Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
1
1. Introduction
Parkinson's disease (PD) is a progressive disorder of the central nervous
system. It occurs predominantly in the elderly and has a substantial impact
on quality of life. Although the etiology of PD is unclear, it is likely to result
from interplay of genetic and environmental factors (1). A Small percentage
of familial PD has often been found to coincide with dominantly inherited
mutations in the gene for alpha-synuclein, or with the recessive gene
mutation for parkin (2). In recent years, attempts to define the disease
genetically have become possible with the discovery of monogenic forms of
the disease. However, such families account for a very small proportion of
cases (3). PD is involving primarily a degeneration of certain nerve cells in
deep parts of the brain called the basal ganglia, and in particular a loss of
neurons in a part of the midbrain called substantia nigra (SN) (4). This
neuronal loss can lead to reduction of the volume of the midbrain and
atrophy of the brainstem as a whole (5). The structural and extent of changes
of the brainstem and total brain as the result of PD is still poorly understood (6).
The SN contains dopaminergic cells that project to the corpus striatum and
are affected by the neurodegenerative process that appears in PD (7).
Dopamine depletion due to the death of dopaminergic neurons in the
striatum and substantia nigra pars compacta (SNc) is among the hallmark of
PD pathology. Selected SN neuronal populations are affected in PD.
However, the neurodegeneration may extend to dopaminergic neurons
outside the SNc, as well as non-dopaminergic neurons (2). PD is traditionally
defined, pathologically, by the finding of Lewy bodies and degeneration of
2
catecholaminergic neurons at post-mortem specimens. Mitochondrial
dysfunction is also found in a large percentage of PD patients (8).
The pathophysiology of the parkinsonian symptoms, especially that of
parkinsonian tremor is under debate (8). The loss of neuronal populations
within the basal ganglia-frontal circuits can have a profound effect upon the
motor and neurobehavioral symptoms in PD. L-3,4-dihydroxyphenylalanine
(L-DOPA) remains the most effective pharmacologic therapy for PD,
however as the disease progresses, the drug lose its efficacy and troublesome
side effects often occur. For better understanding of the symptoms and signs
that accompany PD, the interrelationship of deep brain structures and
cortical areas involved with this neurodegenerative disease must be
investigated (2).
1.1 Statement of the problem
PD is characterized by motor and non-motor deficits. The motor signs of PD
include resting tremor, rigidity, bradykinesia and postural instability (9). In
clinical practice, diagnosis of PD typically depends upon the presence of a
combination of cardinal motor signs, hence there is no definitive test for
diagnosis (10). Confirmation of the diagnosis is often brought by disease
progression and the positive responsiveness of patients to medication
(Levodopa –L-DOPA and other dopamine replacement therapy).
Accordingly PD diagnosis is particularly prone to errors, as this array of
motor symptoms is also present in a wide range of other parkinsonian
conditions such as multiple system atrophy (MSA), progressive supranuclear
palsy (PSP), and dementia with Lewy bodies (11).
3
Neuropathological studies demonstrate that about 10–25% of patients
clinically diagnosed in life as having PD were proved to have another
neurodegenerative disorder at postmortem examinations. So, differential
diagnosis of PD and MSA is often difficult, especially at onset of the disease (12, 13). Inaccuracy in PD diagnosis and the desire to identify presymptomatic
patients have prompted the search for sensitive and specific biomarkers that
include imaging techniques and application of quantitative analysis of brain
atrophy on these images. This will help clinician to differentiate PD from
other movement disorders with overlapping clinical symptoms (13, 14).
So far, neither radiological findings nor laboratory investigations were found
to be specific to confirm the diagnosis of PD. Accordingly, pre-
symptomatic detection of PD seems impossible on clinical basis. Recently,
Jubault et al. (11), conducted a study using anatomical Magnetic resonance
imaging (MRI) and provided in vivo evidence that brainstem damage may
be the first identifiable stage of PD neuropathology, and that the
identification of this consistent damage along with other factors could help
with an earlier diagnosis in the future. This damage could also explain some
of the non-motor symptoms in PD that often precede diagnosis, such as
autonomic dysfunction and sleep disorders.
In recent years, neuroimaging abnormalities, genetic mutations, and
biochemical markers have been increasingly focused as an objective method
for improving PD diagnosis and allowing the identification of persons at risk (10). Most previous studies were based on invasive imaging modalities (15, 14).
A non-invasive biomarker that is able to diagnose PD before the onset of
motor symptoms would provide a better chance to develop interventions
capable of monitoring disease progression and better prognosis (14). Magnetic
4
resonance imaging is considered helpful to facilitate the in vivo diagnosis of
patients with PD, MSA or PSP. It reveals either signal changes or atrophy of
specific brain regions (16). MRI is far more widely available, than other
imaging techniques like positron emission tomography (PET) and single-
photon emission computed tomography (SPECT), and is most commonly
used in clinical practice to differentiate PD from secondary causes of
Parkinsonism (17).
Imaging studies that use MRI volumetry could improve the differential
diagnosis of Parkinsonism, but cost-effectiveness remains to be established.
Complete separation of idiopathic PD from MSA and PSP has been achieved
with sophisticated techniques of MRI volumetry, but this approach is not
widely applicable (3). Two studies (18, 19), addressed the diagnostic
effectiveness of magnetic resonance volumetry against retrospective clinical
diagnosis in determining an accurate diagnosis in patients with parkinsonian
syndrome.
Alterations of the brain volume have been reported in a series of studies,
such as spino-cerebellar ataxia, autism, schizophrenia, epilepsy and
Alzheimer's disease (20-23). However the presence and extent of structural
changes in the brain as a consequence of PD is still poorly understood (6). To
our knowledge very limited amount of studies have been done denoting the
brainstem and/or its subdivisions volume changes in PD (12, 11, 16, 24, 25).
Cordato et al. (18), reported in their study that all MRI measures, including
hippocampal volume, were preserved in PD.
Anatomical measurement of brainstem volume is important in estimating the
pathological changes that affect the brainstem. The brainstem atrophy is a
5
significant finding in many neurological diseases such as cerebral palsy,
spino-cerebellar ataxia, amyotrophic lateral sclerosis, syringobulbia,
craniocervical junction abnormalities, central pontine myelinosis and
multiple sclerosis. For this reason the foundation of data with an objective
and efficient volumetric measurement of the brainstem using modern
imaging techniques is crucially important for the early diagnosis, as well as
to determine the rate of progression and prognosis of these neurological
disorders (5).
1.2 Aim of the study
The aim of this case-control study is to determine the volume of brainstem,
midbrain, pons and medulla oblongata and their volume fraction to total
brainstem in PD patients. The relation between the aforementioned
volumetric changes and age and gender of patients will also be investigated
and compared to normal healthy controls.
1.3 Research objectives
1.3.1 General
• To study the volumetric alteration of midbrain and total
brainstem in PD patients and to study the effect of age, sex, and
onset and duration of the disease on their volume alteration.
1.3.2 Specific
• To measure the volume of the midbrain, pons, medulla
oblongata and brainstem as a whole in patients with PD &
compare the findings with that of healthy controls.
6
• To correlate the variations in the volume of midbrain, pons,
medulla oblongata and brainstem as a whole with sex, age, and
onset and duration of the disease.
1.4 Hypotheses
• The volume of midbrain and brainstem as a whole is reduced in
patients with PD.
• The degree of alteration in the volume of midbrain and total
brainstem is influenced by the age and gender.
• The degree of alteration in the volume of midbrain and total
brainstem volume is correlated to age at onset and duration of
PD.
• The alteration in the volume of the midbrain and brainstem can
be applied in the diagnosis of PD.
7
2.1 Anatomy of the brain
The brain is one of the most complex and magnificent organs in the human
body. The brain is the part of the central nervous system which lies wholly
inside the cranium. The average adult brain weighs between 1300 – 1400
grams and that of new born is 350 – 400 grams (26). The brain gives
awareness of internal and external environment through processing a
constant stream of sensory data that helps in maintaining homeostasis of the
body. Besides that it is responsible for our perceptions, behaviors, and
memories. It also initiates all voluntary movements.
The brain is composed of cerebrum, cerebellum and brainstem. The
cerebrum lies in the anterior and middle cranial fossae constituting the
largest part of the brain, and composed of right and left cerebral
hemispheres. The cerebrum consists of an outer grey matter, inner white
matter and deep structures e.g. basal ganglia, thalamus and hypothalamus.
The cerebellum lies in the posterior cranial fossa under the cerebrum and is
connected to the cerebrum through the brainstem. The brainstem consists of
three parts; midbrain, pons and medulla oblongata, and it connects the spinal
cord to the cerebrum and cerebellum (Figure 2.1) (27).
The brain contains cavities called ventricles filled by cerebrospinal fluid
(CSF); two lateral, a third and a fourth ventricles. The two lateral ventricles
connect with the third ventricle through the interventricular foramina
(foramina of Monro). The third ventricle connects to the fourth ventricle
through a narrow canal called the cerebral aqueduct of Sylvius that traverses
the midbrain (Figure 2.2).
8
Histologically the brain is made up of two type of cells; nerve cells
(neurons) and glial cells. All neurons consist of a cell body, dendrites and an
axon. The neurons convey information through electrical and chemical
signals (neurotransmitter). The neurotransmitter is conveyed through a tiny
gap between neurons or between neuron and receptor called a synaptic cleft
(Figure 2.3).
Figure 2.1: Median sagittal section of the brainstem (28)
9
Figure 2.2: Ventricular system of the brain (28)
Figure 2.3: A neuron showing cell body, dendrites, axon and a synapse (29)
10
2.2 Anatomy of the brainstem
The brainstem is situated in the posterior cranial fossa, and its ventral
surface lies on the clivus (30). It can be divided into three parts; from caudal
to cranial, these are the medulla oblongata, pons and midbrain. It is
connected inferiorly with the spinal cord, superiorly with the cerebrum and
posteriorly with the cerebellum (27). The brainstem is one of the least
understood parts of the human brain despite its prime importance for the
maintenance of basic vital functions (31). The brainstem is the primary relay
center for afferent and efferent connections between the cerebral cortex, the
cerebellum and the spinal cord (32).
Histologically the human brainstem is composed of a multitude of axonal
nerve fibers as well as cranial and non-cranial nerve nuclei (32). It also
contains cholinergic, dopaminergic, noradrenergic, and serotonergic nuclei
whose cortical and subcortical projections are essential to the regulation of
consciousness, sleep, behavior, cognition, muscle tone, posture; and cardiac
and respiratory functions (31, 33). Due to the spatial concentration of important
neural structures in this relatively small brain region, pathological injury of
the brainstem is often life-threatening and results in severe neurological
effect (30, 32). It is involved also in a number of neurodegenerative diseases,
such as PD, Alzheimer's disease, and Huntington's disease (32).
Despite the crucial importance of the brainstem, most of the knowledge
about structure and organization of white and grey matter within the
brainstem is derived from ex vivo dissection and histological studies (34). One
reason for rare in vivo data of the brainstem is due to the anatomical
characteristics of the brainstem, specially its close vicinity to large arteries
11
and ventricles. The small size of the brainstem anatomical substructures
presents inherent challenges to neuroimaging analysis. These properties
make the brainstem a difficult structure for study with non-invasive methods
like MRI, as they place high demands on image acquisition as well as data
analysis methods (31). Nevertheless, in more recent years neuroimaging
studies with different MRI methods provides many valuable insights to
brainstem architecture (31, 32, 34-37).
The cranial limit of the brainstem is identified as the midbrain-diencephalic
junction (or diencephalic-mesencephalic junction) where the brainstem
meets the thalamus and hypothalamus at the level of the tentorium cerebelli.
The cranial border of the brainstem in a sagittal section was defined as the
axial plane passing through posterior commissure (posteriorly) and
mammillary bodies (anteriorly) (38, 21, 39-41). Planes parallel to the mammillary
body-posterior commissure line passing through the cranial and caudal notch
of the pons were used to further separate the brainstem into midbrain, pons
and medulla oblongata (41). Accordingly, the midbrain joins the pons at the
pontomesencephalic junction, and the pons meets the medulla oblongata at
the pontomedullary junction. The caudal border of the brainstem is the
cervicomedullary junction at the level of the foramen magnum and
pyramidal decussation (Figure 2.4) (33, 42). Eichler et al. and Schulz et al. (21,
41), determined the caudal border of the brainstem by a parallel line to
mammillary body-posterior commissure plane at the posterior rim of the
foramen magnum. Brainstem and cerebellum are separated from each other
by a plane through the obex and posterior commissure shifted posteriorly to
include the inferior colliculus.
12
Figure 2.4: Parts of brainstem: cranial and caudal border and planes dividing it to midbrain, pons & medulla oblongata (28)
2.3. Anatomy of the medulla oblongata
The medulla oblongata connects the pons superiorly with the spinal cord
inferiorly. The junction of the medulla oblongata and spinal cord is at the
origin of the anterior and posterior roots of the first cervical spinal nerves,
which corresponds approximately to the level of the foramen magnum. The
medulla oblongata is conical in shape, its broad extremity being directed
superiorly (27). It is approximately 3 cm in length and 2 cm in diameter. The
ventral surface of the medulla oblongata is separated from the basilar part of
the occipital bone and apex of the dens by the meninges and occipito-axial
ligaments. Caudally, the dorsal surface of the medulla oblongata occupies
the midline notch between the cerebellar hemispheres (30).
13
On the anterior surface of the medulla oblongata is the anterior median
fissure, which is continuous inferiorly with the anterior median fissure of the
spinal cord. On each side of the median fissure, there is a swelling called the
pyramid. The pyramids taper inferiorly, and it is here that the majority of the
descending fibers cross over to opposite side, forming the pyramidal
decussation. Posterolateral to the pyramids are the olives, which are oval
elevation produced by the underlying inferior olivary nuclei. In the groove
between the pyramid and the olive emerge the rootlets of the hypoglossal
nerve. Posterior to the olives are the inferior cerebellar peduncles, which
connect the medulla oblongata to the cerebellum. In the groove between the
olive and the inferior cerebellar peduncle emerge the roots of the
glossopharyngeal and vagus nerves and the cranial roots of the accessory
nerve (27).
The posterior surface of the superior half of the medulla oblongata forms the
lower part of the floor of the fourth ventricle. The posterior surface of the
inferior half of the medulla oblongata is continuous with the posterior aspect
of the spinal cord and possesses a posterior median sulcus. On each side of
the median sulcus, there is an elongated swelling, the gracile tubercle,
produced by the underlying gracile nucleus. Lateral to the gracile tubercle is
a similar swelling, the cuneate tubercle, produced by the underlying cuneate
nucleus. The central canal of the spinal cord continues upward into the lower
half of the medulla oblongata; in the upper half of the medulla oblongata, it
expands as the cavity of the fourth ventricle (27).
14
2.4 Anatomy of the pons
The pons is anterior to the cerebellum and connects the medulla oblongata to
the midbrain. It is about 2.5 cm long and owes its name to the appearance
presented on the anterior surface, which is that of a bridge connecting the
right and left cerebellar hemispheres (27). On the ventral surface of the
brainstem, the transition between medulla oblongata and pons is clearly
demarcated by a transverse sulcus. Laterally, in a region known as the
cerebellopontine angle, the facial, vestibulocochlear and glossopharyngeal
nerves emerge. The ventral surface of the pons is separated from the clivus
(basisphenoid and dorsum sellae) by the cisterna pontis. It is markedly
convex transversely, less so vertically, and grooves the petrous part of the
temporal bone laterally up to the internal acoustic meatus (30). The anterior
surface shows many transverse fibers that converge on each side to form the
middle cerebellar peduncle. There is a shallow groove in the midline, the
basilar groove, which lodges the basilar artery. On the anterolateral surface
of the pons, the trigeminal nerve emerges on each side. The posterior surface
of the pons is hidden from view by the cerebellum. It forms the upper half of
the floor of the fourth ventricle and is triangular in shape. The posterior
surface is limited laterally by the superior cerebellar peduncles and is
divided into symmetrical halves by a median sulcus (27).
2.5 Anatomy of the midbrain
The midbrain traverses the hiatus in the tentorium cerebelli, and connects the
pons and cerebellum with the forebrain. It is the shortest brainstem segment,
not more than 2 cm in length and most of it lies in the posterior cranial fossa.
Lateral to it are the parahippocampal gyri, which hide its sides when the
15
inferior surface of the brain is examined (30). On the posterior surface are two
colliculi (corpora quadrigemina), which are partly overlain by the pulvinar
of the thalamus. These are rounded eminences that are divided into superior
and inferior pairs by a vertical and a transverse grooves. The superior
colliculi are centers for visual reflexes, and the inferior colliculi are lower
auditory centers. In the midline below the inferior colliculi, the trochlear
nerves emerge. On the lateral aspect of the midbrain, the superior and
inferior brachia ascend in an anterolateral direction. The superior brachium
passes from the superior colliculus to the lateral geniculate body and optic
tract. The inferior brachium connects the inferior colliculus to the medial
geniculate body (27).
On the anterior aspect of the midbrain, there is a deep depression in the
midline, the interpeduncular fossa, which is bounded on either side by the
crus cerebri (27). The crura cerebri are superficially corrugated and emerge
from the cerebral hemispheres. They converge as they descend and meet as
they enter the pons (30). Many small blood vessels perforate the floor of the
interpeduncular fossa, and this region is termed the posterior perforated
substance. The oculomotor nerve emerges from a groove on the medial side
of the crus cerebri (27).
2.5.1 Internal structure of midbrain
The midbrain comprises two lateral halves, called the cerebral peduncles;
each of these is divided into an anterior part, the crus cerebri, and a posterior
part, the tegmentum, by a pigmented band of grey matter called the SN(27),
(Figure 2.5). The tegmenti are continuous across the midline but the crura
are separated by the interpeduncular fossa (30). The narrow cavity of the
16
midbrain is the cerebral aqueduct, which connects the third and fourth
ventricles and contains cerebrospinal fluid. The tectum is the part of the
midbrain posterior to the cerebral aqueduct; it has the superior and inferior
colliculi. The cerebral aqueduct is lined by ependyma and is surrounded by
central grey matter (27). The red nucleus is a rounded mass of grey matter
situated between the cerebral aqueduct and the SN. Its reddish hue, seen in
fresh specimens, is due to its vascularity and the presence of an iron-
containing pigment in the cytoplasm of many of its neurons. The red nucleus
is connected to the SN through afferent and efferent fibers (27).
2.5.2 Substantia nigra
The SN is a large motor nucleus situated between the tegmentum, and the
crus cerebri throughout the midbrain. Its medial part is traversed by
oculomotor axons passing ventrally to their point of exit in the
interpeduncular fossa. Histologically, the SN is composed of medium-size
multipolar neurons that possess inclusion granules of melanin pigment
within their cytoplasm. The SN is divided into a dorsal pars compacta and a
ventral pars reticulata; the neurons in each part have quite different
connections (30).
2.5.2.1 Pars compacta
The pars compacta of the substantia nigra (SNc) is dorsal and composed of
closely packed dopaminergic neurons that synthesize dopamine as their
neurotransmitter. The dopaminergic neurons contain neuromelanin granules
that appear dark in cut sections. This dark pigment, a polymer derived from
dopamine, gives the SN its name (Latin, black substance). The pigmentation
increases with age and is most abundant in primates, maximal in man, and
17
present even in albinos (43, 44). The medial lemniscus constitutes its dorsal
border and pars reticulata ventral border (44). The dopaminergic neurons of
the SN are located in three cell groups referred to as A8 (or retrorubral cell
group), A9 (or nigral cell group), and A10 (or ventral tegmental area, which
includes the parabrachial pigmentosus and paranigral nuclei). Because these
cell groups are directly continuous with one another, the outlines of the SNc
where A9 cells are located, are difficult to assess (44). Neuromelanin can
interact with many heavy metal ions such as zinc, copper, manganese,
chromium, cobalt, mercury, lead, and cadmium, and it strongly binds with
iron. The iron content of the SN increases with age. Iron is also located in
neuromelanin-containing neurons and its accumulation limits synthesis of
dopamine (44).
In PD, the levels of dopamine in the SN and striatum decrease dramatically
as a result of the degeneration of pars compacta neurons (30). Several
attempts were made to improve visualization of the SN using multiple
magnetic resonance (MR) contrasts, but the correspondence between MR
images and the actual anatomy of the SN was unclear (44). The inputs to the
SNc are predominantly from striatal gamma-aminobutyric acid (GABA)
neurons, but also receive inputs from the frontal lobe. The output from SNc
projects to the caudate nucleus and putamen in a topographically organized
fashion (nigrostriatal fibres) (30).
2.5.2.2 The pars reticulata
The pars reticulata of the substantia nigra (SNr) is the pale ventral zone and
extends cranially as far as the subthalamic region. It is considered to be a
homologue of the medial (internal) segment of the globus pallidus and
resembles it in terms of cell type and connectivity (30). Because of the
18
striking similarities in cytology, connectivity and function of the internal
segments of the globus pallidus and SN pars reticulata, these two nuclei can
be considered as a single structure arbitrarily divided by the internal capsule (43). It consists of clusters of neurons, many of which intermingle with fibers
of the crus cerebri. The cells in the pars reticulata are composed mainly of
GABA neurons (44).
The pars reticulata has afferent (input) and efferent (output) connections.
The main input to the pars reticulata is derived from the striatum
(striatonigral fibres) mainly from the caudate nucleus and putamen. They
come by two routes, known as the direct and indirect pathways. The direct
pathway consists of axons of cells in the striatum that project directly to the
pars reticulata. This direct pathway exerts an inhibitory effect on pars
reticulata neurons. The indirect pathway has three connections before it
reaches the pars reticulata; a) a projection from the striatal cells to the
external part of the globus pallidus, b) a GABAergic projection from
external part of globus pallidus to the subthalamic nucleus and c) a
glutamatergic projection from the subthalamic nucleus to the pars reticulata
(subthalamonigral projection). The subthalamonigral projections are
important in the pathophysiology of movement disorders such as PD and
dyskinesias. The indirect pathway is an excitatory to the pars reticulata.
Some corticonigral fibers appear to exist, passing from precentral and
postcentral gyri to neurons in the pars reticulata (30).
The output from the pars reticulata projects significantly to the thalamus
(nigrothalamic pathway) and superior colliculus. These pathways use GABA
as their neurotransmitter. In addition, the pars reticulata also inhibits
dopaminergic activity in the pars compacta via axon collaterals (30).
19
Figure 2.5: A cross sectional diagram through the superior colliculus of the midbrain showing the SN (45)
2.6 Age and sex-related effects on brain volume
Examination of the structural brain alterations that occur with age may assist
in understanding age-related functional changes and susceptibility to
neurodegeneration (46). The whole brain volume changes throughout the life
span and it includes reduction in the brain volume and increase in brain
ventricles and cerebrospinal fluid volume (47, 48). Brain volume increases
during childhood and adolescence up to the age of ~13 years after which the
whole brain starts to decrease. There is a suggestive evidence of a second
period of growth for brain or stability in brain volume during adolescent up
to age 35 years. After age 35 years a steady volume loss of 0.2% per year is
found, which increases gradually to 0.5% per year at age 60. Over 60 years
there is a steady annual brain volume loss of more than 0.5% (47, 49). It was
20
also reported in the literature that, the brain gains volume until age 40-50
before the shrinkage of advanced age begins (50). Beyond the age of 50 years
the average person will lose 5% of brain volume per decade (46).
Although brain volumes are affected globally by age, the pattern of brain
atrophy is not consistent across different brain regions (51, 52). There is an
increasing consensus on the overall pattern of grey matter development over
the course of childhood and adolescence. In childhood a global increase of
cortical grey matter volume takes place, which is then followed by a gradual
decrease in adolescence and early adulthood. Total brain volume loss is
attributed primarily to loss of grey matter (51, 53-58). The grey matter loss is
greater in cortex than in subcortical structures (52). The total white matter
volume increases until approximately the fifth decade of life and declines
thereafter and no significant decline was shown with age(51, 53-58), whereas
Mortamet et al. found a surprising result that white matter increases
significantly with age (57). On the other hand, another study reported that
total white matter volume is negatively related to age (52).
Concerning sex many studies showed that the total brain volume and total
grey and white matter volumes were larger in males compared to females (48,
55, 59, 56, 60, 61). Erbagci et al. (5), reported a statistically significant gender-
related difference in the total brain volume (1,074.06 ± 111.75 cm³ for male
and 966.81 ± 77.44 cm³ for female). Ekinci et al. (62), also confirmed the
above result and found that the mean total brain volume in males was greater
than in females (1,202.05 and 1,143.65 cm³ respectively). Although the total
brain volume of males is bigger than that of females, there is a significant
more prominent decrease in grey matter in male than in female during
childhood and adolescence (53). The cerebellum and pons are also larger in
21
men than women (63). In general males are more affected than females
through their life-span by age-related changes concerning brain volume (48, 53,
59). Inconsistently it was also reported in another study that annual rates of
brain tissue loss were similar in male and in female and in older and younger
adults. These rates were estimated as 5.4±0.3cm³ per year for total brain
volume loss and 2.4±0.4 cm³ and 3.1±0.4 cm³ per year for that of grey and
white matter respectively (64).
The aging brain undergoes biochemical, molecular, structural and functional
changes, rendering it vulnerable to a range of neuropsychiatric disorders (46).
Many factors affect structural age changes in the brain, among these factors
are; (1) degeneration of myelinated axons which reduces the speed of axonal
conduction, (2) structural changes in the cerebral vessels leading to
generalized decrease of cerebral blood flow and hence resulting in
disturbance of glucose and oxygen supply to the tissues and neuronal death.
The middle cerebral artery seems to be the most affected while arteries to the
cerebellum and brainstem are secured, (3) hypertension, (4) neurotransmitter
changes which cause damage to critical lipid, protein and DNA components
of cells, leading to neuronal dysfunction and eventually cell death, (5)
changes in calcium homeostasis, (6) alteration in genetic expression
resulting in vulnerability to oxidative stress, inflammatory responses and
regulation of DNA repair, and (7) decline in mitochondrial function (47, 46).
2.7 Age and sex-related effects on brainstem volume Although the human brain exhibits a global complex pattern of differential
aging, it is still unclear whether differential aging is observed in the posterior
fossa structures; brainstem and cerebellum. With constantly improving MRI
technology, greater progress has been made over the past 20 years in
22
mapping how the human brain matures and changes across the life span (65).
Despite this, the reported brainstem age-related changes remain sparse due
to the technical and methodological limitations of segmentations and
quantitative assessment of data from this region (35).
Moderate age-related shrinkage of the cerebellum has been noted. There was
a clear agreement in the literature that pons and medulla oblongata do not
show volumetric loss along with age (35, 66, 63, 67, 52).
Many studies(5, 35, 66, 50, 46, 52), have approved that in normal adults, changes in
brainstem volume was unaffected by age. They have explained the reason
for the brainstem volume being constant with age as the presence of
important nuclei in this region, which regulates vital centers of respiratory
and cardiovascular functions. In addition Lambert et al. and Lee et al. (35, 66),
attributed the consistent volume of brainstem with age due to the minimal
effect of decreased blood flow to the brainstem compared to the other
cerebral regions. On the contrary to the above studies Kruggel (68), has
reported that brainstem volume decreases with age in both sexes.
Luft et al. (50), have found an age-related shrinkage in the midbrain. A cross-
sectional study (66), also proved the age-related changes in the midbrain and
no change in the medulla oblongata. Midbrain structures such as SN showed
marked sensitivity to age related volume losses (r = -0.42) (46). Aging is
associated with a linear decline of pigmented neurons in the SN and with
decreased levels of striatal dopamine (50).
In the majority of studies, it is reported that the volumes of brainstem are
significantly bigger in males than in females (62, 5, 68, 66). The inconsistencies
between studies concerning the measurements of brainstem volume obtained
23
may be due to the different methods used for measurement, environmental
factors and age. One of the major reasons that influence the brainstem
volume by gender is that males have proportionately bigger mass index than
females, in addition to the sexual hormonal effects on the brain in males. It
has been reported by Xu et al. (69), that testosterone level has a relation with
brain mass.
2.8 Imaging techniques of the brain
In 1895 Wilhelm Röntgen used the X-rays from a cathode ray tube to expose
a photographic plate and produced the first radiographic exposure of his
wife's hand. Over the past 30 years there has been a revolution in medical
imaging, which has been paralleled by developments in computer
technology (70).
Brain imaging techniques allow doctors and researchers to view structures or
problems within the human brain, without invasive neurosurgery. There are
a number of accepted and safe imaging techniques used in hospitals and
researches like; computed tomography (CT) scanning that builds up a
picture of the brain based on the differential absorption of X-rays. CT scans
reveal the gross features of the brain but do not resolve its structure well.
Positron emission tomography (PET) uses trace amounts of radioactive
materials that are injected into bloodstream to map functional processes in
the brain. Single-photon emission computed tomography (SPECT) is similar
to PET and uses gamma rays and cameras to construct two or three
dimensional images of active brain regions. Magnetic resonance imaging
(MRI) uses strong magnetic fields and radio waves to produce high quality
two or three dimensional images of brain structures without use of
24
radioactive tracers (Figure 2.6). Functional magnetic resonance imaging
(fMRI) relies on the paramagnetic properties of oxygenated and
deoxygenated hemoglobin to see images of changing blood flow in the
brain, so it can show which part of the brain is active or functioning, in
response to a certain task performed by the patient through the recording of
movement of blood flow. Diffusion tensor imaging (DTI) is a type of
diffusion MRI used to observe functions of the brain as they occur (in vivo)
and it is often used to image white matter. Diffuse optical tomography
(DOT) is a non-invasive imaging technique that uses near-infrared lights to
produce brain images for recording oxygenation and other physiological
changes which may occur after a stroke, seizures or hemorrhage. A
resolution obtained from DOT is limited compared to MRI, but its advantage
is that the machine is simple and portable and can therefore easily be taken
to the bedside for constant monitoring of brain activity (71-73).
Figure 2.6: Philips MRI scanner (73).
25
2.9 Magnetic Resonance Imaging (MRI)
2.9.1 Introduction
MRI is a method of imaging which has gained worldwide acceptance and, in
addition to many new indications, has replaced other diagnostic imaging
techniques. MR images are no longer the exclusive domain of radiologist,
but also practiced and/or interpreted by a large number of clinicians and
surgeons. With each examination, one is confronted with a number of MR
image findings that require interpretation in order to reach a general
diagnostic impression and a reasonable differential diagnosis. MR images
are still developing rapidly and new imaging sequences are published almost
daily (74). MR images can provide in vivo anatomic images of portions of the
human body with high contrast resolution and can detect many abnormalities
or tumors. Major advantages of MR images include excellent soft-tissue
contrast resolution, multiplanar imaging capabilities, dynamic rapid data
acquisition, and various available contrast agents (75).
MRI uses the magnetic properties of hydrogen atom to produce images. The
nucleus of the hydrogen atom is a single proton. The hydrogen nuclei
(protons) behave like small, spinning bar magnets and align with the
magnetic force when placed in a strong magnetic field. These nuclei in water
molecules and lipids are responsible for producing the anatomical images in
MRI (76, 77).
The first step in MRI, a patient is placed within a large, powerful magnet.
The hydrogen atoms within the patient align in a direction either parallel or
antiparallel to the strong external field. A greater proportion aligns in the
parallel direction so that the net vector of their alignment, and therefore the
26
net magnetic vector, will be in the direction of the external field. This is
known as the longitudinal magnetization. Although aligned in a strong
magnetic field, the hydrogen nuclei do not lie motionless. Each nucleus
spins around the axis of the magnetic field in a motion known as precession.
The frequency of precession is an inherent property of the hydrogen atom in
a given magnetic field and is known as the Larmor frequency. A second
magnetic field is then applied at right angles to the original external field.
This second magnetic field is applied at the same angle as the Larmor
frequency and is known as the radiofrequency pulse (RF pulse) (78, 79).
A magnetic coil known as the RF coil, placed around the patient, applies the
RF pulse. The RF pulse causes the net magnetization vector of the hydrogen
atoms to turn towards the transverse plane, i.e. a plane at right angles to the
direction of the original, strong external field. Depending on the strength and
duration of the RF pulse the magnetization vector will rotate away from the
longitudinal direction to a varying degree. A 90° pulse rotates the vector into
the transverse plane. This is known as the transverse magnetization. A 180°
pulse rotates the vector to the opposite longitudinal direction. Smaller angles
of rotation, known as 'flip angles', of the order of 15 -30° are used for
gradient- recalled-echo sequences (77).
The component of the net magnetization vector in transverse plane induces
an electrical current in the RF coil. This current is known as the MR signal
and is the basis for formation of an image. Computer analysis of the
complex MR signal from the RF receiver coils is used to produce a magnetic
resonance image. In viewing MR images, white or light grey areas are
referred to as 'high signal' dark grey or black areas referred to as 'low signal'.
27
On certain sequences flowing blood is seen as a black area referred to as a
'flow void' (77, 79).
Following the application of a 90° RF pulse, the net magnetization vector
lies in the transverse plane. Also, all of the hydrogen protons are processing
at the same rate – they are said to be 'in phase'. Upon cessation of the RF
pulse two things begin to happen. The net magnetization vector will rotate
back to the longitudinal direction. This is known as longitudinal relaxation
or T1 relaxation (80, 81). At the same time, the spinning hydrogen atoms will
start to process at slightly varying rates. This dephasing process is known as
transverse relaxation or T2 relaxation (decay).
The T1 is defined as the time taken for the longitudinal magnetization to
resume 63 per cent of its final value. The T2 is defined as the amount of time
for the transverse magnetization to decay to 37 per cent of its original value.
The rates at which T1 and T2 relaxation occur are inherent properties of the
various tissues. Sequences that primarily use differences in T1 relaxation
rates produce T1-weighted images. Tissues with long T1 values are shown
as low signal ' dark grey' while those with shorter T1 values are displayed as
higher signal 'light grey'.
T2-weighted images reflect differences in T2 relaxation rates. Tissues whose
protons dephase slowly have a long T2 and are displayed as high signal on
images. Tissues with shorter T2 valves are shown as lower signals. Both T1
and T2 –weighted sequences are performed very commonly in most parts of
the body (77). Most pathological processes show increased T1 and T2
relaxations times and appear lowered in signal (blacker) on a T1-weighted
scan and higher in signal (whiter) on a T2-weighted scan than the normal
28
surrounding tissues. The T1 & T2 weighting of an image can be selected by
appropriately altering the timing and sequence of radiofrequency pulses (76).
To generate contrast between the various soft tissue structures examined,
MRI signals depend on many varied properties e.g.: the number of hydrogen
atoms present in tissue (proton density), the chemical environment of
hydrogen atom, and T1 & T2 relaxation times. By altering the duration and
amplitude of the RF pulse various imaging sequences use these properties to
produce image contrast. Terms used to describe the different types of MRI
sequences include spin echo, inversion recovery, and gradient recalled echo
(gradient echo) (77, 81).
The standard MRI unit consists of a number of magnetic coils systems. First
is the large magnet itself. This is usually a superconducting magnet that uses
liquid helium. Secondly, a series of gradient coils is used to produce
variations to the magnetic field that allow image formation. It is the rapid
switching of these gradients that causes the loud noises associated with MRI
scanning. Lastly the RF coils which are applied around the area of interest,
they are used to transmit the RF pulse and to receive the RF signals. The
coils come in varying shapes and size depending on the part of the body to
be examined (77).
In some cases contrast agent or medium is used to enhance the contrast of
structures or visibility of blood vessels. The contrast media in MRI depends
on agents that have magnetic and paramagnetic properties, the most widely
used agent is a combination of gadolinium and diethylene triamine
pentaacetic acid (DTPA) which dramatically decreases the T1 relaxation
time (76, 72).
29
The processional frequency of hydrogen nuclei at 1.5 T (Tesla) is 64 MHz.
Soft tissue contrast results from: 1) the density of protons (hydrogen nuclei)
within different tissue; 2) the different rates at which the protons in various
tissues realign themselves with the magnetic field of the magnet (also
referred to as T1 relaxation, longitudinal or spin-lattice relaxation); 3) rates
of signal decay or dephasing (also referred to as T2 relaxation, transverse or
spin-spin relaxation). Using these biophysical properties of different normal
and abnormal tissues allows MRI to have greater soft-tissue contrast than CT (74). The main components of a typical MRI scanner includes: 1) a large-bore
magnet with high field strength (0.3 to 1.5 T); 2) RF coils within the magnet
which can transmit and receive properly tuned RF pulse, as well as set
spatially-dependent magnetic fields (gradients) that allow localization of
specific regions of anatomic interest; 3) a computer that operates the device
and processes the RF signal data received from the patient to form an
anatomic image (74, 79).
To generate an MR image, a person is placed onto a table that can be
specifically located within the bore of the magnet. Once in the magnet the
operator selects programs that include the RF pulse sequences necessary to
generate images with the desired contrast parameters based on the proton
densities, T1 and T2 values of the various tissues. The data received from
the subject or patient is processed by the computer using computer
algorithms (2D or 3D Fourier transformation). The images are displayed on
the monitor console and transferred to film or other computers (79). Many
systems store the image data on digital tape or optical discs for easy
retrieval. Not all patients can have MRI examinations. Intracranial aneurysm
clips, cardiac pacemakers, and metallic foreign bodies in the eyes are
30
absolute contraindications for MRI. In addition, the presence of surgical
clips, metallic rods, wires, and other orthopedic hardware can produce
artifacts obscuring visualization of the anatomic structures in the region of
interest and can harm the patient or person near the magnet (74, 82).
2.9.2 Magnetic resonance imaging of the brain
Over the last two decades, there have been dramatic improvements in the
capabilities of the techniques used for imaging the brain. Methods providing
only indirect evidence of an abnormality, e.g. skull radiography, have been
replaced by those that result in direct visualization of the anatomy, i.e. CT
and MRI, and function, i.e. PET of the brain itself. As a result of these
advances, it is now possible to examine the brain routinely with little risk in
a manner superior to that possible by gross anatomic inspection. CT and MR
images are now the principal techniques used for the evaluation of patients
with neurologic diseases. Foreseeable advances in shortening the time
necessary for an MRI examination, improving the environment of MRI
devices to accommodate critically ill patients, and developing the
availability to obtain accurate anatomic as well as physiologic information
about the vasculature of the brain make it likely that MRI will soon become
the principal method for diagnostic imaging of patients with central nervous
system diseases (83).
MRI has a superior contrast resolution facilities discrimination of the grey
and white matter. MRI based volume quantification is now being
increasingly used to investigate neuro-anatomic structures in neurological
and psychiatric disorders, e.g. schizophrenia, Alzheimer's disease and
epilepsy (84-86). MRI volumetry is also useful to examine structures that
31
require assessment of changes in volume over time as an indicator of
therapeutic effectiveness (87).
MRI has proven to be a powerful imaging modality in the evaluation of: 1)
congenital anomalies of the brain; 2) disorders of histogenesis; 3) neoplasms
of central nervous system, cranial nerves, pituitary gland, meninges, and
skull base; 4) traumatic lesions; 5) intracranial hemorrhage; 6) ischemia &
infarction; 7) infectious and noninfectious diseases; 8) metabolic disorders;
and 9) dysmyelinating and demyelinating diseases. MR data can also be
used to generate images of arteries and veins (MR angiography [MRA]) in
displays similar to conventional angiography. Another option with clinical
MRI scanners is the acquisition of spectral data to characterize the
biochemical properties of selected region of interest in the brain (MR
spectroscopy [MRS]) (74). MRI is also considered helpful to facilitate the
diagnosis of neurodegenerative diseases such as PD, multiple system
atrophy (MSA) or progressive supranuclear palsy (PSP), revealing either
signal changes or atrophy of specific brain regions (16).
The routine techniques used for MRI vary from centre to centre. Axial,
coronal and sagittal projections are all considered standard and two of these
projections are usually chosen for a routine examination. A variety of signal
sequences are used to create the image: usually T1-weighted, T2-weighted
and balanced (proton density) images. No signal is produced from bone, so
there is no bone artifact, which means the posterior fossa structures are more
clearly demonstrated. The characteristics of grey and white matter are
different, and both are clearly different from the CSF in the ventricular
system and subarachnoid space. Therefore, the anatomy of the brain can be
exquisitely displayed (76).
32
The appearance of the brain tissue depends on the MRI pulse sequence used
as well as the age of the patient imaged. Myelination of the brain begins in
the fifth fetal month and progresses rapidly during the first two years of life.
The degree of myelination affects the appearance of the brain parenchyma
on MRI (74).
Limitations to brainstem imaging with MRI are the induced field
inhomogeneities caused by nearby air-tissue or bone tissue interfaces which
lead to distortion of the images. Recently, a novel quantitative MR method
called quantitative susceptibility mapping (QSM) is introduced to the
imaging field. This technique is unique in its sensitivity to tissue
constituents, thus rendering it an excellent method for anatomical
delineation of cortical and deep grey matter structures especially for
brainstem. It is also reported that multiple image contrasts provides a
distinctly improved picture of the morphology of the brainstem (32).
Morphological changes in SN can be detected better by the use of 7 tesla
MRI, which provides an increase in both spatial resolution and contrast of
the image (44).
2.9.3 Normal brainstem appearance in MRI
2.9.3.1 Midbrain
The main identifiable structure of the midbrain with MRI is the cerebral
aqueduct of Sylvius. The cerebral aqueduct is a tubular channel about 2mm
in diameter that is surrounded by grey matter. The cerebral peduncles are
symmetrical rounded protuberance on the anterior surface of the midbrain (83). In axial sections the SN can be recognized as thin bands (slightly
hypodense in MR) just behind the peduncles (44). The red nuclei lie in the
33
rostral part of the midbrain. At the caudal end of the midbrain, the
decussation of the superior cerebellar peduncles appears as a central, oval-
shaped region. On the posterior surface of the midbrain, the superior and
inferior colliculi, which together form the quadrigeminal plate, can be
recognized (83).
2.9.3.2 Pons
The pons which connects the midbrain and medulla oblongata is the largest
segment of the brain stem. Its ventral surface is convex anteriorly, and its
nearly flat posterior surface forms part of the floor of the fourth ventricle.
The pontine cistern is between the clivus and the anterior surface of the pons (88). It contains the basilar and proximal anterior inferior cerebellar arteries,
the anterior pontomesencephalic vein, and cranial nerves V and VI. The
cerebellopontine angle cistern, which lies laterally between the middle
cerebellar peduncle and temporal bone, contains cranial nerves VII and VIII,
petrosal veins, and distal portions of the anterior inferior cerebellar artery.
The flocculus, a part of the flocculonodular lobule of the cerebellum,
projects into the posterior aspect of the cerebellopontine angle cistern (83).
2.9.3.3 Medulla oblongata
The medulla oblongata connects the pons with the spinal cord. Its landmarks
are the pyramids, olives, and inferior cerebellar peduncles that protrude from
the anterior, anterolateral, and lateral margins, respectively, of the medulla
oblongata. Depending on the level studied, axial scans show different
contour of the medulla oblongata. In sections through the upper medulla
oblongata, it has an angular configuration due to the pyramids projecting
from the anterior surface (83). The inferior cerebellar peduncles projecting
34
from the lateral surface; at a slightly lower level, has a different contour due
to the protuberance of the olives, from the anterolateral aspects. Near its
caudal ends, the medulla oblongata becomes circular, and where it joins the
cervical cord, elliptical. In coronal images, the pyramids and olives
projecting from the inferolateral margins of the medulla oblongata can be
identified. The medulla oblongata is related anteriorly to a small anterior
medullary cistern, laterally to the narrow lateral medullary cisterns, and
posteriorly to the cisterna magna. The cisterna magna contains portions of
the vertebral and posterior inferior cerebellar arteries (88, 83).
2.10 Design-based (unbiased) stereology
2.10.1 Definition
Stereology is a set of methods used to make unbiased estimates of biological
features of 3D objects through interpretation of 2D images. In stereology
irregular-shaped 3D structures are sampled using geometric test probes
(slabs, sections, lines and points) for quantification in 2D profiles based on a
sound statistical and stochastic background (89, 90). Stereology can be applied
to a variety of physical and optical imaging techniques. It can measure a
wide range of quantitative parameters including number, length, size, shape,
volume and density. The stereological quantification methods are unbiased,
less time consuming and precise. For these reasons, stereological application
on imaging approaches has become gold standard in quantitative structural
analysis of different organs. According to the sampling strategy this type of
stereology was first known as unbiased stereology. In order to avoid the
controversy involving 'biased vs. unbiased' data, many bio-stereologists
35
now prefer the term design-based stereology on behalf of unbiased
stereology (91, 92).
The objective of stereology is to estimate geometrical parameters that
characterize the composition of the structures using a few samples from the
whole. Typical global parameters are 3D (volume or size), 2D (surface area),
1D (length or thickness), or 0D (number) (89).
2.10.2 History
The term “Stereo” is derived from the Greek word for a “geometric object”.
The stereo set at home or stereo images are not called "stereo" because there
are two speakers or two pictures. They are called "stereo", because they try
to recreate sounds or objects in 3-dimentional (3-D) space. The 3-D analysis
of objects dates to ancient Egypt and the development of Euclidean
geometry (92).
In 1635, Bonaventura Cavalieri showed that the mean volume of solids
could be measured from the sum of their profile areas in cut sections of 2D
"Cavalieri principle". In 1777, Georges-Louis Leclerc Comte de Buffon
showed that a needle tossed onto a grid intersects the lines with a probability
proportional to the length of the needle and the spacing of the grid lines;
"Buffon's needle" led to estimation of total length and surface area of
irregular objects in sections. In 1847, the French mining engineer and
geologist, Auguste Delesse provided the basis for accurate and efficient
estimation of objects and region volumes by point counting "Delesse
principle". This principle enables volume estimation of irregular objects
based on their profile areas on random. These and other stereological
methods followed subsequently minimized the potential bias introduced by
36
measuring 3D objects in 2D profiles sections (89, 93, 92). Stereology was then
introduced into the scientific fields in the early 1960s. In 1961, Hans Elias
suggested stereology as a useful method to be used in different scientific
disciplines like geology, biology, engineering and material sciences (92). In
the 1970s biologists began to develop unbiased sampling strategies for
analysis of anatomically defined reference space in biological tissue (94, 92).
The believe in the use of stereological methods for biological researches
developed gradually until the year 2000, many journal editors and reviewer
and funding organizations began to state preference for modern stereology
approaches, which they regard as the state-of-the-art methodology for
morphological quantification of biological tissue. During the 21st century,
several major developments undergone for modern stereology. One of these
achievements is the development of computerized hard-software systems for
unbiased sampling and probes connected to microscopy. These
computerized systems are now affordable for interested biologists in support
of accurate, precise, and efficient approaches for testing a wide variety of
biological hypotheses (92).
2.10.3 Practical applications
Design-based stereology provides the first step towards accurate, efficient,
and more reliable results in morphometric analysis of biological tissue (92).
Certain terms are commonly used in designed-based stereology; these are
accuracy, bias and precision. Accuracy refers to the validity of the data (i.e.
without bias). Bias refers to methodological errors that cause measurement
to be inaccurate or deviation of the result from the expected value. Precision
refers to the reproducibility of measurements, which depend on data
variance, sampling design, sample size and distribution (89). With the
37
growing improvement in the stereological techniques, volume estimation of
irregular shape of various human organs becomes easily achievable.
Stereology has been recommended as the method of choice for
quantification of structures in kidney, lungs and brain researches (90).
Recently stereology has been increasingly applied by neuroscientists for
diagnosing neurological diseases (95).
2.10.4 Sampling
Stereology depends upon careful sampling which utilizes a systematic
uniform random sampling (SURS) paradigm. This means that every part
within the tissue or image have the same chance of being selected for
analysis, and are not being subjected to human or methodological bias. This
system of sampling combines both the unbiasedness of random sampling
and the efficiency of a systematic sampling. SURS is based on selecting the
final sample with a predetermining interval (systemically) while selecting
the first sample of the set randomly within the first sample interval (96). In
stereology the ratio of systematic random selection of a number of sections
from all the set of sections of the structure is referred to as the section
sampling fraction (SSF). For example, if 25 sections are selected using
SURS out of 250 sections from the whole structure; then the sampling
fraction will be 25/250, or 1/10 (96, 97).
In determining sample size for stereological quantification the paradigm "do
more, less well" is applied. Accordingly the simple guideline suffices for
sample size within a structure of interest is as follows: 100 – 200 counting
events (e.g., point hitting the structure of interest) distributed on 50 – 100
38
fields of view from 6 – 9 blocks are sufficient to obtain an appropriate
precision of stereological parameters (89, 92, 90).
2.10.5 Volumetry
Volumetry is a method of quantitative analysis in which the amount of a
substance is determined by measuring the volume that it occupies. Two
approaches are widely used by stereologists. The first approach is fluid
displacement based on Archimedes’ principle. The second approach is the
Cavalieri method which is a stereological approach. Both methods can easily
be implemented in the experimental protocol without making demands on
equipment or work expenditure (90). Nisari et al. (98), compared the two
methods for volumetric measurement of brain and brain components and
found no difference between fluid displacement and Cavalieri principle.
In vivo morphometric analysis of structural changes of many different
human organs is now possible through imaging techniques such as CT, MRI
and PET. Since the images obtained by these techniques are a series of
sections through organs, stereology can be applied for them to obtain
accurate measurements.
2.10.6 Cavalieri principle
The Cavalieri principle is named in honour of Bonaventura Cavalieri (1598 -
1647) who, as a student of Galileo in the seventeenth century, made
significant advances in the mathematics of numerical integrations and was
the first to consider measurement of volume via the analysis of sections
through three dimensional solid objects. Cavalieri pointed out that the
39
volume of any object could be estimated from a set of slices through the
object, provided that they are parallel, separated by a known distance. It is
known that the volume of regular-shaped objects (i.e., prism, cube, cylinder)
can be estimated by the following formula:
V= t × a,
Where (t) is the height and (a) is the base area of the object (99).
Similar to this approach, the Cavalieri principle is a modern stereological
method that can be applied on either microscopic or CT or magnetic
resonance (MR) images for estimation of volumes. This method allows an
estimation to be made of the volumes of irregular objects in an efficient and
unbiased manner (5).
In the Cavalieri principle the sectioning begins at a random starting point of
the structure of interest and cuts it from end to end with a series of parallel
probes at a constant known distance apart (100). The surface areas of all cut
sections are estimated and multiplied by the known section thickness "cross-
distance between the two cutting edges of a section" to provide the volume
of the examined object (Figure 2.7).
Planimetry and point-counting are two methods for estimating volume based
on the Cavalieri principle. Planimetry is a widely used approach in
volumetry and it depends on manual or automatic delineation of the
boundaries of the object of interest on image sections. The sectional area
delineated is estimated by use of software that read pixels inside the
boundary(96), (Figure 2.8). Examples of the software analyzing systems used
in planimetry method are ImageJ, DicomWorks …etc.). The sum of the
40
measured areas of sections obtained by planimetry is then multiplied by the
section thickness and the volume of structure is estimated according to the
following equation:
V= t ×Σa cm3
• Σa denotes the sum of section areas in cm2 and
• (t) is the sectioning thickness in cm
Figure 2.7: Sectioning for volume estimation by Cavalieri principle (101)
In point-counting method, a set of points at specific densities on a
transparent sheet (point-counting grid), is randomly superimposed on
sections, and the number of points that hit the region of interest are counted.
Finally the volume of the structure (V) is estimated by multiplying section
41
thickness (t), total number of points hitting the structure (ΣP) and the
representing area per point in the grid (a/p) as the following equation:
V= t × (a/p) × ΣP cm3
Figure 2.8: Planimetry manual tracing method of Cavalieri principle (102)
2.10.7 Coefficient of error (CE)
Coefficient of error (CE) or relative standard error represents the total
amount of error arising from sampling estimation procedures in a
stereological study using the Cavalieri principle (96). CE allows the
calculation of the optimum number of sections required to attain a given
precision for a particular scanning direction. It also evaluates the reliability
of the point density of the grid and sectioning intervals (87). A preliminary
estimate of CE can be obtained by dividing the standard deviation by the
42
sample average, showing the difference between the calculated average of
the population and the average variability. Usually, a total CE of less than
5% is considered to be adequate for most of the studies to provide accurate
quantitative data (96). Thus a researcher can see the potential variability in
any given volume measurement. When the CE of the measurements is high,
it can generate obvious problem in accuracy and hence interpretation. A CE
of less than 10% is an acceptable range (62).
CE is calculable before the work has been done i.e. amount of error in the
stereological study can be known before conducting the final measurements.
CE estimation has two different equations used according to whether the
study uses point counting or planimetry methods of Cavalieri volume
estimation. In planimetry methods CE is measured using the following
formula;
Where, i = 1,2,3, …, m is the number of sections. A is the measured area of
sections using planimetry method and the others are constant.
2.10.8 Coefficient of variation (CV)
It is a statistical method for standardizing measurement of dispersion and is
expressed in percentage. CV is obtained by dividing the standard deviation
by the mean of the population. It is an important stereological application
that shows extent of variability in relation to the mean of the population (96).
43
2.10.9 Volume fraction (VF)
The maximum size of brain of an individual is affected by factors related to
the brain growth, like gender and physical size. So, the human brain varies
widely from person to another (62). The volume fraction is a stereological
approach denoting the ratio of body components to each other independently
of the body size of individual (5). The volume fraction is used to express the
proportion of a component within a reference volume (62).
The volume fraction of a phase Y within a reference volume is simply the
proportion of each unit volume of the reference space taken up by Y and it is
measured by the following formula:
Vv(Y,ref) =
Volume of phase Y in reference space
Volume of reference space
Where the notation Vv(Y,ref) indicates volume fraction. The brackets are
used to which phase of interest (e.g. Y) and which volume reference the
volume fraction refers to. Example includes Vv (midbrain, brainstem).
Volume fraction ranges from 0 to 1 and is often expressed as percentage (91).
2.11 Parkinson's Disease (PD)
2.11.1 Introduction
Parkinson's disease (PD) was first described by Dr. James Parkinson in a
little book entitled "An Essay on the Shaking Palsy", published in 1817 (103).
For the next century, the condition was known as the shaking palsy and in
the medical community by its Latin equivalent, paralysis agitans. It is
44
sometimes called idiopathic parkinsonism (the term idiopathic means that
the cause is unknown), but more commonly today it is simply called
Parkinson’s disease, to honor the physician who first described it (4).
PD is the most common movement disorder and the second most common
neurodegenerative disease after Alzheimer disease (15). The primary
pathological feature of PD is death of SN cells and degeneration of
nigrostriatal pathway as well as presence of Lewy bodies (intracytoplasmic
inclusion bodies) in residual dopaminergic neurons (104). The SN neurons
make the neurochemical messenger dopamine, which is partly responsible
for starting a circuit of messages that coordinate normal movement (4). The
death of SN cells may affect to a lesser extent the basal ganglia; globus
pallidus, putamen and caudate nucleus (105).
2.11.2 Etiology
Although the main etiology of PD is unknown, genetic predisposing factors
in combination with environmental factors are thought to be responsible for
the death of dopaminergic and non-dopaminergic cells in the brains of PD
patients (106, 10). In addition also occupational status of the individual (e.g:
farmers exposed to pesticides) may play a role in affection with PD (107).
There is increasing evidence that PD may be inherited. Around 15–16% of
individuals with PD have a first-degree relative who has the disease (108, 109).
Both autosomal dominant and recessive forms of inherited PD are described
in Arabic families, associated with four genes mutations (Parkin, PINK1,
LRRK2, and PARK9) (110). Mutation of PINK1 gene analysis should be
considered in early-onset recessive PD patients, particularly those from Arab
origin (111).
45
Some of the environmental factors that may play a role in the development
of PD are certain toxins, oxidative stress, mitochondrial dysfunction,
inflammation and other pathological mechanisms (10, 112). The toxins
including, cyanide, manganese, carbon monoxide, carbon disulfide, N-
methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), methanol, organic
solvents and some other pesticides (113, 109). The prevalence of PD was found
to be twice as high among the residents of rural areas compared to urban
communities. This could be explained by environmental factors like
exposure to toxins and difference in dietary habits, in addition to greater
numbers of consanguineous marriages among rural communities (114, 107).
Other causes of PD beyond genetic and environmental factors may include
head injury(109), other traumas, and vascular compression of cerebral
peduncles by the posterior cerebral artery. This last factor raised a question
regarding etiology of PD which may be answered over time (115).
2.11.3 Prevalence
Data on prevalence and incidence of PD are of particular interest because it
can provide insight into suspected risk factors, protective factors and
primary causes of the disease. But still there are few reliable data of global
prevalence or morbidity of PD. The prevalence of PD varies among ethnic
and geographic groups around the world. The PD worldwide average
prevalence rate was estimated to be 1% in people 60 years and older (104).
But in some studies it was below this rate (0.5%) and the highest reported
rate was 4 % (116, 104). The incidence rate is very low in China (15/100,000)
and high in Argentina (657/100,000) (107). In Europe & United states, the
overall prevalence of the disease is estimated to be 1.6% among persons
46
older than 65 years (103, 104). The annual incidence of PD in the Unites states
was approximated by 446 cases /100,000 populations (104).
The prevalence of PD among the Arab populations is low compared to
worldwide distribution (107). The incidence of PD in Arabs is reported at
4.5/100,000 person/year and reported prevalence at 27 to 43/100,000
persons (110). A prevalence rate of 27/100,000 and 31.4/100,000 have been
found in Thugban – Saudi Arabia and in Benghazi – Libya respectively.
While in Kelibia – Tunisia, Attia Romdhane et al. found a similar prevalence
rate of PD to the above mentioned studies in Arab countries (117-119). The
overall crude prevalence of PD in populations in Israel region was as low as
43.24/100,000 (107). Recently El-Tallawy et al. (114), reported a high
prevalence rate of PD (213.15/100,000) in Al Kharga district – Egypt,
compared to similar studies conducted in Arab countries. This prevalence of
PD in Arabs is higher than black Africans (Nigeria 10/100,000 and Ethiopia
7/100,000) (1). To our knowledge no study has been conducted reporting
prevalence rate of PD in Sudan.
2.11.4 Onset & duration
PD occurs commonly between the age of 45 -55 and roughly in the same age
range in men and women, (114, 4, 120) although it is slightly more common in
men (107, 105). Solla et al. (121), reported that prevalence of PD is more in male
than female by a ratio of 2:1, suggesting a biological diversity. After age of
fifties, the incidence increases sharply with age, ultimately affecting 1.5% of
population over 65 and 2.5% over 85(122), then declines after 85 years of age (114). The onset of PD is rare before the age of 40 years. If PD onset occurs in
patients before 40 years, it is referred to as early-onset PD, although some
47
authors reported early onset to be up to age 50 years. Late onset PD includes
patients whose onset is after 55 years. Early-onset PD has been further
subdivided into; juvenile PD and young-onset PD (YOPD). The juvenile PD
includes cases with onset before 21 years. It is rare and seems to be more
common in Japanese. YOPD includes cases with onset at or above age
21years (123). It was also reported by Willis et al. (124), and Rana et al. (120), that
the YOPD is commonly defined as PD occurring in those aged
approximately between 20 to 49 years and Willis et al. showed that YOPD is
most common among white males in the United States.
The mean duration of PD from onset of symptoms up to death is
approximately 15 years. The natural progression of PD is variable but it is
usually more rapid in patients with late-onset (10). Non-motor symptoms
depend more on age at onset rather than disease duration. Ageing accelerates
disease prognosis after age of 70 years, regardless of previous disease
duration or age at onset of the disease (125). The mortality ratio of PD has
been reported to range 1 – 3.4 (10).
2.11.5 Signs & Symptoms
PD is characterized by motor and non- motor deficits. The motor symptoms
of PD are likely to appear when the pathology of the disease has caused
significant loss of 50–70% of the nigrostriatal dopaminergic neurons in the
SN (112, 126). The motor cardinal signs of PD include resting tremor,
bradykinesia (slowness to initiate movement), rigidity (cogwheel rigidity)
and postural instability (8, 104, 10). Other secondary motor symptoms may
occur (e.g, dysphagia, shuffling gait & micrographia) (10). Rate of annual
decline in these motor signs depends strongly on age at onset, late-onset
48
having higher rate (3.8%) than those of early-onset (2.4%) (104). Progression
of symptoms in PD may occur over 10 – 30 years but the rate of this
progression varies from person to person (17).
The non-motor symptoms of PD remain the most under-appreciated and
under-researched when taken as a whole. These symptoms occur in over
90% of patients across all stages of the disease and include a range of
symptoms from cognitive and autonomic dysfunction, sleep disturbance,
dementia to sensory abnormality and pain (127, 104, 10). Four distinct non-motor
symptoms of PD such as olfactory problems, rapid eye movement behavior
disorder, constipation and depression may be recognized as pre-motor
features. However these symptoms can be unrecognized as clinic
consultation focus largely on the motor aspects of PD which are easier to
manage with a reasonable evidence base for treatment (127). The spectrum
and severity of non-motor symptoms may be presented in different ways for
male and female PD patients, suggesting possible sex-related effects (121).
2.11.6 Pathophysiology
The pathophysiology of PD occurs at multiple levels: at the beginning,
molecular pathogenesis occurs at the targeted areas which in turn lead to
cellular/tissue abnormalities. This tissue abnormality results in
neurochemical changes which then cause site and circuit dysfunction. The
circuit dysfunction will lead to global network activity dysfunction finally
ending by abnormal behavior of PD patients (128).
The early clue to the pathology of the disease came from Brissaud, who
speculated that damage in the SN might lead to PD (13). Therefore the SN
was the focus of attention for decades. In late 1950s, Arvid Carlsson
49
observed that 80% of the dopamine in the brain is localized in the basal
ganglia. Soon afterwards, Oleh Hornykiewicz, studied brains obtained at
postmortem examinations and found that the content of dopamine,
norepinephrine and serotonin in patients with PD was low. He next observed
that, out of the three biogenic amines, dopamine was most drastically
reduced. Therefore PD became the first example of diseases of the brain
associated with a deficiency in a specific neurotransmitter (43).
The SN is now known to be central to the pathology of PD, because the SNc
contains many of the dopaminergic nerve cell bodies (30, 44, 27). Degeneration
of the neurons of the SN results in reduction of the release of the
neurotransmitter dopamine that controls muscle action leading to loss of the
body movements' control. Reduction or loss of more than 80% of the
normal level of dopamine and 50% of pars compacta cells leads to an
inhibitory output activity from basal ganglia to the ventral thalamus and
frontal cortex and subsequent impairment of movement with tremor,
slowness, stiffness, or balance problems, among other symptoms of
parkinsonism (17).
This severity of changes in the SN parallels the reduction of dopamine in the
striatum, this observation suggested that the dopaminergic pathway from the
SN to the striatum is disturbed in PD (43). The nigrostriatal dopaminergic
system originates in the SN and projects primarily to the putamen and
caudate nucleus. So, partial degeneration of this system contributes to the
symptoms of PD (129). With greater understanding of basal ganglia
physiology, the concept of the "circuit abnormalities" caused by the SN
lesions provided a simplistic but workable model for the symptoms that
reduce the normal functions of PD patient (128). The available data strongly
50
support the hypothesis that parkinsonian symptoms are related to abnormal
activity within the basal ganglia. The cerebello-thalamo-cortical loop plays a
role for the frequency of PD tremors (8). But the most famous circuit
involved in PD is the frontostriate circuit dysfunction. This comes from the
converging evidence that striatal dopamine depletion alters the basal ganglia
output to the frontal cortex through the thalamus. Frontal cortical areas are
commonly affected by Lewy pathology (128).
The cardinal pathological feature of the disease consists of the formation of
proteinaceous intraneuronal inclusion called Lewy body and progressive
neuronal loss particularly targeting the SN (17). Because of this, the disorder
is sometimes called Lewy body PD, Lewy body parkinsonism, or simply
Lewy body disease (4).
Braak et al. (130), proposed six point staging procedure for the pathological
process in PD; stage 1 – 2 are presymptomatic and the inclusion bodies are
confined to medulla/pontine tegmentum and olfactory bulb and nucleus,
stage 3 – 4 where the pathological changes are focused on the SN and other
nuclei of midbrain and forebrain, stage 5 – 6 are also called the end or late
stage where the disease can be clinically assessed. The pathological findings
in the late stage extend into the mature neocortex.
Non-motor symptoms correlate with advancing of the disease due to
involvement of lower brainstem nuclei. These nuclei are thought to be key
areas mediating non-motor symptoms such as olfaction, sleep homeostasis,
depression and cognition, pain, constipation and central autonomic vagal
control (127).
51
2.11.7 Diagnosis
Even with recent advances in our understanding of the disease mechanisms,
there is no definitive test for the diagnosis of PD. In clinical practice, the
diagnosis is essentially based on the presence of a combination of the motor
cardinal features and responsiveness to levodopa, which provides only
accuracy of 75 – 90% (10, 131, 13). The percentage rate for PD misdiagnosis is
approximately 10 – 25%. This percentage accounts for those diagnosed as
PD, but exhibited similar disorders such as PSP, MSA, Alzheimer disease,
or cerebrovascular pathology (132, 15, 13). The diagnosis of PD in Arabic
countries is based on two or more cardinal signs: resting tremor,
bradykinesia, rigidity and changes in postural reflexes (110).
A number of rating scales are used for evaluation of the motor signs of PD,
but most of these scales are not fully evaluated for validity and reliability.
The Hoehn and Yahr (H&Y) scale is commonly used to compare group of
patients and ranges from scale 0 (no signs) to stage 5 (wheel-chaired). The
Unified Parkinson's Disease Rating Scale (UPDRS) is the most well
established scale (10).
Over the past two decades neuroimaging techniques such as MRI, SPECT,
PET, and TCUS has increasingly been employed in diagnosis of PD patients
to study their morphological and functional characteristics (17, 25). So many
studies tried neuroimaging markers like, MRI and PET, to help improve
accuracy of the diagnosis of PD.
In the past functional brain imaging with PET had been proved to be of
some value for the differential diagnosis of parkinsonism. However PET
52
imaging was largely restricted to measurement of straital function. Because
of the recent development of high resolution PET and three dimensional
magnetic resonance imaging (3D MRI) based on methods of PET data
analysis, extrastriatal cerebral regions can now also be investigated (12).
TCUS has emerged as a viable tool in differential diagnosis of PD and
recently has been shown to have promising potential as a screening
technique for early detection of PD, even before onset of motor symptoms.
TCUS yields promising results with a sensitivity and specificity of around
83% (131). Another example was conducted by Menke et al. (7), who used
driven equilibrium single pulse observation of T1 (DESPOT1) method
combined with diffusion tensor imaging (DTI). Their study showed that this
method provides a useful set of markers that can be used to differentiate PD
patients from controls. Images synthesized by DESPOT1 provide clear
views of SN allowing this technique to be useful tool for accurate
segmentation of the SN.
In addition, 3D MRI Volumetry has become available and has been recently
applied in patients with PD, MSA, and PSP (12). MRI findings of PD patients
can reveal reduced T2-weighted putamen signal, and midbrain and cortical
atrophy (17).
New application of other techniques has been done by Morgen et al. (126), to
investigate structural damage occurring in PD patients before measurable
atrophy. They used voxel-based magnetic transfer imaging technique and
found that it constitutes a potentially effective method to track early PD-
related pathology. The wealth of methods and applications covered by the
authors indicates that functional and structural brainstem-MRI methods have
53
developed to a point where they can be applied to study a wide range of
neuroscientific problems. It is the hope of the editors that the brainstem will
soon lose its label of a terra incognita and become a region of major interest
in the neuroscience community (31).
2. 11. 8 MRI appearance and volumetric brain changes in PD
The neurodegenerative process of neuronal atrophy and death seen in normal
aging is accelerated and premature in PD; therefore, focal or diffuse atrophy
with sulcal enlargement is a common finding in imaging of these disorders.
Acceleration or abnormal deposition of brain iron, hyperintense foci in the
grey or white matter, and lack of interruption of the blood-brain barrier are
also seen. In general, these disorders clinically and radiologically show a
progressive decline that can continue for years or decades and occasionally
shows relative stability (44).
Pathologically, in PD, there is loss of pigmented cells in the SN, which leads
to malfunction of the major efferent tracts of the SN, the nigrostriatal tract.
Remaining cells may contain eosinophilic cytoplasmic inclusions named
Lewy bodies. A deficiency in striatal dopamine is identified in PD. MRI
findings in PD are relatively nonspecific. The thickness of the SNc has been
described to be diminished in some patients with PD, possibly reflecting
increased iron deposition. MRI may show evidence of atrophy of the
midbrain (74, 133).
Previous studies examining volumetric age-related decline in brainstem
volume in normal aging have reported that there is no volume alteration in
total brainstem, pons and medulla oblongata volumes. However some
studies have supported the finding that the most significantly atrophied part
54
of the brainstem is the midbrain due to shrinkage of its nuclei and a marked
increase in iron concentration (35, 66). A 30-35% increase of iron (total and
ferric iron) content in the SNc of patients with PD has been reported (44).
The primary loss occurs in the neurons of SN, although additional
degeneration occurs in several brain areas including neocortex (134). Nair et
al. (135), reported the mean volumes of putamen, pons and cerebellum in PD
to be 3.3cm³, 11.9cm³ and 121cm³ respectively
Schulz et al. (25), also reported that brainstem volume were normal in PD
patient compared to age-matched controls subjects. In addition they reported
that idiopathic Parkinson's disease (IPD) patients normally do not exhibit
neuronal loss or atrophy in posterior fossa structures. Messina et al. (16),
supported the idea of Schluz in that patients with PD did not show evidence
of volumetric changes in the brainstem with respect to healthy control
(19.87±2.4cm³ v 19.12±2.0 cm³ respectively).
2.11.9 Treatment
Treatments used in PD, unfortunately only provide temporary relief from
early symptoms and do not halt disease progression. In addition,
pathological changes outside of the motor symptoms leading to cognitive,
autonomic, and psychiatric symptoms are not sufficiently treated by current
therapies (13).
Ehringer and Hornykiewicz in 1960 discovered that PD patients have a
marked decrease in dopamine concentration in the striatum and hence
carried out the first trials of dopamine precursor levodopa as a PD therapy (10). Rational therapies of PD aim at correcting the deficiency of dopamine
55
through the use of levodopa which is proved to be a powerful PD treatment
drug. With subsequent advances in therapy, combination of levodopa with
carbidopa or benseraide which are peripheral decarboxylase inhibitor, is a
gold standard treatment for PD (104). This combination reduces the side-
effects associated with levodopa such as nausea and vomiting. The focus of
therapeutic design was also on limiting the breakdown of endogenous
dopamine, so they used selegiline (monoamine oxidase type B inhibitor)
which provides symptomatic benefit (13). Recent discoveries concerning the
role of specific genes in PD pathology will lead to the next revolution in the
disease treatment (13). Drugs used in the treatment of PD are often associated
with side effects in elderly patients. In particular domaminergic drugs can
impair cognitive function and cause postural hypotension (136).
Motor complications in PD result from the short half-life and irregular
plasma fluctuations of oral levodopa. When strategies of providing more
continuous dopaminergic stimulation by adjusting oral medication fail,
patients may be candidates for one of three device-aided therapies: deep
brain stimulation (DBS), continuous subcutaneous apomorphine infusion, or
continuous duodenal/jejunal levodopa/carbidopa pump infusion (DLI) (137).
In DBS, an electrode is surgically implanted in the subthalamic neucleus,
globus pallidus, or ventral intermediate nucleus, introducing continuous
high-frequency stimulation (104).
Despite these advances in symptomatic PD therapy, the ability of these
treatments to facilitate an acceptable quality of life for the patients wanes
with advancing age. This is due to development of motor complications
including decreased levodopa responsiveness, more severe gait and postural
impairment, and cognitive decline with development of dementia (138, 13).
56
The isolation of human embryonic stem cells has provided a potential source
for transplantation materials. Over the past few years, a good effort was
done to develop a protocol that induces the proper dopaminergic
characteristics in these undifferentiated cells to make them suitable
candidates for transplant. But transplant in animal models led to
disappointing results due to poor cell survival (13).
57
3.1 Introduction
The aim of this study is to assess the relation between midbrain, pons and
medulla oblongata volumes to total brainstem volume in Parkinson’s disease
(PD) patients, and in age and sex matched control group using magnetic
resonance imaging (MRI). The volume and volume fraction approaches of
modern stereological techniques is applied on MR images. In this chapter,
the study design, area, duration and groups, the methods used for data
collection (MRI), protocol used for the volumetric analysis of the obtained
images and the statistics used to analyze the measures obtained will be
included.
3.2 Study design
This is an observational case-control study. The cases are patients diagnosed
clinically as having idiopathic Parkinson’s disease and the controls are age
and sex matched normal, neurological disease free volunteers.
The study was ethically approved from the research ethical committee,
Faculty of Graduate Studies and Scientific Research, the National Ribat
University - Sudan. Official permission was also obtained from the
administration of Alamal National Hospital (North Khartoum – Sudan)
where the MR imaging was conducted.
3.3 Study area and duration
The study was conducted in Khartoum city in Sudan during the period from
2012 -2015.
58
3.4 Study groups
The original data set was for 82 subjects; 40 patients with PD and 42 healthy
controls. The total number of cases included in the study was 78 individuals.
The study excluded 4 subjects with extreme young ages; 1 patient and 3
controls. Therefore a total of 78 subjects were enrolled in this study; 39
patients with PD & 39 healthy controls.
Patients with PD (n = 39, 28 males & 11 females), age ranges from 35 – 80
years were included in the study. The mean age of the patients was 59.49 ±
13.02 years. The diagnosis of PD was made using clinical criteria (10) by
expert consultant neurologists, at Neurosciences Specialist Center and
Neurology Outpatient Clinic in Ibrahim Malik and Soba Teaching Hospitals
in Khartoum state - Sudan. Patients with stroke, head trauma, tumor,
multiple system atrophy (MSA), progressive supranuclear palsy (PSP) or
drug induced Parkinsonism were excluded from the study.
Age and sex matched healthy volunteers (n = 39, 25 males & 14 females),
age ranges from 36 – 80 were selected to participate in the study. The mean
age of controls was 56.79 ± 12.39 years. All volunteers were subjected to
MRI scanning and those with normal MRI findings, diagnosed by the
consultant radiologist at the MRI unit in the Diagnostic Center at Alamal
National Hospital (Khartoum - Sudan), were included in the control group.
All patients and controls were subjected to T1-weighed 3D brain MRI at
Alamal National Hospital – MRI unit. Before conducting the MRI scanning
all subjects were informed of the study and written informed consents were
taken. They were also requested to fill constructed questionnaires under
supervision. The questionnaires included socio-demographic data and
59
clinical history for all subjects and clinical data concerning PD for the
patients only.
3.5 Magnetic resonance imaging
The following protocol was used for the accumulated MR imaging data. T1-
weighted coronal images were obtained using three-dimensional acquisition
by Magnetization Prepared Rapid Acquisition Gradient Echo (MP-RAGE).
It provides good grey/white matter contrast in a very short acquisition time.
A 1.5 Tesla Philips Intera MR system (Release 2.6 Level 3 2010 – 11- 24,
The Netherlands) was used. The following scanning parameters were used
for the imaging process: slice distance was zero, the field of view was 230
mm feet to head, 184 mm right to left phase & 184 mm antroposterior. TE=
shortest 4ms, TR=25 ms, bandwidth 189.7 Hz/pixel, flip angle 30º, ECHO
spacing= 1 ÷ 1, phase resolution=100%, slice resolution=50%, and
acquisition time = 3 minutes and 38 seconds
The images of both patients and controls were transferred from the
computer linked with the MRI scanner and saved in CDs. Then the data
from CDs were downloaded to the personal PC and backed up in an external
hard disk.
3.6 Volumetric image analysis
Cavalieri principle was applied to MRI sections for calculation of the
volume and volume fraction of the brainstem and the three subdivisions of
the brainstem.
60
3.6.1 MRI image processing
This was carried out in the Department of Anatomy, Medical Faculty,
Ondokuz Mayis University, Samsun, Turkey. The T1 sequences were
transferred to the PC and further morphometric measurements were
conducted blind to the clinical data, using Image Processing and Analysis in
Java (ImageJ) software. The author of ImageJ, Wayne Rasband
([email protected]), is at the Research Services Branch, National
Institute of Mental Health (NIH), Bethesda, Maryland, USA. ImageJ
software is found on the public domain and freely downloadable from
http://imagej.nih.gov.ij//. It runs on any computer with Java 1.4 or later
virtual machine.
3.6.2 Planimetry method of Cavalieri principle
This is a widely used approach in morphometry, which relies on manual or
automatic boundary delineation of an object in order to estimate its cross-
sectional surface area. Planimetry is generally conducted using software that
automatically counts pixel dimensions enclosed within the traced area (96).
This method gives more accurate results than point counting method despite
that it is more time consuming (139). In this study planimetry method was
applied using ImageJ software by manual tracing of the boundaries of the
region of interest (ROI) (139, 140). The midbrain, pons and medulla oblongata
boundaries were traced manually on each MRI section using the computer
mouse. The surface areas (mm²) were measured automatically on ImageJ.
Since the surface area and the slice thickness were known the volume of
ROI could be calculated according to Cavalieri principle of volume
estimation.
61
3.6.3 Preparation of images to use on ImageJ software
The MRI images were generated and saved on the PC in digital imaging and
communications in medicine (DICOM) format. DICOM viewer software
was installed on the PC to view these images. The software used in this
study was RadiAnt DICOM viewer software, installed freely from
http://www.radiantviewer.com/. After installation the MRI images were
opened in RadiAnt then exported and saved on the PC in two formats;
DICOM and JPEG. This was done to magnify the area of interest without
any resolution loss.
3.6.4 Protocol of MRI image processing for volume estimation of the
brainstem and its subdivisions
The ImageJ software was installed and the measurement was set on the
software to read area in stack position with decimal spaces zero. The
DICOM and JPEG saved images were open using the ImageJ. First the
JPEG image was used to set the scale and then the DICOM image was
reoriented to adjust the head tilting and other deviation in the position of the
head of patients during imaging. The reorient stack(s) window of the ImageJ
opens sections in three views; coronal, axial and sagittal views. The three
views were reoriented and the coronal image was saved to a directory in the
PC for further analysis.
The reoriented coronal image was then transferred to the ImageJ software
and converted into stacks for re-slicing. The output space of re-slice was
adjusted to be one millimeter. The stack of sagittal image series was then
obtained. The mid-sagittal image of the brainstem, where the cerebral
aqueduct was most visible, was selected of the series. This image was
62
rotated to make the brainstem at right angle orientation, and then the rotation
automatically occurs in all of the stacks.
From the midsagittal view of the stack the lower and upper boundaries of the
brainstem were marked on the image using the arrow drawing tool from the
ImageJ software. For the cranial boundary of the brainstem a straight line
was drawn from the posterior commissure crossing anteriorly. To mark the
caudal boundary of the brainstem a straight line was drawn from the upper
border of the arch of the atlas crossing anteriorly.
Because the borders of the foramen magnum are not seen well in MR images
and the atlas was the most fixed clear point in all MR images. The upper
border of the arch of the atlas was considered as the caudal border of the
brainstem and was standardized for all subjects. This landmark which was
referenced in books that the medulla oblongata ends caudally by joining the
spinal cord at the level of the superior border of the posterior arch of the
atlas was chosen as the reference point (141-143).
For segmentation of the three parts of the brainstem also two lines were
drawn selecting the arrow drawing tool from the software, one between
medulla oblongata and pons and the other between the pons and midbrain.
To divide the medulla oblongata from the pons the anterior notch between
them was used as a landmark and a straight line was drawn going posteriorly
from the notch. For dividing the midbrain from the pons a straight line was
drawn from the notch between them anteriorly and to the back. A transparent
sheet was mounted on the PC screen, and then these four lines were drawn
on it to maintain the boundaries landmarks when shifting on the series of
stacks laterally (Figure 3.1).
63
Figure 3.1: Separation of brainstem from the cerebrum and segmentation of
the subdivisions
3.6.4.1 Image processing protocol for volume estimation of the midbrain
Considering the aforementioned procedure as a base for measuring the
different parts of the brainstem in each subject midbrain was scrolled
laterally over the series of slices until last part of it disappears then the
previous slice was selected as a starting point of sampling. Systematic
random sampling was done and the sampling fraction used for midbrain was
64
1/2 "skip one slice and delineate the next". The outer boundary of the
midbrain between the two drawn lines was delineated using the polygon
selection tool from the ImageJ software (Figure 3.2). The sectional cut
surface area of interest was measured by the software automatically. This
was repeated for all selected cuts of midbrain until all samples were finished.
Figure 3.2: Delineation of the midbrain
3.6.4.2 Image processing protocol for volume estimation of the pons
The researcher scrolled laterally over the series of slices of the pons until last
part disappears then the previous slice was selected as a starting point of
sampling. Systematic random sampling was done and the sampling fraction
65
used for pons was 1/2 "skip one slice and delineate the next". The posterior
boundary of the pons was determined to split it from the cerebellum. A
straight line was drawn, by selecting the line drawing tool from the software,
at the back of the pons where the most lateral part of the fourth ventricle
appears to cut the middle cerebellar peduncle (Figure 3.3). This line was
then drawn over the transparency sheet. The outer boundary of the pons was
delineated using the polygon selection tool from the ImageJ software (Figure
3.4). The sectional cut surface area of interest was measured by the software
automatically. This was repeated for all selected cuts of pons until all
samples were finished.
Figure 3.3: Marking the posterior boundary of the pons
66
Figure 3.4: Delineation of the pons
3.6.4.3 Image processing protocol for volume estimation of the medulla
oblongata
For the measurement of the medulla oblongata volume the researcher
scrolled laterally over the series of slices until the last part of medulla
oblongata disappears then the previous slice was selected as a starting point
of sampling. All the slices of medulla oblongata were measured as the
medulla oblongata gives very limited number of sections over the series of
images. This increase in examined sections was done to increase precision of
stereological parameters (90) after tracing the coefficient of error which was
high when the sampling fraction was 1/2. The outer boundary of the medulla
67
oblongata was delineated using the polygon selection tool from the ImageJ
software (Figure 3.5). The sectional cut surface area of interest was
measured by the software automatically. This was repeated for all selected
cuts of medulla oblongata until all samples were finished.
Figure 3.5: Delineation of the medulla oblongata
3.6.5 Estimation of volume by Cavalieri Principle
Estimation of volume was obtained according to Cavalieri principle (144),
which estimates the volume of three-dimensional structures based on two
dimensional slices of objects.
68
The surface area measured from all slices of midbrain, pons and medulla
oblongata on ImageJ were transferred to a prepared spreadsheet on
Microsoft Excel program for measurement of their volume according to
Cavalieri principle. The volume was estimated by multiplication of the total
of sectional area measured of each region by the section thickness which
was 1 mm, as shown in the formula below:
V = t × ∑a,
Where "V" is the volume of midbrain, pons or medulla oblongata, "t" is the
section thickness and "∑a" is the total sectional area of the consecutive
sections millimeter square (m²) of the midbrain, pons or medulla oblongata
3.6.6 Estimation of volume fraction
The volume fraction of stereological methods provides information about
volumetric relations of the components of structure. It ranges from 0 to 1
and is often expressed as percentage. In present study the volume fraction of
midbrain, pons and medulla oblongata was calculated. For the measurement
of the volume fraction of the midbrain the following formula was used:
Vv(midbrain, brainstem) = ∑s midbrain ∑s total brainstem
Where ∑s midbrain is the total surface area of the midbrain and ∑s total
brainstem is the total surface area of the brainstem. The value obtained is the
volume fraction of the midbrain to total brainstem expressed as percentage.
For the measurement of volume fractions of the pons and medulla oblongata
the same formula was used with the change of total surface area of midbrain
by total surface area of pons or medulla oblongata.
69
3.6.7 Reliability of the measurement
All MRI datasets were measured by one investigator blinded to the clinical
data of the subject. Reliability of the measurement was expressed by intra-
observer reliability to indicate how stable were the results obtained. This
was done by repeating the measurement twenty-times at different times for
same MRI sample for a single patient and the coefficient of error (CE) was
measured according to the following formula:
Where, i=1,2,…, m is the number of sections. A is the measured area of
sections using planimetry and the others are constant.
3.7 Statistical analysis
The data was collected and verified by hand then coded before computerized
data entry. The data in the questionnaires were coded and entered in a sheet
prepared in Microsoft Excel. The surface areas measured by the ImageJ
software were transferred to a spreadsheet prepared in Microsoft Excel. The
statistical Package for Social Sciences (SPSS) software version 16.0 was
used for all data entry and analysis. Descriptive statistics (e.g. number,
percentage, mean, range, standard deviation) and analytical tests like t-test
and ANOVA were used to compare variables between the two groups. Also
correlation tests were used and a p-value <0.05 was considered as
statistically significant.
70
4.1 Introduction
The original data set was for 82 subjects; 40 patients with PD and 42 healthy
controls. The total number of cases included in the study was 78 individuals.
The study excluded 4 subjects with extreme young ages; 1 patient and 3
controls. Therefore a total of 78 subjects were enrolled in this study; 39
patients with PD & 39 healthy controls.
In this chapter first the main demographic data and age of the subjects were
compared between and across the groups of patients and controls. Then
analysis of the main clinical characteristics of the PD patients (n=39) was
performed. Next volumetric analysis of the region of interest (ROI) of both
patients and controls was done and their results were compared together.
After that volume of the ROI was correlated with age and main clinical
characteristics of the disease. Finally estimation of the coefficient of error
(CE) for the measurement of the volumes of the data was calculated.
4.2 Demographic data
Patients’ group was consisting of 39 patients with PD; 28 male and 11
female. Their age ranged from 35 to 80 years with a mean age of 59.49 ±
13.02 years.
Controls’ group was consisting of 39 healthy controls; 25 male and 14
female. Their age ranged from 36 to 80 years with a mean age of 56.79 ±
12.39 years.
No significant differences between patients and controls were found with
respect to age, gender and body mass index. Table 4.1 summarizes patients
and control profiles. Concerning the residence of the subjects most of the
71
patients and controls were from urban area. The percentage of patients living
in urban was higher when compared to the controls. Almost half of the
subjects among the patients and controls were having secondary and higher
education. Comparing the occupation among the patients and controls it was
observed that equal percentage of controls and patients were involved in
labour work. About half or more of the patients and controls were
considered as poor according to their financial status (Table 4.1).
Table 4.1: Demographic Characteristics of patients & controls (Cont...)
Variables Patients Controls N % N %
Sex:
Male
Female
28
11
71.8
28.2
25
14
64.1
35.9
Residence:
Rural
Urban
04
35
10.3
89.7
13
26
33.3
66.7
Education:
Illiterate
Primary
Secondary
University & above
06
14
03
16
15.4
35.9
07.7
41.0
05
12
12
10
12.8
30.8
30.8
25.6
72
Table 4.1: Demographic Characteristics of patients & controls
Variables Patients Controls
N % N %
Occupation:
Professional
Govt. Employee
Military
Labour
Housewife
13
04
01
13
08
33.3
10.3
02.5
33.3
20.5
04
04
06
15
10
10.3
10.3
15.4
38.4
25.6
Marital status:
Single
Married
Widow
00
37
02
00.0
94.9
05.1
03
36
00
07.7
92.3
00.0
Monthly income (SDG*)
≤500
600 – 2000
>2000
22
16
01
56.4
41.0
02.6
25
12
02
64.1
30.8
05.1
*SDG = Sudanese Pounds
73
4.3 Age of all subjects
The mean age for all subjects was 58.13±12.70 years and the median was 61
years as shown in (Table 4.2). The age distribution was almost normally
distributed with slight left skewness (- 0.188), (Figure 4.1).
The age range for PD patients was 35 – 80 years, while that of the controls
was 36 – 80 years. The mean age of PD patient (59.46 years) was slightly
higher than that of controls (56.79 years). However, the difference between
them did not rise to a significant level (p>0.05). When comparing the mean
age of the two groups sex wise, there was no statistical difference (p>0.05)
between the mean age of male patients (59.29 years) and male controls
(57.76 years). The same finding exists between the female patients (59.91
years) and female controls (55.07 years). Accordingly there was no
statistical difference of the mean age of males and females between and
across the groups (Table 4.3).
Both patients and controls were divided according to their age into three
groups; first group of age ranges 35-49 years, second group 50-64 and third
group equals or above 65 years (Table 4.4).
Table 4.2: Age (year) of the patient and control groups and their details
Subject N Age range Mean ± SD
Patient 39 35 – 80 59.46 ± 13.02
Control 39 36 – 80 56.79 ± 12.39
Total 78 35 – 80 58.13 ± 12.70
74
Figure 4.1: Age distribution for all study groups
Table 4.3: Age (years) of the patient and control groups depending on sex and their details
Subject N Age Range Mean ± SD
Male Patient 28 35 – 80 59.29 ± 12.67
Female Patient 11 35 – 78 59.91 ± 14.51
Male Control 25 39 – 80 57.76 ± 12.66
Female Control 14 36 – 71 55.07 ± 12.16
Total 78 35 – 80 58.13 ± 12.70
75
Table 4.4: Age groups distribution of all the subjects
Age range No. of Control No. of Patients
35-49 years 13 10
50-64 years 15 10
≥ 65 years 11 19
Total 39 39
4.4 Clinical characteristics of the PD patients
4.4.1 Onset of the disease
The age range at onset of the disease among PD patients was 25 – 76 years.
This range was divided into small groups of 10 years interval to see effect of
age at onset of the disease. The mean age at onset of PD was 54.00 ± 12.22
years and the median age at onset of PD was 55.0 years. The onset of PD
among patients was highest in the age group from 55–64 years (Figure 4.2).
The possibility of onset of PD may increase with age up to 64 years and then
decreases after that (Figure 4.3).
76
Figure 4.2: Percentages of age distribution at onset of Parkinson’s disease
Figure 4.3: Age distribution (years) at onset of Parkinson’s disease
77
4.4.2 Duration of the disease
Concerning the duration of the PD after the patients have first been
diagnosed; the mean duration of the disease was 5.68 ± 4.21 years and the
median was 5 years (Figure 4.4). Regarding the distribution of the duration,
the patients were divided into three groups; the first group from less than 4
years, the second group more than 4years up to 8 years and the third group
more than 8 years. Slightly more than a half of the PD patients were
suffering of the disease for a period less than four years from diagnosis
(Table 4.5). The mean duration interval was 1.74 ± 0.85 years and the
median was 1 year.
Figure 4.4: Duration (years) of Parkinson’s disease
78
Table 4.5: Distribution of duration of Parkinson’s disease
Years interval N Percent (%) Mean ± SD ≤1 – 4 20 51.3
1.74 ± 0.85 > 4 – 8 09 23.1 > 8 10 25.6 Total 39 100
4.4.3 Duration of the symptoms before medical diagnosis of PD
Most of the patients (84.6 %) seek medical diagnosis after more than one
year from the start of symptoms (Table 4.6). The mean duration was 1.85 ±
0.37 years and the median was 2.0 years.
Table 4.6: Duration of symptoms before medical diagnosis of Parkinson’s disease
Duration N Percent (%) Mean ± SD ≤ one year 06 15.4
1.85 ± 0.37 >one year 33 84.6 Total 39 100
4.4.4 Medical characteristics of the Parkinson’s disease patients
Most of the PD diagnosed patients (76.9%) had no family history of PD
while only the remaining percent (23.08%) were having PD in their families.
Regarding the other medical family histories, slightly more than a half
(51.28%) of the PD patient were not having any of the medical histories, a
quarter (25.64%) of them were either having diabetes or hypertension and
79
the remaining were having combination or other medical histories (Figure
4.5). More than half of the patients were under a combination of medical
treatment e.g. Levodopa and other dopamine agonist (Figure 4.6).
Concerning the compliance with medication; most of the patients (74.36%)
were using their medications regularly, 20.51% of them were using
medication only during acute phase of symptoms and 5.13% of the patients
were not using their medication (Figure 4.7).
Figure 4.5: Medical history of Parkinson’s patients
80
Figure 4.6: Medications used by Parkinson patients
Figure 4.7: Compliance of Parkinson patients with medication used
81
4.5 Volumetric analysis
4.5.1 Volumetric analysis of the midbrain for patients and controls
The overall comparison of the mean volumes of midbrain between the
patients and controls reveals high significance difference (p<0.001); the
details of this is shown in (Table 4.7 and Figure 4.8).
Multiple comparisons test shows the details of interaction between and
across the groups (Tables 4.7) as follows:
• The mean volume of midbrain in the male controls was (8.62±1.34
cm³). It was slightly larger compared to the mean volume of midbrain
in female controls (8.45±1.44 cm³). However, this difference did not
reach a statistical significant value (p>0.05).
• While the mean volume of midbrain in the male patients (7.72±1.16
cm³) was larger compared to the female patients’ midbrain volume
(6.53±1.14 cm³). However, the difference of mean volume of
midbrain within the PD patients was also not significant (p>0.05).
• The mean volume of midbrain in the male patients (7.72±1.16 cm³)
was smaller compared to the male controls’ midbrain volume
(8.62±1.34 cm³). However, the difference of mean volume of
midbrain between males of the two groups was not significant
(p>0.05).
• While the mean volume of midbrain in the female patients (6.53±1.14
cm³) was much smaller compared to the female controls’ midbrain
volume (8.45±1.44 cm³). This difference of mean volume of midbrain
between females of patients and controls was statistically significant
(p<0.05).
82
Table 4.7: Volume (cm³) of midbrain for all groups and overall
Groups N Mean ± SD Range Total
Male Patient 28 7.72 ± 1.16 5.76 – 10.24 7.38 ± 1.26¥
Female Patient 11 6.53 ± 1.14* 4.93 – 8.87
Male Control 25 8.62 ± 1.34 6.44 – 11.08 8.56 ± 1.36¥
Female Control 14 8.45 ± 1.44* 5.56 – 11.46
Total 78 7.97 ± 1.43 4.93 – 11.46
* and ¥= p<0.05
Figure 4.8: Boxplot of the mean volume (cm³) of the midbrain for the
patients and controls by gender
83
4.5.2 Volumetric analysis of the pons for patients and controls
The overall comparison of the mean volumes of pons between the patients
and controls reveals significant difference (p=0.015), the details of this is
shown in (Table 4.8 and Figure 4.9).
Multiple comparisons test shows the details of interaction between and
across the groups (Tables 4.8) as follows:
• The mean volume of pons in the male controls (15.50±2.14 cm³) was
larger than that of female controls (14.37±2.34 cm³). This difference
was not significant (p>0.05).
• The mean volume of pons in the male patients (14.11±1.81 cm³) was
larger compared to the female patients’ (13.09±2.61 cm³). However,
the difference of mean volume of pons in this group was not
significant (p>0.05).
• The mean volume of pons in the male patients (14.11±1.81 cm³) was
smaller compared to the male controls’ (15.50±2.14 cm³). However,
the difference of mean volume of pons between males of the two
groups was not significant (p>0.05).
• The mean volume of pons in the female patients (13.09±2.61 cm³)
was smaller compared to the female controls’ (14.37±2.34 cm³).
However this difference of mean volume of pons between females of
patients and controls was not significant (p>0.05).
Multiple comparison test indicates that the significance difference of the
volume of the pons between the patients and controls is due to the effect of
female patients over male controls. Female patients mean volume of pons
was 2.41 cm³ less than that of male controls (p=0.02).
84
Table 4.8: Volume (cm³) of pons for all groups and overall
Groups N Mean ± SD Range Total
Male Patient 28 14.11 ± 1.81 10.28 – 17.29 13.82 ± 2.25¥
Female Patient 11 13.09 ± 2.61* 9.27 – 17.19
Male Control 25 15.50 ± 2.14* 11.80 – 21.03 15.11 ± 2.25¥
Female Control 14 14.37 ± 2.34 11.35 – 18.22
Total 78 14.46 ± 2.25 9.27 – 21.03
* and ¥= p<0.05
Figure 4.9: Boxplot of the mean volume (cm³) of the pons for the patients and controls by gender
85
4.5.3 Volumetric analysis of the medulla oblongata for patients and
controls
The overall comparison of the mean volumes of medulla between the
patients and controls reveals significant difference (p=0.007), the details of
this is shown in (Table 4.9 and Figure 4.10).
Multiple comparisons test shows the details of interaction between and
across the groups (Tables 4.9) as follows:
• The mean volume of medulla in the male controls (4.76±0.78 cm³)
was slightly larger compared to the female controls’ medulla volume
(4.67±0.64 cm³). But, the difference of mean volume of medulla
within the controls did not rise to a significant level (p>0.05).
• The mean volume of medulla in the male patients (4.28±0.49 cm³)
was slightly larger compared to the female patients’ medulla volume
(4.09±0.60 cm³). However, the difference of mean volume of medulla
oblongata within the PD patients was not significant (p>0.05).
• The mean volume of medulla oblongata in the male patients
(4.28±0.49 cm³) was smaller compared to the male controls’ medulla
volume (4.76±0.78 cm³). This difference of mean volume of medulla
between males of the two groups was statistically significant (p<0.05).
• The mean volume of medulla in the female patients (4.09±0.60 cm³)
was smaller compared to the female controls’ medulla volume
(4.67±0.64 cm³). However this difference of mean volume of medulla
between females of both groups was not significant (p>0.05).
Multiple comparison test indicates that the significant difference of the
volume of the medulla between the patients and controls is due to the effect
86
of male patients over the male controls and female patients over male
controls (p=0.03).
Table 4.9: Volume (cm³) of medulla oblongata for all groups and overall
Groups N Mean ± SD Range Total
Male Patient 28 4.28 ± 0.49* 3.46 – 5.49 4.22 ± 0.52¥
Female Patient 11 4.09 ± 0.60 3.10 – 5.18
Male Control 25 4.76 ± 0.78* 3.41 – 7.23 4.73 ± 0.73¥
Female Control 14 4.67 ± 0.64 3.67 – 6.20
Total 78 4.48 ± 0.68 3.10 – 7.23
* and ¥= p<0.05
Figure 4.10: Boxplot of the mean volume (cm³) of the medulla oblongata for the patients and controls by gender
87
4.5.4 Volumetric analysis of the brainstem for patients and controls
The overall comparison of the mean volumes of brainstem between the
patients and controls reveals high significant difference (p=0.001), the
details of this is shown in (Table 4.10 and Figure 4.11).
Multiple comparisons test shows the details of interaction between and
across the groups (Tables 4.10) as follows:
• The mean volume of the brainstem in the male controls (28.89±3.68
cm³) was larger compared to the female controls’ volume (27.48±3.79
cm³). But, the difference of mean volume of the brainstem within the
controls did not rise to a significant level (p>0.05).
• The mean volume of brainstem in the male patients (26.10±2.80 cm³)
was larger compared to the female patients’ volume (23.71±4.11 cm³).
However, the difference of mean volume of brainstem within the PD
patients was not significant (p>0.05).
• The mean volume of the brainstem in the male patients (26.10±2.80
cm³) was smaller compared to the male controls’ volume (28.89±3.68
cm³). This difference of mean volume of the brainstem between
males of the two groups was statistically significant (p<0.05).
• The mean volume of the brainstem in the female patients (23.71±4.11
cm³) was much smaller compared to the female controls’ volume
(27.48±3.79 cm³). This difference of mean volume of the brainstem
between females of the two groups shows a statistical significance
(p<0.05).
88
Multiple comparison test indicates that the significance difference of the
volume of brainstem between the patients and controls is due to the effect
of patients over controls
Table 4.10: Volume (cm³) of brainstem for all groups and overall
Groups N Mean ± SD Range Total
Male Patient 28 26.10 ± 2.80* 21.81 – 31.61 25.43 ± 3.35¥
Female Patient 11 23.71 ± 4.11# 17.87 – 29.66
Male Control 25 28.89 ± 3.68* 23.37 – 37.54 28.38 ± 3.73¥
Female Control 14 27.48 ± 3.79# 22.63 – 34.65
Total 78 26.90 ± 3.82 17.87 – 37.54
*, # and ¥= p<0.05
Figure 4.11: Boxplot of the mean volume (cm³) of the brainstem for the patients and controls by gender
89
4.5.5 Volume fraction analysis of midbrain
The mean volume fraction of midbrain in males was higher than in females
in patients group (29.55±2.88 % vs 27.62±1.10 %). While the mean volume
fraction of midbrain in males was lower than in females in controls group
(29.82±2.54 % vs 30.73±3.36 %).
The mean volume fraction of midbrain in male patients was slightly lower
than male controls (29.55±2.88 % vs 29.82±2.54 %). The mean volume
fraction of midbrain in female patients was lower than female controls
(27.62±1.10 % vs 30.73±3.36 %).
Over all there was a statistical difference in the volume fractions of midbrain
between the patients and controls (p≤0.05), this is shown in (Table 4.11 &
Figure 4.12).
Table 4.11: Volume fractions (%) of midbrain among the groups
Groups N Mean ± SD Range
Male Patient 28 29.55 ± 2.88 24.77 – 36.44
Female Patient 11 27.62 ± 1.10 24.58 – 31.39
Male Control 25 29.82 ± 2.54 25.37 – 34.86
Female Control 14 30.73 ± 3.36 24.57 – 35.54
Total 78 29.58 ± 2.85 24.57 – 36.44
p=0.051
90
Figure 4.12: Boxplot of the mean volume fraction (%) of the midbrain for
the patients and controls by gender
4.5.6 Volume fraction analysis of pons
The mean volume fraction of pons in male was slightly lower than female in
patients group (54.01±3.08 % vs 54.98±2.22 %). While the mean volume
fraction of pons in male was higher than female in controls group
(53.67±3.01 % vs 52.21±3.55 %).
The mean volume fraction of pons in male patients was higher than male
controls (54.01±3.08 % vs 53.67±3.01 %). The mean volume fraction of
pons in female patients was higher than female controls (54.98±2.22 % vs
52.21±3.55 %).
91
Over all there was no statistical difference in the volume fractions of pons
between the patients and controls (p>0.05), this is shown in (Table 4.12 &
Figure 4.13).
Table 4.12: Volume fractions (%) of pons among groups
Groups N Mean ± SD Range
Male patient 28 54.01 ± 3.08 47.14 - 59.04
Female patient 11 54.98 ± 2.22 51.90 - 57.95
Male control 25 53.67 ± 3.01 47.86 - 59.14
Female control 14 52.21 ± 3.55 47.41 - 56.59
Total 78 53.72 ± 3.09 47.14 - 59.14
p= 0.15
Figure 4.13: Boxplot of the mean volume fraction (%) of the pons for the
patients and controls by gender
92
4.5.7 Volume fraction analysis of medulla oblongata
The mean volume fraction of medulla oblongata in male was lower than
female in patients group (16.45±1.65 % vs 17.40±1.79 %). The mean
volume fraction of medulla oblongata in male was also lower than female in
controls group (16.51±1.76 % vs 17.05±1.59 %). The mean volume fraction
of medulla oblongata in male patients was slightly lower than male controls
(16.45±1.65 % vs 16.51±1.76 %). While the mean volume fraction of
medulla oblongata in female patients was slightly higher than female
controls (17.40±1.79 % vs 17.05±1.59 %).
Over all there was no statistical difference in the volume fractions of
medulla oblongata between the patients and controls (p>0.05), this is shown
in (Table 4.13 & Figure 4.14).
Table 4.13: Volume fraction (%) of medulla oblongata among groups
Groups N Mean ± SD Range
Male patient 28 16.45 ± 1.65 13.20 - 19.72
Female patient 11 17.40 ± 1.79 14.86 - 20.49
Male control 25 16.51 ± 1.76 14.17 - 20.59
Female control 14 17.05 ± 1.59 13.88 - 19.83
Total 78 16.71 ± 1.70 13.20 - 20.59
p-value = 0.34
93
Figure 4.14: Boxplot of the mean volume fraction (%) of the medulla oblongata for the patients and controls by gender
4.6 Correlation of volumes and volume fractions of midbrain,
pons, medulla oblongata & total brainstem with age
Pearson’s correlation test was computed to assess the relationship between
the volumes and volume fractions of the ROI and age.
4.6.1 Correlation of age with volume of midbrain
Overall, there was a significant negative correlation between the age and
volume of midbrain (r = - 0.514 & p ≤0.001) (Figure 4.15). This explains
that when the subjects become older, the volume of midbrain become
94
smaller in size. When analyzed separately with patients, there was a
significant negative correlation between the age of PD patients and volume
of midbrain (r = - 0.452 & p = 0.004) (Figure 4.16). In controls there was
also a significant negative correlation between the age and volume of
midbrain (r = - 0.587 & p ≤0.001) (Figure 4.17). A significant negative
correlation was found when analyzing age of male patients and male
controls with volume of midbrain (r = - 0.532 & p ≤0.001) (Figure 4.18). A
significant negative correlation was found when analyzing age of female
patients and female controls with volume of midbrain (r = - 0.537 & p =
0.006) (Figure 4.19).
Figure 4.15: Correlation of age with volume of midbrain for all groups
95
Figure 4.16: Correlation of age with volume of midbrain for PD patients
Figure 4.17: Correlation of age with volume of midbrain for controls
96
Figure 4.18: Correlation of age with volume of midbrain among male patient and male controls
Figure 4.19: Correlation of age with volume of midbrain among female patients and female controls
97
4.6.2 Correlation of age with volume of pons
Overall, there was a significant negative correlation between the age and
volume of pons (r = - 0.305 & p = 0.007) (Figure 4.20). Along with increase
of age, volume of pons was decreased. When analyzed separately with
patients, there was a significant negative correlation between the age of PD
patients and volume of pons (r = - 0.352 & p = 0.025) (Figure 4.21). In
controls there was a non significant negative correlation between the age and
volume of midbrain (r = - 0.223 & p = 0.172) (Figure 4.22). A significant
negative correlation was found when analyzing age of male patients and
male controls with volume of pons (r = - 0.310 & p = 0.024) (Figure 4.23).
A non significant negative correlation was found when analyzing age of
female patients and female controls with volume of pons (r = - 0.346 & p =
0.090) (Figure 4.24).
Figure 4.20: Correlation of age with volume of pons for all groups
98
Figure 4.21: Correlation of age with volume of pons for PD patients
Figure 4.22: Correlation of age with volume of pons for controls
99
Figure 4.23: Correlation of age with volume of pons for male patients and male controls
Figure 4.24: Correlation of age with volume of pons for female patients and female controls
100
4.6.3 Correlation of age with volume of medulla oblongata
Overall, there was a significant negative correlation between the age and
volume of medulla oblongata (r = - 0.304 & p = 0.007) (Figure 4.25). Along
with increase of the age, volume of medulla oblongata was decreased. When
analyzed separately with patients, there was a non significant negative
correlation between the age of PD patients and volume of medulla oblongata
(r = - 0.160 & p = 0.332) (Figure 4.26). In controls there was a significant
negative correlation between the age and volume of medulla oblongata (r = -
0.392 & p = 0.014) (Figure 4.27). A significant negative correlation was
found when analyzing age of male patients and male controls with volume
of medulla oblongata (r = - 0.272 & p = 0.049) (Figure 4.28). A non
significant negative correlation was found when analyzing age of female
patients and female controls with volume of medulla oblongata (r = - 0.385
& p = 0.058) (Figure 4.29).
Figure 4.25: Correlation of age with volume of medulla oblongata for all groups
101
Figure 4.26: Correlation of age with volume of medulla oblongata for PD patients
Figure 4.27: Correlation of age with volume of medulla oblongata for controls
102
Figure 4.28: Correlation of age with volume of medulla oblongata for male patients & male controls
Figure 4.29: Correlation of age with volume of medulla oblongata for female patients & female controls
103
4.6.4 Correlation of age with volume of brainstem
Overall, there was a significant negative correlation between the age and
volume of brainstem (r = - 0.426 & p ≤0.001) (Figure 4.30). Along with
increase of age, the volume of brainstem was decreased. When analyzed
separately with patients, there was a significant negative correlation between
the age of PD patients and volume of brainstem (r = - 0.418 & p = 0.008)
(Figure 4.31). In controls there was also a significant negative correlation
between the age and volume of brainstem (r = - 0.426 & p = 0.007) (Figure
4.32). A significant negative correlation was found when analyzing age of
male patients and male controls with volume of brainstem (r = - 0.437 & p =
0.001) (Figure 4.33). A significant negative correlation was found when
analyzing age of female patients and female controls with volume of
brainstem (r = - 0.463 & p = 0.020) (Figure 4.34).
Figure 4.30: Correlation of age with volume of brainstem for all groups
104
Figure 4.31: Correlation of age with volume of brainstem for PD patients
Figure 4.32: Correlation of age with volume of brainstem for Controls
105
Figure 4.33: Correlation of age with volume of brainstem for male patients & male controls
Figure 4.34: Correlation of age with volume of brainstem for female patients & female controls
106
4.6.5 Correlation of age groups with total volume of the brainstem and
each of its subdivisions
The subjects have been divided into three groups according to age range;
first group from 35-49 years, the second group from 50-64 years and the
third group equal or above 65 years. This has been performed to investigate
the extent of reduction in the volume of the brainstem and its subdivisions in
relation to different age ranges between PD patients and control group. A
more reduction was observed in the volume of midbrain in age group 35-49
years than in the other groups, however this did not reach a significant value
(Table 4.14).
Table 4.14: The volume of brainstem and its subdivisions in controls and PD patients in relation to different age groups
Brainstem
(cm³ ± SD)
Medulla oblongata
(cm³ ± SD)
Pons
(cm³ ± SD)
Midbrain
(cm³ ± SD) Subject
Age range
(years)
30.60±3.40 5.14±0.88 15.88±1.96 9.58±1.24 * Control(n=13)
35 -49 27.15±2.86 4.23±0.46 14.85±1.9 8.07±1.16 * PD (n=10)
0.40 0.13 0.91 0.71 p-value
27.34±3.53 4.51±0.60 14.62±2.36 8.20±1.20 Control(n=15)
50 - 64 26.47±2.57 4.44±0.47 13.98±1.40 8.04±1.24 PD(n=10)
0.68 0.35 0.53 0.91 p-value
27.17±3.45 4.54±0.49 14.81±2.38 7.82±1.02 Control(n=11)
≥ 65 23.10±3.44 4.10±0.57 13.20±2.32 6.68±0.94 PD (n=19)
0.68 0.38 0.79 0.78 p-value
* Reduction in midbrain volume between PD and control was 1.51 cm³
107
4.6.6 Correlation of age with volume fraction of midbrain
Overall, there was a significant negative correlation between the age and
volume fraction of midbrain (r = - 0.318 & p = 0.005) (Figure 4.35). Along
with increase of age, volume fraction of midbrain was decreased. When
analyzed separately with patients, there was a non-significant negative
correlation between the age of PD patients and volume fraction of midbrain
(r = - 0.218 & p = 0.182) (Figure 4.36). In controls there was a significant
negative correlation between the age and volume fraction of midbrain (r = -
0.393 & p = 0.013) (Figure 4.37). A significant negative correlation was
found when analyzing age of male patients and male controls with volume
fraction of midbrain (r = - 0.310 & p = 0.024) (Figure 4.38). A non-
significant negative correlation was found when analyzing age of female
patients and female controls with volume fraction of midbrain (r = - 0.343 &
p = 0.093) (Figure 4.39).
Figure 4.35: Correlation of age with volume fraction of midbrain for all groups
108
Figure 4.36: Correlation of age with volume fraction of midbrain for PD patients
Figure 4.37: Correlation of age with volume fraction of midbrain for controls
109
Figure 4.38: Correlation of age with volume fraction of midbrain for male patients & male controls
Figure 4.39: Correlation of age with volume fraction of midbrain for female patients & female controls
110
4.6.7 Correlation of age with volume fraction of pons
Overall, there was a non-significant positive correlation between the age and
volume fraction of pons (r = 0.189 & p = 0.097) (Figure 4.40). Along with
increase of age, the volume fraction of pons is not affected. When analyzed
separately with patients, there was a non-significant negative correlation
between the age of PD patients and volume fraction of pons (r = - 0.004 & p
= 0.982) (Figure 4.41). In controls there was a significant positive
correlation between the age and volume fraction of pons (r = 0.339 & p =
0.035) (Figure 4.42). A non-significant positive correlation was found when
analyzing age of male patients and male controls with volume fraction of
pons (r = 0.183 & p = 0.190) (Figure 4.43). A non-significant positive
correlation was found when analyzing age of female patients and female
controls with volume fraction of pons (r = 0.194 & p = 0.354) (Figure 4.44).
Figure 4.40: Correlation of age with volume fraction of pons all groups
111
Figure 4.41: Correlation of age with volume fraction of pons PD patients
Figure 4.42: Correlation of age with volume fraction of pons controls
112
Figure 4.43: Correlation of age with volume fraction of pons for male patients & male controls
Figure 4.44: Correlation of age with volume fraction of pons for female patients & female controls
113
4.6.8 Correlation of age with volume fraction of medulla oblongata
Overall, there was a non-significant positive correlation between the age and
volume fraction of medulla (r = 0.189 & p = 0.097) (Figure 4.45). This
explains that there was no correlation between the age and volume fraction
of medulla. When analyzed separately with patients, there was a significant
positive correlation between the age of PD patients and volume fraction of
medulla (r = 0.357 & p = 0.026) (Figure 4.46). In controls there was a non-
significant positive correlation between the age and volume fraction of
medulla (r = 0.012 & p = 0.941) (Figure 4.47). A non-significant positive
correlation was found when analyzing age of male patients and male
controls with volume fraction of medulla (r = 0.169 & p = 0.227) (Figure
4.48). A non-significant positive correlation was found when analyzing age
of female patients and female controls with volume fraction of medulla (r =
0.278 & p = 0.178) (Figure 4.49).
Figure 4.45: Correlation of age with volume fraction of medulla oblongata for all groups
114
Figure 4.46: Correlation of age with volume fraction of medulla oblongata for PD patients
Figure 4.47: Correlation of age with volume fraction of medulla oblongata for controls
115
Figure 4.48: Correlation of age with volume fraction of medulla oblongata for male patients & male controls
Figure 4.49: Correlation of age with volume fraction of medulla oblongata for female patients & female controls
116
4.7 Correlation of volumes and volume fractions of midbrain, pons,
medulla oblongata & total brainstem with onset of Parkinson's disease
Pearson’s correlation test was computed to access the relationship between
the volumes and volume fractions of the ROI and onset of the disease.
4.7.1 Correlation of onset of Parkinson’s disease with volume of
midbrain
Overall, there was a significant negative correlation between the onset of PD
and volume of midbrain (r = - 0.412 & p = 0.009). A significant negative
correlation was found between onset of PD in male patient and volume of
midbrain (r = - 0.405, n = 28 & p = 0.032). There was no correlation
between the onset of PD in female patients and volume of midbrain (r = -
0.556, n = 11 & p = 0.076). This explains that along with increase of age of
onset of PD, the volume of midbrain was decreased. A scatter plot
summarizes the results (Figure 4.50).
Figure 4.50: Correlation of onset of PD with volume of midbrain
117
4.7.2 Correlation of onset of Parkinson’s disease with volume of pons
Overall there was a significant negative correlation between the onset of PD
and volume of pons (r = - 0.352 & p = 0.028). When the onset of PD was
correlated separately with male patients there was no correlation (r= - 0.235,
n = 28 & p = 0.228). There was no correlation between the onset of PD in
female patients and volume of pons (r=- 0.338, n=11 & p=0.088). This
explains that; although there was no relationship with gender, in overall
patient group when the age of onset of the disease was increased, the volume
of pons was decreased. A scatter plot summarizes the results (Figure 4.51).
Figure 4.51: Correlation of onset of PD with volume of pons
118
4.7.3 Correlation of onset of Parkinson’s disease with volume of medulla
oblongata
Overall there was no correlation between the onset of PD and volume of
medulla oblongata (r = - 0.183 & p = 0.264). When the onset of PD was
correlated separately with male patients and female patients there was no
correlation (r = - 0.173, n = 28 & p = 0.380) and (r = - 0.202, n = 11 & p =
0.551) respectively. This explains that there is no relationship between age
of onset of the disease and volume of medulla oblongata. A scatter plot
summarizes the results (Figure 4.52).
Figure 4.52: Correlation of onset of PD with volume of medulla oblongata
119
4.7.4 Correlation of onset of Parkinson’s disease with volume of
brainstem
Overall there was a significant negative correlation between the onset of PD
and volume of brainstem (r = - 0.403 & p = 0.011). When the onset of PD
was correlated with volume of brainstem separately with male and female
patients there was no correlation (r = - 0.350, n = 28 & p = 0.068) and (r = -
0.526, n = 11 & p = 0.097) respectively. This explains that; although there
was no relationship with gender, when the overall age of onset of the disease
was increased, the volume of brainstem was decreased. A scatter plot
summarizes the results (Figure 4.53).
Figure 4.53: Correlation of onset of PD with volume of brainstem
120
4.7.5 Correlation of onset of Parkinson’s disease with volume fraction of
midbrain
Overall there was no correlation between the onset of PD and volume
fraction of midbrain (r = - 0.173 & p = 0.293). When the onset of PD was
correlated with volume fraction of midbrain separately among the male and
female patients there was no correlation (r = - 0.214, n=28 & p=0.274) & (r
= - 0.092, n = 11 & p = 0.789) respectively. This explains that there is no
relationship between age of onset of the disease and volume fraction of
midbrain. A scatter plot summarizes the results (Figure 4.54).
Figure 4.54: Correlation of onset of PD with volume fraction of midbrain
121
4.7.6 Correlation of onset of Parkinson’s disease with volume fraction of
pons
Overall there was no correlation between the onset of PD and volume
fraction of pons (r = - 0.023 & p = 0.891). When the onset of PD was
correlated with volume fraction of pons separately among the male and
female patients there was no correlation (r = 0.121, n=28 & p = 0.541) and (r
= - 0.0453, n = 11 & p = 0.162) respectively. This explains that there is no
relationship between age of onset of the disease and volume fraction of pons.
A scatter plot summarizes the results (Figure 4.55).
Figure 4.55: Correlation of onset of PD with volume fraction of pons
122
4.7.7 Correlation of onset of Parkinson’s disease and volume fraction of
medulla oblongata
Overall there was a non significant positive correlation between the onset of
PD and volume fraction of medulla oblongata (r = 0.315 & p = 0.051).
When the onset of PD was correlated with volume fraction of medulla
oblongata separately for male patients there was a non significant positive
correlation (r = 0.148, n = 28 & p = 0.451). A significant positive correlation
was found between the onset of PD in female patients and volume fraction
of medulla oblongata (r = 0.663, n = 11 & p = 0.026). This explains that
there is no overall relationship between age of onset and volume fraction of
medulla oblongata. Although in female patients when age of onset of the
disease was increased, the volume of brainstem was also increased. A scatter
plot summarizes the results (Figure 4.56).
Figure 4.56: Correlation of onset of PD in with volume fraction of medulla oblongata
123
4.8 Correlation of volumes and volume fractions of midbrain,
pons, medulla oblongata & total brainstem with duration of
Parkinson's disease
Pearson’s correlation test was computed to assess the relationship between
the volumes and volume fractions of the ROI and duration of the disease.
Overall there was no correlation between the duration of PD and the volume
and volume fraction of midbrain, pons, medulla oblongata and brainstem.
This correlation was also performed between male and females separately
and the same results exist (P>0.05). This results is shown in table (4.15)
4.9 Estimations of coefficient of error for the measurements of
the volumes of the data
The coefficient of error (CE) for measurement of volumes of the midbrain,
pons and medulla oblongata were estimated. The mean of CE of midbrain
and medulla oblongata were almost the same (0.21 %). The mean of CE of
pons (0.16 %) was estimated as the minimum of the three regions of the
brainstem, details are shown in (Table 4.16).
124
Table 4.15: Correlation of duration of PD with volume and volume fraction of midbrain, pons, medulla oblongata and brainstem
Variable Patients
(n=39)
Male
(n=28)
Female
(n=11)
Volume of midbrain (cm³) r-value - 0.15 - 0.25 0.27
p-value 0.36 0.20 043
Volume of pons (cm³) r-value - 0.06 - 0.12 0.12
p-value 0.71 0.54 0.72
Volume of medulla oblongata (cm³) r-value 0.05 0.15 - 0.12
p-value 0.74 0.45 0.73
Volume of brainstem (cm³) r-value - 0.09 - 0.15 0.13
p-value 0.60 0.43 0.70
Volume fraction of midbrain (%) r-value - 0.14 - 0.21 0.34
p-value 0.41 0.28 0.31
Volume fraction of pons (%) r-value 0.03 0.01 0.05
p-value 0.84 0.95 0.90
Volume fraction of medulla oblongata
(%)
r-value 0.16 0.35 - 0.43
p-value 0.32 0.07 0.19
* Correlation is significant when the p-value is ≤ 0.05
125
Table 4.16: Results of the coefficient of error (CE in %) for volume of data
Structure Mean SD Range
Midbrain volume 0.21 0.04 0.15 – 0.37
Pons volume 0.16 0.03 0.11 – 0.23
Medulla oblongata volume 0.21 0.05 0.13 – 0.35
126
5.1 Introduction
Parkinson's disease is a neurodegenerative disorder characterized by
progressive loss of dopaminergic neurons of the substantia nigra pars
compacta (SNc). It is clinically diagnosed on the basis of its motor
symptoms (11). In this case-control study, the researcher is trying to make a
contribution to explore the in vivo volume changes of the brainstem and its
subdivisions in patients with PD. This is a novel study to quantify in vivo
the regional brainstem and its subdivisions atrophy in brains of patients with
PD using T1-weighted 3D brain MRI and manual planimetry tracing method
of the region of interest (ROI), and applying the volumetric method of
Cavalieri on the data of patients (n=39) and healthy controls (n=39). The
volume fraction of brainstem regions, which is a stereological approach
denoting the proportions of the body components independent of body mass
index was also calculated (5).
The motor cardinal signs of PD include resting tremor, rigidity, bradykinesia
and postural instability (9). In clinical practice, diagnosis of PD typically
depends upon the presence of a combination of motor cardinal signs and
responsiveness to levodopa, so its diagnosis is subjected to a rate of
misdiagnosis that varies from 10% to 50 % (14). This is mainly due to the fact
that there is no sensitive and specific biomarker valid for clinicians to use in
the differential diagnosis of PD from other neurological disorder with
overlapping motor cardinal signs such as multiple system atrophy (MSA)
and progressive supranuclear palsy (PSP) (10, 11, 14).
With the advancing development in imaging techniques of the brain
researchers have started employing different anatomical methods on MRI, to
127
evaluate the structural changes in brain regions critical to PD (145). Most
researches have focused on neuroimaging and biochemical markers of PD to
improve diagnosis of the disease as well as monitoring its progression (14).
The use of multi-modality approach methods such as a combination of
structural and iron sensitive imaging increased diagnostic sensitivity to the
disease status and progression (145). In the study of Nair et al. (135), a
combination of conventional MRI and diffusion tensor imaging techniques
gave a reliable data to differentiate PD from MSA.
Many studies have tried to contribute in providing a neuroimaging
biomarker as a better diagnostic tool for PD, and several of them
concentrated in quantifying structural and functional changes in the
substantia nigra (SN) and basal ganglia components mainly putamen and
caudate nucleus (146, 147, 7, 6). Other studies were directed towards evaluation
of atrophy in cerebral regions mainly frontal lobe and cerebellum (148-150).
Few studies have aimed at measuring atrophy of brainstem of PD patients (12,
11, 16, 24, 25).
Magnetic resonance imaging has advantages over other imaging modalities
such as CT which have been used previously to diagnose disorders of the
brain. CT requires ionizing radiation while MRI is a noninvasive imaging
method. In addition MRI has a superior contrast resolution that facilities
discrimination of the grey and white matter of the brain tissue. MRI affords
a multiplanar imaging capabilities and dynamic rapid data acquisition (74). In
addition MRI is far more widely available than other imaging techniques
like PET and SPECT and is most commonly used in clinical practice (17).
128
However, the spatial resolution of MRI scanners was insufficient to detail
the anatomy of the SN. Several attempts were made to improve visualization
of the SN using multiple MR contrasts, but the correspondence between MR
images and the actual anatomy of the SN was unclear (44). Due to the
diversity of imaging findings of SN and the inaccurate delineation of the SN
on MR images(151), The researcher used T1-weighted 3D images with rapid
acquisition gradient-echo MR sequences, and applied Cavalieri stereological
method on these images for volumetric measurements of the brainstem as a
whole and each of its subdivisions of PD patients and healthy controls. This
imaging sequences allow the acquisition of T1-weighted 3D data sets of the
brain that can be reformatted to provide images in different orientations, a
process often called multiplanar reconstruction (81).
To date, the most widely used T1-weighted 3D GRE sequence is a
magnetization-prepared rapid acquisition gradient-echo (MP RAGE)
sequence. Images obtained by T1-weighted MP RAGE sequence provide
appropriate timing parameters, and give excellent contrast between grey and
white matter which facilitates perfect anatomical quantification of brain
structures (81). In addition T1-weighted 3D MR sequence does not produce
signals from bones and therefore there is no effect of bone artifact on the
images. This facilitates better visualization of the posterior cranial fossa
structures and hence better delineation of brainstem boundaries (76). Still, the
full potential of MRI has not yet been reached; there is continuing
refinement of the equipment, contrast agent and software in progress every
day. Therefore, this study is designed to work on the volumetric evaluation
of the brainstem. The whole structure and its components are clearly
recognized in the MR images.
129
Cavalieri principle is a modern stereological method that is applied on MR
images for estimation of volumes of irregular objects based on their profile
areas on random sections (89, 93, 92). Stereology estimates biological features
of 3D objects through interpretation of 2D images and can be applied to a
variety of imaging techniques (89, 90). These stereological quantification
methods are unbiased, less time consuming and precise. For these reasons,
stereological application on imaging approaches has become a gold standard
in quantitative structural analysis of different human organs. MRI based
volume quantification is now being increasingly used to investigate
neuroanatomic structures in neurological disorders (5, 95). In the present study,
the researcher applied the volume and volume fraction approach of the
stereological methods over the Cavalieri principle to reveal the size changes
in the PD.
5.2 Main findings
The main finding of the present study was that patients with PD showed
significant atrophy in total brainstem volume and in all of its subdivisions;
midbrain, pons and medulla oblongata compared to healthy controls. The
greatest effect of the disease was observed in the midbrain volume
suggesting that the SN neuronal cell death may play a role in this atrophy.
This study is in accordance with a study conducted by Jubault et al. (11), who
found that brainstem damage may be the first exclusive point of PD
neuropathology. They evaluated the reduction of white matter in the
brainstem region and found that cranial part of the medulla oblongata and
caudal part of the pons showed reduction in white matter. On contrary, a few
case-control studies, have reported that the volume of brainstem was not
affected in PD (12, 16, 25). This could be explained by variation in segmentation
130
methods, software used for measurements and sample size, age and type of
patients in the present study compared to the aforementioned studies. They
used fully-automated segmentation method whereas manual tracing method
was used in this study. They used FreeSurfer software while this study used
ImageJ software. Their sample included patients with PD, PSP and MSA
and the present study was confined to patients with PD only. It is worth
mentioning here that the mean age of their patients was also older than that
of the present study group.
Brainstem studies have been neglected until recently because it was hard to
approach experimentally due to its location in a relatively inaccessible
region of the nervous system. But these difficulties have been solved by
technical imaging advances and computational methods (152). However, the
reported brainstem volume changes remain sparse due to the technical and
methodological limitations of segmentations and quantitative assessment of
data from this region (35).
5.3 The volumes of ROI by Age
In this study, the effects of age and gender on brainstem volume and its
subdivisions as well as comparing these effects between controls and PD
patients was investigated. During aging, both morphological and
neurochemical changes occur in the human central nervous system, which
increase vulnerability toward physiological disturbances and
neurodegenerative disorders such as PD (46). Therefore aging is the strongest
risk factor for the development of PD. Although a marked age-related
decline in the dopaminergic neuronal levels suggest that nigrostriatal system
dysfunction occurs, this also happens during normal aging (153). Application
131
of the modern imaging techniques adds a lot in understanding the pattern of
volume loss in normal ageing and diseases related to ageing (47, 46).
Concerning age effects on brain volume in general, studies have shown that
the total cerebral brain volume is reduced and the ventricles size is increased
with age (47-49, 52). The brain atrophy has variable patterns & degree of
changes along different brain regions (52). Several factors affect the regional
vulnerability of brain atrophy, such as decreased blood flow, demyelination
of nerve fibers, and regional gene expression (47, 46). The grey matter declines
with age in a linear constant function throughout adult life, whereas that of
the white matter seems to be delayed or not affected until middle adult life
and then decline after that (54, 55).
Although total cerebral brain volume declines with age, most studies have
reported that the total brainstem volume remains stable with advancing of
age. Mainly the volume of pons and medulla oblongata seemed not to be
affected along with age (5, 35, 50, 46, 52). Their justification to this stability of
brainstem volume with age was based on the minimal effect of decreased
blood flow to the brainstem in comparison to other cerebral regions and
presence of the vital cardiovascular and respiratory centers in the region of
the brainstem (35, 66). In healthy control group studied in the current study a
significant negative correlation in the volumes of brainstem and each of its
subdivision with age was noticed except in the pons this reduction was not
significant. This finding is in discordance with the aforementioned studies.
A possible source of variation in the findings across previous studies and
that of this study may be due to the different imaging parameters, scanning
sequence, slice thickness and technique used for segmentation of ROI. The
present study has the advantage to use 1mm slice thickness images with no
132
inter slice gap, enabling the evaluation of the full extent of the ROI. In
addition, T1-weighted MP RAGE sequence applied to imaging in this study
provides an excellent visualization of brainstem boundaries.
Although previous studies examining volumetric age-related decline in
brainstem volume in normal aging have reported that there is no volume
alteration in total brainstem, pons and medulla oblongata volumes. However
some studies have supported the present findings that the most significantly
atrophied part of the brainstem is the midbrain due to shrinkage of its nuclei
and a marked increase in iron concentration (35, 66). The dopaminergic
neurons of the SN are characterized by their pigmentation which is a
resultant of the intraneuronal accumulation of neuromelanin. Neuromelanin
has strong ability to bind with iron. This explains the high concentration of
iron seen in stained histological sections of SN. The SN shows loss of
melanized neurons and deposition of iron with increasing age which appears
as hyperintense areas in T1-weighted images (44). Our results were in
accordance with the previous studies regarding age-related volumetric
alterations in midbrain.
The discrepancy in the age-related reduction and volume of medulla
oblongata between this and the others studies could be due to the different
methodologies used. Most of the literature based their delineation of the
caudal border of the medulla oblongata along a plane parallel to the
mammillary body-posterior commissure plane at the posterior rim of the
foramen magnum (21, 41). However borders of the foramen magnum are not
seen well in MR image and the atlas was the most fixed clear point in all
MR images. Accordingly during the present assessment of the volume of the
medulla oblongata in this study and to standardize the lower border of the
133
medulla oblongata for all subjects, the upper border of the posterior arch of
the atlas was considered as the landmark. This landmark was recommended
on what have been referenced in books that the medulla oblongata ends
caudally by joining the spinal cord at the level of the superior border of the
posterior arch of the atlas (141-143). In addition to the present knowledge, in
judicial hangings that breaking of the odontoid process is held to be the
actual cause of death and not only strangulation because it makes pressure
on the medulla oblongata. This may also explain that medulla oblongata
extends to the level of the arch of the atlas.
In PD patients, a highly significant reduction in the volumes of brainstem as
well as that of the midbrain with age was found. The reduction of these
volumes was significant when compared to healthy control. There is also a
significant reduction in the volume of pons with age however the reduction
of the volume of the medulla oblongata was insignificant in PD patients. It
can be concluded that the reduced brainstem volume in PD patients was
mainly due to the reduction in the volume of their midbrains reflecting a
progressive neurodegenerative process in the neurons of SNc, which is a
hallmark of PD (153, 44). Also it is reported that the iron concentration in SN is
30-35% more in PD patients than in healthy aging people, and hence more
loss in melanized neurons (44).
5.4 The volume of ROI by gender
There is a generalized agreement in the literature that the total volume of the
brain is bigger in males than in females (54, 55). According to Erbagci et al. (5),
the average brainstem volume of healthy individuals, in the age group 20-40
years, was found to be 22.05 ± 4.01 cm³ in males and 18.99 ± 2.36 cm³ in
134
females. This difference was found to be statistically significant when
compared gender wise. Ekinci et al. (62), conducted a study on healthy
volunteers and measured volume of the brainstem, their results showed that
males were having greater brainstem volumes than females (24.3 and 22.9
cm³ respectively). However, this difference did not rise to a statistically
significant level (p>0.05). Kruggel (68) and Lee et al. (66), have found the
brainstem volume to be 30.6 ± 6.9 cm³ in males, 27.9 ± 5.3 cm³ in females,
and 27.81 ± 2.95 cm³ in males and 25.15 ± 2.23 cm³ in females respectively.
One of the major reasons that influence the brain volume by gender is that
males have proportionately bigger body size than females, in addition to the
sex hormonal effects on the brain in males (69). Although the total brain
volume of males is bigger than that of females, it is reported that generally
males are more affected than females through their life-span by age-related
changes concerning total brain volume (48, 53, 59). De Bellis et al. (53), reported
that there is a significant decrease in grey matter in males than in females
during childhood and adolescence.
With regard to the brainstem volume, almost all the studies have agreed that
brainstem volume is greater in males than in females (62, 5, 66, 63). The current
study supports the above studies as it was found that the volumetric
measurements of total brainstem and all its subdivisions were bigger in
males than in females. Despite this, it was reported that brainstem showed
more age-related atrophy in males, whereas that of females almost remains
constant (154). In the current study with regards to the total brainstem
volumes and its subdivisions in both healthy controls and PD patients groups
there was no gender discrepancy in the reduction of the volumes in ROI, this
was in accordance with Lee et al. (66), and Kurggel et al. (68), found also no
135
gender discrepancy in the volume of total brainstem, midbrain, pons and
medulla oblongata.
When overall males and females in the study were compared in regard to
their ROI volume reduction there was more decline in the volume of total
brainstem and its subdivisions particularly that of the midbrain in males
more than in females, which is in accordance with what has been reported
that males are more affected by age related changes (48, 53, 59).
Also at the level of the clinical outcome of non-motor symptoms of PD, the
spectrum and severity was presented in different ways for male and female
patients, suggesting a possible sex-related effect (121). One possible source of
male-female differences in the clinical and cognitive characteristics of PD is
the effect of estrogen on dopaminergic neurons and pathways in the brain.
This effect is not yet understood, as insight into how the fluctuation of
estrogen over the lifetime affects the brain is currently limited (155).
5.5 Comparison of brainstem volumetric changes between PD
and other neurological disorders
Camargos et al. (156), have reported that the brainstem volume was 19.79 ±
2.67 cm³ in healthy individuals and 14.15 ± 2.35 cm³ in spinocerebellar
ataxia patients. This difference was statistically significant and the most
affected brainstem structure was the pons. In another case-control study
conducted by Eichler et al. (21), the mean volume of the brainstem in normal
adult was 28.8 ml. They reported a marked loss of brainstem volume in
patients with spinocerebellar ataxia. In multiple sclerosis, a case-control
study conducted by Alper et al. (157), showed that the brainstem volume of the
control and patients was 4,517 ± 553 mm³ and 3,515 ± 512 mm³
136
respectively. Fearing et al. (158), have reported in their study that the
brainstem volume of children with traumatic brain injury 21.77 ± 1.8 cm³,
while the average of the healthy children was 22.78 ± 2.54 cm³
Nair et al. (135), found that the mean volumes of putamen, pons and
cerebellum in MSA patients lower than in PD patient and this difference was
statistically significant.
5.6 The volumes of ROI by Age at onset & duration
The average age at onset of PD was estimated to be 54.00 ± 12.22 years in
this study which was around what estimated previously (125). In the current
study, the volumes of midbrain, pons and total brainstem showed correlation
with age at onset of PD except the medulla oblongata which did not show
correlation with age at onset of PD. Only one study commented on age at
onset and disease progression(125), and reported that age at onset is
independent determinant of the disease clinical features. In this study,
gender wise, males' midbrain volume decreased significantly with early
onset of the disease in comparison to females’ midbrain volume, this may be
due to the small sample size of female PD patients. Apart from the midbrain
volume, all other parts of the ROI; pons, medulla oblongata and total
brainstem volume did not show differences in male and female in relation to
age at onset of PD. On clinical basis the average rate of annual decline in
the motor signs of PD depends strongly on age at onset, late-onset having
higher rate (3.8%) than those of early-onset (2.4%) (104).
Motor symptoms of PD are likely to appear when the pathology of the
disease has caused significant damage to the SN (126). Progression of these
137
motor symptoms in PD may occur over 10 – 30 years but the rate of this
progression varies from person to person (17). In the current study, when
duration of the disease was correlated with the volume findings of the
brainstem and its subdivisions, the extent of brainstem, midbrain, pons and
medulla oblongata neurodegeneration did not show correlation with the
duration of the disease. This was in contrast to what have been reported that
the volume loss became marked in parallel with disease severity and
duration (146).
To avoid confounding variables, in the current study, the volume fraction of
the midbrain, pons and medulla oblongata in relation to total brainstem
volume were calculated. The volume fraction calculates the absolute volume
and reduces variability of the data and corrects effect of factors affecting
brain size, other than age and disease, like body mass index (5). In the current
study regarding the volume fraction of the midbrain, pons and medulla
oblongata, the only part which showed significant difference between PD
patients and controls was in the midbrain.
The volume fraction of midbrain was lower in male controls than in female
controls. This is in accordance with the above mentioned studies that males
have more decline in their brain volumes with age. The volume fraction of
midbrain in male PD patients was higher than that in females, indicating that
the volumetric fractional changes of midbrain were more severe in females.
This may be due the variation of the severity and duration of the disease.
Concerning the volume fraction of both pons and medulla oblongata, there
was no significant difference between male and females in both control and
patients group. The males and females were almost similarly affected by the
disease.
138
6. 1 Conclusions
• This quantitative volumetric study demonstrated a global atrophy of
midbrain, pons, medulla oblongata and the total brainstem volumes in
PD patients compared to healthy control. It was also obvious that the
progressive reduction in the midbrain region leads to the reduction in
the total brainstem volume. Apart from the midbrain, both pons and
medulla oblongata showed variation in atrophy when analyzed with
other variables like, age, gender, age at onset and duration of the
disease.
• When the volumes of midbrain, pons, medulla oblongata and total
brainstem volumes were correlated to age of both healthy controls and
PD patients an obvious age related effects was observed. In the
controls group there was a reduction in the volumes of the brainstem
and its subdivision except the pons seemed to be not affected. When
this normal age-related reduction in volumes of the brainstem and its
subdivisions in healthy controls were compared to the reduction in PD
patients with age there was a significant reduction of these volumes in
PD patients. This means that the disease-related effects have caused
more progressive reduction in the volume of the region of interest in
PD patients beside the normal age-related changes.
• Gender wise, there was no correlation found in this study between
gender and volume reduction in both healthy control and PD patients
this may be due to the small sample size of female group in this study
compared to the number of males.
139
• The volumes of midbrain, pons and total brainstem showed
correlation with age at onset of PD except the medulla oblongata
which did not show correlation with age at onset of PD. Gender wise,
only the males’ midbrain volume showed significant correlation with
age at onset of PD, i.e. early diseased more reduction. The duration of
PD did not show correlation with volumetric changes of total
brainstem and its subdivisions.
• The volume fraction of the PD patients’ midbrain showed significant
difference when compared to healthy controls. Gender wise the
volume fraction of females was higher than that of males in the
control group. In the patient group this was the opposite, males were
having higher volume fraction of midbrain than females. The pons
and medulla oblongata did not show significant difference of volume
fraction when comparing patients with PD with the controls group.
6.2 Recommendations
• Although this volumetric neuroimaging study is promising, future
refinement in resolution of images and improvement in techniques
sensitivity of methods are needed before their diagnostic potential in
PD are fully realized.
• It is believed that the volumetric measurement of midbrain and
brainstem can help in early diagnosis of PD patients. In addition this
will provide an opportunity to initiate neuroprotective therapy which
can assist prognosis of the disease and valued outcomes of its
treatment.
140
• Further longitudinal studies of physiological ageing with large number
of subjects and wide range of age groups will be necessary to verify
the results of normal aging on the brainstem and to compare this with
PD patients.
• Volumetric measurement of the SN in PD needs further investigation.
• As PD has significant negative impact on the quality of life of patients
and their families, establishment of specialized center for PD in
Khartoum-Sudan will be valuable resource for patients, physician and
researchers by providing referral services, education materials and
programs.
6. 3 Limitations
• Limitation in structural imaging techniques and sequences for
visualization of SN.
• The relationship between brainstem volumes changes and clinical
dysfunction based on different stages of the disease was not tested in
this study. This will require diagnosis of PD patients according to
Hoehn and Yahr scale (H&Y) and /or Unified Parkinson's Disease
Rating Scale (UPDRS). It will also require greater number of patient
at different age groups to participate in the analysis.
• Recruitment difficulties
141
7. References
1. Okubadejo NU, Bower JH, Rocca WA, Maraganore DM (2006).
Parkinson's disease in Africa: A systematic review of epidemiologic
and genetic studies. Movement Disorders: 21(12):2150-2156.
2. Mandir AS, Vaughan C (2000). Pathophysiology of Parkinson's
disease. International Review of Psychiatry: 12(4):270-280.
3. Tolosa E, Wenning G, Poewe W (2006). The diagnosis of Parkinson's
disease. Lancet Neurology: 5(1):75-86.
4. Golbe LI, Mark MH, Sage JI (2010).Parkinson's disease handbook: a
guide for patients and their families. In: Mark MH, editor. New Jersey:
American Parkinson Disease Association, Inc.: p. 1-14.
5. Erbagci H, Keser M, Kervancioglu S, Kizilkan N (2012). Estimation of
the brain stem volume by stereological method on magnetic resonance
imaging. Surgical and Radiologic Anatomy: 34(9):819-824.
6. Pitcher TL, Melzer TR, Macaskill MR, Graham CF, Livingston L,
Keenan RJ, Watts R, Dalrymple-Alford JC, Anderson TJ (2012).
Reduced striatal volumes in Parkinson's disease: a magnetic resonance
imaging study. Translational Neurodegeneration: 1(1):17.
7. Menke RA, Scholz J, Miller KL, Deoni S, Jbabdi S, Matthews PM,
Zarei M (2009). MRI characteristics of the substantia nigra in
Parkinson's disease: a combined quantitative T1 and DTI study.
Neuroimage: 47(2):435-441.
8. Bergman H, Deuschl G (2002). Pathophysiology of Parkinson's
disease: from clinical neurology to basic neuroscience and back.
Movement Disorders: 17 Suppl 3:S28-40.
142
9. Camlidag I, Kocabicak E, Sahin B, Jahanshahi A, Incesu L, Aygun D,
Yildiz O, Temel Y, Belet U (2014). Volumetric analysis of the
subthalamic and red nuclei based on magnetic resonance imaging in
patients with Parkinson's disease. International Journal of
Neuroscience: 124(4):291-295.
10. Jankovic J (2008). Parkinson's disease: clinical features and diagnosis.
Journal of Neurology, Neurosurgery and Psychiatry: 79(4):368-376.
11. Jubault T, Brambati SM, Degroot C, Kullmann B, Strafella AP,
Lafontaine AL, Chouinard S, Monchi O (2009). Regional brain stem
atrophy in idiopathic Parkinson's disease detected by anatomical MRI.
PLoS One: 4(12):e8247.
12. Ghaemi M, Hilker R, Rudolf J, Sobesky J, Heiss WD (2002).
Differentiating multiple system atrophy from Parkinson's disease:
contribution of striatal and midbrain MRI volumetry and multi-tracer
PET imaging. Journal of Neurology, Neurosurgery and Psychiatry:
73(5):517-523.
13. Savitt JM, Dawson VL, Dawson TM (2006). Diagnosis and treatment
of Parkinson disease: molecules to medicine. Journal of Clinical
Investigation: 116(7):1744-1754.
14. Wang J, Hoekstra JG, Zuo C, Cook TJa, Zhang J (2013). Biomarkers of
Parkinson's disease: current status and future perspectives. Drug Discov
Today: 18(3-4):155-162.
15. Long D, Wang J, Xuan M, Gu Q, Xu X, Kong D, Zhang M (2012).
Automatic classification of early Parkinson's disease with multi-modal
MR imaging. PLoS One: 7(11):e47714.
143
16. Messina D, Cerasa A, Condino F, Arabia G, Novellino F, Nicoletti G,
Salsone M, Morelli M, Lanza PL, Quattrone A (2011). Patterns of brain
atrophy in Parkinson's disease, progressive supranuclear palsy and
multiple system atrophy. Parkinsonism Relat Disord: 17(3):172-176.
17. Pavese N, Brooks DJ (2009). Imaging neurodegeneration in Parkinson's
disease. Biochimica et Biophysica Acta: 1792(7):722-729.
18. Cordato NJ, Pantelis C, Halliday GM, Velakoulis D, Wood SJ, Stuart
GW, Currie J, Soo M, Olivieri G, Broe GA, Morris JG (2002). Frontal
atrophy correlates with behavioural changes in progressive
supranuclear palsy. Brain: 125(Pt 4):789-800.
19. Paviour DC, Price SL, Stevens JM, Lees AJ, Fox NC (2005).
Quantitative MRI measurement of superior cerebellar peduncle in
progressive supranuclear palsy. Neurology: 64(4):675-679.
20. Diaz-de-Grenu LZ, Acosta-Cabronero J, Pereira JM, Pengas G,
Williams GB, Nestor PJ (2011). MRI detection of tissue pathology
beyond atrophy in Alzheimer's disease: introducing T2-VBM.
Neuroimage: 56(4):1946-1953.
21. Eichler L, Bellenberg B, Hahn HK, Koster O, Schols L, Lukas C
(2011). Quantitative assessment of brain stem and cerebellar atrophy in
spinocerebellar ataxia types 3 and 6: impact on clinical status. AJNR
American Journal of Neuroradiology: 32(5):890-897.
22. Prestia A, Boccardi M, Galluzzi S, Cavedo E, Adorni A, Soricelli A,
Bonetti M, Geroldi C, Giannakopoulos P, Thompson P, Frisoni G
(2011). Hippocampal and amygdalar volume changes in elderly
patients with Alzheimer's disease and schizophrenia. Psychiatry
Research: 192(2):77-83.
144
23. Yamasaki S, Yamasue H, Abe O, Suga M, Yamada H, Inoue H,
Kuwabara H, Kawakubo Y, Yahata N, Aoki S, Kano Y, Kato N, Kasai
K (2010). Reduced gray matter volume of pars opercularis is associated
with impaired social communication in high-functioning autism
spectrum disorders. Biological Psychiatry: 68(12):1141-1147.
24. Paviour DC, Price SL, Jahanshahi M, Lees AJ, Fox NC (2006).
Regional brain volumes distinguish PSP, MSA-P, and PD: MRI-based
clinico-radiological correlations. Movement Disorders: 21(7):989-996.
25. Schulz JB, Skalej M, Wedekind D, Luft AR, Abele M, Voigt K,
Dichgans J, Klockgether T (1999). Magnetic resonance imaging-based
volumetry differentiates idiopathic Parkinson's syndrome from multiple
system atrophy and progressive supranuclear palsy. Annals of
Neurology: 45(1):65-74.
26. Blinkov SM, Glezer II (1968).The human brain in figures and tables: A
quantitative handbook. New York: Plenum Press: p. 333-334.
27. Snell RS (2010).Clinical neuroanatomy. 7th ed. Baltimore: Wolters
Klumer - Lippincott Williams & Wilkins: p. 196- 212.
28. Netter FH, Craig JA, Perkins J (2002).Atlas of Neuroanatomy and
Neurophysiology: Selections from the Netter Collection of Medical
Illustrations. Special ed. Teterboro: Icon Custom Communications: p.
2.
29. http://www.mayfieldclinic.com/PE-AnatBrain.htm#.U6KhcGDlqFE.
30. Crossman AR (2008).Neuroanatomy: Brain stem. In: Standring S,
editor. Gray's Anatomy: The anatomical basis of clinical practice. 40th
ed. London: Churchill Livingstone - Elsevier Limited: p. 275- 290.
31. Beissner F, Baudrexel S (2014). Investigating the human brainstem
with structural and functional MRI. Front Hum Neurosci: 8:116.
145
32. Deistung A, Schafer A, Schweser F, Biedermann U, Gullmar D,
Trampel R, Turner R, Reichenbach JR (2013). High-Resolution MR
Imaging of the Human Brainstem In vivo at 7 Tesla. Front Hum
Neurosci: 7:710.
33. Blumenfeld H (2010).Neuroanatomy through clinical cases. 2nd ed.
Sunderland: Sinauer Associates, Inc: p. 494.
34. Ford AA, Colon-Perez L, Triplett WT, Gullett JM, Mareci TH,
Fitzgerald DB (2013). Imaging white matter in human brainstem. Front
Hum Neurosci: 7:400.
35. Lambert C, Chowdhury R, Fitzgerald TH, Fleming SM, Lutti A, Hutton
C, Draganski B, Frackowiak R, Ashburner J (2013). Characterizing
aging in the human brainstem using quantitative multimodal MRI
analysis. Front Hum Neurosci: 7:462.
36. Singleton O, Holzel BK, Vangel M, Brach N, Carmody J, Lazar SW
(2014). Change in Brainstem Gray Matter Concentration Following a
Mindfulness-Based Intervention is Correlated with Improvement in
Psychological Well-Being. Front Hum Neurosci: 8:33.
37. Yeo SS, Chang PH, Jang SH (2013). The ascending reticular activating
system from pontine reticular formation to the thalamus in the human
brain. Front Hum Neurosci: 7:416.
38. Barsottini OG, Ferraz HB, Maia AC, Jr., Silva CJ, Rocha AJ (2007).
Differentiation of Parkinson's disease and progressive supranuclear
palsy with magnetic resonance imaging: the first Brazilian experience.
Parkinsonism Relat Disord: 13(7):389-393.
146
39. Luft AR, Skalej M, Welte D, Kolb R, Burk K, Schulz JB, Klockgether
T, Voigt K (1998). A new semiautomated, three-dimensional technique
allowing precise quantification of total and regional cerebellar volume
using MRI. Magnetic Resonance in Medicine: 40(1):143-151.
40. Naidich TP, Duvernoy HM, Delman BN, Sorensen AG, Kollias SS,
Haacke EM (2009).Duvernoy's Atlas of the Human Brain Stem and
Cerebellum: Structural Organization of the Mesencephalic-
Diencephalic Junction. Vienna: SpringerWienNewYork: p. 149-157.
41. Schulz JB, Borkert J, Wolf S, Schmitz-Hubsch T, Rakowicz M,
Mariotti C, Schols L, Timmann D, van de Warrenburg B, Durr A,
Pandolfo M, Kang JS, Mandly AG, Nagele T, Grisoli M, Boguslawska
R, Bauer P, Klockgether T, Hauser TK (2010). Visualization,
quantification and correlation of brain atrophy with clinical symptoms
in spinocerebellar ataxia types 1, 3 and 6. Neuroimage: 49(1):158-168.
42. Guerrini L, Lolli F, Ginestroni A, Belli G, Della Nave R, Tessa C,
Foresti S, Cosottini M, Piacentini S, Salvi F, Plasmati R, De Grandis D,
Siciliano G, Filla A, Mascalchi M (2004). Brainstem neurodegeneration
correlates with clinical dysfunction in SCA1 but not in SCA2. A
quantitative volumetric, diffusion and proton spectroscopy MR study.
Brain: 127(Pt 8):1785-1795.
43. Cote L, Crutcher MD (1991).The Basal ganglia. In: Kandel ER,
Schwartz JH, Jessell TM, editors. Principles of Neural Sciences. 3rd ed:
Elsevier: p. 648- 655.
44. Lehéricy S, Bardinet E, Poupon C, Vidailhet M, François C (2014). 7
tesla magnetic resonance imaging: A closer look at substantia nigra
anatomy in Parkinson's disease. Movement Disorders: 29(13):1574-
1581.
147
45. http://en.wikipedia.org/wiki/Substantia_nigra.
46. Trollor JN, Valenzuela MJ (2001). Brain ageing in the new millennium.
Australian and New Zealand Journal of Psychiatry: 35(6):788-805.
47. Anderton BH (2002). Ageing of the brain. Mechanisms of Ageing and
Development: 123(7):811-817.
48. Coffey CE, Lucke JF, Saxton JA, Ratcliff G, Unitas LJ, Billig B, Bryan
RN (1998). Sex differences in brain aging: a quantitative magnetic
resonance imaging study. Archives of Neurology: 55(2):169-179.
49. Hedman AM, van Haren NE, Schnack HG, Kahn RS, Hulshoff Pol HE
(2012). Human brain changes across the life span: a review of 56
longitudinal magnetic resonance imaging studies. Human Brain
Mapping: 33(8):1987-2002.
50. Luft AR, Skalej M, Schulz JB, Welte D, Kolb R, Burk K, Klockgether
T, Voight K (1999). Patterns of age-related shrinkage in cerebellum and
brainstem observed in vivo using three-dimensional MRI volumetry.
Cerebral Cortex: 9(7):712-721.
51. Bozzali M, Cercignani M, Caltagirone C (2008). Brain volumetrics to
investigate aging and the principal forms of degenerative cognitive
decline: a brief review. Magnetic Resonance Imaging: 26(7):1065-
1070.
52. Walhovd KB, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N,
Agartz I, Salat DH, Greve DN, Fischl B, Dale AM, Fjell AM (2011).
Consistent neuroanatomical age-related volume differences across
multiple samples. Neurobiology of Aging: 32(5):916-932.
148
53. De Bellis MD, Keshavan MS, Beers SR, Hall J, Frustaci K,
Masalehdan A, Noll J, Boring AM (2001). Sex differences in brain
maturation during childhood and adolescence. Cerebral Cortex:
11(6):552-557.
54. Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL
(2002). Age-related total gray matter and white matter changes in
normal adult brain. Part I: volumetric MR imaging analysis. AJNR
American Journal of Neuroradiology: 23(8):1327-1333.
55. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ,
Frackowiak RS (2001). A voxel-based morphometric study of ageing in
465 normal adult human brains. Neuroimage: 14(1 Pt 1):21-36.
56. Koolschijn PC, Crone EA (2013). Sex differences and structural brain
maturation from childhood to early adulthood. Dev Cogn Neurosci:
5:106-118.
57. Mortamet B, Zeng D, Gerig G, Prastawa M, Bullitt E (2005). Effects of
healthy aging measured by intracranial compartment volumes using a
designed MR brain database. Med Image Comput Comput Assist Interv:
8(Pt 1):383-391.
58. Passe TJ, Rajagopalan P, Tupler LA, Byrum CE, MacFall JR, Krishnan
KR (1997). Age and sex effects on brain morphology. Progress in
Neuro-Psychopharmacology and Biological Psychiatry: 21(8):1231-
1237.
59. Gur RC, Mozley PD, Resnick SM, Gottlieb GL, Kohn M, Zimmerman
R, Herman G, Atlas S, Grossman R, Berretta D, et al. (1991). Gender
differences in age effect on brain atrophy measured by magnetic
resonance imaging. Proceedings of the National Academy of Sciences
of the United States of America: 88(7):2845-2849.
149
60. Murphy DG, DeCarli C, McIntosh AR, Daly E, Mentis MJ, Pietrini P,
Szczepanik J, Schapiro MB, Grady CL, Horwitz B, Rapoport SI (1996).
Sex differences in human brain morphometry and metabolism: an in
vivo quantitative magnetic resonance imaging and positron emission
tomography study on the effect of aging. Archives of General
Psychiatry: 53(7):585-594.
61. Raz N, Gunning FM, Head D, Dupuis JH, McQuain J, Briggs SD,
Loken WJ, Thornton AE, Acker JD (1997). Selective aging of the
human cerebral cortex observed in vivo: differential vulnerability of the
prefrontal gray matter. Cerebral Cortex: 7(3):268-282.
62. Ekinci N, Acer N, Akkaya A, Sankur S, Kabadayi T, Sahin B (2008).
Volumetric evaluation of the relations among the cerebrum, cerebellum
and brain stem in young subjects: a combination of stereology and
magnetic resonance imaging. Surgical and Radiologic Anatomy:
30(6):489-494.
63. Raz N, Gunning-Dixon F, Head D, Williamson A, Acker JD (2001).
Age and sex differences in the cerebellum and the ventral pons: a
prospective MR study of healthy adults. AJNR American Journal of
Neuroradiology: 22(6):1161-1167.
64. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C
(2003). Longitudinal magnetic resonance imaging studies of older
adults: a shrinking brain. Journal of Neuroscience: 23(8):3295-3301.
65. Dennis EL, Thompson PM (2013). Typical and atypical brain
development: a review of neuroimaging studies. Dialogues Clin
Neurosci: 15(3):359-384.
150
66. Lee NJ, Park IS, Koh I, Jung TW, Rhyu IJ (2009). No volume
difference of medulla oblongata between young and old Korean people.
Brain Research: 1276:77-82.
67. Sullivan EV, Rosenbloom M, Serventi KL, Pfefferbaum A (2004).
Effects of age and sex on volumes of the thalamus, pons, and cortex.
Neurobiology of Aging: 25(2):185-192.
68. Kruggel F (2006). MRI-based volumetry of head compartments:
normative values of healthy adults. Neuroimage: 30(1):1-11.
69. Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, Yamashita K
(2000). Gender effects on age-related changes in brain structure. AJNR
American Journal of Neuroradiology: 21(1):112-118.
70. Richard LD, Wayne V, Adam WMM (2012).Gray's basic anatomy.
International ed. Philadelphia: Churchill Livingstone - Elsevier: p. 4.
71. Tartaglia MC, Rosen HJ, Miller BL (2011). Neuroimaging in dementia.
Neurotherapeutics: 8(1):82-92.
72. Turner R, Jones T (2003). Techniques for imaging neuroscience.
British Medical Bulletin: 65:3-20.
73. Wright A (2010).Brain scanning techniques (CT, MRI, fMRI, PET,
SPECT, DTI, DOT). wwcerebraorguk: Cerebra.: p. 1-14.
74. Burgener FA, Meyers SP, Tan RK, Zaunbauer W (2002).Differential
diagnosis in magnetic resonance imaging. New York: Thieme: p. 1 - 4.
75. Keller SS, Roberts N (2009). Measurement of brain volume using MRI:
software, techniques, choices and prerequisites. J Anthropol Sci:
87:127-151.
76. Armstrong P, Wastie M, Rochall A (2004).Diagnostic imaging. 5th ed.
Oxford: Blackwell Ltd: p. 311-398.
151
77. Lisle DA (2007).Imaging for students. 3rd ed. New York: Oxford
University Press Inc.: p. 18 - 21.
78. Mulkern RV (2009).Fast imaging Principles. In: Atlas SW, editor.
Magnetic resonance imaging of the brain and spine. Philadelphia:
Wolters Kluwer- Lippincott Williams & Wilkins: p. 94-150.
79. Schenck JF, Kelley DA, Marinrlli L (2009).Instrumentation: magnet,
coils, and hardware. In: Atlas SW, editor. Magnetic resonance imaging
of the brain and spine. 4th ed. Philadelphia: Wolters Kluwer- Lippincott
Williams & Wilkins: p. 2-24.
80. Smith RC, Lange RC (1998).Understanding magnetic resonance
imaging. Florida: Library of Congress Cataloging p. 21-59.
81. Wetzel SG, Johnson G, Tan AG, Cha S, Knopp EA, Lee VS,
Thomasson D, Rofsky NM (2002). Three-dimensional, T1-weighted
gradient-echo imaging of the brain with a volumetric interpolated
examination. AJNR American Journal of Neuroradiology: 23(6):995-
1002.
82. Skinner S (2012). MRI of the knee. Australian Family Physician:
41(11):867-869.
83. Strother CM (1994).The brain: techniques for evaluation of the brain.
In: Putman CE, Ravin CE, editors. Texbook of diagnostic imaging. 2nd
ed: W.B. Saunders: p. 126 - 246.
84. Bernasconi A, Bernasconi N, Natsume J, Antel SB, Andermann F,
Arnold DL (2003). Magnetic resonance spectroscopy and imaging of
the thalamus in idiopathic generalized epilepsy. Brain: 126(Pt
11):2447-2454.
152
85. Ho BC, Andreasen NC, Nopoulos P, Arndt S, Magnotta V, Flaum M
(2003). Progressive structural brain abnormalities and their relationship
to clinical outcome: a longitudinal magnetic resonance imaging study
early in schizophrenia. Archives of General Psychiatry: 60(6):585-594.
86. Murphy C, Jernigan TL, Fennema-Notestine C (2003). Left
hippocampal volume loss in Alzheimer's disease is reflected in
performance on odor identification: a structural MRI study. Journal of
the International Neuropsychological Society: 9(3):459-471.
87. Sahin B, Emirzeoglu M, Uzun A, Incesu L, Bek Y, Bilgic S, Kaplan S
(2003). Unbiased estimation of the liver volume by the Cavalieri
principle using magnetic resonance images. European Journal of
Radiology: 47(2):164-170.
88. Naidich TP, Delman BN, Fowkes ME, Smethurst ME, Steinberger JD,
Doshi AH, Tang CY, Pasik P (2013).Normal brain anatomy:
Brainstem. In: Naidich, Castillo, Cha, Smirniotopoulos, editors.
Imaging of the brain. Philadelphia: Saunders-Elsevier: p. 297-327.
89. Hsia CCW, Hyde DM, Ochs M, Weibel ER (2010). An Official
Research Policy Statement of the American Thoracic Society/European
Respiratory Society: Standards for Quantitative Assessment of Lung
Structure. American Journal of Respiratory and Critical Care
Medicine: 181(4):394-418.
90. Tschanz S, Schneider JP, Knudsen L (2014). Design-based stereology:
Planning, volumetry and sampling are crucial steps for a successful
study. Annals of Anatomy - Anatomischer Anzeiger: 196(1):3-11.
91. Howard CV, Reed MG (1998).Unbiased Stereology: Three-
dimensional measurement in microscopy. 1st ed. Oxford: BIOS
Scientific: p. 55-56.
153
92. Mouton PR (2002).Principles and Practice of Unbiased Stereology: An
Introduction for Bioscientists. 1st ed: The Johns Hopkins University
Press, Baltimore, Maryland: p. 1-215.
93. Hyksova M, Kalousova A, Saxl I (2012). Early history of geometric
probability and stereology. Image Anal Stereol: 31(1):1-16.
94. Cruz-Orive LM, Weibel ER (1981). Sampling designs for stereology.
Journal of Microscopy: 122(Pt 3):235-257.
95. Evans SM, Janson AM, Nyengaard JR (2004).Quantitative methods in
neuroscience: a neuroanatomical approach. 1st ed: Oxford University
Press: p. 327.
96. Altunkaynak BZ, Onger ME, Altunkaynak ME, Ayranci E, Canan S
(2012). A Brief Introduction to Stereology and Sampling Strategies:
Basic Concepts of Stereology. NeuroQuantology: 10(1):31-43.
97. Witgen BM, Grady MS, Nyengaard JR, Gundersen HJ (2006). A new
fractionator principle with varying sampling fractions: exemplified by
estimation of synapse number using electron microscopy. Journal of
Microscopy: 222(Pt 3):251-255.
98. Nisari M, Ertekin T, Ozcelik O, Cinar S, Doganay S, Acer N (2012).
Stereological evaluation of the volume and volume fraction of
newborns' brain compartment and brain in magnetic resonance images.
Surgical and Radiologic Anatomy: 34(9):825-832.
99. Akbas H, Sahin B, Eroglu L, Odaci E, Bilgic S, Kaplan S, Uzun A,
Ergur H, Bek Y (2004). Estimation of breast prosthesis volume by the
Cavalieri principle using magnetic resonance images. Aesthetic Plastic
Surgery: 28(5):275-280.
154
100. Roberts N, Puddephat MJ, McNulty V (2000). The benefit of
stereology for quantitative radiology. British Journal of Radiology:
73(871):679-697.
101. Sonmez OF, Odaci E, Bas O, Colakoglu S, Sahin B, Bilgic S, Kaplan S
(2010). A stereological study of MRI and the Cavalieri principle
combined for diagnosis and monitoring of brain tumor volume. J Clin
Neurosci: 17(12):1499-1502.
102. Sahin B, Elfaki A (2012). Estimation of the volume and volume
fraction of brain and brain structures on radiological images.
NeuroQuantology: 10(1):87-97.
103. Adams RD, Victor M (1985).Principles of neurology 3rd ed. New York
; London ; Sydney: McGraw-Hill p. 915.
104. Fritsch T, Smyth KA, Wallendal MS, Hyde T, Leo G, Geldmacher DS
(2012). Parkinson disease: research update and clinical management.
Southern Medical Journal: 105(12):650-656.
105. Snell RS (2001).Clinical Neuroanatomy for medical students. 5th ed.
Baltimore: Lippincott Williams & Wilkins: p. 189 - 206.
106. Gandhi S, Wood NW (2005). Molecular pathogenesis of Parkinson's
disease. Human Molecular Genetics: 14 Spec No. 2:2749-2755.
107. Masalha R, Kordysh E, Alpert G, Hallak M, Morad M, Mahajnah M,
Farkas P, Herishanu Y (2010). The prevalence of Parkinson's disease in
an Arab population, Wadi Ara, Israel. Isr Med Assoc J: 12(1):32-35.
108. Samii A, Nutt JG, Ransom BR (2004). Parkinson's disease. Lancet:
363(9423):1783-1793.
109. Stoessl AJ (1999). Etiology of Parkinson's disease. Canadian Journal
of Neurological Sciences: 26 Suppl 2:S5-12.
155
110. Benamer HT, de Silva R, Siddiqui KA, Grosset DG (2008). Parkinson's
disease in Arabs: a systematic review. Movement Disorders:
23(9):1205-1210.
111. Cazeneuve C, San C, Ibrahim SA, Mukhtar MM, Kheir MM, Leguern
E, Brice A, Salih MA (2009). A new complex homozygous large
rearrangement of the PINK1 gene in a Sudanese family with early onset
Parkinson's disease. Neurogenetics: 10(3):265-270.
112. Lesage S, Brice A (2009). Parkinson's disease: from monogenic forms
to genetic susceptibility factors. Human Molecular Genetics:
18(R1):R48-59.
113. Langston JW, Forno LS, Tetrud J, Reeves AG, Kaplan JA, Karluk D
(1999). Evidence of active nerve cell degeneration in the substantia
nigra of humans years after 1-methyl-4-phenyl-1,2,3,6-
tetrahydropyridine exposure. Annals of Neurology: 46(4):598-605.
114. El-Tallawy HN, Farghaly WM, Shehata GA, Rageh TA, Hakeem NM,
Hamed MA, Badry R (2013). Prevalence of Parkinson's disease and
other types of Parkinsonism in Al Kharga district, Egypt.
Neuropsychiatr Dis Treat: 9:1821-1826.
115. Jannetta PJ, Whiting DM, Fletcher LH, Hobbs JK, Brillman J, Quigley
M, Fukui M, Williams R (2011). Parkinson's disease: an inquiry into
the etiology and treatment. Neurol Int: 3(2):e7.
116. Costa J, Lunet N, Santos C, Santos J, Vaz-Carneiro A (2010). Caffeine
exposure and the risk of Parkinson's disease: a systematic review and
meta-analysis of observational studies. J Alzheimers Dis: 20 Suppl
1:S221-238.
156
117. Al Rajeh S, Bademosi O, Ismail H, Awada A, Dawodu A, al-Freihi H,
Assuhaimi S, Borollosi M, al-Shammasi S (1993). A community
survey of neurological disorders in Saudi Arabia: the Thugbah study.
Neuroepidemiology: 12(3):164-178.
118. Ashok PP, Radhakrishnan K, Sridharan R, Mousa ME (1986).
Epidemiology of Parkinson's disease in Benghazi, North-East Libya.
Clinical Neurology and Neurosurgery: 88(2):109-113.
119. Attia Romdhane N, Ben Hamida M, Mrabet A, Larnaout A, Samoud S,
Ben Hamda A, Ben Hamda M, Oueslati S (1993). Prevalence study of
neurologic disorders in Kelibia (Tunisia). Neuroepidemiology:
12(5):285-299.
120. Rana AQ, Siddiqui I, Yousuf MS (2012). Challenges in diagnosis of
young onset Parkinson's disease. Journal of the Neurological Sciences:
323(1-2):113-116.
121. Solla P, Cannas A, Ibba FC, Loi F, Corona M, Orofino G, Marrosu
MG, Marrosu F (2012). Gender differences in motor and non-motor
symptoms among Sardinian patients with Parkinson's disease. Journal
of the Neurological Sciences: 323(1-2):33-39.
122. Brown RH, Jr. (1996).Myotonia and periodic paralysis. In: Samuels
MA, Feske S, editors. Office Practice of Neurology Churchill
Livingstone Inc.: p. 610.
123. Schrag A, Schott JM (2006). Epidemiological, clinical, and genetic
characteristics of early-onset parkinsonism. Lancet Neurology:
5(4):355-363.
157
124. Willis AW, Schootman M, Kung N, Racette BA (2013). Epidemiology
and neuropsychiatric manifestations of Young Onset Parkinson's
Disease in the United States. Parkinsonism Relat Disord: 19(2):202-
206.
125. Cilia R, Cereda E, Klersy C, Canesi M, Zecchinelli AL, Mariani CB,
Tesei S, Sacilotto G, Meucci N, Zini M, Ruffmann C, Isaias IU,
Goldwurm S, Pezzoli G (2014). Parkinson's disease beyond 20 years.
Journal of Neurology, Neurosurgery and Psychiatry:Epub ahead of
print.
126. Morgen K, Sammer G, Weber L, Aslan B, Muller C, Bachmann GF,
Sandmann D, Oechsner M, Vaitl D, Kaps M, Reuter I (2011).
Structural brain abnormalities in patients with Parkinson disease: a
comparative voxel-based analysis using T1-weighted MR imaging and
magnetization transfer imaging. AJNR American Journal of
Neuroradiology: 32(11):2080-2086.
127. Chaudhuri KR, Odin P, Antonini A, Martinez-Martin P (2011).
Parkinson's disease: the non-motor issues. Parkinsonism Relat Disord:
17(10):717-723.
128. Caviness JN (2014). Pathophysiology of Parkinson's disease behavior--
a view from the network. Parkinsonism Relat Disord: 20 Suppl 1:S39-
43.
129. Kandel ER (1991).Disorders of thoughts: Schizophrenia. In: Erik R.
Kandel, Schwartz JH, Jessell TM, editors. Principles of Neural
Sciences. 3rd ed: Elsevier: p. 863.
130. Braak H, Ghebremedhin E, Rub U, Bratzke H, Del Tredici K (2004).
Stages in the development of Parkinson's disease-related pathology.
Cell and Tissue Research: 318(1):121-134.
158
131. Pauly O, Ahmadi SA, Plate A, Boetzel K, Navab N (2012). Detection
of substantia nigra echogenicities in 3D transcranial ultrasound for
early diagnosis of Parkinson disease. Med Image Comput Comput
Assist Interv: 15(Pt 3):443-450.
132. Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992). Accuracy of clinical
diagnosis of idiopathic Parkinson's disease: a clinico-pathological study
of 100 cases. Journal of Neurology, Neurosurgery and Psychiatry:
55(3):181-184.
133. Tien RD (1994).The neurodegenerative diseases metabolic diseases,
white matter diseases, and the neurocutaneous syndromes. In: Putman
CE, Ravin CE, editors. Textbook of Diagnostic Imaging. 2nd ed: W.B.
Saunders: p. 243-246.
134. Biundo R, Formento-Dojot P, Facchini S, Vallelunga A, Ghezzo L,
Foscolo L, Meneghello F, Antonini A (2011). Brain volume changes in
Parkinson's disease and their relationship with cognitive and
behavioural abnormalities. Journal of the Neurological Sciences:
310(1-2):64-69.
135. Nair SR, Tan LK, Mohd Ramli N, Lim SY, Rahmat K, Mohd Nor H
(2013). A decision tree for differentiating multiple system atrophy from
Parkinson's disease using 3-T MR imaging. European Radiology:
23(6):1459-1466.
136. Meara J, Bhowmick BK, Hobson P (1999). Accuracy of diagnosis in
patients with presumed Parkinson's disease. Age and Ageing: 28(2):99-
102.
159
137. Volkmann J, Albanese A, Antonini A, Chaudhuri KR, Clarke CE, de
Bie RM, Deuschl G, Eggert K, Houeto JL, Kulisevsky J, Nyholm D,
Odin P, Ostergaard K, Poewe W, Pollak P, Rabey JM, Rascol O,
Ruzicka E, Samuel M, Speelman H, Sydow O, Valldeoriola F, van der
Linden C, Oertel W (2013). Selecting deep brain stimulation or
infusion therapies in advanced Parkinson's disease: an evidence-based
review. Journal of Neurology: 260(11):2701-2714.
138. Levy G (2007). The relationship of Parkinson disease with aging.
Archives of Neurology: 64(9):1242-1246.
139. Acer N, Sahin B, Usanmaz M, Tatoglu H, Irmak Z (2008). Comparison
of point counting and planimetry methods for the assessment of
cerebellar volume in human using magnetic resonance imaging: a
stereological study. Surgical and Radiologic Anatomy: 30(4):335-339.
140. Gundersen HJ, Bendtsen TF, Korbo L, Marcussen N, Moller A, Nielsen
K, Nyengaard JR, Pakkenberg B, Sorensen FB, Vesterby A, et al.
(1988). Some new, simple and efficient stereological methods and their
use in pathological research and diagnosis. APMIS: 96(5):379-394.
141. Gray H (1858).Anatomy: descriptive and surgical. London: John W.
Parker and Son, West Strand: p. 451.
142. Jones HR, Burns TM, Aminoff MJ, Pomeroy SL (2013).Nervous
system: Part II - spinal cord and peripheral motor and sensory systems.
In: Netter FH, editor. The Netter collection of medical illustrations. 2nd
ed. Philadelphia: Elsevier - Saunders: p. 83.
143. Kulkarni NV (2006).Clinical anatomy for students: problem solving
approach. New Delhi: Jaypee Brothers Medical Publisher Ltd: p. 384.
160
144. Gundersen HJ, Jensen EB (1987). The efficiency of systematic
sampling in stereology and its prediction. Journal of Microscopy:
147(Pt 3):229-263.
145. Tuite PJ, Mangia S, Michaeli S (2013). Magnetic Resonance Imaging
(MRI) in Parkinson's Disease. J Alzheimers Dis Parkinsonism: Suppl
1:001.
146. Kashihara K, Shinya T, Higaki F (2011). Neuromelanin magnetic
resonance imaging of nigral volume loss in patients with Parkinson's
disease. J Clin Neurosci: 18(8):1093-1096.
147. Menke RA, Jbabdi S, Miller KL, Matthews PM, Zarei M (2010).
Connectivity-based segmentation of the substantia nigra in human and
its implications in Parkinson's disease. Neuroimage: 52(4):1175-1180.
148. Burton EJ, McKeith IG, Burn DJ, Williams ED, O'Brien JT (2004).
Cerebral atrophy in Parkinson's disease with and without dementia: a
comparison with Alzheimer's disease, dementia with Lewy bodies and
controls. Brain: 127(Pt 4):791-800.
149. Pagonabarraga J, Corcuera-Solano I, Vives-Gilabert Y, Llebaria G,
Garcia-Sanchez C, Pascual-Sedano B, Delfino M, Kulisevsky J,
Gomez-Anson B (2013). Pattern of regional cortical thinning associated
with cognitive deterioration in Parkinson's disease. PLoS One:
8(1):e54980.
150. Wu T, Hallett M (2013). The cerebellum in Parkinson's disease. Brain:
136(Pt 3):696-709.
151. Lehericy S, Sharman MA, Dos Santos CL, Paquin R, Gallea C (2012).
Magnetic resonance imaging of the substantia nigra in Parkinson's
disease. Movement Disorders: 27(7):822-830.
161
152. Nicholls JG, Paton JF (2009). Brainstem: neural networks vital for life.
Philosophical Transactions of the Royal Society of London Series B:
Biological Sciences: 364(1529):2447-2451.
153. Eriksen N, Stark AK, Pakkenberg B (2009). Age and Parkinson's
disease-related neuronal death in the substantia nigra pars compacta.
Journal of Neural Transmission Supplementum: 73:203-213.
154. Oguro H, Okada K, Yamaguchi S, Kobayashi S (1998). Sex differences
in morphology of the brain stem and cerebellum with normal ageing.
Neuroradiology: 40(12):788-792.
155. Miller IN, Cronin-Golomb A (2010). Gender differences in Parkinson's
disease: clinical characteristics and cognition. Movement Disorders:
25(16):2695-2703.
156. Camargos ST, Marques W, Jr., Santos AC (2011). Brain stem and
cerebellum volumetric analysis of Machado Joseph disease patients.
Arquivos de Neuro-Psiquiatria: 69(2B):292-296.
157. Alper F, Kantarci M, Altunkaynak E, Varoglu AO, Karaman A, Oral E,
Okur A (2006). Quantitative magnetic resonance imaging of brainstem
volumes, plaques, and surface area in the occipital regions of patients
with multiple sclerosis. Acta Radiologica: 47(4):413-418.
158. Fearing MA, Bigler ED, Wilde EA, Johnson JL, Hunter JV, Xiaoqi L,
Hanten G, Levin HS (2008). Morphometric MRI findings in the
thalamus and brainstem in children after moderate to severe traumatic
brain injury. Journal of Child Neurology: 23(7):729-737.