusively

settled.

The aim of the present study was to determine whether changes in grey matter density

occur in human visual cortex once a retinal visual field defect has been established. We

studied this in two groups of subjects with either AMD or glaucoma, using magnetic

resonance imaging (MRI). AMD is caused by accumulated waste products in the tissues

underneath the macula that interferes with retinal metabolism and leads to retinal

atrophy (Holz et al., 2004; Zarbin, 2004). This disease affects the retinal pigment

epithelium and the photoreceptor layer, and causes centrally located visual field defects.

In glaucoma, progressive retinal ganglion cell (RGC) loss and optic nerve damage

48 Chapter 1

occur, in most cases induced by an elevated intra-ocular pressure (IOP; (Fechtner &

Weinreb, 1994; Nickells, 1996)). In glaucoma, visual field loss starts peripherally.

If visual deprivation affects adult visual cortex, we expect to find a lower grey matter

density in the projection zones of the acquired retinal field defects. Thus, in glaucoma

patients, we would expect lower grey matter density in anterior medial occipital cortex,

whereas in AMD patients we would expect differences in more posterior visual cortex.

Methods

Subjects Subjects with visual field defects were recruited from a database of the Department of

Ophthalmology of the University Medical Center Groningen (Groningen, The

Netherlands) and through advertisements in magazines of patient associations. The

group consisted of nine patients suffering from AMD (two female and seven males;

mean age 73 years, range 52-82) and eight patients with primary open-angle glaucoma

(one female and seven males; mean age 73 years, range 61-84). Patients had to have

homonymous scotoma of at least 10 degrees diameter located centrally in at least one

quadrant, for a minimum of 3 years. Patients with any other (neuro-) ophthalmic disease

that might affect the visual field were excluded.

The homonymous visual field defects in the AMD group were located at the fovea, while

the glaucoma group showed primarily large peripheral visual field defects (though

heading towards fixation due to the inclusion criterion). This different location of visual

field losses is reflected in the visual acuity (logMAR; minimum angle of resolution) and

average visual field sensitivity (MD; mean deviation) scores of both groups. Table 1 lists

these characteristics.

For the control group, 12 healthy age-matched subjects (three female and nine male;

mean age 66 years, range 60-82) were recruited either by advertisement in a local

newspaper, or were the partners of the visual field impaired participants. Control

subjects were required to have good visual acuity (logMAR≤0), not to have any visual

field defect, and had to be free of any ophthalmic, neurologic, or general health problem.


T-tests showed no significant age-differences between the two patient groups nor with

the control group.

subjects diagnosis visual acuity

(logMAR) visual field

sensitivity (MD) age

1 AMD 1.3 -7.6 75

2 AMD 0.7 -3.6 82

3 AMD 1 -3.5 79

4 AMD 1 -12 52

5 AMD 1 -5 82

6 AMD 0.7 -2.7 63

7 AMD 0.8 -2.6 76

8 AMD 0.4 -2.2 82

9 AMD 0.1 -4.5 68

mean AMD 0.8 -4.9 73

10 glaucoma 0 -23 67

11 glaucoma 0.1 -13.8 69

12 glaucoma 0 -8.8 84

13 glaucoma 0.1 -5.2 82

14 glaucoma 0.7 -14.5 65

15 glaucoma 0.05 -3.7 61

16 glaucoma 0.1 -18.3 75

17 glaucoma 0.1 -6.4 80

mean glaucoma 0.1 -11.7 73

Table 1: Subject characteristics. Subject characteristics. Visual acuity of

the best eye (expressed in logMAR; minimum angle of resolution), visual field

sensitivity of the best eye (expressed as the mean deviation (MD) in sensitivity

(dB)) and age of the two patients group (AMD and glaucoma).

This study conformed to the tenets of the Declaration of Helsinki and was approved by

the medical review board of the University Medical Center Groningen (Groningen, The

Netherlands). All participants gave their informed written consent prior to participation.

50 Chapter 1

Materials and data acquisition Visual fields were recorded using the Humphrey Field Analyzer (HFA; Carl Zeiss

Meditec, Dublin, California, USA) running the 30-2 program Sita Fast.

High-resolution MRI was performed on a 3.0 Tesla Philips Intera (Eindhoven, The

Netherlands). A 3-D structural MRI was acquired on each subject using a T1 weighted

magnetization sequence T1W/3D/TFE-2, 8 degrees flip angle, matrix size 256 • 256,

field of view 230.00 160.00 179.69, yielding 160 slices, voxel size 1x1x1 mm, TR 8.70

ms.

Analysis The MRI data were analysed by means of voxel-based morphometry (VBM) (Ashburner

& Friston, 2000), a method that is part of SPM99 (Statistical Parametric Mapping)

software (Wellcome Department Imaging Neuroscience, London, UK;

http://www.fil.ion.ucl.ac.uk/spm/spm99.html). VBM statistically assesses local changes

in grey matter density using anatomical MRI scans. The anatomical scans were first

normalised to a common coordinate system using the standard MNI (Montreal

Neurological Institute) template of SPM99. The normalisation process inevitably

introduces volumetric changes when warping series of brain images to match a

template. In principle, these could be corrected for. However, in this study, we are

interested in differences in grey matter density. Therefore no correction for volumetric

changes was applied. After that, the images were smoothed at 10-mm full width at half

maximum (FWHM). The next step was the image segmentation. After correcting for non-

homogeneities in the image intensity, each voxel was classified using probabilistic maps

into one of the 3 different tissues: grey matter, white matter and cerebrospinal fluid.

Non-brain voxels were excluded from the statistical analysis by applying a brain mask.

The statistical analysis consisted of a one-way ANOVA test comparing grey matter

densities between the 3 groups (AMD, glaucoma and controls). Subjects’ age was

added to the analysis as a covariate. The test was performed with three different

statistical uncorrected thresholds: p<0.00001, p<0.0001 and p<0.001.


Results

Figure 1 shows the clusters of difference for the comparison of grey matter density

between the glaucoma patients and the control group (xyz-coordinates of the voxel with

the highest signal in the cluster: -1 -83 15). Table 2 gives the results of the statistical

analysis at the cluster level for three different probability thresholds.

Curiously, the comparison between AMD patients and the control group did not reveal

any significant difference in grey matter density.

Figure 1: Results. Regions of difference in grey matter density resulting from the VBM comparison

glaucoma<controls (for convenience shown at three different threshold cut-offs). In the analysis, the subjects’

age was used as a covariate. The results are displayed on the average image of normalised brains of the

glaucoma and control groups. Table 2 lists the cluster level statistics.

uncorrectedp<0.00001

uncorrectedp<0.0001

uncorrected p<0.001

corrected p(for search volume)

0.005 0.0001 0.0001

uncorrected p(for search volume)

0.009 0.0001 0.0001

voxels 440 2573 6376 T 5.24 4.35 3.45

Table 2: Statistics. Summary of the results at a cluster level of the statistical analysis for the

VBM comparison glaucoma<controls at each of the three different thresholds.

52 Chapter 1

Discussion

The main finding of this study is that, in comparison to a group of age-matched control

subjects, the anterior occipital region of subjects with glaucoma contains a lower grey

matter density. The location of the regional change in grey matter density agrees with a

lesion projection zone associated with a loss of peripheral retinal input, which in turn

agrees with the more peripheral location of the field defect of the glaucoma group. This

suggests that the difference is indeed a consequence of the pathology. Our results

therefore indicate that an acquired retinal visual field defect can lead to selective atrophy

in visual cortex.

Conversely, no difference was observed in the AMD group compared with the control

group. Based on the foveal location of their retinal field defect, we expected comparable

atrophy in more posterior regions of visual cortex. Even though the AMD group’s

scotoma were smaller (both in terms of degrees of visual field and mean deviation),

based on the known over-representation of the fovea in visual cortex (Dougherty et al.,

2003) (magnification factor), the expected lesion projection zone should still have been

considerable. Below, we discuss a number of possible explanations for this finding.

It is conceivable that cortical degeneration would be correlated with the severity of the

retinal visual field defect (as assessed by the mean deviation, see methods). When

evaluated for all subjects in our study, grey matter density in the anterior occipital cortex

is, although modestly, indeed correlated with the severity of the visual field defect

(R2=0.4). (Voxel intensity data was extracted from the cluster resulting from the

comparison controls – glaucoma at uncorrected threshold p<0.0001). In addition to a

different location of the retinal field defect, in the AMD group the severity of the retinal

field defect was less than in the glaucoma group. In our present study, thus, it cannot be

dismissed that only the more severe degeneration associated with the glaucoma group

has been able to detect.

On the other hand, there may be more variability in the anatomical location of the foveal

than of the peripheral representations of the visual field. This could lead to a decreased

accuracy of data normalization for the foveal representations. Moreover, the occipital

pole is a difficult region to segment because it is very convoluted (Dougherty et al.,


2003). It is therefore possible that resulting normalization and segmentation errors make

it harder to detect regional grey matter density differences in the foveal projection zone

of visual cortex.

Furthermore, the difference in our results could be explained by the different type of

pathology of our experimental groups. RGC and optic nerve damage occurs in

glaucoma but is absent in AMD. Several studies show a clear correspondence between

optic nerve damage and atrophy in visual cortex. For example, from animal studies, it

has been observed that lesions at retinal and optic nerve levels result in histological

changes in the visual pathway (Haddock & Berlin, 1950). Moreover, neuronal loss in

lateral geniculate nucleus and striate cortex occurs as a consequence of enucleation in

monkeys (Haseltine et al., 1979). Deprivation early in postnatal life leads to loss of

visual cortical neurons as well (Nucci et al., 2003, Tigges et al., 1984). Furthermore,

glaucoma induced through experimentally elevated IOP in non-human primates, (Yucel

et al., 2003) and cats (Chen et al., 2003) provoked cell loss in both lateral geniculate

nucleus and visual cortex. In the human, a decrease in size of lateral geniculate nucleus

and visual cortex as a consequence of a reduction in the size of the optic nerves and

tracts has been measured in post-mortem samples (Andrews et al., 1997).

In agreement, in a recent neuro-imaging study using VBM, a reduced optic nerve and

visual cortex were found in a group of human albinos (von dem Hagen et al., 2005).

Retinal abnormalities in albinism are restricted to central retina, where RGC density is

significantly reduced (Guillery et al., 1984). When considering both ours and their

results, it can be argued that RGC cell damage is responsible for cortical atrophy

independently from the location of the deteriorated area in the retina.

Experimental studies in cats and monkeys suggest that retinal damage induced by high

IOP and causing cortical atrophy propagates by means of transneuronal degeneration

(Chen et al., 2003, Gupta & Yucel, 2003). Although we here investigated grey matter

density and therefore no axonal atrophy along the visual pathway could be measured,

transneuronal degeneration can be considered as the major process behind our present

findings in the glaucoma group.

In AMD, the unimpaired RGC layer may still provide enough (spontaneous) activity that

could prevent the degeneration of neurons in visual cortex. In the rabbit, there is

54 Chapter 1

evidence that electrical stimulation of the eye can elicit evoked potentials in the visual

cortex despite an experimentally severely damaged photoreceptor layer (Humayun et

al., 1995). Moreover, two subjects with AMD were recently reported to show large-scale

functional reorganisation in visual cortex (Baker et al., 2005), while no sign of cortical

reorganisation was found in the peripheral visual field representation of macaque

monkeys after seven months of experimentally induced scotoma where the RGC layer

was destroyed (Smirnakis et al., 2005). The presence of a functioning RGC is an

important factor behind the occurrence of cortical reorganisation. Further, this suggests

an important link between cortical deterioration and disruption between retina and visual

cortex (as by RGC and nerve damage).

In conclusion, we demonstrated the occurrence of structural changes in visual cortex

following a retinal visual field defect acquired later in life. Relative to controls, we find

lower grey matter density in the case of glaucoma. We suggest that the cortical atrophy

is the result of transneuronal degeneration following the loss of RGCs in glaucoma. The

absence of atrophy in subjects with AMD is possibly related to the sparing of the RGC in

these subjects. A better understanding of the relation between retinal visual field defects

and structural changes in visual cortex may help understand disease symptoms as well

as their progression. Moreover, cortical degeneration may limit the efficacy of

rehabilitation and training programs (Safran & Landis, 1996), retinal prostheses

(Hossain et al., 2005), and may require new therapeutic strategies (Taub et al., 2002) to

prevent blindness.

Acknowledgement

The authors want to thank Michiel Kunst for assistance in setting up the VBM analysis

and Remco Renken for fruitful suggestions regarding data analysis.

C.C.B. is supported by an Ubbo Emmius grant from the University of Groningen, The

Netherlands. The study was further supported by an equipment grant from the Prof.

Mulder foundation.


References

1. Andrews,T.J., Halpern,S.D. & Purves,D. (1997) Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J.Neurosci., 17, 2859-2868.

2. Ashburner,J. & Friston,K.J. (2000) Voxel-based morphometry--the methods. Neuroimage., 11, 805-821.

3. Baker,C.I., Peli,E., Knouf,N. & Kanwisher,N.G. (2005) Reorganization of visual processing in macular degeneration. J.Neurosci., 25, 614-618.

4. Chan,S.T., Tang,K.W., Lam,K.C., Chan,L.K., Mendola,J.D. & Kwong,K.K. (2004) Neuroanatomy of adult strabismus: a voxel-based morphometric analysis of magnetic resonance structural scans. Neuroimage., 22, 986-994.

5. Chen,X., Sun,C., Huang,L. & Shou,T. (2003) Selective loss of orientation column maps in visual cortex during brief elevation of intraocular pressure. Invest Ophthalmol.Vis.Sci., 44, 435-441.

6. dem Hagen,E.A., Houston,G.C., Hoffmann,M.B., Jeffery,G. & Morland,A.B. (2005) Retinal abnormalities in human albinism translate into a reduction of grey matter in the occipital cortex. Eur.J.Neurosci., 22, 2475-2480.

7. Dougherty,R.F., Koch,V.M., Brewer,A.A., Fischer,B., Modersitzki,J. & Wandell,B.A. (2003) Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J.Vis., 3, 586-598.

8. Fechtner,R.D. & Weinreb,R.N. (1994) Mechanisms of optic nerve damage in primary open angle glaucoma. Surv.Ophthalmol., 39, 23-42.

9. Guillery,R.W., Hickey,T.L., Kaas,J.H., Felleman,D.J., DeBruyn,E.J. & Sparks,D.L. (1984) Abnormal central visual pathways in the brain of an albino green monkey (Cercopithecus aethiops). J.Comp Neurol., 226, 165-183.

10. Gupta,N. & Yucel,Y.H. (2003) Brain changes in glaucoma. Eur.J.Ophthalmol., 13 Suppl 3, S32-S35.

11. Haddock,J.N. & Berlin,L. (1950) Transsynaptic degeneration in the visual system; report of a case. Arch.Neurol.Psychiatry, 64, 66-73.

12. Haseltine,E.C., DeBruyn,E.J. & Casagrande,V.A. (1979) Demonstration of ocular dominance columns in Nissl-stained sections of monkey visual cortex following enucleation. Brain Res., 176, 153-158.

13. Holz,F.G., Pauleikhoff,D., Klein,R. & Bird,A.C. (2004) Pathogenesis of lesions in late age-related macular disease. Am.J.Ophthalmol., 137, 504-510.

14. Hossain,P., Seetho,I.W., Browning,A.C. & Amoaku,W.M. (2005) Artificial means for restoring vision. BMJ, 330, 30-33.

15. Humayun,M., Sato,Y., Propst,R. & de Juan E Jr (1995) Can potentials from the visual cortex be elicited electrically despite severe retinal degeneration and a markedly reduced electroretinogram? Ger J.Ophthalmol., 4, 57-64.

16. Johansson,B.B. (2004) Brain plasticity in health and disease. Keio J.Med., 53, 231-246.

17. Kitajima,M., Korogi,Y., Hirai,T., Hamatake,S., Ikushima,I., Sugahara,T., Shigematsu,Y., Takahashi,M. & Mukuno,K. (1997) MR changes in the calcarine area resulting from retinal degeneration. AJNR Am.J.Neuroradiol., 18, 1291-1295.

18. Mendola,J.D., Conner,I.P., Roy,A., Chan,S.T., Schwartz,T.L., Odom,J.V. & Kwong,K.K. (2005) Voxel-based analysis of MRI detects abnormal visual cortex in children and adults with amblyopia. Hum.Brain Mapp., 25, 222-236.

19. Merzenich,M.M., Nelson,R.J., Stryker,M.P., Cynader,M.S., Schoppmann,A. & Zook,J.M. (1984) Somatosensory cortical map changes following digit amputation in adult monkeys. J.Comp Neurol., 224, 591-605.

20. Nickells,R.W. (1996) Retinal ganglion cell death in glaucoma: the how, the why, and the maybe. J.Glaucoma., 5, 345-356.

21. Nucci,C., Piccirilli,S., Nistico,R., Morrone,L.A., Cerulli,L. & Bagetta,G. (2003) Apoptosis in the mechanisms of neuronal plasticity in the developing visual system. Eur.J.Ophthalmol., 13 Suppl 3, S36-S43.

56 Chapter 1

22. Resnikoff,S., Pascolini,D., Etya'ale,D., Kocur,I., Pararajasegaram,R., Pokharel,G.P. & Mariotti,S.P. (2004) Global data on visual impairment in the year 2002. Bull.World Health Organ, 82, 844-851.

23. Safran,A.B. & Landis,T. (1996) Plasticity in the adult visual cortex: implications for the diagnosis of visual field defects and visual rehabilitation. Curr.Opin.Ophthalmol., 7, 53-64.

24. Smirnakis,S.M., Brewer,A.A., Schmid,M.C., Tolias,A.S., Schuz,A., Augath,M., Inhoffen,W., Wandell,B.A. & Logothetis,N.K. (2005) Lack of long-term cortical reorganization after macaque retinal lesions. Nature, 435, 300-307.

25. Taub,E., Uswatte,G. & Elbert,T. (2002) New treatments in neurorehabilitation founded on basic research. Nat.Rev.Neurosci., 3, 228-236.

26. Tigges,M., Hendrickson,A.E. & Tigges,J. (1984) Anatomical consequences of long-term monocular eyelid closure on lateral geniculate nucleus and striate cortex in squirrel monkey. J.Comp Neurol., 227, 1-13.

27. Yucel,Y.H., Zhang,Q., Weinreb,R.N., Kaufman,P.L. & Gupta,N. (2003) Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog.Retin.Eye Res., 22, 465-481.

28. Zarbin,M.A. (2004) Current concepts in the pathogenesis of age-related macular degeneration. Arch.Ophthalmol., 122, 598-614


58 Chapter 2

Cortical thickness and visual field defects 59

CHAPTER 2 Visual field defects and the structural brain

Part 2

Cortical thickness and visual field defects

Authors:

Christine C. Boucard Brian T. Quinn

Nomdo M. Jansonius Bruce Fischl


Submitted

60 Chapter 2

Abstract

Developmental ocular disorders are known to affect the structure of visual cortex, but

little is known about the effects of acquired retinal disorders. In a previous study

(Boucard et al., 2006; submitted for publication), we investigated whether prolonged

cortical deprivation, due to retinal field defects acquired later in life, leads to cortical

changes in the adult brain. Compared to controls, we found lower grey matter (GM)

concentration in the visual cortex of a group of patients suffering from glaucoma. Such a

change was absent in a group of subjects with age-related macular degeneration. In

order to better understand the origin of this change, we here examine cortical grey

matter thickness in the same subjects. Magnetic resonance images (MRI) were obtained

in subjects with glaucoma, age-related macular degeneration (AMD) and controls.

Cortical thickness was compared between the groups using Freesurfer. A significant

cortical grey matter thinning was found in the occipital area of the glaucoma group when

compared to controls. In AMD, no difference was found. The present results corroborate

and specify our previous findings. Cortical degeneration following visual deprivation later

in life affects occipital cortical grey matter thickness in case of glaucoma, but not of

AMD. Transneuronal degeneration, in response to the disrupted link between brain and

retina caused by RGC layer loss and optic nerve damage, is discussed as the main

cause of this thinning.


Introduction

Age-related macular degeneration (AMD) and glaucoma, both eye diseases associated

with the occurrence of retinal visual field defects, are the two leading causes of visual

impairment in the developed world (Resnikoff et al., 2004). AMD is caused by

accumulated waste products in the tissues underneath the macula that interfere with

retinal metabolism and lead to retinal atrophy (Holz et al., 2004; Zarbin 2004). The

disease causes centrally located visual field defects. In glaucoma, where visual field

loss starts peripherally and grows towards the fovea, progressive retinal ganglion cell

(RGC) loss and optic nerve damage occur, in most cases induced by an elevated intra-

ocular pressure (IOP) (Fetchner et al., 1994; Nickell, 1996). The major distinction

between these two diseases is that the RGC and optic nerve are damaged in glaucoma

but remain intact in AMD.

Due to the retinotopic cortical organisation (Inouye, 1909; Holmes et al., 1916; Dougerty

et al., 2003), when field defects occur in both eyes and overlap, the retinotopic

corresponding part of visual cortex is no longer stimulated. The fact that cortical tissue is

affected by an absence of stimulation (Johansson, 2004; Merzenich et al., 1984) makes

it pertinent to ask whether retinal field defects deteriorate the structure of the occipital

cortex. Besides, this issue is highly relevant from a clinical point of view since cortical

degeneration might affect functional recovery after treatment of retinal disease. Recent

studies have demonstrated that developmental visual disorders such as strabismus

(Chan et al., 2004), amblyopia (Mendola et al., 2005) and albinism (von dem Hagen et

al., 2005) affect the structure of human occipital cortex. However, very few studies have

yet examined the influence of visual deprivation later in life.

In a previous study from our group (Boucard et al., 2006; submitted for publication), we

investigated this question in glaucoma and AMD. Compared to controls, in the glaucoma

group a lower grey matter (GM) concentration was found in the cortical projection zone

corresponding to the damaged region of the retina. At the same time, no difference was

observed in the AMD group, suggesting loss of RGC and optic nerve damage to be the

62 Chapter 2

essential factor for cortical degeneration to occur. This previous analysis was performed

using “non-modulated” voxel-based morphometry (VBM), a technique that allows

estimating differences in GM concentration in local structures between two or more

groups (Ashburner et al., 2000).

Yet, the precise origin of this lower GM concentration as detected by VBM remains

unclear at present. Regional GM differences assessed by VBM (namely the proportion

of GM relative to other tissue types within a region) can reflect either cortical thickness

differences or the effect of different amounts of folding. In addition, low voxel intensity

resolution, smoothing or whole brain deformation are aspects that could lead to

erroneous conclusions in VBM comparisons. In order to overcome these issues and

look for corroborating evidence, we here performed a further analysis examining cortical

grey matter thickness per se. The analysis was performed using the Freesurfer method

(Fischl et al., 2000). With Freesurfer, cortical thickness is determined by measuring the

distance between the GM/white matter (WM) boundary and the pial surface. The

present analysis revealed a thinning of cortical grey matter in the lesion projection zone

in the glaucoma group, while in the AMD group no such difference was found. An

acquired retinal visual field defect associated with RGC layer loss and optic nerve

damage can thus result in changes in the thickness of visual cortex.

Methods




group consisted of nine patients suffering from AMD (two female and seven males;

mean age 73 years, range 52-82) and eight patients with primary open-angle glaucoma

(one female and seven males; mean age 73 years, range 61-84). Patients had to have

homonymous scotoma of at least 10 degrees diameter centrally located in at least one


quadrant. The visual field defect had to exist at least 3 years. Patients with any other

(neuro-) ophthalmic disease that could affect the visual field were excluded.

In the AMD group, the homonymous visual field defects were primarily located in the

foveal region, while in the glaucoma group larger peripheral visual field defects were

present (although heading towards fixation due to the inclusion criterion). This difference

is depicted in the visual acuity (logMAR; minimum angle of resolution) and average

visual field sensitivity (MD; mean deviation) scores of both groups. Table 1 shows these

characteristics.

subjects diagnosis visual acuity

(logMAR) visual field

sensitivity (MD) age

1 AMD 1.3 -7.6 75

2 AMD 0.7 -3.6 82

3 AMD 1 -3.5 79

4 AMD 1 -12 52

5 AMD 1 -5 82

6 AMD 0.7 -2.7 63

7 AMD 0.8 -2.6 76

8 AMD 0.4 -2.2 82

9 AMD 0.1 -4.5 68

mean AMD 0.8 -4.9 73

10 glaucoma 0 -23 67

11 glaucoma 0.1 -13.8 69

12 glaucoma 0 -8.8 84

13 glaucoma 0.1 -5.2 82

14 glaucoma 0.7 -14.5 65

15 glaucoma 0.05 -3.7 61

16 glaucoma 0.1 -18.3 75

17 glaucoma 0.1 -6.4 80

mean glaucoma 0.1 -11.7 73

Table 1: Subject characteristics. Visual acuity of the best eye (expressed in logMAR; minimum angle of

resolution), visual field sensitivity of the best eye (expressed as the mean deviation (MD) in sensitivity (dB))

and age of the two patients group (AMD and glaucoma).

64 Chapter 2

For the control group, 12 healthy age-matched subjects (three female and nine male;

mean age 66 years, range 60-82) were recruited either by advertisement in a local

newspaper, or were the partners of the visual field impaired participants. Control

subjects were required to have good visual acuity (logMAR≤0), not to suffer from any

visual field defect, and had to be free of any ophthalmic, neurologic, or general health

problem.

No significant age differences between the two patient groups nor with the control group

were assessed by means of t-test analysis.

This study conformed to the tenets of the Declaration of Helsinki and was approved by

the medical review board of the University Medical Center Groningen (Groningen, The

Netherlands). All participants gave their informed written consent prior to participation.


Meditec, Dublin, California, USA) running the 30-2 Sita Fast program.

High-resolution MRI was performed on a 3.0 Tesla Philips Intera (Best, The

Netherlands). A 3-D structural MRI was acquired on each subject using a T1 weighted

magnetization sequence T1W/3D/TFE-2, 8 degrees flip angle, matrix size 256 · 256,

field of view 230.00 160.00 179.69, yielding 160 slices, voxel size 1x1x1 mm, TR 8.70

ms.

Analysis A detailed description of the Freesurfer (http://surfer.nmr.mgh.harvard.edu/) automated

procedures used here for cortical thickness measurements can be found in Fischl et al.,

(2000). In short, after intensity non-uniformity corrections in the MR data and Talairach

normalisation, voxels are classified as WM or something else than WM based on

intensity and neighbour information (Dale et al., 1999). Next, the skull is stripped using a

template (Segonne et al., 2004) and a second intensity correction is performed on the

brain volume. The obtained segmentations of each individual subject are visually

inspected, and any obvious inaccuracies manually corrected. This is followed by an


inflation step during which metric distortion is minimized to preserve original areas and

distances (Fischl et al., 1999). Finally, models are constructed of the white surface

following the intensity gradients between the WM and GM, and of the pial surface

according to the intensity contrast between the GM and Cerebral-Spinal Fluid (CSF)

(Dale et al., 1999; Fischl et al., 2001). The distance between the white and pial surfaces

defines the thickness at each location of cortex across the entire brain volume.

Statistics Statistical maps of thickness differences were constructed using a t statistics. For each

vertex in the cortical thickness map, group (AMD, glaucoma and controls) effects on

cortical thickness were calculated using a general linear model. The participant’s age

was taken as a covariate factor in order to control for its potential contribution to the

differences.

In the resulting statistical difference maps, thresholds were set using the false discovery

rate (FDR), which corrects for the vertices that falsely show differences among those

that truly display differences (Genovese et al, 2002). Our hypothesis was that as a

consequence of interrupted cortical stimulation, the visual field defect groups would

show lower thickness values than the control group. Because of the unidirectional

nature of these expectations, we choose a FDR threshold of 0.1 (which would

correspond to the commonly accepted 0.05 threshold if the hypothesis would consider

the possibility of effects occurring in both directions).

The resulting regions with a significant difference in cortical thickness between groups

were mapped on the mean inflated surface of all participants (figure 1).

Results

Figure 1 shows the results of the comparison of cortical thickness between the control

and the glaucoma group. In both left and right hemispheres, the comparison led to lower

values of cortical thickness in regions in the anterior medial part of the occipital lobe of

the glaucoma group. Conversely, the analysis did not reveal any significant differences

66 Chapter 2

in cortical thickness between AMD patients and controls. The cortical thickness of both

groups included in these corresponding regions is displayed in figure 2.

Figure 1: Results. Regions of difference in cortical thickness resulting from the comparison

glaucoma<controls. The results are mapped on the mean inflated surface from all participants’ brains.

Figure 2. Results. Boxplots displaying the cortical thickness of both control and glaucoma group in the ROI

resulting from the comparison controls>glaucoma. The data includes the average thickness from both left and

right hemispheres.


Discussion

In this study, we observed that in comparison to a group of age-matched controls,

patients with glaucoma show a local cortical thinning in the occipital region. Conversely,

in the case of AMD no such differences in cortical thickness were found. These results

corroborate those of a previous study in which GM concentration was compared

between these groups using VBM. Hence, two substantially different types of analysis

result in the same conclusion. We consider this as additional evidence for the idea that

cortical degeneration is associated with acquired retinal visual field defects in glaucoma.

Most likely this degeneration is a response to the disruption of the connection between

brain and retina as a result of RGC and optic nerve damage.

Comparison of methods In order to understand the value of performing an additional analysis on the same

groups of subjects, we here discuss the main differences between the VBM and

Freesurfer methods for evaluating structural changes.

In short, VBM identifies differences in the proportion of GM relative to other tissue types

within a region. This is achieved by spatially normalising to the same stereotactic space,

segmenting the normalised images into GM, WM and CSF, smoothing the segmentation

of interest and finally performing a statistical analysis to localise significant differences

between two or more experimental groups. However, with GM concentration, one can

only measure relative amounts of GM within a region. These regional characteristics do

not allow discriminating between differences in cortical thickness and differences in the

amount of folding. The reason why the cortical thickness analysis was performed is to

verify if the GM decrease we found using the VBM method reflects actual cortical

reduction. The Freesurfer method (Dale et al., 1999; Fischl et al., 1999, Fischl et al.,

2000) has been validated using post-mortem brains (Rosas et al., 2002), and manual

measurements (Kuperberg et al., 2003).

Because of the brain’s natural cortical folding, with VBM a direct measurement of

cortical thickness is not possible. Adjacent gyri can be mistaken as a single thick GM

area and be reported to show a local high GM concentration (Mechelli et al., 2005).

68 Chapter 2

Instead, the surface-based approach in Freesurfer allows following exactly the GM/WM

boundary and pial surfaces avoiding possible errors coming from the folded structure.

The result is an accurate approximation about how the cortical ribbon appears in real.

During the voxel classification process, VBM assigns to each voxel a specific brain

tissue (GM, WM and CSF) according to its intensity and location. In order to construct a

GM segmented volume that can be used in the final statistical comparison, the

segmentation algorithm is required to detect intensity variations very precisely.

Estimation on the basis of absolute intensity can easily result in segmentation errors.

For example, a voxel containing only WM and CSF could erroneously be classified as

GM because of its average intensity. A slightly different approach is used in Freesurfer

(Fischl et al., 2000) that avoids this kind of segmentation errors. First, a WM volume is

created by classifying voxels as WM or something else than WM based on intensity and

neighbour constraints. Then, using intensity contrast information, models of the

boundary GM/WM as well as the pial surface (GM/CSF boundary) are constructed (Dale

et al., 1999). Because cortical thickness is defined as the shortest distance between

these two surfaces models, the GM volume model does not result from specific

classifications depending on voxel intensities and is independent of voxel resolution. As

a consequence, GM comparisons are directly linked to the cortical ribbon enabling a

more accurate approximation of reality.

Another issue suggesting that Freesurfer’s modelling of the GM is more in agreement

with reality is the fact that thickness measures are independent of any smoothing. On

the contrary, in VBM, in order to compensate for inaccuracies in the normalisation

process, the segmented data are smoothed. While this can induce mistakes in the

localisation of differences, the Freesurfer analysis permits to exactly locate thickness

differences. Besides, since the maps are not restricted to the voxel resolution of the

original data, the detection of submillimeter differences between groups is possible

(Fischl et al., 2000).

The VBM normalisation process only fits the overall brain shapes. Changes detected by

this method can be dramatically influenced by possible whole brain deformation.

Consider as an example the case where GM atrophy occurs on the parietal temporal

border. This could cause the brain to shrink in that area pulling the parietal region and


the occipital lobe towards the void created by the atrophy. This could subsequently

enlarge the gap between the left and right occipital lobes which consequently could be

detected as a region of lower GM concentration by VBM. When directly investigating

cortical thickness, as with Freesurfer, this cannot be the case.

Discussion of results The region with diminished cortical thickness in the glaucoma group is located in the

medial anterior occipital lobe. The location is in accordance with the cortical projection

zone associated with loss of peripheral retinal input (Inouye, 1909; Holmes et al., 1916;

Dougerty et al., 2003). The fact that the field defects of the glaucoma group had a more

peripheral location supports the idea that cortical thinning is specifically associated with

the field defect. Our results therefore indicate that an acquired retinal visual field defect

can lead to selective atrophy in the visual cortex.

Taking into account that in AMD field defects are located in the foveal region, a similar

thinning in more posterior occipital regions would have been expected if an absence of

normal stimulation is the underlying cause. One could argue that the reason why no

cortical thinning was detected was that the extent of the field defect was not as large as

in the glaucoma group, where it covered not only the centre but also part of the

periphery of the visual field. But, as a result of cortical magnification the foveal region is

overrepresented in visual cortex, so the expected corresponding cortical lesion should

still be considerable.

Another argument could be that in the AMD group the severity of the retinal field defect

was less pronounced than in the glaucoma group (see table 1, visual field sensitivity).

Although one could think that only the more severe degeneration associated with

glaucoma has been detected, the fact that RGC and optic nerve damage occurs in

glaucoma but is absent in AMD is more likely to explain the difference in cortical

thickness. There is strong evidence linking optic nerve damage and atrophy in visual

cortex. For example, from animal studies, it is known that retinal and optic nerve lesions

produce histological changes in the visual pathway (Haddock et al., 1950). Furthermore,

enucleation in monkeys leads to neuronal loss in lateral geniculate nucleus (LGN) and

striate cortex (Haseltine et al., 1979). Visual deprivation in postnatal life results in a

70 Chapter 2

decay of visual cortical neurons as well (Nucci et al., 2003, Tigges et al., 1984).

Moreover, induced glaucoma using experimentally elevated IOP in cats (Chen et al.,

2003) and non-human primates (Yucel et al., 2003) causes cell reduction in LGN and

visual cortex. In post-mortem human brains, a strong correlation was found between the

size of the LGN and visual cortex and the size of optic nerves and tracts (Andrews et al.,

1997).

Another finding supporting the idea that differences in RGC and optic nerve damage are

the main causal factor in explaining the presence or absence of atrophy in our groups

comes from a recent neuro-imaging study. In this study, in human albinos a reduced

optic nerve and visual cortex were found (Von Dem Hagen et al., 2005). In albinism,

RGC density in the central retina is significantly reduced (Guillery et al., 1984). This, in

addition to our findings, suggests that cortical degeneration may occur irrespective of

the retinal location of the atrophic area and is mainly a consequence of RGC damage.

By transneuronal degeneration the atrophy from damaged parts of the retina can

propagate towards the cortex provoking its atrophy. This has been shown, for example,

in cats and monkeys where the retina was experimentally injured by an induced

elevated IOP (Chen et al., 2003; Gupta et al., 2003). Because of the presence of optic

nerve damage in the glaucoma group, we consider transneuronal degeneration as the

main mechanism responsible for cortical thinning.

In the case of the AMD group, degeneration of neurons in visual cortex could be

prevented by the intact RGC layer producing spontaneous activity. Evoked potentials

have been measured in the rabbit visual cortex after electrical stimulation of the eye in

which the photoreceptor layer had been experimentally destroyed (Humayun et al.,

1995).

Besides, in recent papers, large-scale functional reorganisation in visual cortex was

described in two subjects suffering from AMD (Baker et al., 2005), whereas

experimentally induced scotoma that included destruction of the RGC layer in the

peripheral visual field of macaque monkeys showed no sign of reorganisation after

seven months (Smirnakis et al., 2005). These two studies suggest that the existence of

a functioning RGC correlates with the occurrence of reorganisation in visual cortex.


This, in turn, is consistent with the idea that cortical thinning occurs only when visual

cortex is isolated from the retina as happens in the case of optic nerve damage.

Finally, the correspondence between the results we obtained by using the two methods

strongly suggests that the lower GM concentrations found in the glaucoma group are a

consequence of cortical thinning. However, a common observation that is valid for both

methods is that it is not clear if the observed cortical changes (GM concentration as well

as cortical thickness) originate from neuronal loss or reflect other kind of alterations

such as changes in neuronal size, neuropil, dendritic or axonal arborisation.

Unfortunately, this issue can only be investigated by methods other than MRI (Mechelli

et al., 2005).

In conclusion, using a method that calculates cortical thickness, we corroborate and

extend the findings of our previous study demonstrating a decreased GM concentration

in glaucoma compared to age-matched controls. We now show that these lower GM

concentrations are indeed a consequence of cortical thinning. Hence, occipital cortical

thinning can be observed in visual cortex following retinal visual field defects acquired

later in life. Previous studies and the absence of cortical atrophy in subjects with AMD

suggest that cortical thinning results from transneuronal degeneration following loss of

RGCs. This study thus contributes as well to understanding brain plasticity at later age

in general.

In the clinical point of view, a better understanding of the relation between retinal visual

field defects and structural changes in visual cortex may help understand disease

symptoms as well as their progression. Cortical degeneration may limit the efficacy of

rehabilitation and training programs (Safran et al., 1996), retinal prostheses (Hossain et

al., 2005), and may require new therapeutic strategies (Taub et al., 2002) to prevent

blindness.

72 Chapter 2

Acknowledgement


Netherlands. A visit of C.C.B. to the Athinoula A Martinos Center was additionally

supported by a grant from the Prof. Mulder foundation. Support for this research at the

Athinoula A Martinos Center was provided in part by the National Center for Research

Resources (P41-RR14075, R01 RR16594-01A1 and the NCRR BIRN Morphometric

Project BIRN002, U24 RR021382), the National Institute for Biomedical Imaging and

Bioengineering (R01 EB001550) as well as the Mental Illness and Neuroscience

Discovery (MIND) Institute. We thank Paul Maguire for suggesting the cortical thickness

measure in addition to the grey matter concentration and for his guidance in the data

analysis. We also thank Martin Pavlovsky for his contribution to the discussion about the

methodology.


References

1. Andrews TJ, Halpern SD, Purves D. Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J Neurosci 1997; 17: 2859-2868.

2. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000; 11: 805-821.

3. Baker CI, Peli E, Knouf N, Kanwisher NG. Reorganization of visual processing in macular degeneration. J Neurosci 2005; 25: 614-618.

4. Chan ST, Tang KW, Lam KC, Chan LK, Mendola JD, Kwong KK. Neuroanatomy of adult strabismus: a voxel-based morphometric analysis of magnetic resonance structural scans. Neuroimage 2004; 22: 986-994.

5. Chen X, Sun C, Huang L, Shou T. Selective loss of orientation column maps in visual cortex during brief elevation of intraocular pressure. Invest Ophthalmol Vis Sci 2003; 44: 435-441.

6. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999; 9: 179-194.

7. dem Hagen EA, Houston GC, Hoffmann MB, Jeffery G, Morland AB. Retinal abnormalities in human albinism translate into a reduction of grey matter in the occipital cortex. Eur J Neurosci 2005; 22: 2475-2480.

8. Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA. Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J Vis 2003; 3: 586-598.

9. Fechtner RD, Weinreb RN. Mechanisms of optic nerve damage in primary open angle glaucoma. Surv Ophthalmol 1994; 39: 23-42.

10. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999; 9: 195-207.

11. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000; 97: 11050-11055.

12. Fischl B, Liu A, Dale AM. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human

cerebral cortex. IEEE Trans Med Imaging 2001; 20: 70-80.

13. Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002; 15: 870-878.

14. Guillery RW, Hickey TL, Kaas JH, Felleman DJ, DeBruyn EJ, Sparks DL. Abnormal central visual pathways in the brain of an albino green monkey (Cercopithecus aethiops). J Comp Neurol 1984; 226: 165-183.

15. Gupta N, Yucel YH. Brain changes in glaucoma. Eur J Ophthalmol 2003; 13 Suppl 3: S32-S35.

16. HADDOCK JN, BERLIN L. Transsynaptic degeneration in the visual system; report of a case. Arch Neurol Psychiatry 1950; 64: 66-73.

17. Haseltine EC, DeBruyn EJ, Casagrande VA. Demonstration of ocular dominance columns in Nissl-stained sections of monkey visual cortex following enucleation. Brain Res 1979; 176: 153-158.

18. Holmes G, Lister WT. Disturbances of vision from cerebral lesions with special reference to the macula. Brain 1916; 39: 34-73.

19. Holz FG, Pauleikhoff D, Klein R, Bird AC. Pathogenesis of lesions in late age-related macular disease. Am J Ophthalmol 2004; 137: 504-510.

20. Hossain P, Seetho IW, Browning AC, Amoaku WM. Artificial means for restoring vision. BMJ 2005; 330: 30-33.

21. Humayun M, Sato Y, Propst R, de Juan E Jr. Can potentials from the visual cortex be elicited electrically despite severe retinal degeneration and a markedly reduced electroretinogram? Ger J Ophthalmol 1995; 4: 57-64.

22. Inouye T. Die Sehstorungen bei Schussverletzungen der kortikalen Sehsphare nach Beobachtungen an Verwundeten der letzten japanischen Kriege. Leipzig: W Engelmann 1909.

23. Johansson BB. Brain plasticity in health and disease. Keio J Med 2004; 53: 231-246.

24. Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F et al. Regionally localized thinning of the cerebral cortex in

74 Chapter 2

schizophrenia. Arch Gen Psychiatry 2003; 60: 878-888.

25. Mechelli A, Price CJ, Friston KJ, Ashburner J. Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews 2005; 1: 105-113.

26. Mendola JD, Conner IP, Roy A, Chan ST, Schwartz TL, Odom JV et al. Voxel-based analysis of MRI detects abnormal visual cortex in children and adults with amblyopia. Hum Brain Mapp 2005; 25: 222-236.

27. Merzenich MM, Nelson RJ, Stryker MP, Cynader MS, Schoppmann A, Zook JM. Somatosensory cortical map changes following digit amputation in adult monkeys. J Comp Neurol 1984; 224: 591-605.

28. Nickells RW. Retinal ganglion cell death in glaucoma: the how, the why, and the maybe. J Glaucoma 1996; 5: 345-356.

29. Nucci C, Piccirilli S, Nistico R, Morrone LA, Cerulli L, Bagetta G. Apoptosis in the mechanisms of neuronal plasticity in the developing visual system. Eur J Ophthalmol 2003; 13 Suppl 3: S36-S43.

30. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP et al. Global data on visual impairment in the year 2002. Bull World Health Organ 2004; 82: 844-851.

31. Rosas HD, Hevelone ND, Zaleta AK, Greve DN, Salat DH, Fischl B. Regional cortical thinning in preclinical Huntington disease and

its relationship to cognition. Neurology 2005; 65: 745-747.

32. Safran AB, Landis T. Plasticity in the adult visual cortex: implications for the diagnosis of visual field defects and visual rehabilitation. Curr Opin Ophthalmol 1996; 7: 53-64.

33. Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004; 22: 1060-1075.

34. Smirnakis SM, Brewer AA, Schmid MC, Tolias AS, Schuz A, Augath M et al. Lack of long-term cortical reorganization after macaque retinal lesions. Nature 2005; 435: 300-307.

35. Taub E, Uswatte G, Elbert T. New treatments in neurorehabilitation founded on basic research. Nat Rev Neurosci 2002; 3: 228-236.

36. Tigges M, Hendrickson AE, Tigges J. Anatomical consequences of long-term monocular eyelid closure on lateral geniculate nucleus and striate cortex in squirrel monkey. J Comp Neurol 1984; 227: 1-13.

37. Yucel YH, Zhang Q, Weinreb RN, Kaufman PL, Gupta N. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res 2003; 22: 465-481.

38. Zarbin MA. Current concepts in the pathogenesis of age-related macular degeneration. Arch Ophthalmol 2004; 122: 598-614


76 Chapter 3

Reorganisation in visual cortex associated with visual field defects? 77

CHAPTER 3 Visual field defects and the functional brain

Reorganisation in visual cortex associated with visual field defects?

Authors:

Christine C. Boucard Nomdo M. Jansonius


78 Chapter 3

Abstract

Due to the retinotopic organisation of visual cortex, visual field defects overlapping in

both eyes prevent the stimulation of the corresponding cortical area. Prolonged absence

of stimulation causes the brain to adapt its neuronal circuits. With the use of fMRI and

common techniques, retinotopic maps of age-related macular degeneration (AMD) and

glaucoma patients, as well as controls, were examined in an attempt to investigate

whether cortical reorganization occurs in visual cortex as a result of acquired retinal

visual field defects. From a clinical perspective, this question is highly significant as

functional rehabilitation might be affected by cortical reorganisation.

We present here abnormal retinotopic maps in two cases of AMD. In one case, we

argue that the atypical pattern was caused by extrafoveal fixation. In the other case, the

pattern cannot be explained on the basis of a deviant fixation. Hence, some form of

cortical reorganisation may have occurred in the right hemisphere of this subject.

Further, no evident differences were found between the retinotopic maps of the

glaucoma and control groups.

The assessment of cortical (re-)organisation in subjects with visual field defects is

especially complicated as uncontrolled variables, such as extrafoveal fixation, can lead

to abnormal retinotopic maps.


Introduction

Due to the retinotopic cortical organisation [1-4], when visual field defects occur in both

eyes and overlap, a section of visual cortex no longer receives stimulation. Under

prolonged absence of stimulation, the brain may respond adapting the cortical neuronal

circuits [5,6].

Acquired visual field defects provide an exceptional opportunity to examine if the mature

human visual cortex reorganises in response to abnormal visual experience. There is

evidence that developmental visual disorders such as strabismus [7], amblyopia [8] and

albinism [9] affect the structure of human occipital cortex. Previous research in our

group also showed occipital cortical thinning in glaucoma (Boucard, 2006 a,b, submitted

for publication). However, it has not been entirely settled whether long-term

reorganization occurs in visual cortex during absence of visual input. In the human,

while Sunness (2004) [10] reported a silent region in the visual cortex corresponding to

the lesion projection zone in one subject known with age-related macular degeneration

(AMD) subject, cortical reorganisation was found in two cases of AMD [11]. Abnormal

retinotopic organisation was also found in rod monochromats, where the cones, and

thus the fovea, are dysfunctional [12,13]. In a study using electrophysiology in macaque,

after monocular retinal lesion, in spite of perceptual filling-in, no topographic

reorganisation was measured in visual cortex [14]. Likely, in a recent fMRI paper,

experimentally induced scotoma, including destruction of the RGC layer, in the

peripheral visual field of macaque monkeys showed no sign of reorganisation even after

seven months [15]. On the other hand, a number of studies reported reorganisation of

receptive fields following induced retinal lesions in cats and monkeys [16-24], but the

extend of RGC damage is unknown. There is a clear diversity of results in both animal

and human research.

With our study, we aim to help to shed light on the diversity of findings in the current

literature. By means of commonly used retinotopic techniques [1,25-27] in fMRI, we are

examining retinotopic maps of AMD and glaucoma patients, as well as controls, in an

80 Chapter 3

attempt to investigate the occurrence of cortical reorganisation in acquired retinal visual

field defects. The question is highly relevant from a clinical point of view as cortical

reorganisation might affect functional recovery after treatment of retinal disease.

Here, we present abnormal retinotopic maps in two cases of AMD. In the first case, the

atypical pattern might have been generated by eccentric or extrafoveal fixation.

However, in the second case, we did not find such an explanation for the abnormal

retinotopic pattern. This may suggest that cortical reorganisation has occurred in one

hemisphere. Furthermore, the retinotopic maps of the glaucoma and control groups

showed no evident differences, suggesting most likely absence of cortical

reorganisation.

Methods

Subjects AMD1 is an 81-year-old male with visual acuity of 0.2 in the left eye and 0.08 in the

right. AMD2 is a 68-year-old male with visual acuity of 0.8 in the left eye and 0.1 in the

right. Both subjects are diagnosed with AMD since at least 3 years. They presented

homonymous scotoma of at least 10 degrees diameter located in the foveal region. The

glaucoma patients showed primarily large peripheral visual field defects heading

towards fixation and were required to have homonymous scotoma of at least 10 degrees

diameter located centrally in at least one quadrant, for a minimum of 3 years. In both

groups, no other (neuro-) ophthalmic disease affecting the visual field was present. The

control subjects have good visual acuity, no visual field defect, and are free of any

ophthalmic, neurologic, or general health problem.

AMD is caused by accumulated waste products in the tissues underneath the macula

that interfere with retinal metabolism and lead to retinal atrophy [28,29]. The disease

causes centrally located visual field defects.



Meditec, Dublin, California, USA) running the 30-2 program Sita Fast.

High-resolution MRI was performed on a 3.0 Tesla Philips Intera (Best, The

Netherlands). A “full” 3-D structural MRI was acquired on each subject using a T1

weighted magnetization sequence T1W/3D/TFE-2, 8 degrees flip angle, matrix size 256

• 256, yielding 170 slices, voxel size 0.9x0.9x1 mm, TR 8.70 ms. The scanning time was

approximately of 10 min. In addition, a “partial” 3-D structural MRI was acquired on each

subject using a T1 weighted magnetization sequence T1W/3D/TFE-2, 8 degrees flip

angle, matrix size 256 • 256, yielding 108 slices, voxel size 0.8x0.8x2 mm, TR 8.70 ms.

Scanning time was approximately 2 min. This “partial” anatomy was only performed in

the occipital area and was used to align the functional data to the “full” anatomy.

Functional data was acquired using a T2*-weighted gradient-recalled echo planar

imaging (EPI) sequence with a SENSE-head coil. Technical data for the measurements

were TE 35 ms, TR 2000 ms, flip angle 79 degrees, 108 slices in one volume, voxel size

1.6x1.6x2.0 mm. The scan duration was 224 s. The field of view was 210 mm for all

subjects. The functional scanning was only performed in the occipital area.

Stimulation Stimuli were presented using a modified version of the RET software developed by the

Vision Science and Technology Activities (VISTA) group at Stanford University

(http://white.stanford.edu/software/). The software was developed in Matlab using

routines of the Psychophysics Toolbox [30,31]; http://psychtoolbox.org/).

Stimuli were displayed with an Apple Macintosh iBook with 8-bit resolution per gun and

projected onto a screen at the top end of the bore of the MR-scanner by means of a

BARCO LCD-projector G300. Subjects viewed the stimuli through a mirror system

supplied with the scanner. The viewing distance was 90 cm. Due to their central visual

field defects, fixation in our group of visual field defects patients is peculiarly difficult.

Therefore, in order to attain good fixation, the subjects were instructed to direct their

82 Chapter 3

gaze towards the centre of a fixation cross that covered the whole screen. The cross

changed colour at random intervals (between 1 and 3 s). To maintain the attention,

subjects had to press a button as soon as they noted a colour change. Stable fixation

was further controlled by means of an eye-tracker device (MR-Eyeview, SMI, Teltow,

Germany) and its corresponding software IView which recorded eye movements during

the whole experiment. All subjects maintained sufficiently accurate stable fixation.

Visual field maps were measured using an expanding ring and two (horizontal and

vertical) bifield wedge-shaped conventional stimuli able to create travelling waves of

neural activity in visual cortex [1,25,32,33]. Both stimuli consisted of drifting, achromatic

(mean luminance ~50 cd/m2), dartboard contrast patterns (~90 % contrast) with contrast

reversal rate of 8Hz. Stimuli were presented from the central fixation point to 9° of

eccentricity during 6 cycles of 36 s each (approximately 3.5 minutes per run). Between

each run, subjects could rest during 1 or 2 minutes.

Retinotopically organized visual areas share their borders at the vertical meridian

representations. Therefore, boundaries between the different retinotopically organised

areas were identified using bifield vertical wedge stimuli (figure 1). This permitted to

localise primary visual cortex. Besides, a bifield horizontal wedge (figure 1) evoked

activity in the centre of the different retinotopically organised areas in the hemisphere

contralateral to the stimulation. Because the upper visual field is represented in the

ventral cortical areas while the lower visual field in the dorsal areas, the ventral and

dorsal edges of primary visual cortex could also be distinguished. Eccentricity was

measured with the use of dynamic expanding rings (figure 2). As the ring moved from

fovea to periphery, the activity at locations containing neurons with peripheral receptive

fields is delayed relative to locations containing neurons with foveal receptive fields,

creating a travelling wave of neural activity.

Figure 1. Horizontal and vertical bifield wedges.


Figure 2. Two snapshots of the expanding ring pattern.

Data analysis The first steps of data processing were performed using SPM (Statistical Parametric

Mapping) software (Wellcome Department Imaging Neuroscience, London, UK;

http://www.fil.ion.ucl.ac.uk/spm/spm99.html). To correct for the eventual motion during

or between the functional runs, the EPI functional volumes were realigned to the first

volume of the first run. Then, “partial” anatomy was co-registered to a functional volume.

Next, using the VISTA toolbox, the “partial” anatomy was aligned to the “full” anatomy.

In this way, the functional data spatially matches the “full” anatomy. Functional data was

then averaged for every stimulus type (rings and wedges). The “full” anatomy was

segmented separately by hemisphere. Finally, activations resulting from both rings and

wedges were displayed on a flattened grey matter model of each hemisphere.

Using the wedge activations and their anatomical representations, we specified the

location of V1. Next, the eccentricity information expressed by the rings within that area

told us about the organisation of the representation of the visual fields in V1. Finally, the

retinotopic map in V1 was assessed by visually examining the patterns.

Results

Figure 3 displays the representation of visual field eccentricity resulting from the

expanding ring stimulation in a control subject (a), and two AMD patients (AMD1(b) and

AMD2(c)).

84 Chapter 3

Figure 3. Retinotopic maps of the left and right hemispheres of a) control subject; b) patient AMD1; and c)

patient AMD2 where the arrow points to the area of interest. The colours on the retinotopic map correspond to

the location where the visual field was stimulated by the different rings (or stimulus phase). The icon on the

right top indicates the relationship between colour and location where the visual field was stimulated (which

corresponds to each phase of the stimulus). The black lines, drawn by hand along the activation

corresponding to the vertical wedge, indicate the boundaries of V1. Letter “v” stands for ventral edge of V1,

letter “d” for the dorsal edge.


A conventional retinotopic map is first shown in a). As it is expected by the retinotopic

topography under which V1 is organised, each of the rings evoked activity preserving

the eccentric order in which they were presented and without interruptions. This, of

course, is based on the assumption that the fovea of both eyes were directed at the

centre of the fixation cross.

When compared to the retinotopic map of a control subject, patients AMD1 and AMD2

exhibit abnormal retinotopic patterns.

In the case of AMD1 (b), in both hemispheres, the activation induced by the two most

inner rings (red and yellow) is located on the dorsal edge of V1, while the central and

ventral part of V1 responded to more outer rings (blue). The same pattern is repeated in

the neighbouring retinotopic areas, such as V2. In this abnormal pattern, the activation

seems to be shifted suggesting that the participant may have been using an extrafoveal

part of the retina to fixate the center of the cross (figure 4).

AMD2 (c) presents a more or less conventional pattern in the left hemisphere, whereas

in the right hemisphere the representation of the expanding rings is rather abnormal.

Central fixation in this participant is confirmed by the conventional pattern in the left

hemisphere. In the right hemisphere, under such central fixation, the expected

continuous bands of yellow and green are interrupted by a section of blue that runs

approximately through the middle of primary visual cortex. Hence, a section of cortex

expected to be primarily responsive to the second and third inner rings (yellow and

green) appears to have been activated by the outer rings (blue).

The retinotopic patterns from the glaucoma group did not show any clear difference from

the maps from the control group, and are therefore not reproduced here.

Discussion

We here presented two atypical retinotopic maps of patients (AMD1 and AMD2) known

with AMD, an acquired retinal visual field defect resulting in central scotoma.

86 Chapter 3

First, we show how, after a careful analysis, the abnormal pattern found in AMD1 can be

explained by eccentric or extrafoveal fixation. In the case of AMD2, we did not find other

explanation than cortical reorganisation for the retinotopic pattern in the right

hemisphere. Further, the retinotopic patterns from the glaucoma group did not show any

clear difference from the maps from the control group.

In the case of AMD1, the pattern (figure 3b) can be explained by the fact that the subject

did not fixate in the middle of the cross, as instructed, but opted for eccentric extrafoveal

fixation. Fixation was additionally controlled by means of an eye-tracker device.

However, the eye-tracker only controls for eye movements, reporting about the stability

of the fixation, but fails in reporting about possible eccentric fixation. In the case of

central scotoma, fixation is always an arduous task and complicates retinotopic

measurements. As a strategy to overcome their foveal impairment, very often patients

with central scotoma automatically adopt an extrafoveal preferred retinal locus (PRL) for

fixation [34-36]. The schematic figure 4 shows the possible consequences of extrafoveal

fixation along the vertical meridian in the upper visual field. In this case, the central

stimulation (red) would fall onto the peripheral visual field. On the other hand, the foveal

part of the visual field would be stimulated by a more peripheral phase (blue). Central

and eccentric stimulation would no longer correspond with foveal and peripheral

activation resulting in an atypical pattern of the central and eccentric phases, in the

dorsal edge of V1. Such a retinotopic map can be seen in patient AMD1, where on the

ventral part of V1, the blue colour prevails as a sign of peripheral stimulation. On its

dorsal part, the whole visual field appears to be represented, but in a rather patchy

manner. AMD1’s retinotopic map is therefore consistent with extrafoveal fixation. No

cortical reorganisation can be thus deduced from this atypical pattern.


Figure 4. Schematic representation of the hypothetical retinotopic map resulting from eccentric or extrafoveal

fixation along the vertical meridian in the upper visual field. On the left, the stimulation of the rings is

represented by the different colours. The black point shows where the participant is hypothetically fixating with

the fovea. The vertical and horizontal lines indicate how the visual field is represented in visual cortex. On the

right, a drawing shows the expected retinotopic pattern within V1 in one hemisphere. The star (*) indicates the

location of the foveal representation.

On the other hand, in the retinotopic map of patient AMD2 (figure 3c), the visual field is

represented in a normal fashion, except for the central area (see arrow) in the right

hemisphere. That area is normally expected to respond to parafoveal stimulation, as it

holds projections from that area. However, here it reacted to the stimulation of the

peripheral visual field (blue). The fact that the atypical pattern is only present in one

hemisphere rejects the possibility of eccentric or extrafoveal fixation along the vertical

meridian, since both hemispheres would show abnormal patterns in that case. Yet, the

inner ring (red) representation in the pattern of the left hemisphere is located at the

expected foveal area. The rest of the eccentricity map in the left hemisphere follows the

conventional pattern, as well. It seems thus that in this case, fixation was centrally

directed. Eccentric or extrafoveal fixation does not appear to be able to explain the

observed retinopic pattern. Therefore, a possible explanation is that cortical

88 Chapter 3

reorganisation may have occurred in the right hemisphere of AMD2. The causes of such

a lateralisation are not clear to us. Manifestly, the complicate mechanisms behind

cortical reorganisation still need to be investigated in depth.

A possible mechanism by which peripheral stimulation activates visual cortex in the

location where the foveal and parafoveal representations are expected could be new

intracortical horizontal connections formed by axonal sprouting. By such a mechanism,

the visual input reaching active areas would eventually spread to deprived areas.

Previous work in cats and monkeys assigned intracortical horizontal connections as the

main factor accounting for the observed reorganisation after retinal lesions [19,37,38]. In

our case, foveal and parafoveal silent cortical regions would attract connections from the

unimpaired retinal periphery. The resulting retinotopic map would show invasion of

central areas with peripheral ones.

In the case of glaucoma, there is progressive retinal ganglion cell (RGC) and optic nerve

damage which leads to visual field loss starting peripherally and growing towards the

fovea. As the retina presents additional peripheral loss, new connections would be more

improbable to occur. On the other hand, there is substantial evidence linking cortical

degeneration to glaucoma. Experimentally induced glaucoma in cats [39] and non-

human primates [40] results in cell loss in visual cortex. Recent neuro-imaging work in

our group also showed a lower grey matter concentration as a result of cortical thinning

in the cortical lesion projection zone in human patients with glaucoma but not in AMD

(Boucard et al., 2006a,b; submitted for publication). Degenerative changes in the visual

cortex were also very recently reported in an autopsy examination of one glaucoma

patient [41]. The fact that no obvious atypical patterns were seen in the retinotopic maps

of our glaucoma group may suggest that cortical reorganisation does not occur in case

of cortical degeneration. Perhaps, cortical atrophy prevents the formation of new

horizontal connections.

The measurement of cortical organisation associated with abnormal visual fields is

especially complicated because it is linked to a series of possible artefacts which can


lead to erroneous interpretations. Indeed, although finding atypical maps in the cortical

projection zones can be a sign of cortical reorganisation, before linking an abnormal

map to cortical reorganisation, one should primarily exclude any other possible

explanation that could have originated the atypical pattern. Uncontrolled variables, such

as extrafoveal fixation, can lead to abnormal retinotopic maps making the task of

interpretation a very delicate one.

In the present study, the fact that we find only one possible case of cortical

reorganisation implies caution in the conclusions and requires further investigation.

Finally, it should be mentioned that, because of the relative limited spatial resolution of

fMRI, the technique we employ here only allows measuring the presence of large

cortical reorganisation. Small changes in cortical organisation would most likely go

unnoticed.

A better understanding of the relation between retinal visual field defects and functional

changes in visual cortex may help understand disease symptoms as well as their

progression. Moreover, cortical reorganisation may limit the efficacy of rehabilitation and

training programs [42] as well as retinal prostheses aimed at restoring some degree of

vision in the blind [43].

In order to understand the mechanisms behind cortical reorganisation, future retinotopic

research should be directed towards the comparison of maps obtained from different

disorders. For example, retinitis pigmentosa, which presents intact RGCs together with

peripheral vision loss, would clarify the question if cortical reorganisation is related to

intact RGC or to central location of scotoma. Likely, optical neuritis, where nerve

damage is present, would also help bring additional insight to the issue.

Acknowledgement


Netherlands. The study was further supported by an equipment grant from the Prof.

90 Chapter 3

Mulder foundation, Behavioural and Cognitive Neuroscience (BCN) school and the

Medical Faculty of the University of Groningen. We would like to thank Ronald van den

Berg and Just van Es for adapting the VISTA software, and Debora Zandbergen for her

help in the experiments and data analysis.


References

1. Engel SA, Glover GH, Wandell BA. Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb Cortex 1997; 7:181-192.

2. Horton JC, Hoyt WF. The representation of the visual field in human striate cortex. A revision of the classic Holmes map. Arch Ophthalmol 1991; 109:816-824.

3. Inouye T. Die Sehstorungen bei Schussverletzungen der kortikalen Sehsphare nach Beobachtungen an Verwundeten der letzten japanischen Kriege. W Engelmann: Leipzig, 1909.

4. Tootell RB, Switkes E, Silverman MS, Hamilton SL. Functional anatomy of macaque striate cortex. II. Retinotopic organization. J Neurosci 1988; 8:1531-1568.

5. Johansson BB. Brain plasticity in health and disease. Keio J Med 2004; 53:231-246.


7. Chan ST, Tang KW, Lam KC, Chan LK, Mendola JD, Kwong KK. Neuroanatomy of adult strabismus: a voxel-based morphometric analysis of magnetic resonance structural scans. Neuroimage 2004; 22:986-994.

8. Mendola JD, Conner IP, Roy A, Chan ST, Schwartz TL, Odom JV, et al. Voxel-based analysis of MRI detects abnormal visual cortex in children and adults with amblyopia. Hum Brain Mapp 2005; 25:222-236.

9. von dem Hagen EA, Houston GC, Hoffmann MB, Jeffery G, Morland AB. Retinal abnormalities in human albinism translate into a reduction of grey matter in the occipital cortex. Eur J Neurosci 2005; 22:2475-2480.

10. Sunness JS, Liu T, Yantis S. Retinotopic mapping of the visual cortex using functional magnetic resonance imaging in a patient with central scotomas from atrophic macular degeneration. Ophthalmology 2004; 111:1595-1598.


12. Baseler HA, Brewer AA, Sharpe LT, Morland AB, Jagle H, Wandell BA. Reorganization of human cortical maps caused by inherited photoreceptor abnormalities. Nat Neurosci 2002; 5:364-370.

13. Morland AB, Baseler HA, Hoffmann MB, Sharpe LT, Wandell BA. Abnormal retinotopic representations in human visual cortex revealed by fMRI. Acta Psychol (Amst) 2001; 107:229-247.

14. Murakami I, Komatsu H, Kinoshita M. Perceptual filling-in at the scotoma following a monocular retinal lesion in the monkey. Vis Neurosci 1997; 14:89-101.










92 Chapter 3


25. DeYoe EA, Carman GJ, Bandettini P, Glickman S, Wieser J, Cox R, et al. Mapping striate and extrastriate visual areas in human cerebral cortex. Proc Natl Acad Sci U S A 1996; 93:2382-2386.

26. Slotnick SD, Yantis S. Efficient acquisition of human retinotopic maps. Hum Brain Mapp 2003; 18:22-29.

27. Warnking J, Dojat M, Guerin-Dugue A, Delon-Martin C, Olympieff S, Richard N, et al. fMRI retinotopic mapping--step by step. Neuroimage 2002; 17:1665-1683.



30. Brainard DH. The Psychophysics Toolbox. Spat Vis 1997; 10:433-436.

31. Pelli DG. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat Vis 1997; 10:437-442.

32. Engel SA, Rumelhart DE, Wandell BA, Lee AT, Glover GH, Chichilnisky EJ, et al. fMRI of human visual cortex. Nature 1994; 369:525.

33. Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 1995; 268:889-893.

34. Cheung SH, Legge GE. Functional and cortical adaptations to central vision loss. Vis Neurosci 2005; 22:187-201.

35. Varsori M, Perez-Fornos A, Safran AB, Whatham AR. Development of a viewing strategy during adaptation to an artificial central scotoma. Vision Res 2004; 44:2691-2705.

36. Sunness JS, Applegate CA, Haselwood D, Rubin GS. Fixation patterns and reading rates in eyes with central scotomas from advanced atrophic age-related macular degeneration and Stargardt disease. Ophthalmology 1996; 103:1458-1466.

37. Das A, Gilbert CD. Receptive field expansion in adult visual cortex is linked to dynamic changes in strength of cortical connections. J Neurophysiol 1995; 74:779-792.

38. Obata S, Obata J, Das A, Gilbert CD. Molecular correlates of topographic reorganization in primary visual cortex following retinal lesions. Cereb Cortex 1999; 9:238-248.


40. Yucel YH, Zhang Q, Weinreb RN, Kaufman PL, Gupta N. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res 2003; 22:465-481.

41. Gupta N, Ang LC, Noel de Tilly L, Bidaisee L, Yucel YH. Human Glaucoma and Neural Degeneration in the Intra-cranial Optic Nerve, Lateral Geniculate Nucleus and Visual Cortex of the Brain. Br J Ophthalmol 2006.

42. Safran AB, Landis T. Plasticity in the adult visual cortex: implications for the diagnosis of visual field defects and visual rehabilitation. Curr Opin Ophthalmol 1996; 7:53-64.

43. Hossain P, Seetho IW, Browning AC, Amoaku WM. Artificial means for restoring vision. Bmj 2005; 330:30-3


94 Chapter 4

Occipital 1H-MRS reveals normal metabolite concentrations in retinal visual field defects 95

CHAPTER 4 Visual field defects and the metabolic brain

Part 1

Occipital 1H-MRS reveals normal metabolite concentrations

in retinal visual field defects

Authors:

Christine C. Boucard Johannes M. Hoogduin Jeroen van der Grond Frans W. Cornelissen

Submitted

96 Chapter 4

Abstract

We investigated whether progressive retinal visual field defects, which prevent

stimulation of visual cortex, affect metabolite concentrations in the occipital region such

as N-acetylaspartate (NAA), a marker for degenerative processes, creatine and choline.

Participants known with glaucoma, age-related macular degeneration and controls were

examined by proton MR spectroscopic imaging. Absolute NAA, Creatine and Choline

concentrations were derived from a single voxel in the occipital region of each

hemisphere. No significant differences were found between the three groups among any

metabolite concentration. We conclude that progressive retinal visual field defects do

not reduce metabolite concentration in visual areas suggesting that there is no ongoing

occipital degeneration. We discuss the possibility that metabolite change is too slow to

be detectable.


Introduction


macular degeneration (AMD) and glaucoma [1], are affected by progressive retinal

visual field defects. Due to the retinotopic cortical organisation, when these field defects

occur in both eyes and overlap, a corresponding section of visual cortex no longer

receives stimulation. It is known that prolonged absence of stimulation may result in

cortical changes [2,3]. The question addressed here is: are occipital concentrations of

N-acetylaspartate (NAA) (a marker of neuronal integrity), Creatine (Cr) and Choline

(Cho) metabolites affected as a consequence of progressive retinal field defects? To

study this question, we measured absolute concentrations of these compounds using

single-voxel proton magnetic resonance spectroscopy (1H-MRS) in the occipital region

in each hemisphere of AMD and glaucoma patients and a control group. 1H-MRS is a non-invasive technique that allows detection and quantification of certain

biochemical compounds in brain tissue, such as NAA, Cr, Cho, and lipids [4,5]. NAA is

found at relatively high concentrations in the human central nervous system and is

particularly localized within neurons and related to neuronal processes [4,6]. A decrease

in its concentration is routinely considered as an indicator of neuronal loss or

dysfunction [7,8] and has been observed in different brain regions in various

neurodegenerative disorders [5,9-13] and neuro-ophthalmology [14]. Its decrease is

mostly observed at the moment when the disease is in progression. Cr, which is known

to play an important role in energy metabolism, has been reported to be constant

throughout the brain and resistant to change in several degenerative brain diseases

[4,15,16]. Cho is considered a marker for cell turnover [4].

AMD is caused by the accumulation of waste products in the tissues underneath the

macula preventing normal retinal metabolism and leading to gradual retinal atrophy

[17,18]. AMD affects the retinal pigment epithelium and the photoreceptor layer, and

causes centrally located visual field defects. In glaucoma, visual field loss starts

peripherally. Progressive retinal ganglion cell loss and optic nerve damage occur, in

98 Chapter 4

most cases, induced by an elevated intra-ocular pressure [19,20]. In both cases, the

retinal damage is in continuous progress.

NAA is considered a major marker for neuronal integrity. Therefore, if the progressive

visual deprivation affects the metabolism of adult visual cortex, we expect to find lower

local NAA concentrations in the occipital areas for the visual field defect groups when

compared to the control group.

Methods




group consisted of seven patients suffering from AMD (two female and five males; mean

age 72 years, range 52-82) and seven patients with primary open-angle glaucoma (one

female and six males; mean age 73 years, range 61-84). Patients had to have for a

minimum of 3 years homonymous scotoma of at least 10 degrees diameter located

centrally in at least one quadrant (as recorded using the 30-2 program Sita Fast of

Humphrey Field Analyzer (Carl Zeiss Meditec, Dublin, California, USA)). Patients with

any other (neuro-) ophthalmic disease that might affect the visual field were excluded.

For the control group, 12 healthy subjects (four female and eight male; mean age 62

years, range 46-82) were recruited either by advertisement in a local newspaper, or

were the partners of the visual field impaired participants. Control subjects were

required to have good visual acuity, not to have any visual field defect, and had to be

free of any ophthalmic, neurologic, or general health problem.

This study was approved by the medical review board of the University Medical Center

Groningen (Groningen, The Netherlands). All participants gave their informed consent

prior to participation.


Materials and data acquisition Single voxel 1H-MRS was performed on a whole body 3.0 Tesla Philips Intera scanner

(Eindhoven, The Netherlands) in both the left and right occipital pole using the standard

T/R headcoil. Scanning parameters were: TE=144ms, TR=2s and 128 signal averages.

An elongated PRESS box was located along the calcarine sulcus as far to the back of

the occipital pole and the midline of the brain as possible while avoiding the inclusion of

fat and vasculature (figure 1). Total scan time was 5 min per 1H-MRS voxel including

acquisition of an unsuppressed water signal with identical scanner settings.

Raw signals were post processed using the scanner software. Post-processing

included: 1) DC baseline correction using the last 10% of the signal. 2) Multiplication

with a Gaussian and exponential function resulting in 2 Hz line broadening and 1 Hz line

sharpening, respectively. 3) Zero filling from 1024 to 4096 samples. 4) Fourier

transformation from the time to the frequency domain. 5) Manual zero and first order

phase correction.

Baseline and peak heights were determined manually by 3 independent operators,

naïve with respect to the subject’s classification. The measurements showed good

correlation between operators and were averaged.

Absolute metabolite concentrations were obtained by using the unsuppressed water

spectrum as a reference and assuming a water volume percentage of 71%.

Figure 1. Example of PRESS box location (left hemisphere) and spectra.

100 Chapter 4

Statistics Because no significant differences between the right and left hemispheres were

detected, these values were averaged. A one-way analysis of variance (ANOVA) was

used to determine any significant difference in the concentration of the three metabolites

(NAA, Cr and Cho) between the three groups (AMD, glaucoma and controls).

Results

Figure 2 depicts the average of NAA, Cr and Cho absolute concentrations for each

group. The one-way ANOVA between the three groups (AMD, glaucoma and controls)

showed no significant differences for any of the three metabolites concentration: NAA:

F(2,23)=2.433, p<0.110; Cr: F(2,23)=2.144, p<0.140; and Cho: F(2,23)=1.754, p<0.195.

Figure 2. NAA, Cr and Cho absolute concentrations averaged for each group.

Discussion

The main finding of this study is that, the NAA absolute levels in the occipital brain of

subjects with progressive visual field defects (AMD and glaucoma) do not differ from the

levels of a group of control subjects. Hence, our results indicate that progressive retinal

visual field defects do not induce a measurable decrease in NAA metabolite

concentration in the visual brain areas.


AMD and glaucoma are both accompanied by progressive visual field defects. Because

of cortical retinotopic organisation, the section of visual cortex corresponding to the

dysfunctional area of the retina will no longer receive input. Indeed, previous work by

our group (Boucard et al., 2006a,b; submitted for publication) showed that compared to

controls, glaucoma, but not AMD, patients showed a lower grey matter (GM)

concentration as a result of cortical thinning in the cortical lesion projection zone.

Knowing that non-stimulated cortical tissue tends to degenerate [2] and that occurring

cell loss is linked to a decrease in NAA metabolite concentration [7,8,21], we would

have expected to find a reduction in NAA concentration in the visual brain of patients

suffering from progressive visual field defects, in particular those with glaucoma.

However, this is not the case. The fact that in our sample there was no significant lower

level of NAA suggests that cell loss is not currently occurring in the occipital brain of our

patient groups. In agreement, longitudinal studies have emphasized that a substantial

proportion of the decreases in NAA occurs in the acute phase of cell degeneration [21].

Visual field degeneration in both AMD and glaucoma progresses rather slowly [22]. This

fact suggests that perhaps the rate of progression is not high enough to evoke a

decrease in NAA metabolite concentration. Alternatively, the cortical area corresponding

to the affected retinal region may be too small to provoke NAA changes that can be

measured using single voxel 1H-MRS.

On the other hand, the region of interest (ROI) defined by our single voxel may not

completely cover a degenerated region. In our previous MRI studies (Boucard et al.,

2006a,b; submitted for publication) we investigated changes in grey matter. The area

where differences were detected did correspond to the projection zone of the damaged

region of the retina and was located in the anterior occipital lobe, along the

interhemispheric fissure. Because of its proximity to the fissure, and consequently its

vicinity to large amounts of pulsating blood, this area is unsuitable for a single voxel 1H-

MRS ROI. As a consequence, our present ROI locations may not have been placed

exactly in the cortical region previously associated with a reduction in grey matter. This

may have reduced our ability to demonstrate small local changes in metabolite

concentration.

102 Chapter 4

Our results show no significant differences in Cr concentrations between all three

groups. This was expected since this metabolite has been observed to stay invariable

throughout the brain, also in the case of degenerative brain disorders [4,15,16].

Therefore, normalized changes in NAA are mostly assessed in relation to Cr in terms of

the ratio NAA/Cr [23]. However, despite being resistant to change in degenerative

diseases, we preferred not to make use of the ratio NAA/Cr to measure cell loss.

Variations in Cr levels do occur as general loss together with other metabolites in tissue

necrosis [4]. Thus, if the process of degeneration has already taken place and has

stopped, NAA/Cr levels will not decrease but both NAA and Cr compounds will be

comparably reduced as a result of the decay of cell number. The ratio NAA/Cr will

consequently be inadequate to evaluate any changes in NAA.

Increases in Cho levels have been related to cell turnover [4]. Again, the fact that in our

experiment no changes were measured in Cho levels among all three groups reveals

that no cortical metabolic changes are associated with visual field defects.

With the relatively small size of our experimental groups, the present study has only the

power to detect big effects. This is not necessarily a disadvantage, because relatively

large effects would have had more potential clinical implication than subtle differences.

On the other hand, larger sample sizes could allow identification of more subtle

differences, the presence of which is suggested by trends in our analysis.

Finally, AMD and glaucoma are both associated with the occurrence of visual field

defects but differ in the pathology. In glaucoma, the optic nerve is damaged, while in

AMD it remains intact. We thus observe that nerve damage does not necessarily affect

NAA levels in the occipital region. Conversely, an immunohistochemistry study targeting

the mechanism behind multiple sclerosis linked artificially induced optic nerve damage

in the rat to a decrease in NAA concentration, which returned to normal level after 24

days [24]. Likewise, a lower NAA concentration in the chiasm of two patients suffering

from optic neuritis was found. The NAA levels increased after visual field improvements

[25]. This is in agreement with the idea that reduced NAA concentrations can only be

found when degenerative processes are currently taking place. In the case of our patient

groups, the absence of a detectable change suggests either that occipital degeneration


had already occurred (as presumably is the case of glaucoma) or perhaps occurs at a

rate that is too slow to induce detectable NAA change.

Conclusion

No significant differences in NAA, Cr and Cho absolute concentrations were found in the

occipital brain of visual field defect patients when compared with controls. The absence

of a reduction in NAA concentrations compared to controls primarily most likely indicates

that no degeneration is currently occurring in the occipital region of AMD and glaucoma

patients. This absence might also be due to the fact that both diseases progress at a

very slow rate, which may prevent detectable NAA changes. Further research

concerning ocular disorders with a faster degenerative process (for instance, as in

ischemic optic neuropathy or retinal vascular occlusion) could clarify this issue. The

application of 1H-MRS for the metabolic evaluation of consequences of retinal visual

field defects in the visual brain may help understand disease symptoms and progression

as well as mechanism of brain plasticity in general.

Acknowledgement

We want to thank Anita Kuiper for assistance during scanning.

104 Chapter 4

References

1. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, et al. Global data on visual impairment in the year 2002. Bull World Health Organ 2004; 82:844-851.

2. Johansson BB. Brain plasticity in health and disease. Keio J Med 2004; 53:231-246.


4. Gujar SK, Maheshwari S, Bjorkman-Burtscher I, Sundgren PC. Magnetic resonance spectroscopy. J Neuroophthalmol 2005; 25:217-226.

5. Passe TJ, Charles HC, Rajagopalan P, Krishnan KR. Nuclear magnetic resonance spectroscopy: a review of neuropsychiatric applications. Prog Neuropsychopharmacol Biol Psychiatry 1995; 19:541-563.

6. Simmons ML, Frondoza CG, Coyle JT. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 1991; 45:37-45.

7. Block W, Traber F, Flacke S, Jessen F, Pohl C, Schild H. In-vivo proton MR-spectroscopy of the human brain: assessment of N-acetylaspartate (NAA) reduction as a marker for neurodegeneration. Amino Acids 2002; 23:317-323.

8. Demougeot C, Garnier P, Mossiat C, Bertrand N, Giroud M, Beley A, et al. N-Acetylaspartate, a marker of both cellular dysfunction and neuronal loss: its relevance to studies of acute brain injury. J Neurochem 2001; 77:408-415.

9. Block W, Karitzky J, Traber F, Pohl C, Keller E, Mundegar RR, et al. Proton magnetic resonance spectroscopy of the primary motor cortex in patients with motor neuron disease: subgroup analysis and follow-up measurements. Arch Neurol 1998; 55:931-936.

10. Karitzky J, Block W, Mellies JK, Traber F, Sperfeld A, Schild HH, et al. Proton magnetic resonance spectroscopy in Kennedy syndrome. Arch Neurol 1999; 56:1465-1471.

11. Kuzniecky R. Clinical applications of MR spectroscopy in epilepsy. Neuroimaging Clin N Am 2004; 14:507-516.

12. Sijens PE, Mostert JP, Oudkerk M, De Keyser J. (1)H MR spectroscopy of the brain in multiple sclerosis subtypes with analysis of the metabolite concentrations in gray and white matter: initial findings. Eur Radiol 2006; 16:489-495.

13. Tedeschi G, Litvan I, Bonavita S, Bertolino A, Lundbom N, Patronas NJ, et al. Proton magnetic resonance spectroscopic imaging in progressive supranuclear palsy, Parkinson's disease and corticobasal degeneration. Brain 1997; 120 ( Pt 9):1541-1552.

14. Ettl A, Fischer-Klein C, Chemelli A, Daxer A, Felber S. Nuclear magnetic resonance spectroscopy. Principles and applications in neuroophthalmology. Int Ophthalmol 1994; 18:171-181.

15. Chan YL, Yeung DK, Leung SF, Cao G. Proton magnetic resonance spectroscopy of late delayed radiation-induced injury of the brain. J Magn Reson Imaging 1999; 10:130-137.

16. Schuff N, Amend D, Ezekiel F, Steinman SK, Tanabe J, Norman D, et al. Changes of hippocampal N-acetyl aspartate and volume in Alzheimer's disease. A proton MR spectroscopic imaging and MRI study. Neurology 1997; 49:1513-1521.



19. Fechtner RD, Weinreb RN. Mechanisms of optic nerve damage in primary open angle glaucoma. Surv Ophthalmol 1994; 39:23-42.

20. Nickells RW. Retinal ganglion cell death in glaucoma: the how, the why, and the maybe. J Glaucoma 1996; 5:345-356.

21. De Stefano N, Matthews PM, Arnold DL. Reversible decreases in N-acetylaspartate after


acute brain injury. Magn Reson Med 1995; 34:721-727.

22. Jansonius NM. Bayes' theorem applied to perimetric progression detection in glaucoma: from specificity to positive predictive value. Graefes Arch Clin Exp Ophthalmol 2005; 243:433-437.

23. De Stefano N, Narayanan S, Mortilla M, Guidi L, Bartolozzi ML, Federico A, et al. Imaging axonal damage in multiple sclerosis by means of MR spectroscopy. Neurol Sci 2000; 21:S883-887.

24. Bjartmar C, Battistuta J, Terada N, Dupree E, Trapp BD. N-acetylaspartate is an axon-specific marker of mature white matter in vivo: a biochemical and immunohistochemical study on the rat optic nerve. Ann Neurol 2002; 51:51-58.

25. Hashimoto M, Ohtsuka K, Harada K. N-acetylaspartate concentration in the chiasm measured by in vivo proton magnetic resonance spectroscopy. Jpn J Ophthalmol 2004; 48:353-3

106 Chapter 5

Visual Stimulation, 1H-MR Spectroscopy and fMRI of the Human Visual Pathways 107

CHAPTER 5 Visual field defects and the metabolic brain

Part 2

Visual Stimulation, 1H-MR Spectroscopy and fMRI of the Human Visual Pathways

Authors:

Christine C. Boucard Jop P. Mostert

Frans W. Cornelissen Jacques H.A. de Keyser

Matthijs Oudkerk Paul E. Sijens

Published in: European Radiology 2005 Jan;15(1):47-52

108 Chapter 5

Abstract

The purpose was to assess changes in lactate content and other brain metabolites

under visual stimulation in optical chiasm, optic radiations and occipital cortex using

multiple voxel MR spectroscopy (MRS). 1H chemical shift imaging (CSI) examinations of

transverse planes centered to include the above structures were performed in four

subjects at an echo time of 135 ms. Functional MRI (fMRI) was used to confirm the

presence of activity in the visual cortex during the visual stimulation. Spectral maps of

optical chiasm were of poor quality due to field disturbances caused by nearby large

blood vessels and/or eye movements. The optic radiations and the occipital lobe did not

show any significant MR spectral change upon visual stimulation, i.e., the peak areas of

inositol, choline, creatine, glutamate and N-acetylaspartate were not affected.

Reproducible lactate signals were not observed. fMRI confirmed the presence of strong

activations in stimulated visual cortex. Prolonged visual stimulation did not cause

significant changes in MR spectra. Any signal observed near the 1.33 ppm resonance

frequency of the lactate methyl-group was artefactual, originating from lipid signals from

outside the volume of interest (VOI). Previous claims about changes in lactate levels in

the visual cortex upon visual stimulation may have been based on such erroneous

observations.


Introduction

According to the astrocyte-neuron lactate shuttle hypothesis, lactate is formed in the

astrocyte, subsequently transferred to the mitochondria of the neuron, and serves there

as the main fuel for oxidative metabolism (1). In line with this hypothesis, several single-

voxel MR spectroscopy (MRS) studies on the effect of visual stimulation on brain

metabolism, reported that lactate signals increased in the occipital part of the brain upon

visual stimulation (2-5). Other MRS investigators, however, did either not observe such

increased lactate signals at all (6,7) or found only an extremely short-lived increase

followed by a decline (8). In all published examples, the CH3-lactate signals detected

near 1.33 ppm appear to be small. In addition, signals may potentially have been

contaminated with -(CH2)n- lipid signals, arising from the fatty tissue between brain and

skull that is very close to the posterior part of the MRS voxel, that resonate at 1.30 ppm.

In the present MRS study, multiple voxel chemical shift imaging (CSI) was used to

assess any metabolic changes in the visual pathway during visual stimulation. The

visual pathway runs from the retina through the optical chiasm to the lateral geniculate

nucleus. From there the optic radiations project to the visual cortex. Multiple voxel CSI

of transverse planes centered on optical chiasm, optic radiations and visual cortex

allowed for direct comparison of any spectral changes observed in the main structures

of the optical pathways with results in brain areas outside theses regions of interest.

fMRI was used to confirm the presence of activity in visual cortex during similar visual

stimulation.

Materials and methods

MR examinations of four healthy volunteers were performed at the Department of

Radiology of the University Hospital Groningen at a field strength of 1.5 T using the

standard head coil of a Siemens Magnetom Vision MR scanner (Siemens AG, Erlangen,

110 Chapter 5

Germany). Age of volunteers was 21, 25, 26 and 40 years. Informed consent was

obtained after the nature of the procedures had been fully explained. In all subjects

MRS was preceded by the acquisition of T2 weighted MRI scans in order to position the

volumes of interest on the anatomical structures of interest. Automated hybrid PRESS

(point resolved spectroscopy) 2D-CSI measurements with a repetition time (TR) of 1500

ms and an echo time of 135ms (SE, double spin echo) were performed. Hybrid-CSI

includes pre-selection of a VOI that is located within the brain to prevent the strong

interference from subcutaneous fat and is smaller than the phase-encode field of view

(FOV) that must be large enough to prevent wraparound artefacts (9). CSI 16x16 phase

encoding of a transverse FOV of 16x16 cm2 was thus combined with VOIs of

dimensions allowing for optimal measurement of optical chiasm, optic radiations and of

the visual cortex. Automated localized multiple angle projection (MAP) shimming

resulted in water peak line widths of less than 8 Hz in the VOI. Excitation with 2.56 ms

sinc-Hanning shaped RF pulses preceded by 25.6 ms Gaussian shaped RF pulses for

chemical shift selective excitation (CHESS) and subsequent spoiling of the resultant

water signal, was followed by collection of the second spin echo using 1024 data points

and a spectral width of 500 Hz. All 16x16 2D-CSI measurements were 1 acquisition per

phase encoded step with 4 prescans and TR's of 1500 ms (acquisition time 7 min). Time

domain data were multiplied with a Gaussian function (centre 0 ms, half width 256 ms),

2D-Fourier transformed, phase and baseline corrected and quantified by means of

frequency domain curve fitting with the assumption of Gaussian line shapes, using the

standard "Numaris-3" software package provided with the MR system. Sixth order

polynomial lines with a 0-4.3 ppm calculation range were used for baseline correction. In

the curve fitting the number of peaks fitted included the chemical shift ranges restricted

to 3.4-3.6 ppm for inositol 3.1-3.3 ppm for choline (Cho), 2.9-3.1 for creatine (Cr), 2.2-

2.4 for glutamate (Glu), 1.9-2.1 for N-acetyl aspartate (NAA), and 1.2-1.5 ppm for lactate

(Lac), and their line widths and peak intensities unrestricted. Using standard post-

processing protocols the raw data were thus processed automatically, allowing for

operator-independent quantifications. Metabolite concentrations were compared

between a visual stimulation condition and a base line condition during which subjects

had their eyes closed. During visual stimulation subjects viewed an 8 Hz flickering high-


contrast dartboard pattern (pattern size about 15 deg diameter, 0.5 deg checks, central

fixation cross present). Such patterns are known to strongly activate visual cortex (10).

Stimulation and control blocks lasted typically 14 minutes and were repeated twice for

each of the four subjects.

fMRI experiment: One subject (F.W.C.) was tested immediately after the MRS

experiment. During the block design fMRI experiment, a baseline condition (blank

screen with only a central fixation cross present) was alternated with a visual stimulation

condition (flickering dartboard pattern identical to the one reported above). Blocks lasted

30 s. and the sequence was presented 12 times. fMRI data were acquired using a T2*

weighted gradient recalled echo planar imaging sequence. Technical data for the

measurements were: TE 60 ms, TR 3080 ms, flip angle 90°, 26 slices in one volume,

matrix 64x64. Field of view 240 mm. Voxel size:3.75x3.75x3 mm. fMRI data analysis

was performed with SPM99 software (SPM99; Wellcome Department Imaging

Neuroscience, London, UK. http://www.fil.ion.ucl.ac.uk/spm/spm99.html). The EPI

functional volumes were matched to the first volume to eliminate movement artefacts

(SPM: realignment) and spatially smoothed (SPM: smooth, gaussian kernel, FWHM of 5

mm). An fMRI block design analysis procedure (t-test, p>0.05, corrected for multiple

comparisons) was used to compare “stimulation” with “baseline” activation in order to

assess brain activity induced by our stimulation.

Results

Optical chiasm and visual cortex. The spectral map of a transverse slice with a

FOV of 6x11x1 cm3 that was angulated to include optical chiasm as well as occipital

brain tissue is shown in Fig.1. Three baseline CSI measurements were performed as

well as two measurements acquired during optical stimulation. The two anterior rows in

which the optical chiasm is located, show poor spectra due to field interference caused

by nearby large blood vessels and/or eye movement. The quality of the rest of the

spectra is negatively affected by the selection of a FOV that did not allow for an entirely

112 Chapter 5

successful shimming procedure. Thus, upon optical stimulation no significant change in

any metabolite level could be observed inside or outside the occipital lobe.

Figure 1. The CSI (TE135/TR1500) spectral map of a transverse slice with a FOV of

6x11x1 cm3 that was angulated to include optical chiasm as well as occipital brain tissue

(visual cortex).

Optic radiations and visual cortex. The next step was to examine a 9x7x2 cm3

VOI including the optic radiations and the occipital lobe. Spectral quality is much better

(Fig.2). Again the protocol included three baseline and two stimulation measurements.

Over the total number of quantified voxels Cho, Cr and NAA peak areas showed mean

percent standard deviations of 11.3, 8.2 and 4.4 respectively (baseline). Table 1 shows

the peak areas of these metabolites for the optic radiation and occipital voxels included


in the spectral map. The changes in these compounds and those in inositol, Glu and

lactate were not significant. (The apparent lack of inositol in the stimulated spectrum of

Fig.2 rather reflects a signal-to noise ratio inadequate for accurate detection of this

particular compound than any change as a result of visual stimulation).

Figure.2. CSI (TE135/TR1500) spectral map a 9x7x2 cm3 VOI including the optic radiations

and the occipital. The spectra shown are from the same optic radiation voxel with (middle right)

and without optic stimulation (lower right).

Single voxel occipital: visual cortex. In the above examinations, lactate was not

significantly present in any voxel, irrespective of whether it was part of the visual

pathways or not. We therefore included a single voxel TE135/TR1500 MRS examination

114 Chapter 5

of a 3x3x3 cm3 volume measured six times without and ten times with visual

stimulation. Measurements of 128 acquisitions resulted in a time resolution of 3:12 min.

The spectra shown have a (slightly) better signal-to-noise ratio and a lower resolution

between peaks as expected (Fig.3). However, during both presence and absence of

visual stimulation no significant levels of lactate were present. The peaks labelled Lac in

Fig. 3 appear at the wrong frequency, and represent noise rather than lactate. The Cho,

Cr and NAA peak areas showed mean percent standard deviations of 7.2, 5.7 and 4.5

respectively (baseline) and upon stimulation Cho, Cr and NAA showed mean percent

changes of -6.4, 0.3 and 2.0 (not significant). Glutamate (Glu) and inositol signals were

not observed.

Figure 3. Single voxel (TE135/TR1500) spectra of 3x3x3 cm3 volume with

(upper right) and without optical stimulation (lower right).


CSI occipital: visual cortex. The final experiment was a CSI with a VOI narrower

than in the third study (5x7x2cm3) in order to cover as much as possible area of the

occipital lobe. The protocol included four baseline and four visual stimulation MRS

measurements. Over the total number of quantified voxels, Cho, Cr and NAA peak

areas showed mean percent standard deviations of 10.0, 10.6 and 4.3 respectively

(baseline). Upon stimulation Cho, Cr and NAA showed mean percent changes of 7.3,

5.7 and –0.4 (not significant) and these observations did not differ significantly between

occipital lobe (Table 1, last line) and other areas. Glu and inositol were not observed

(peak areas equal to zero). In the most posterior row of spectra, a signal that could have

a lactate contribution was seen twice, once in a baseline and once in a stimulation CSI

(Fig.5).

Figure 4. CSI (TE135/TR1500) spectral map a 5x7x2 cm3 VOI including the occipital lobe. The spectra

shown are from the same occipital voxel with (middle left) and without optic stimulation (middle right).

116 Chapter 5

Figure 5. Posterior parts of CSI (TE135/TR1500) spectral maps from the same

examination as Fig.4. In two of eight maps, once with (upper right) and once without

stimulation (lower right) one voxel shows artifact signal from the adipose tissue

between brain and skull that one could easily mistake for lactate signal

The fourth MRS experiment was immediately followed by fMRI. Strong activations were

observed throughout the occipital lobe upon presentation of our stimulation patterns

(Fig.6). The activity in the visual cortex covered both occipital lobes, and extended from

the occipital poles (were the central visual field representations are known to be

situated) into the interhemispheric sulcus (peripheral visual field representation). The

maxima of activation thus corresponded with the MRS ROI used for this subject.


Figure 6. fMRI pattern of activity in visual cortex obtained applying the comparison

“stimulation” > “baseline” in SPM (p>0.05 corrected). The fMRI stimulation consisted of 12 series of 30 sec. of central fixation cross, followed by 30 sec. of an 8 Hz flickering

dartboard pattern.

Stimulated / Control Cho Cr NAA

Examination Fig.2:

Optic rad. (2 voxels)

Occipital (2 voxels)

109 ± 11

92 ± 12

98 ± 6

108 ± 6

102 ± 8

95 ± 4

Examination Fig.3:

Occipital (1 voxel)

94 ± 10

100 ± 8

102 ± 5

Examination Fig.4:

Occipital (4 voxels)

108 ± 15

101 ± 12

99 ± 5

Table 1. Metabolite peak areas in visually stimulated brain relative to the corresponding areas

before stimulation (% with SD)

118 Chapter 5

Discussion

In this study, we assessed changes in lactate content and other brain metabolites under

visual stimulation in optical chiasm, optic radiations and occipital cortex. Our results

indicate an absence of any significant changes in any of the studied metabolites as a

result of prolonged visual stimulation. We conclude that visual stimulation that does

result in strong fMRI activation does not cause significant changes in MR spectra, at

least for visual stimulations lasting up to 14 min (and a time resolution of 7 min in our

CSI experiments and 3:12 min in our single voxel study). Our observations are thus in

agreement with those of others who did not observe lactate increases (6,7) or only very

early and brief changes in lactate level (increases reversed after 12 sec after the onset

of visual stimulation) (8). Our failure to detect true lactate signals does not reflect

inadequate sensitivity of the MRI equipment used in this study; to the contrary, in terms

of signal-to-noise ratio and resolution the spectra shown here (Fig.2-5) are not inferior to

those published by others. When one considers the entire spectral map with inclusion of

the signals from outside the VOI, it is easily visualized that the “lactate” represents out-

of-phase lipid signals originating from the fatty tissue between brain and skull (fig.5). We

suggest that claims about increased lactate levels made in several publications (2-5)

may have been based on such artefacts.

With the use of higher field MRS equipment (3T or higher) it might still be possible to

achieve a sensitivity for MRS to visual stimulation that approaches that of fMRI. Our

conclusion is that in the visual pathways running from the retina through the optical

chiasm and the lateral geniculate nucleus to the visual cortex, the lactate level remains

very low (<0.5 mM level, the detection limit at 1.5T MRS), even after checkerboard

stimulation.


References

1. Magistretti PJ, Pellerin L, Rothman DL, Shulman RG (1999) Energy on demand. Science 283:496-7.

2. Prichard J, Rothman D, Novotny E, Petroff O, Kuwabara T, Avison M, Howseman A, Hanstock C, Shulman R (1991), Lactate rise detected by 1H NMR in human visual cortex during physiologic stimulation. Proc Natl Acad Sci USA 88:5829-5831.

3. Sappey-Marinier D, Calabrese G, Fein G, Hugg JW, Biggins C, Weiner MW (1992) Effect of photic stimulation on human visual cortex lactate and phosphates using 1H and 31P magnetic resonance spectroscopy. J Cereb Blood Flow Metab 12:584-592.

4. Kuwabara T, Wanatabe H, Tanaka K, Tsuji S, Ohkubo M, Ito T, Sakai K, Yuasa T (1994), Mitochondrial encephalopathy: elevated visual cortex lactate unresponsive to photic stimulation – a localized 1H MRS study. Neurology 44:557-559.

5. Frahm J, Krüger G, Merboldt KD, Hänicke W, Kleinschmidt A (1996), Dynamic uncoupling and recoupling of perfusion and oxidative

metabolism during focal brain activation. Magn Reson Med 35:143-148.

6. Merboldt K-D, Bruhn H, Haenicke W, Michaelis T, Frahm J (1992). Decrease of glucose in the human visual cortex during photic stimulation. Magn Reson Med 25:187-194.

7. Etta A, Fischer-Klein C, Chemelli A, Daxer A, Felber S. Nuclear magnetic resonance spectroscopy (1994) Principles and applications in neuro-opthalmology. Int Opthalmology 18:171-181.

8. Mangia S, Garreffa G, Bianciardi M, Giove F, Di Salle F, Maraviglia B (2003), The aerobic brain: lactate decrease at the onset of neural activity. Neuroscience 118:7-10.

9. Sijens PE, van den Bent MJ, Nowak PJCM, van Dijk P, Oudkerk M (1997), 1H Chemical shift imaging reveals loss of brain tumor choline signal after administration of Gd-contrast agent. Magn Reson Med 37:222-225.

10. Wandell BA (1999), Computational neuroimaging of human visual cortex. Annu Rev Neurosci 22:145-173

120 Chapter 6

fMRI of brigthness induction in human visual cortex 121

CHAPTER 6 Exploring activity in the visual brain

under no physical stimulation

Functional MRI of brightness induction in human visual cortex

Authors:

Christine C. Boucard Just J. van Es

R. Paul Maguire Frans W. Cornelissen

Published in: Neuroreport 2005 Aug 22;16(12):1335-8

122 Chapter 6

Abstract

A grey surface on a bright background appears to be darker than the same surface on a

dark background. We used fMRI to study this phenomenon called brightness induction.

While being scanned, subjects viewed centre-surround displays in which either centre-

or surround-luminance was modulated in time. In both cases, subjects perceive similar

brightness changes in the central surface. In the region of visual cortex encoding this

central surface, both modulations evoked comparable fMRI responses. However, the

surround modulation signal showed a considerable delay relative to the onset of the

brightness percept. This suggests that, although correlated, the fMRI signals do not bear

a direct relationship with perceived brightness. We conclude that retinotopically

organised visual cortex does not represent brightness per se.


Introduction

One of the fundamental questions in visual science is whether the brightness of a

surface, i.e. its perceived luminance, is explicitly represented in the primary visual areas

(often referred to as “early visual cortex”). In the powerful brightness induction illusion

(figure 1), the perceptual experience of brightness is dissociated from the actual

physical stimulation, thus providing an elegant manner to study this issue.

Measurements obtained in cat and monkey single-cell physiological research showed

that some V1 and V2 cells responded to luminance changes occurring far outside their

classical receptive fields suggesting they may play a role in brightness perception [1-6].

Qualitatively and quantitatively, the perception of brightness in macaques and humans

has been shown to be similar [7]. This suggests that also in humans, brightness could

be explicitly represented in the responses of neurons in striate cortex. Indeed, the

results of some recent human neuro-imaging studies tie filling-in and brightness-related

processing to primary visual cortex [8-10].

Figure 1. Brightness induction. A grey surface on a bright background appears to be darker than the

same surface on a dark background.

Thus far, none of human imaging studies varied the magnitude of the brightness (or

filling-in) percept to test for the existence of an explicit representation of brightness in

visual cortex. In this functional magnetic resonance imaging (fMRI) study, subjects were

shown a surface changing in brightness due to various levels of modulation of the

luminance of either the surface itself or its immediate surround. While both types of

124 Chapter 6

stimulation resulted in fMRI signals of similar magnitude, the signal in the central surface

representation evoked by modulating surround luminance showed a large onset latency

that was not present in the perception of the brightness modulations. Although

correlated, the fMRI signal did not bear a direct temporal relationship with subjects’

perceptual experience suggesting that brightness is not explicitly represented in

retinotopically organised visual cortex.

Materials and methods

8 healthy volunteers (5 women and 3 men, mean age 26, range 20-38) participated in

the fMRI experiment. All of them had normal or corrected to normal vision. This study

was approved by the medical review board of the University Medical Centre Groningen.

All subjects gave their informed consent.

Scanning was performed using a 1.5T Siemens Magnetom Vision fMRI scanner with a

head-volume coil (Siemens, Erlangen, Germany). Functional data was acquired using a

T2*-weighted gradient-recalled echo planar imaging sequence. Technical data for the

measurements were TE 60 ms, TR 3.080 s, flip angle 90 degrees, twenty-six slices in

one volume, matrix 64x64, and a slice thickness of 3 mm. The field of view ranged from

200 to 240 mm.

Stimuli were presented by means of custom software written in Matlab, using the

Psychophysics Toolbox extensions (http://psychtoolbox.org). A Panasonic LCD

projector (1024x768 pixel resolution) and translucent screen, located at the foot of the

scanner, were used for display. Subjects viewed the stimuli through an angled mirror,

attached to the head-coil. Non-linearity in the projector’s output was corrected in

software. Background luminance was approx. 100 cd/m2. Screen size was approx. 15 x

10 deg, limited by the scanner’s bore.

Subjects were shown two experimental conditions. In the centre luminance condition,

the luminance of a central oval surface (5.0 deg horizontal by 3.75 deg vertical) was

modulated sinusoidally in time at 1 Hz around the mean background luminance, while

the surround luminance was kept stable. The surround started from the outer edge of


the central figure and was limited by the scanner’s bore. The central figure was oval to

make it as large as possible and at the same time have the surround visible on all sides.

In the surround luminance condition, only the luminance of the surround was modulated.

Stimulus on- and offsets were masked by a temporal gaussian window of 6 s. Three

different levels of luminance modulation were used: 10%, 20% and 40% of contrast

relative to the background. Experimental conditions were alternated with a baseline

condition (“fixation”) with mean luminance values during which subjects gazed at a

screen that was blank except for a central fixation dot. A pilot psychophysical

experiment demonstrated that in a simultaneous matching task, subjects matched

induced brightness changes with real luminance modulations of about 40% of the

magnitude of the inducing modulation. Subject report and personal observation

indicates that induced brightness changes start almost immediately after onset of the

inducing stimulation (see also [11]).

To determine the cortical regions representing the central figure and the surround we

used contrast-reversing (8 Hz) central and peripheral checkerboard localizer stimuli of

the same size as the experimental stimuli.

In order to ensure attention and maintenance of fixation, a coloured fixation dot was

displayed in the centre of the screen during all conditions. The dot changed colour at

random intervals (between 1 and 3 sec) while subjects had to press a button as soon as

they noted the change. All subjects performed the task with high accuracy providing

confirmation of correct fixation and attention maintenance.

Anatomical and functional MRI data for each subject were acquired in a single

experimental session. The scanning session started with the acquisition of a T1-

weighted anatomical image. Next, in each of four functional runs, 330 T2* weighted EPI

volumes were acquired over a time period of 16.9 minutes. Of each run, the first two

volumes contaminated with signal bias due to saturation effects were discarded.

Experimental conditions were presented in a block wise fashion. 12 blocks of

experimental stimuli (36 s or 12 volumes each, with either central or surround luminance

modulation at one of three levels of contrast modulation) were interleaved with blocks of

fixation (18 s, 6 volumes each). This was immediately followed by 6 blocks of localizer

126 Chapter 6

stimuli (36 s, 12 volumes each), interleaved as well with blocks of fixation (18 s, 6

volumes each).

Data were analysed using SPM 99 software (Wellcome Department Imaging

Neuroscience, London, UK. http://www.fil.ion.ucl.ac.uk/spm/spm99.html) in combination

with the MarsBaR toolbox (http://marsbar.sourceforge.net/).

The EPI functional volumes from all runs within a single subject were aligned to the first

volume of the first run to eliminate movement artefacts. These volumes were then co-

registered with the subject’s T1 anatomical image. No spatial smoothing was applied.

Each value in the volume was normalized to the corresponding volume global mean. For each individual subject, functional ROIs representing the central figure and its

surround were determined from the activity obtained during checkerboard stimulation. A

design matrix, describing the data in terms of a general linear model was used to

estimate effect levels for each of the localizer conditions. For our purpose, it is important

to exclude any signal related to the representation of the border between the centre and

surround. In order to achieve “clean” ROIs we used the SPM mask procedure. We first

contrasted the activations corresponding to the central checkerboard > fixation and

peripheral checkerboard > fixation (p<0,0001). Subsequently, we defined the foveal ROI

(i.e. the cortical region representing the central surface) as the cluster of voxels

activated by the contrast central checkerboard > fixation masked exclusively by the

contrast peripheral checkerboard > fixation (p<0,05). In the same manner, a peripheral

ROI (i.e. the cortical region representing the surface surround) was defined by the

cluster of voxels activated by the contrast peripheral checkerboard > fixation masked

exclusively by the contrast central checkerboard > fixation (p<0.05).

Using MarsBaR, we extracted, for each subject, a mean time varying signal across all

voxels in each cluster (both foveal and peripheral) from the data (high-pass temporally

filtered with a 1080 s period). Next, for each subject and each cluster, we calculated the

mean activation relative to fixation for each experimental condition at each luminance

contrast level. The means of each subject (averaged across hemisphere, runs and

replications of the same condition within a run) were entered into a repeated measures

ANOVA with “region of interest” (foveal or peripheral), “type of modulation” (central

figure or surround), and “contrast” (10%, 20% and 40%) as factors. Additionally, time-


courses were plotted of the fMRI signal in the foveal ROI for both centre and surround

modulations and in the peripheral ROI for the surround modulation. To facilitate the

comparison between time-courses, the mean amplitude of the signal obtained during

surround modulation was scaled to approximately match that during central modulation.

Results

Figure 2a shows the mean fMRI blood oxygen level-dependent (BOLD) modulation in

the foveal and peripheral ROIs for each experimental condition. In the foveal ROI, the

fMRI signal obtained when we modulated the central surface in luminance increases

with increasing contrast. During this central surface modulation, there was no significant

change in the signal in the peripheral ROI. During surround luminance modulation, we

did find an increase in fMRI signal in the peripheral ROI. Despite the absence of any

physical changes in the central surface, during surround modulation, the activity in the

foveal ROI was not different from that during actual luminance modulation. The three-

way interaction between region of interest (foveal or peripheral), type of modulation

(centre or surround) and contrast level (10%, 20% and 40%) was significant (F(2,14) =

17.1; p <0.0002).

One further critical test to determine whether the measured BOLD signal changes are

indeed correlated with our subject’s perceptual experience exists of examining the

temporal properties of the signals. Figure 2b shows the BOLD time-course (averaged

over contrast level) for the foveal ROI during both central and surround luminance

modulation as well as for the peripheral ROI during surround modulation. The graph

indicates a delay of at least 3 seconds in the onset of the BOLD in the foveal ROI during

surround luminance change relative to the other two conditions.

128 Chapter 6

Figure 2. (a) Average fMRI signal change. Extracted signal corresponding to the three different contrast

values between central figure and surround (10%, 20% and 40%) for both experimental conditions (central &

surround luminance modulation) and region of interest (foveal & peripheral ROI). (b) Time-course of the fMRI

signal (averaged over contrast level) in the foveal ROI for both centre and surround modulations and in the

peripheral ROI for the surround modulation. Note the delay of at least 3 seconds for the signal onset in the

foveal ROI during surround modulation relative to the other two conditions.

Discussion

We have compared fMRI signals in visual cortex when subjects viewed brightness

changes evoked by luminance modulations of a central surface with those occurring

when similar brightness changes were induced by varying the luminance of the

surface’s surround. Although we found fMRI signals of approximately similar magnitude,

the fMRI signal obtained during induced brightness changes showed a large delay (~3

s) compared to the one measured during direct luminance modulation. Such a delay

was not experienced by the observers (Note that at 1 Hz modulation frequency, during a

period of 3 s, subjects already had observed three cycles of brightness increments and

decrements. This implies that the fMRI signals are not an indication of explicit brightness

representation in human retinotopically organised visual cortex.


It is known that the visual cortex responds very strongly to contrast edges [12]. In our

luminance-modulated stimuli, the location of the outer edge of the central surface and

the inner edge of its surround completely coincided. Could the fact that our stimuli

shared a common edge explain the similarity of the signals obtained during centre and

surround modulation in the foveal ROI? We deem this unlikely since our ROIs were

selected by masking one localizer’s response with the other. Therefore, by definition, the

resulting ROI avoids this common edge area. The dissimilar results for central and

surround luminance modulation in the peripheral ROI confirm this notion (figure 2a). In

case of a delayed positive BOLD signal (caused by blood spreading), an increase of

response would also be expected in the peripheral ROI during central luminance

modulation. We conclude that the present results are not an artefact of our stimulation

or imaging method.

Although we know that our activations are located in retinotopically organised visual

cortical areas, we cannot tie these to specific visual areas such as primary or secondary

visual cortex. As our goal for the present study was to determine if any explicit

representation of brightness could be found we do not see this as an essential limitation.

Our results appear to be somewhat at odds with previous findings of single-cell [2-6]. It

is possible that the fMRI technique, that can measure only responses of large

populations of neurons, was simply not sensitive enough to pick up brightness-

correlated signals. However, only few cells in the animal studies appear to have

responded to brightness per se, as many required probing with contrast stimuli in order

to evoke a “brightness” response. The use of such contrast probes in our view

invalidates labelling responses as strictly brightness-related. Also a number of human

studies have examined brightness signals in primary visual cortex. McCourt & Foxe [9]

recently claimed to have observed rapid (50-80 ms post stimulus) electrical potentials

related to brightness processing. However, considering the relatively low spatial

resolution of their imaging technique and the use of a fine-patterned stimulus, it is not

quite certain that contrast and surface responses were dissociated. In their fMRI study,

Haynes et al. [10] reported surface related responses in V1 during luminance

modulation. Although they showed these responses correlated well with the subjective

judgment of brightness, brightness changes were not separated from luminance

130 Chapter 6

changes, as in the present study. In the light of our present findings, the surface

responses reported on by Haynes et al. [10] were probably not related to the percept of

brightness, but most likely reflected luminance stimulation. Likewise, some

psychophysical studies claim to have found evidence for an explicit cortical brightness

representation [13-17]. However, these psychophysical results do not bind this

representation to a precise cortical region. In fact, our present results are not

inconsistent with the idea of a cortical role in the representation of brightness but

suggest that the role of retinotopically organised areas may be more indirect.

Indeed, the signals obtained in the foveal ROI during induced brightness modulation,

while delayed, are nevertheless of similar magnitude to those obtained during actual

luminance modulation. In addition, the fMRI signal only shows a positive correlation with

contrast in the ROIs and conditions during which surfaces had been perceived to

change in brightness.

A possibility is that the delayed fMRI signal results from neural feedback. Yet, most

known processes such as active cortical filling-in [13,18-22], long-range (e.g. inhibitory)

interactions [3,23], or re-entrant input to V1 [24] related to processes such as stimulus

selection or figure/ground segregation are generally thought to occur at a time scale that

is much smaller than the delay that we find here. Therefore this is less likely. A

speculative option is that the observed delayed signal is related to a form of long-term

potentiation. Repetitive presentation of a visual stimulus leads to a persistent

enhancement of one of the early components of the visual evoked potential [25].

Interestingly, as in our experiment, this signal increase was also observed in regions not

directly stimulated. A final option is that the delayed signal is related to attention. In a

recent fMRI study, Sasaki and Watanabe [8] found that activity in V1 correlated with the

surface filling-in. Yet we cannot be certain that their signal explicitly coded surface

characteristics since the magnitude of the perceived change was not varied and time

courses were not provided. As the activity was enhanced when subjects’ attention was

not distracted by another task, it is thus possible that such filling-in signals and our

delayed signal have the same origin in attentional processes.


Conclusion

We conclude that the human retinotopically organised visual cortex does not explicitly

represent brightness. Yet, we did observe delayed BOLD activity suggesting that those

areas might play a more indirect role in the perception of surface brightness. Finally, our

results emphasize that in spatial fMRI tasks, it is important to consider the magnitude of

activations as well as their temporal characteristics.

Acknowledgments

We want to thank the Department of Radiology of the University Medical Centre

(UMCG) for use of their MR scanner, Anita Kuiper for assistance during scanning,

Christian Keysers and Remko Renken for their fruitful suggestions regarding data

analysis, and Tony Vladusich for useful comments on an earlier version of this

manuscript.

Support

Author CCB is supported by an Ubbo Emmius grant from the University of Groningen.

Authors JJE and FWC are supported by grant 051.02.080 of the Cognition program of

the Netherlands Organization for Scientific Research (NWO).

132 Chapter 6

References

1. Hung CP, Ramsden BM, Chen LM, Roe AW. Building surfaces from borders in Areas17 and 18 of the cat. Vision Res 2001; 41: 1389-1407.

2. Kinoshita M, Komatsu H. Neural representation of the luminance and brightness of a uniform surface in the macaque primary visual cortex. J Neurophysiol 2001; 86: 2559-2570.

3. MacEvoy SP, Kim W, Paradiso MA. Integration of surface information in primary visual cortex. Nat Neurosci 1998; 1: 616-620.

4. Rossi AF, Rittenhouse CD, Paradiso MA. The representation of brightness in primary visual cortex. Science 1996; 273: 1104-1107.

5. Rossi AF, Desimone R, Ungerleider LG. Contextual modulation in primary visual cortex of macaques. J Neurosci 2001; 21: 1698-1709.

6. Rossi AF, Paradiso MA. Neural correlates of perceived brightness in the retina, lateral geniculate nucleus, and striate cortex. J Neurosci. 1999; 19(14):6145-6156.

7. Huang X, MacEvoy SP, Paradiso MA: Perception of brightness and brightness illusions in the macaque monkey. J Neurosci 2002; 22: 9618-9625.

8. Sasaki Y, Watanabe T. The primary visual cortex fills in color. Proc Natl Acad Sci USA 2004; 101(52): 18251-18256.

9. McCourt ME, Foxe JJ. Brightening prospects for early cortical coding of perceived luminance: a high-density electrical mapping study. Neuroreport 2004; 15(1): 49-56.

10. Haynes JD, Lotto RB, Rees G. Responses of human visual cortex to uniform surfaces. Proc Natl Acad Sci USA 2004; 101: 4286-4291.

11. De Valois RL, Webster MA, De Valois KK, Lingelbach B. Temporal properties of brightness and color induction. Vision Res 1986; 26: 887-897.

12. Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol 1962; 160: 106-154.

13. Davey MP, Maddess T, Srinivasan MV. The spatiotemporal properties of the Craik-O'Brien-

Cornsweet effect are consistent with 'filling-in'. Vision Res 1998; 38: 2037-2046.

14. De Weerd P, Desimone R, Ungerleider LG. Perceptual filling-in. a parametric study. Vision Res 1998; 38: 2721-2734

15. Motoyoshi I. Texture filling-in and texture segregation revealed by transient masking. Vision Res 1999; 39: 1285-1291.

16. Paradiso MA, Nakayama K. Brightness perception and filling-in. Vision Res 1991; 31: 1221-1236.

17. Rossi AF, Paradiso MA. Temporal limits of brightness induction and mechanisms of brightness perception. Vision Res 1996; 36: 1391-1398.

18. Gerrits HJ, Vendrik AJ. Simultaneous contrast, filling-in process and information processing in man's visual system. Exp Brain Res 1970; 11: 411-430.

19. Komatsu H, Murakami I, Kinoshita M. Surface representation in the visual system. Brain Res Cogn Brain Res 1996; 5: 97-104.

20. Komatsu H, Kinoshita M, Murakami I. Neural responses in the retinotopic representation of the blind spot in the macaque V1 to stimuli for perceptual filling-in. J Neurosci 2000; 20: 9310-9319.

21. Ramachandran VS, Gregory RL. Perceptual filling in of artificially induced scotomas in human vision. Nature 1991; 350: 699-702.

22. Walls GL. The filling-in process. Am J Optom Arch 1954; 31: 329-341.

23. Das A, Gilbert CD. Topography of contextual modulations mediated by short-range interactions in primary visual cortex. Nature 1999; 399: 655-661.

24. Di Russo F, Martínez A, Hillyard SA. Source Analysis of Event-related Cortical Activity during Visuo-spatial Attention. Cerebral Cortex 2003; 13(5): 486-499.

25. Teyler TJ, Hamm JP, Clapp WC, Johnson BW, Corballis MC, Kirk IJ. Longterm potentiation of human visual evoked responses. Eur J Neurosci 2005;21:2045–2050


SECTION 3:

CONCLUSION

136

Conclusion 137

3.1. Summary of results

The possible presence of degeneration is studied in chapters 1 and 2, where we

investigate the structural changes in our two visual field defects groups by means of

anatomical magnetic resonance imaging (aMRI). In chapter 1, using voxel-based

morphometry (VBM), we found that compared to controls, glaucoma, but not age-related

macular degeneration (AMD), patients had a lower grey matter concentration in the

cortical area which corresponds to the projection zone of their scotoma. In chapter 2,

using a surface-based morphometric method (Freesurfer), cortical thinning was found in

approximately the same region again in glaucoma, but not AMD. These latter results

provide evidence that the lower grey matter concentration found in glaucoma is due to

cortical thinning. Furthermore, both results suggest a significant association between

cortical degeneration and retinal ganglion cell (RGC) and optic nerve damage.

The question of degeneration was also explored in chapter 4. With the use of proton

magnetic resonance spectroscopy (1H-MRS) we aimed to measure the metabolites in

the visual brain areas, especially N-acetyl aspartate (NAA). NAA is considered a

neuronal marker, whereby a decrease in its concentration indicates disease

progression. We hypothesised that, in case of degeneration, low NAA levels would be

measured. The results, however, did not show any significant differences between the

groups. The absence of a reduction may either be due to the fact that disease progress

occurs at a very slow rate, or indicate that no degeneration is currently occurring in the

groups.

In case of reorganisation, cortical neurons may survive and establish new connections

where sufficient input is still available. This would lead to the establishment of a new

cortical map, where the representation of the retina would differ from the norm. Chapter

3 was dedicated to studying the possible remapping in our two experimental groups.

The maps obtained by means of retinotopic techniques using functional magnetic

138

resonance imaging (fMRI) revealed atypical representations in two AMD subjects.

However, in one case, we demonstrated that the atypical pattern might have been

generated by eccentric or extrafoveal fixation. In the other case, we could not find

another explanation than cortical reorganisation for the abnormal pattern obtained from

the right hemisphere. Furthermore, no evident differences were found between the

retinotopic maps of the glaucoma and control groups. According to these results, we

cautiously suggest that perhaps nerve damage, present in glaucoma but not in AMD,

prevents cortical reorganisation (maybe due to the consequent induced cortical

degeneration),

An additional pilot study using 1H-MRS is presented in chapter 5. Increase in lactate

concentrations has been controversially related to brain activity related to visual

stimulation. The aim of the experiment was to test the possibility of measuring lactate

changes during neuronal activation, along the visual pathways and in visual brain areas.

Such changes may provide an alternative means to probe activity in the visual pathway

and cortex which is not dependent on the blood oxygen level-dependent (BOLD)

response used in fMRI. However, no significant increase in lactate levels was measured

during visual stimulation. The fact that the same stimulus evoked robust visual cortex

activation measured by fMRI indicates that lactate levels remain very low, even after

strong visual stimulation. Therefore, it cannot be used to measure neuronal activity in

the visual system.

As described in the introduction, filling-in is often present in visual field defects. In

chapter 6, we investigated the neuronal correlates of the phenomenon in normal

subjects. By means of a perceptual illusion (brightness induction), we measured brain

activity related to brightness induction and visual filling-in in the absence of actual

physical stimulation. The fMRI results indicated that the brightness induction illusion

requires activity in other visual areas, in addition to early visual cortex.

Conclusion 139

3.2. General discussion

In the general discussion, we examine to what extend the hypothesis put forward at the

beginning of the introduction can be accepted. To review, the main hypothesis was that

as a consequence of acquired visual field defects, the visual cortex would either

degenerate or reorganise. Moreover, it was hypothesised that cortical degeneration

would occur in the case of retinal ganglion cell (RGC) and optic nerve damage, while

reorganisation would occur with intact RGCs and optic nerve.

In chapters 1 and 2, we saw that the glaucoma group underwent cortical degeneration,

while AMD most likely did not. The conclusion is strengthened by the fact that significant

cortical degeneration was found specifically in the visual cortex, and that similar results

were obtained using two different analysis methods. The selective occurrence of

degeneration can be interpreted as an indication of a significant association between

cortical degeneration and lesions in the RGC layer. This is in line with the first part of our

hypothesis. Moreover, these results corroborate those of previous studies in non-human

primates [1] and cats [2], where experimentally induced glaucoma through elevation of

the intraocular pressure (IOP) resulted in cell loss in the visual cortex. In the human, a

very recent paper describes optic nerve, lateral geniculate nucleus (LGN) and visual

cortex degeneration in one glaucoma patient, by means of autopsy [3].

The hypothesis of degeneration was further tested in chapter 4 with the use of 1H-MRS.

We hypothesised that, in case of degeneration, the NAA concentration (a neuronal

marker and indicator of disease progression) would be decreased. However, no

significant differences in metabolite levels were found between the groups. This

absence of reduction can be interpreted as an indication that degeneration is not

currently occurring or that disease progress is too slow to induce detectable changes.

The second part of our hypothesis concerned reorganisation. As we presented in

chapter 3, by means of retinotopic analysis, we found a retinotopy pattern which

140

suggested the possible presence of cortical reorganisation in one AMD participant. In

that case, stimulation of the peripheral visual field evoked activity in the area normally

expected to receive projections from the parafovea. None of the glaucoma cases

presented a pattern that clearly differed from the control group. This suggests that when

RGCs are intact, cortical reorganisation is possible. This is in line with the second part of

our hypothesis. Moreover, these results shed light on the diversity of findings in the

current literature. Cortical reorganisation was found in two cases of AMD [4]. However,

Sunness (2004) [5] reported a silent region in the visual cortex that corresponded to the

lesion projection zone in one AMD subject. Furthermore, in the animal research, there is

a diversity of results. A number of studies reported reorganisation of receptor fields

following induced retinal lesions [6-14], but the extent of RGC damage is unknown. In

one monkey study, in which the RGC layer had been experimentally damaged, no

reorganisation was found, even after 7.5 months after the lesion [15].

The results of this thesis, as well as previous findings, suggest that the way visual cortex

adapts to retinal visual field defects may be dependent on the presence or absence of

RGC damage.

Finally, filling-in is often observed in connection with visual field defects. In chapter 6,

brain activity related to brightness filling-in was investigated in normal subjects. The

results indicated that brightness filling-in is not dependent on spatially localised activity

in early visual cortex, but presumably requires activity in later visual areas. This finding

is consistent with recent reports on brightness and colour filling-in [16-18]. The fact that

spatially localised activity is not a necessary condition for creating a continuous percept

makes it understandable that visual field defects, which may also lack such activity, can

be filled-in. The study also adds to the controversial heated discussion about the neural

mechanism of filling-in [19].

Conclusion 141

3.3. Future research

Considering the relatively low number of subjects in the patient studies of this thesis,

confirmation of the present findings in larger groups would be appropriate. Additionally,

a large number of subjects could allow designs whereby groups could be stratified

according to different ages, as well as extent of retinal damage. This would permit a

better control of the age and extent of damage variables. On the other hand, anatomical

and functional measures of each subject at different times would enable longitudinal

studies of the progression of the effect on the cortex. The set-up of such a study for the

purpose of only examining cortical degeneration and reorganisation would likely be very

costly. Therefore, integrating existing or future large-scale population studies that

include neuro-imaging measurements would be a solution.

In addition, investigating the cortical consequence of visual field defects in other

disorders, with different symptoms, would allow for the control of more aspects. For

example, the same experiments described in this thesis could be performed on patients

with retinitis pigmentosa, where similarly to AMD, there is no RGC damage but as in the

case of glaucoma, are attained with peripheral vision loss. The results would permit to

separate the influence of the RGC from the possible influence of the field defect

location. Likely, optical neuritis, where nerve damage is present, would bring additional

insight into the issue.

Finally, it is possible that differences in the performance during rehabilitation of patients

with comparable retinal visual field defects may show a relation with the extent of

degeneration or reorganisation in cortical visual areas. In the same manner, some of the

findings presented in this thesis may have consequences for the use of visual implants,

aimed at restoring some degree of vision in the visually impaired and blind.

Degeneration, as well as reorganisation of visual cortex, may limit –or perhaps enhance-

the usefulness of this kind of prosthesis.

142

References

1. Yucel YH, Zhang Q, Weinreb RN, Kaufman PL, Gupta N. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res 2003; 22:465-481.


3. Gupta N, Ang LC, Noel de Tilly L, Bidaisee L, Yucel YH. Human Glaucoma and Neural Degeneration in the Intra-cranial Optic Nerve, Lateral Geniculate Nucleus and Visual Cortex of the Brain. Br J Ophthalmol 2006.


5. Sunness JS, Liu T, Yantis S. Retinotopic mapping of the visual cortex using functional magnetic resonance imaging in a patient with central scotomas from atrophic macular degeneration. Ophthalmology 2004; 111:1595-1598.











16. Perna A, Tosetti M, Montanaro D, Morrone MC. Neuronal mechanisms for illusory brightness perception in humans. Neuron 2005; 47:645-651.

17. Vladusich T, Lucassen MP, Cornelissen FW. Do cortical neurons process luminance or contrast to encode surface properties? J Neurophysiol 2005.

18. Cornelissen FW, Wade AR, Vladusich T, Dougherty RF, Wandell B. No fMRI evidence for brightness and colour filling-in in early human visual cortex. Journal of Neuroscience 2006; (in press).

19. Komatsu H. The neural mechanisms of perceptual filling-in. Nat Rev Neurosci 2006; 7:220-231

Conclusion 143

144 Samenvatting

145

Samenvatting

Maculadegeneratie en glaucoom, de twee voornaamste oorzaken van blindheid in de ontwikkelde wereld, resulteren in gezichtsvelddefecten. Gezichtsvelddefecten zijn gebieden in het netvlies die blind zijn of een gereduceerde licht-sensitiviteit hebben. De primaire visuele gebieden in de hersenen zijn retinotopisch georganiseerd. Dat betekent dat er bestaat een directe relatie tussen het deel van het netvlies dat wordt gestimuleerd en het deel van de cortex dat actief is. Bij een gezichtsvelddefect zal door het ontbreken van retinale activiteit het corresponderende corticale gebied niet langer worden gestimuleerd. Het is bekend dat tijdens de vroege ontwikkeling, maar ook op latere leeftijd, het niet gebruiken van zenuwweefsel kan leiden tot degeneratie ofwel reorganisatie van de cortex. Het doel van dit promotieonderzoek is om inzicht te krijgen in de consequenties van retinale gezichtsvelddefecten op de hersenen. Het gaat hierbij om gezichtsvelddefecten, zoals maculadegeneratie en glaucoom, die op latere leeftijd verworven zijn. Een belangrijk verschil tussen maculadegeneratie en glaucoom is dat bij glaucoom de optische zenuw en de retinale ganglioncellen beschadigd zijn, terwijl deze bij maculadegeneratie intact blijven. Door dit verschil zijn wij in staat geweest om de samenhang tussen de ganglioncel en optische zenuw beschadiging, corticale degeneratie en corticale reorganisatie te bestuderen. Met het combineren van verschillende neuro-imaging technieken hebben wij de structurele, metabolische en functionele gevolgen van gezichtsvelddefecten in de hersenen bestudeerd. In hoofdstukken 1 en 2 hebben wij de mogelijk corticale degeneratie met behulp van anatomische magnetic resonance imaging (MRI) bestudeerd. Hoofdstuk 1 beschrijft een onderzoek waarin met gebruikmaking van de statistische software voxel-based morphometry (VBM) de grijze stof concentratie is bestudeerd in de hersenen van proefpersonen met glaucoom, maculadegeneratie en in qua leeftijd vergelijkbare controle proefpersonen. In vergelijking met deze controlegroep werd bij de glaucoomgroep een lagere grijze stof concentratie gevonden in de corticale representatie van de retinale lesie. Er werd geen significant verschil gevonden tussen de controle -en maculadegeneratiegroepen. In hoofdstuk 2, met behulp van een surface-based morphometry methode (Freesurfer), werd onderzocht of de cortex mogelijk dunner wordt bij glaucoom en maculadegeneratie. Bij glaucoom patiënten bleek een corticale verdunning aanwezig te zijn in ongeveer hetzelfde corticale gebied dat naar voren kwam in de studie van hoofdstuk 1. Wederom werden bij maculadegeneratie ook geen significante effecten gevonden.

146 Samenvatting

Beide resultaten wijzen op een mogelijke significante relatie tussen corticale degeneratie en schade aan retinale ganglioncellen en de optische zenuw. Transneuronale degeneratie (het proces waarbij de atrofie van een beschadigde neuron via zijn axon wordt doorgegeven aan de volgende neuron) is de meest waarschijnlijke verklaring voor onze bevindingen. Het feit dat zeer vergelijkbare resultaten zijn verworven met twee verschillende analysetechnieken ondersteunt de conclusie. De vermindering in grijze stof concentratie kan tevens geassocieerd worden met corticale verdunning. Daarnaast komen onze resultaten sterk overeen met de bevindingen van eerdere studies bij dieren. Bij katten en apen resulteerde experimenteel geïnduceerde glaucoom (door verhoging van de intraoculaire druk) in celverlies in de visuele cortex. Ook bij de mens zijn onlangs degeneratie van de optische zenuw, laterale geniculate nucleus en visuele cortex, gemeten door middel van post-mortem onderzoek bij een glaucoom patiënt. De kwestie van degeneratie wordt eveneens onderzocht in hoofdstuk 4. Het doel van de studie in dit hoofdstuk was om de metabolieten in de visuele hersengebieden te meten met proton magnetic resonance spectroscopy (1H-MRS). N-acetyl aspartate (NAA) in hersenweefsel is een neuronale marker: een daling van de concentratie wijst op een progressie van de ziekte. In het geval van degeneratie werden lage NAA niveaus verwacht. Er was echter geen verschil in NAA concentraties tussen de groepen (maculadegeneratie, glaucoom en controle). De afwezigheid van een afname zou kunnen komen doordat op het moment van meten geen degeneratie plaatsvond, of omdat de progressie van de ziekte te langzaam verliep om tot meetbare veranderingen te leiden. Bij neuronale reorganisatie zullen neuronen overleven en nieuwe functionele verbindingen aan gaan, mits ze voldoende neuronale input krijgen. Dit zou leiden tot een nieuwe functionele kaart in de cortex waarin de representatie van het netvlies anders zou zijn dan normaal. Hoofdstuk 3 is gewijd aan het onderzoek van mogelijke corticale remapping bij onze twee soorten gezichtsvelddefecten. Kaarten van de visuele cortex werden verworven met behulp van retinotopische technieken en functionele MRI (fMRI). Bij twee maculadegeneratie patiënten waren de retinotopische patronen atypisch. Bij een van deze proefpersonen werd aangetoond dat het patroon heeft kunnen resulteren uit excentrische of extrafoveale fixatie. Bij de andere proefpersoon werd echter duidelijke corticale degeneratie waargenomen in de rechter hemisfeer. De stimulatie van het perifere gezichtsveld riep activiteit op in het hersengebied waar normaal de parafoveale representatie wordt verwacht. Verder werden er geen evidente verschillen gevonden tussen de retinotopische kaarten van de glaucoom- en controlegroep. Voorzichtig concluderend zouden deze resultaten er mogelijk op kunnen wijzen dat schade aan de optische zenuw, welke aanwezig is bij glaucoom maar niet bij maculadegeneratie, corticale reorganisatie zou kunnen tegenhouden (misschien ten gevolge van corticale degeneratie). Tot op heden is er geen consensus over dit

147

onderwerp binnen de wetenschappelijke wereld. Onze resultaten werpen nieuw licht op de diversiteit van bevindingen in de huidige literatuur. De resultaten van dit proefschrift, alsmede eerdere bevindingen, suggereren dat de manier waarop de visuele cortex zich aanpast aan retinale gezichtsvelddefecten, afhankelijk is van de aanwezigheid of afwezigheid van schade aan retinale ganglioncellen en de optische zenuw. Hoofdstuk 5 behandelt een extra pilot studie waarbij 1H-MRS werd gebruikt. In de wetenschappelijke literatuur is de relatie tussen de verhoging van de lactaat concentratie in de hersenen en hersenactiviteit controversieel. Het doel van ons experiment was om te testen of mogelijke veranderingen in lactaat concentraties langs de visuele pathways en visuele hersengebieden meetbaar zijn met 1H-MRS. Dergelijke veranderingen zouden een alternatief meetinstrument kunnen zijn om de activiteit in de visuele pathways en cortex te meten, met als belangrijk kenmerk dat het onafhankelijk is van de blood oxygen level-dependent (BOLD) respons die normaal met fMRI wordt gemeten. Er werd echter geen significante verhoging van lactaatniveaus gemeten tijdens visuele stimulatie. Het feit dat dezelfde stimulus robuuste activiteit opriep in de visuele cortex tijdens traditionele fMRI metingen, wijst erop dat de lactaatniveaus zeer laag blijven, ook tijdens sterke visuele stimulatie. Daarom concluderen wij dat lactaat concentraties niet geschikt zijn om activiteit in het visuele systeem te meten. Tenslotte, het fenomeen “filling-in” is vaak aanwezig bij gezichtsvelddefecten. Om meer van het neuronale mechanisme achter “filling-in” te begrijpen, hebben we in hoofdstuk 6, doormiddel van een fMRI experiment, helderheidsinductie onderzocht bij normale proefpersonen. Helderheidsinductie is een perceptuele illusie waarbij een waargenomen helderheidsverandering in een deel van het visuele veld niet wordt veroorzaakt door een fysische verandering ter plekke, maar door een verandering van het omringende deel van het visuele veld. De waargenomen helderheidsverandering is niet fysisch aanwezig en correspondeert dus met een vorm van “filling-in”. De resulaten toonden aan dat helderheidsinductie niet afhankelijk is van spatieel gelokaliseerde activiteit in vroege visuele corticale gebieden, maar mogelijk activiteit vereist van latere (visuele) hersengebieden. Deze bevinding is verenigbaar met recente rapporten over helderheid en kleur “filling-in” en maakt het begrijpelijk dat “filling-in” plaats zou kunnen vinden bij gezichtsvelddefecten, waarbij tevens spatieel gelokaliseerde activiteit ontbreekt, namelijk door de uitval van een deel van het visuele veld. Deze studie voegt ook nieuwe informatie toe aan de discussie over het neurale mechanisme achter “filling-in”. De bevindingen die in deze proefschrift naar voren worden gebracht, kunnen consequenties hebben voor het gebruik van visuele implantaten die gericht zijn op het (gedeeltelijk) herstel van de visus bij slechtzienden. Degeneratie, evenals reorganisatie van de visuele cortex, zou kunnen de bruikbaarheid van dit soort prothesen kunnen beperken of misschien verbeteren.

148

Acknowledgements

… and the last part of the book has come… I do not like endings… but they are useful… somehow, it is when you reach the end of a story that you think about the whole episode… and here I am, at the end of this book, at the end of my Groningen period, at the end of another episode of my life… I must say that this one was a very fruitful episode! …an episode full of adventures (of course), friendship and science. So many people did contribute to the successful outcome of my exciting PhD crusade, that I cannot recall how many times I said “thank you” during these extraordinary 4 years. I would like to use the opportunity of this “dankwoord” section to re-thank everybody for making this adventure possible. I will start with Frans. Frans is a super supervisor. He was the one that guided me through the whole PhD mission. Many times with him, I had the impression of us being companions in a fascinating expedition. Through all the adventurous vicissitudes, he always had the idea that was missing and was always capable of creating in me the feeling of “ma… of course! how didn’t I think about it before?!”. Not only he gave me freedom and support to develop my ideas, but also always looked after my work … and that made me feel I was in good hands. His frequent criticism and endless remarks were sometimes hard to handle, but at the end always resulted in healthy comments accompanied with fruitful discussions. Of course, Frans also had his irritating side… ohlala!, how stressful were those “last minute” strategies! But, one thing that I was always sure of is that we would safely cross the finishing line, because… “alles komt goed”! Frans, dank je wel voor alles! Wanneer gaan wij sushi eten? The greatest ideas and most productive comments and advices that I received during my PhD, came from Paul Maguire. When the period that I thought I was dealing with “Policia Militar” (=PM) vanished, Paul became a friend always ready to have a chat and share impressions about life accompanied with a delightful sense of humour. Always supportive, he was the one that pushed me to expose my photography work. Paul, super thanks for being there! See you around! To my promotors, Anneke and Aart, I want to express my sincere gratitude for periodically looking after the course of my project and contribute to it with helpful advice and kind support. Big thanks also to Gert, with whom I enjoyed more than one enjoyable discussion about the world of science in general.

Acknowledgements 149

I would like to express my gratitude to the leescommissie (reading committee), composed by Prof. A.B. Safran, Prof. R.J.W. de Keizer and Prof. P. van Dijk ,for reading and evaluating this thesis and providing useful comments. A big applause goes to the participants (proefpersonen) of the experiments. I must admit that recruiting subjects was one of the most difficult tasks of my project. But, the moments that I spent with my subjects were the most rewarding. It was extremely gezellig to have a chat with them (in my artistic dutch), and exchange some life experiences. It was in those moments when I felt that my work would one day hopefully help someone. This was really of great value!! Bedankt, proefpersonen om deel te nemen in mijn project! Zonder jullie, er was geen mogelijk onderzoek! Super bedankt, dus, voor de moeite te nemen om van (soms) verreweg naar Groningen te komen! Bedankt voor liggen in de scanner (en niet in slaap vallen)! Bedankt voor jullie ogen en hersennen en ... vooral bedankt voor de gezelligheid! Speciaal dank gaat naar Menheer de Groot, met wie heb ik nu de kans om een mooie vriendschap te genieten! A big bedankt! goes also to all blind en slechtziende associations that advertised my research among their members in order to provide me with subjects. A fundamental element in an MRI project is the scanner. I must then thank Hans, Remco and Anita for their contribution in the data acquisition process. Many subjects and many experiments mean many hours to spend in the acquisition room. It’s amazing, when I look back, how highly pleasant but also deeply unpleasant those moments could be. Good luck to the future scanning victims! To (always busy) Hans, I am also super grateful for leading and enjoying the spectroscopy project. For this particular project, a big thanks goes also to Jeroen van der Grond (I am still amazed by this name!) who, from Leiden, provided us with highly advanced analysis methods. Working with Paul Sijens on the other spectroscopy project was very fruitful as well. It is just pity (and a shame) that politics rule … many times against science. For their outstanding advices in data analysis, that saved me more than once from despair, I want to say 1000 thanks! to Remko and Christian Keysers. Debora, thanks for your work... I am still amazed that the brain segmentation didn’t segment your brain! At NIC, I was always hiper busy. But, fortunately, I also found some time for jokes, laughs and revolution at the kapitalistik kantine … those were moments of healthy craziness when I became myself again! A big uhuuu! to all NICers, especially to Mbembix, Paolix, Gerkix, Lavix, Carlix, Valerix, Chriix, Christiaaaaanix, Meltemix, Maaaaangix, gdje si?! Of course, I don’t forget the LEO people. Tony, thank you for all your useful remarks. Ronald, always available when I needed a hand, thank you for all the programming that you did, together with Just, for the retinotopy project. Check in your agenda when we can escape to Tatras! I worked with Just since the beginning … to the end! Extra bedankt voor alles, Just… we made a super samenvatting! :D Excellent memories will always come back when thinking about UMCG / AZG. I will never forget all the people I met at the ophthalmology department, all of them very gezellig people, always ready to help. Enormous bedankt to all the stuff at the

150

polikliniek, administratie (for all the dossiers), oogartsen, assistenten, TOA’s … for support with eye-related topics … the list is loooong … Govert, Khay, Kochoi, Rogier, Tanja, Lonneke, Karen, Henk, Dieneke, Dirk, Jose, Bert, Peter Hardus, Nomdo, Wim, Joke ... and Klaas Jan, my first super office-mate! and Xin-Li and Kim ... bedankt voor alles 1.000 keer!!! For making things easier and possible (especially with the inexplicable non-intuitive bureaucracy), for solving problems, and being there for advice, the BCN and RUG extraordinary people were always available. Britta, Diana, Caesar Hulstaert and Rob, bedanktissimo! Rob Visser is one of my idols. Whenever there is a problem to solve, he fishes in milliseconds the most valid and original idea and converts it into a practical solution. What would have I done without the secretary assistance from Aafke and Tinie at NIC and Fenna, Ella and Stella at the ophthalmology department? Super bedankt, especially to SUPER Fenna for providing me with all the tralala impossible-to-find-online articles!!! My computers and I would like to thank super Erik and super Ardy and super Albert for making our (online) life easier and happier with their solutions à la informatikus. Advised by Paul Maguire and Frans, I spent 2 weeks at the Martinos Center in Boston. Many thanks to Bruce Fischl, Brian Quinn, Jennie Pacheco and super Nick Schmansky for having me in your lab and making possible the Freesurfer study of this thesis! Thanks to all of you that I mentioned here and to all of you that I forgot to mention here :D , doing science in Groningen was mostly enjoyable… I just regret having lost my smile now and then! Working under (time) pressure might sometimes result in high productivity, but it is certainly non-human… Even thought many times it didn’t looked like that, these 4 years of my life were not only work… luckily, there was (sometimes) some time left to live life with explendid first-class fruits and vegetables. Meeting you, my friends, made these 4 years exxxtra special! Such an excellent combination of fruits and vegetables from all over the planet Earth (or does maybe someone come from Space?) can only result in a big family living in mushiness, true friendship, peace and love! I will never forget those delightful parties where all of us coming from all possible latitudes, longitudes and altitudes came together to jump on (mostly) Balkan rhythms… I won’t be there for the next parties, but they will for sure appear in my dreams… Patricio, Anita, Cesar, Edwin, Lara, Karol, Paola & Gilles Tomato (it was a real honour marrying you!), Bore, Brani orange & Graeme Cherry-fish (odlicna vjencjanje!) & Danilo Mandarine, Alarm Andrija (ma… kako si glasan, bre!), Vibor Sunflower, Danijela, Diana, Mladen, Haris, Kengo Walnut, Franciscu olive, Primož artichoke , Janja Lemon , Edita Cherry, Aave Cherry, Martin Cauliflower, Sonja Mango, Lenny Carrot, Eleonora Apple, Cristiano Kiwi, Maria the ghost, Dimitri Suikerbiet, Paolo Almond, Lena Palmtree, Sandrine, Angeliki Hot chili pepper & Oleh, Stasinos Cactus, Isabel, Gertrudis, Zuzanka Bananka, Gerke Prei, Charmaine Onion, Toni Strawberry, Peter Paprika, Paco Payaso, Burhan, Heidi, Bas, Haris, Gabriel Blueberry, Jeroen Citroen (keep on spreading the tropical mood), Harmen, Bram, Koen the Grrrushi, Juraj, Kees (wanneer gaan wij nog

Acknowledgements 151

eens wandelen in the middle of the nothing?), Malik, Mylene, Laurent, Teresa Peach (ke vaya bien!), Ilir the Great, Giorgio Tulip, Theo the toy… please, in real or in dreams come to visit wherever I am! ...long live frutology! I also want to thank my family who, although physically faraway, were always present. Big kisses go a bit everywhere in, Spain, Catalunya, Italy, France, Croatia and Slovakia... The biggest kiss goes to Puerto de Santa Maria for my parents (Mr. Pig and Mrs. Lion), and to Calceranica al Lago (the most beautiful place in the world). Grazie zio e cugini for printing this thesis! For the super art appearing now and then in this thesis, a big applause to the international artists: Frenk Asparragus from Koper, Alex from Barcelona and Limuna, Artičok and Ananas (me) from Groningen! Excellent work, we are the best!!! Special thanks run to my paranimfe, Sonja Mango and Janja Lemon and paranimfa-in law, Branislava Orange. We are a good team, eh! :D Big thank you also to those faraway who did not stop watching the series filmed in Groningen and made now and then an appearance :D : Jorge Cherry, Jordi Ofertix Grape, Berta Cinnamon (spunkix!), Mario, Toni Mickey Mouse, David & Maria & Guiomar, Ariel el señor del lenguaje, sAtomix Papaya, Peter Pan & Gabika Tinkerbell (kedy ideme na Elbrus?), Igor Konj, Niklas Potato, Johannes, Danka Apricot, Vlado Kapusta (why?), Kaori, crazy Simon, Tiago Ibericus, Jan Hazelnut, Honza Grape, Matthias, Heli, Ivica Jagoda (pusafone!), Emanuel il nostro eroe, Alex Papito, Gaetana & Serge & Laura, Rohini Plumb and strong bird... and to Aavix, I guess one word is enough: armenghe!! I would like to thank the director of all of these 4 years. My dear Toni alias Mickey Mouse, congratulations for this brilliant movie and thank you again for choosing me as principal actress! I also want to thank the group of Delft with who I enjoyed nice birthday celebrations (I know you won’t forget the last one, at Penguin beach :D ): Petra Banana & Marcel Cernica & Frederic, Jelena & Marc, Eric & Marijke, Michiel BubbleGum, Milan. To the ex-YU community, especially Mango, Alarm, Artičok, Limuna i Naranča, želim reći hvalix za otvoriti moje oči :D i omogućiti da postajem Titova pionirka! nositi ću maramu i kapu uvijek s ponosom! i ja ću uvijek marlijvo učiti i raditi i cijeniti sve ljude svijeta koji žele slobodu i mir! Naprijed! oooohhh!.. i naravno, hvala za odličnu hranu... burek, baklavu, kajmak... To my close fruit family, Mango, Bloemkool, Mela, Kiwi, Mrkva, Naranča, Trešnja-riba :D, Artičok, Limuna, ... thank you for the exxxtra dinners, trips (thanks Twingwie!), art-evenings, laughs, Pieterburen excursions, mushiness... I also want to thank Burek, Kaja, Maka and Suki for being so mushis... a big grhrhrhrhhh! to all of you! abericado for being there! … and because in special circumstances the laws of physics vanish very easily, I am sure distance won’t take us apart. Thank you! all my bikes for the exceptional transportation during these years! Especially one bike had a significant positive effect on my work efficiency: my bike at ACLO (the sport centrum). Training on that bike after statically spending the whole day in front of the computer was a really good therapy. Yoga also helped keeping my body and mind

152

healthy... dankjewel, Anneke! Another glorious contribution to my scientific life is the one of van den Budenmayer, whose concerto en mi mineur version 1798 has the power to make me enter in a state of total concentration during which I am able to write and write and write... There are still 4 fruits I want to thank in a deeper way. Janja, my dearest Lemon... hiper sretna sam da sam mogla s tobom uživati the last part of the Groningen series. Sa tobom, sve je bilo uhuuu!, ihihiii!, yeah! FIMO art-evenings were the best discovery of the year 2005! kmeek... kmeek! Eleonora, maialaska, bella melina, popeta del me cor! Ele is the best house-mate of the world! 3 years with her will never be enough... life with her is a mix of dalinian critical paranoia and fine common sense... what else would you like? endives au jambon and soupe à l’oignon? no problem! Ele, exxxtra thanks for being there, I mean here, at home, especially during the last turbulent PhD moments... no way I could survive without you! mushi, ne veden da qualche banda! muuuah! Mango, lipota - krasiVa žena - piOnirka - veLika glava - najbolja prIjatelica - ljubavI - draga Moja! even thoughT we knEw each other from previous life, meeting you again in Groningen was the best part of everything! Mickey Mouse is genious! During these 4 years of magic connexions, we went through so many adventures that we could write a whole book! but, because I just finished writing this thesis, I won’t be too long here. Mango, thank you for teaching me Croatian (language without the vowels and an articles!) and for telling me so many stories about partisans and Tito! and for... ohlalala... so many things... well, I said I would keep it short...so... thank you for being my best friend! and stay tuned! uuuuuuuuuuuuuuuvijeeeeeeeek zajeeeeeeeeeeeeeeeeeedno!!! Finally, life wouldn’t be so beautifully special without Martin, my cauliflower! He is one of the people with who I had the most fruitful and interesting discussions about my thesis ... and the one who helped me the most with all the non-intuitive editing! Moja laska, with you, it is so easy to enjoy every step in life! Thank you for all your help, thank you for your deep understanding, your splendid friendship, your intense love... thank you for all the past and future adventures ... and thank you for everyday life! you are the only person with who my dreams of adventures can come true, so let’s go!

1

Documents

Boucard - Neuroimaging of visual field defects › research › portal › files › 14548089 › 06_thesis.pdf · Visual field defects provide a unique opportunity to examine how