Introduction to Brain Science and fMRI · MR Lab Co-Director Center for Mind Brain Sciences University of Trento [email protected] Tokyo Institute of Technology February 2011

Introduction to Brain Science and fMRI

Jorge Jovicich, Ph.D.MR Lab Co-Director

Center for Mind Brain SciencesUniversity of Trento

[email protected]

Tokyo Institute of Technology February 2011

Dates Topics

Friday Feb 4, 2011(13:50-16:40)

• Overview of a brain fMRI experiment

• Basic MRI concepts o Signal source. Image formation. Contrast. Safety.

• Anatomical MR images: oAcquisition: T1-weighted contrast imagingoAnalysis: brain segmentation o Potential image artifacts

Mon Feb 7, 2011(13:20-16:30)

• Functional MR images: o fMRI Contrasto fMRI Acquisitiono fMRI Analysis

Lectures Outline

Jorge Jovicich, February 2011

Review from last class• Equilibrium magnetization (main magnet, B0)• Excitation of the magnetization (RF pulse, B1, flip angle, head coil)• Relaxation of the magnetization (tissue specific properties: T1, T2, T2*)• MR Signal

• Transverse magnetization• Depends on parameters I control: B0, flip angle, TR, TE• Depends on parameters I don’t control: T1, T2, T2*

• MR Image• Gradients encode spatial information in the signal (images)• Gradients generate acoustic noise

• MR Image Contrast• Manipulating sequence parameters we can chose image contrast• Typical contrast for structural scans in fMRI: T1 contrast• Typical contrast for functional scans in fMRI: T2* contrast

• MR Safety• B0: projectile• RF: heating • Magnetic gradients: peripheral nerves stimulation, noise


Today• Brain fMRI: functional contrast

– Hemoglobin and magnetic properties– BOLD: Blood Oxygenation Level Dependent Contrast– BOLD: specificity– BOLD: temporal resolution– BOLD: spatial resolution

• Brain fMRI: image acquisition– Echo Planar Imaging– Challenges in EPI– Parallel Imaging– Example of EPI artifacts

• Brain fMRI: Analysis Overview – Pre-processing – Statistcs– Experimental design


Brain Functional MRI Contrasts

• Invasive– Vascular volume (gadolinium)

• Non-invasive– Blood Oxygenation Level Dependent (BOLD)– Perfusion (CBF: cerebral blood floow)– CMRO2 (Cerebral metabolic rate O2 consumption)– Vascular volume changes (VASO)– … ongoing research (currents, diffusion, etc.)


Brain Vasculature

Source: Menon & Kim, TICS

Slide courtesy of Jody Culham

• Brain weight: 2% of body weight• Brain energy budget: ~20% of body• Very efficient irrigation system


Some facts about hemoglobin (Hb)

• Each red blood cell: about 250 million Hb molecules

• Function: transport of ‘goods’ and ‘wastes’• carries O2 to tissue and CO2 away from tissue• one Hb molecule can carry up to 4 atoms of O2

• Hemoglobin molecule structure: - four protein globin chains- each globin chain contains a heme group- at center of each heme group is an iron atom (Fe)- each heme group can attach an oxygen atom (O2)

Source: http://wsrv.clas.virginia.edu/~rjh9u/hemoglob.html,


http://wsrv.clas.virginia.edu/~rjh9u/hemoglob.html�

Oxygenation states give rise to different magnetic states: • oxygenated-Hb

• isomagnetic with respect to the surrounding tissue, no net magnetization, paired electrons

• deoxygenated-Hb • paramagnetic with respect to the surrounding tissue, net magnetization, unpaired electrrons

• Pauling & Coryell, PNAS 1936.

Magnetic properties of hemoglobin (Hb)

oxygenated deoxygenated Jorge Jovicich, February 2011

NMR relaxation properties of hemoglobin

De-oxygenation: shorter T2


Thulborn et al., 1982

Gradient EchoT2

* changes

Test tubes, Ogawa 1990b

Spin EchoT2 changes

Little difference

Large difference

With fast T2*-weightedimage, we can measureoxygenation changes

in blood!

Maybe we can use blood oxygenationchanges specific

to brain function?

MRI properties of hemoglobin


Dynamics of hemoglobin:Brain Hemodynamic Response

rCBF

dHg

rCBV

rCBV: regional Cerebral Blood Volume

rCBF: regional Cerebral Blood Flow

dHg: deoxygenated Hemoglobin

Hg: oxygenated Hemoglobin

Capillary during baseline

Capillary during activation

neuron

O2


Integration and signaling in ensembles of neurons

ATP consumption by neurons and astrocytes

Glucose ↑ Oxygen ↑

Blood flow ↑↑ Blood volume ↑

Displacement of deoxyhemoglobin

Increase coherent spin in H nuclei of diffusing H20

Increase local MR signal

Indirect relationship between fMRI signal and cognitive processes

Sensory, motor and cognitive processes

Modified from Huettel, Song and McCarthy

O2 supply exceedsMetabolic needs


-10 -5 0 5 10 15 20 25

-10 -5 0 5 10 15 20 25

MR characterization of the Hemodynamic Response

Baseline

Baseline

Rise

Rise

Peak

Peak

Undershoot

Undershoot

Sustained Response

Initial Dip

Initial Dip

Blockdesign

Single-eventdesign

t (seconds after stimulation)

Response to a series of

brief stimulus

Response to one brief stimulus

~5s~12s

~2s

Courtesy of Allen Song

If [dHb]↑ ⇒ MR signal↓If [dHb]↓ ⇒ MR signal ↑


BOLD Time Course

% signal change=(peak-baseline)/baselineUsually 0.5-3% (if much higher it’s a vessel)

Initial signal dip-More specific to neuronal activation location-Elusive, very small effect, difficult to obtain

Time to riseSignal begins to rise ~2s after stimulus

Time to peakSignal peaks ~4-6s after stimulus starts

Post stimulus undershootSignal suppressed ~15-20s after stimulus ends


BOLD sensitivity: T2* and TEM

R s

igna

l (M

xy)

Action (less dHg)Rest (more dHg)

TE t

S(TE) = S0 e–TE R2*


BOLD sensitivity: T2* and TE

Jorge Jovicich, February 2011Courtesy of P. Bandettini

TE Dependence of MR Signal Changewith echo-time (TE)

TE (ms)

TEoptimum ≈ T2* (active_tissue)

Optimalrange


BOLD specificity: what are we measuring actually?

• So far: With fast T2*-weighted MRI (optimal choice of TE) we can measure deoxy-Hb changes per voxel

• But:– Relative concentration of deoxy-Hb can change because:

• Relative oxygenation changes – Metabolism to support neuronal activation

• Blood flow changes• Blood volume changes

– We measure the combined effect


Relative % changes in CBF, CBV and BOLD signals

Changes measured with external contrast agents

From Huettel, Song and McCarthy Jorge Jovicich, February 2011

Evidence that BOLD response reflects pooled local field potential activity

(Logothetis 2001)

Neural specificity: hope for BOLD


BOLD signal: spatial properties

Because of vascular contamination,

functional spatial resolution

IS NOT EQUIVALENT TO

fMRI voxel size


Harrison, Harel et al., Cerebral Cortex 12:225 (2002)

100µmHarrison, Harel et al., Cerebral Cortex 12:225 (2002)

Duvernoy et al., (1981) Brain Res. Bull. 7:518

BOLD fMRI is differentially sensitive to large and small vessels


Different effects on extravascular spins by large and small vessels

Modified from Huettel, Song and McCarthy

Spin echo does not form BOLD contrast is measured

Spin echo forms BOLD contrast is erased


Gradient Echo vs. Spin Echo in fMRI

• Contribution of large vessels and capillary beds to the BOLD signals;

• Separation/suppression of signals from large vessels.

Diameter (µm)1.0 10.0

∆R2,

∆R2

* (1

/sec

)

20

10

0

∆R2

∆R2*

∆R2 = 1/T2(baseline) - 1/T2(activation)

∆R2* = 1/T2*(baseline) - 1/T2

*(activation)

Measured with spin-echo

Measured with grad-echo

Capillaries Larger vessels


BOLD signal: spatial specificity

Advantages at high fields

• Voxel size• Sequence: spin-echo or gradient echo• Field strength


BOLD sensitivity: field strength effects

Courtesy of Stuarte Clare Jorge Jovicich, February 2011

Signal contributions: gradient echo (T2*)

100µm

IntravascularSmall venuole/capillary

Large venuoleField strength

Extravascular protons near large vessels

Extravascular protons near small vessels

Rel

ativ

e co

ntrib

utio

n

Blood signal

Harrison, Harel et al., Cerebral Cortex 12:225 (2002)


100µm

Signal contributions: spin echo (T2)

IntravascularSmall venuole/capillary

Large venuoleField strength

Rel

ativ

e co

ntrib

utio

n

Blood signal

Extravascular protons near small vessels





• Brain fMRI Analysis Overview: Pre-processing & Statistcs• Experimental design


Echo Planar Imaging (EPI) • EPI: standard imaging sequence for fast MRI

(brain fMRI, diffusion, perfusion, etc.)

• EPI advantages for brain fMRIo Nowadays easily availableo Dominant contrast is T2

* (BOLD)o Spin-echo EPI can have T2 & T2

*

o Typically allows full brain coverage in ∼1-3 sec with 3-4 mm isotropic voxels

• EPI limitations:o High sensitivity to local magnetic field inhomogeneities

- Signal loss and image distortionso Gradient switching: Nyquist ghosto Loudo Signal decay during acquisition limits spatial resolution


What’s the difference between the structural and functional sequences?

S(t)

“slice select”

“freq. enc”(read-out)

RFtGz

Gy

Gx

kx

ky

RF

tS(t)

tGz

Gy

Gx etc...T2*

Conventional MRI(gradient-echo)

kx

ky

Echo-planar imaging(gradient-echo)

one RF excitation, one line of kspace...

one RF excitation, many lines of k-space...


Important EPI parameters that you choose

• Slice– Field of view / Matrix size / in-plane voxel size– Slice thickness– Slice gap– Slice orientation– Number of slices– Acquisition order (interleaved, sequential, asc./desc.)

• Echo Time (TE) • Repetition time (TR)• Flip angle • Receiver bandwidth (echo-spacing)• Number of volumes to acquire & dummy scans• Paralel imaging• Distortion correction method (field map, etc.)

FOV=192 x 192 mm2

Matrix= 64 x 64 voxelsIn-plane voxel = 192/64

= 3mm

FOV


Effects of these parameters• Signal dropout

– TE– Slice thickness– Slice orientation

• Image distortions– Matrix size (FOV, voxel)– Slice orientation– Receiver bandwidth– Paralel imaging– Distortion correction method

• BOLD sensitivity• TE• Slice thickness• Slice orientation

•Signal-to-noise ratio– Voxel size (in plane & thickness)– Receiver bandwidth– Paralel imaging– Flip angle

•Ghosts• Receiver bandwidth• TR

Modified from Stuart Clare Jorge Jovicich, February 2011

Experiment overview

Blocked Experimental

Design

Data Acquisition

Condition A Condition ACondition B Condition B

One 2D slice

One 3D Volume

(many slices)

RFGz

Gy

Gx

Signal

Important acquisition parameters

• In plane spatial resolution (acquisition time per slice, distortions, signal loss, resolution)

• Limited by gradient switching capabilities of our hardware• Spatial thickness (signal loss, distortions, resolution)

• Limited by TR• Flip angle

• There is an optimal flip angle for maximal signal for a given TR and tissue T1

• Total number of images (experiment duration, gradient heating)

TRTime

Time


Let’s look at sample 4T EPI:what do you think?


Let’s look at sample 4T EPI:what do you think?


Magnetic susceptibility MRI artifacts

The good.

The bad.

The ugly.

Courtesy of Larry Wald Jorge Jovicich, February 2011

Enemy #1 of EPI: local magnetic susceptibility gradients

B0 Inhomogeneity Map

Strong localInhomogeneities

(B=B0 + ∆B)

Homogeneous (B= B0)

Sinus cavities

Ear cavities

“Magnetic Susceptibility”• Definition: a material’s tendency to magnetize when placed in an external field

• Origin of gradients: interfaces of differing magnetic susceptibilities

• Effects: signal loss and distortions


Susceptibility effects occur near magnetically dis-similar materials

Field disturbance around air surrounded by water (e.g. sinuses)

B0 field map (coronal image) 1.5T

Bo

Ping-pong ball in water…

Air

Water

Courtesy of Larry Wald Jorge Jovicich, February 2011

Susceptibility effects increase with Bo

1.5T 3T 7T

Ping-pong ball in H20:Field maps (∆TE = 5ms), black lines spaced by 0.024G (0.8ppm at 3T)

Courtesy of Larry Wald

Odd/Evenechoesaligned

Odd/Evenechoes

misaligned

EPI problem: N/2 (Nyquist) ghost

N/2 ghost

Modified from Huettel, Song, McCarthy Jorge Jovicich, February 2011

Greadout

-

S(t)

Sample points

tAcquisitionwindow

+ +

Acquisitionwindow

S(t)

Odd/Evenechoesaligned

Odd/Evenechoes

misaligned

EPI problem: N/2 (Nyquist) ghost


• Can we speed things up?• Spatial information from RF coil array• Acquisition acceleration

– Without need of faster-switching gradients– Without additional RF power deposition

New frontiers: parallel imaging


New frontiers: parallel imaging

Acquisition: SMA

SH

SENSE

Reconstruction:

SMASH: Use spatial harmonicparameters of coils to fill-in parts of k-space

Reduced k-space sampling

{

SENSE: Acquire Smaller FOVimages and utilize sensitivity

maps of coils to correct aliasing

Courtesy of Larry Wald

Independent surface coils

Quick Review of some EPI artifacts

MR Image Artifacts: EPI

• Signal loss in gradient echo EPI– Brain areas with T2* << TE

Structural MPRAGE EPI GE


MR Image Artifacts: EPIStructural GE EPI GE

• Geometric distortions in gradient echo EPI– Local B0 inhomogeneities interfere with low

bandwidth EPI phase-encoding gradientsJorge Jovicich, February 2011

• Nyquist ghost in gradient echo EPI– Imperfect gradient waveforms and Eddy currents

induce shifts of the echo peaks relative to the acquisition window center

– Always there in standard single-shot EPI, hard to get below ~ 5%



8_1475

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40

SLICES

%G

HOST

ROI1ROI2ROI3ROI4

7_1445

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40

SLICES

%G

HOST

ROI1ROI2ROI3ROI4

Same WindowWidth&WindowCenter

7_1445_actual 8_1475_newROIs

1 2

34

%Ghost in different ROIs in every slices

5

Reducing Ghosts by optimizing parameters

Courtesy of Dr. Paolo Ferrari Jorge Jovicich, February 2011


• Fat suppression in gradient echo EPI– No fat suppression can lead to a Nyquist

ghost fat signal that interferes with the main image, potentially producing artifactualactivation or hiding real activation


MR Image Artifacts: EPIOne volume (TR)

Another volume (TR)

• RF transmit/receive chain problems– Some slices are hypointense– Data useless: vendor service call!


MR Image Artifacts: A Story

Strange activation found outside of brain


• Data co-registation ok it is no brain signal. • Visual examination of data in area close to artifact seems ok.• Temporal pattern shows no obvious problems.• Experimental setup has been previously used without problems

• Can we reproduce the problem on a phantom? ⇒ No• Is it interference with fat signal? ⇒ Fat suppression gives same

effects on another subject




• Four spots of activation• Geometry suspiciously similar to that of our head RF coil

• This is the only experiment in which we use a tactile stimulator inside the scanner.

• This is the only protocol for which we find the artifact

• RF pick up? • Reproduce with phantom?• Only when stimulator on AND headphones inside RF coil



Experimental run with both response box and piezo

Let’s look at the data again, now with standard deviation images


Equal settings in brightness and contrast

Response box: connected. Piezo: connected. Bkg noise: 4.67±0.89

Response box: removed with cables. Piezo: connected. Bkg noise: 5.11±0.94

Response box: removed. Piezo: turned off control unit in console room. Bkg noise: 3.30±0.54

Response box: removed. Piezo: removed from MR room. Bkg noise: 3.24±0.57

Standard deviation images



Noise filter mounted: • 0 spikes • STD images from calc_tSNR_func• tSNR images from calc_tSNR_func

Noise filter unmounted: • 5 spikes • STD images from calc_tSNR_func• tSNR images from calc_tSNR_func

Equal settings in brightness and contrast

⇒ Spikes and tSNR are not a good indicator for this artifact

⇒ the STD map is a better



• But:– We had tested the RF filters of the tactile stimulator and

verified they worked well.– What has actually changed ????????

• We later find that:– One day one of the filters broke during an experiment.– To continue working a researcher replaced it with an

untested filter because it seemed to work. MR staff was not informed.



MR Image Artifacts: A StoryFaulty RF filter for tactile stimulator: sample standard deviation run

Repaired RF filter for tactile stimulator: sample standard deviation run


OverviewTypical Structural MRI Typical Functional MRI

Contrast • 3D T1 (gray-white matter separation)• Signal changes across voxels in one image

• 2D T2* (BOLD, TE ∼T2*gray_matter for the

used voxel size)• Signal changes across time for each voxel

Spatial resolution

Nominal ∼ 1x1x1 mm3 • Nominal ∼3x3x3 mm3 (or smaller)• Functional: limited by hemodynamics and acquisition protocol (SE vs GE, high field)

Temporal resolution

Nominal ∼ 9 minutes for full volume

• Nominal ∼ 2 sec for full volume• Functional ∼ limited by hemodynamics

and experimental designArtifacts Movement, wrap around, dental

work, RF coil inhomogeneities, RF interference from periph. Equipment, etc.

• Signal loss (areas with short T2*)• Geometric distortions (wrong phase)• Nyquist ghosts• Image drifts (gradient heating)• RF interference from periph. equipment





• Brain fMRI: Analysis Overview – Pre-processing – Statistcs– Experimental design


Statistical Mapsuperimposed on

anatomical MRI image

Functional images

Time

~ 5 min

Time

fMRI signal changesIn area of interest

Condition

~2s

Brain region of interest

Source: Modified from Jody Culham’s fMRI Newbies web site

What do we want to do with this MRI data ?

Assumptions we would like to make for fMRI statistical analyses

• You can trust your images for your study!Right image parameters, no abnormal artifacts, brain lesions, etc.

• Voxel signal comes from the same brain area throughout time series but there is motion

• Voxels in any given image-volume acquired simultaneouslybut different slices are acquired at different times

• Voxel intensity variability mostly related to stimulation paradigmbut signal changes can have other sources

• Multiple scans from a subject can be combined (each voxel same structure) but the subject can move during the experiment

• Multiple subjects can be combined (each voxel same structure)but brains will be generally different and oriented differently

DATA PREPROCESSINGJorge Jovicich, February 2011

Signal pre-processing: basic steps

Modified from Source: Huettel S.A., Song A.W., McCarthy G.

GOAL: To maximize the spatial & temporal accuracy of the functional data in relation to brain activity


Basic steps signal pre-processing



MRI Data Quality Assurance

• Examine MRI data during acquisition• Is the brain normal?• Movie-like observation is useful to detect

• Abnormal artifacts/distortions• RF spikes• Abnormally high motion

• At this stage you can decide to:• Cancel your experiment• Talk to your subject to reduce motion• Not analyze the dataset

Save a lot of time!


Always look at your structural MRI at the beginning of the experiment

http://wwwrad.pulmonary.ubc.ca/stpaulsstuff/MRartifacts.html

What is wrong here?


Always look at your structural MRI!

• Always check anatomical MRI• If suspicious:- run CLINICAL-BRAIN protocol

(T1,T2,FLAIR,Diffusion)- just use the default parameters- but set slices to cover all head!- inform the Physician responsible- run your fMRI?- what to tell the subject?

3DSagittalT1

T2 Axials

Is your normal volunteer ‘normal’?

‘Normal’ volunteerwith arachnoid cyst





Head Motion


• The problem:• MRI signal variability unrelated to neuronal activation• Motion causes

• fatigue, swallow, unconfort, sleep• task-related (THE WORST!)

• Solutions:• Prepare the subject• Experimental design• Postprocessing corrections:

• Co-register all volumes in a time series to a reference volume

Motion Effects


Motion (even 1 voxel) = Large artificial ‘brain activation’


Motion correction: registration to reference

• Determine the rigid body transformation that minimises the sum of the squared difference between two images.

• Rigid body transformation is defined by:3 translations - in X, Y & Z directions.3 rotations - about X, Y & Z axes.

• Note:o Each brain volume is treated as an instantaneous acquisitiono Motion correction needs to re-slicing to create an interpolated volumeo Interpolation will combine data from spatially adjacent slices to create new slices

o If the slice aacquisition was sequential then voxels that are close in space were also acquired close in time -> no big problem

o If the slice acquisition was interleaved, original adjacent slices correspond to temporally distant acquisitions -> slice timing correction should be done before motion correction


Motion Correction ParametersCould be acceptable motion Too much motion (>> 1mm)


Effects of Motion Correction




Slice Acquisition Timing Correction• The problem:

• Ideally • Each brain volume is acquired instantaneously • Each volume time point represents the cognitve state of the whole brain related to that time point

• In reality • Echo image slice is acquired at a different time

• Sequential slice acquisition • Interleaved slice acquisition

• Consequence: • inacurrate representation of hemodynamic response

• Solutions:• Reduce the time for whole-brain sampling• Postprocessing: temporal interpolation


Slice Acquisition Timing Correction

The problem

Different slice orders

- ascending vs. descending

- sequential vs. interleaved


Interleaved AcquisitionSlice 15, Slice 17, …, Slice 16Ideal hemodynamic

response for the activearea: same for all slices

Time errors: different responses for different slices.

Slice Acquisition Timing Correction

• Correction shifts each voxel's time series so that all voxels in a given volume "appear" to have been captured at exactly the same time

SEQUENTIAL slice acquisition: functional volume are shifted in

time.

This is correctedby sinc (or linear)

interpolation in time.



Geometric distortions in fMRI data


• The problem:• Ideally B0 is uniform in the space of the sample, and this is assumed in the image reconstruction process

• In reality the magnetic field is distorted• Echo planar images show

• areas with signal loss: nothing to do!• areas with geometric distortions: something to do!

• Solutions:• Improve the homogeneity of B0 (shimming)• Postprocessing corrections:

• Measure field distortions • Reconstruct image with field inhomogeneity information



Functional-Structure:Co-registration and normalization

• Single subject or group analyses:• For each subject:

• Co-registration between fMRI and anatomical scans• Rigid body transformation (cost function minimized)• This adds an interpolation step (smoothing)

• Co-registration (normalization) of both (now aligned) to standard atlas • For spatial reference• Talairach space, or probabilistic atlases, cortical surface based• This adds an interpolation step (smoothing)• Caveats: normalization space may not represent all subjects

EPI Anatomical Standard TemplateJorge Jovicich, February 2011

Standardized Spaces (brain atlas)

• Talairach space (proportional grid system)– From atlas of Talairach and Tournoux (1988)– Based on single subject (60y, Female, Cadaver)– Single hemisphere– Related to Brodmann coordinates

• Montreal Neurological Institute (MNI) space– Combination of many MRI scans on normal controls

• All right-handed subjects– Approximated to Talaraich space

• Slightly larger• Taller from AC to top by 5mm; deeper from AC to bottom by 10mm

– Used by SPM, National fMRI Database, International Consortium for Brain Mapping


Should you co-register to a standard space (normalize)?

• Advantages– Allows generalization of results to larger population– Improves comparison with other studies– Provides coordinate space for reporting results– Enables averaging across subjects

• Disadvantages– Reduces spatial resolution– May reduce activation strength by subject averaging– Time consuming, potentially problematic

• Doing bad normalization is much worse than not normalizing




“In neuroimaging, filters are used to remove uninteresting variation in the data that can be

safely attributed to noise sources, while preserving signals of interest.”

Spatial-Temporal Filtering

Source: Huettel S.A., Song A.W., McCarthy G.

Temporal Filtering• Identify unwanted frequency variation

– Drift (low-frequency)

– Physiology: cardiac (1-1.5 Hz), respiratory (0.2-0.3 Hz)

– Task overlap (high-frequency)

• Reduce power around those frequencies through application of filters

• Potential problem: removal of frequencies composing response of interest


Physiological effects in the MR signal

http://www.fil.ion.ucl.ac.uk/spm/course/


0.025 Hz

Temporal Filtering


Spatial FilteringSpatial smoothing essentially ‘blurs’ your functional data.

Why would you ever want to reduce the spatial resolution of your data?

Spatial smoothing may often be required when averaging data across several subjects due to the individual variations in brain anatomy and functional organization.

Gaussian convolution is separable Gaussian smoothing kernel

Effects of Smoothing on ActivityUnsmoothed Data

Smoothed Data (kernel width 5 voxels)


Smoothing: reduction of false positive rate


Should you spatially smooth?• Advantages

– Increases Signal to Noise Ratio (SNR)• Matched Filter Theorem: Maximum increase in SNR by filter

with same shape/size as signal– Reduces number of comparisons

• Allows application of Gaussian Field Theory– May improve comparisons across subjects

• Signal may be spread widely across cortex, due to intersubject variability

• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of signal

is not known




fMRI Statistical Analysis• Model driven

– Assume a model for the hymodynamic response function (HRF) and assume that it is uniform across the brain

– Correlate a temporal waveform of expected HRF events with temporal time course

– The model includes confound sources: motion correction parameters, physiological-derived regressors (heart, respiration), signal drifts

– Advantages: easy to understand and use– Challenges: You get what you model for, may fail to detect non-

antincipated or transient task-related components

• Data driven– Classify networks with temporally/spatially correlated voxels

(clustering, ICA, PCA, multi-voxel patterns, etc.)– Advantages: no assumptions on the relationship between stimuli and

responses– Challenges: Statistical significance and accuracy of automatically

defined classifiers

• HybridsJorge Jovicich, February 2011










defined classifiers











defined classifiers


Model: - External stimulation- Hemodynamic response

Voxel that cares about the stimulus

Voxel that does not care about the stimulus


Standard Statistical Analyses

Standard generalized linear model

• Correlate each voxel separately to the convolution of the hemodynamic response and the stimulation paradigm

• Measure residual noise amplitude• T-statistics

Model fit / noise amplitude• Threshold t-statistics ⇒ show map on anatomic scan

However:• Other signals not related to activation can give activity• Use preprocessing to minimize these

Ys = α M(ts) + εs Üt-statistic for H0: α > 0data fit model noise


modelling & parameter estimation

construction of statistic

f MRI time series

statistical image

voxel time series

Voxel based statistics


Data model functionvoxel time series

Ys = µ + α f(ts) + εs

= αµ + + εs

f(ts) = 0 or 1

t-statistic for H0: α > 0

Linear Regression

Mean

Amplitude

Error (unexplained variance)

statistical image(map of α)

We want a data modelthat gives the error term

as low as possible


Experimental Design Overview

Hypothesis• why?• where? (neuroanatomy)• what? (behaviour)

Experimental design• how?

This section has several slides from:http://web.mit.edu/hst.583/www/course2001/LECTURES/hst583_lect10_slides.pdf


Experimental Design Concepts

• Remember what fMRI may give you– Relative local “neural” activity (mm/s)– NOT absolute neural activity– NOT excitation vs. Inhibition– NOT about necesity of a given region for a task


• Key parameters for any experiment– Ethical committee approval– Subjects’ demographics– Pheripherical equipment needs– Brain area/volume to cover – Spatial and temporal resolution of the acquired data– Switch many times conditions within a scan– Signal-to-noise: run as many scans as possible per subject– Total scan time: usually under 1.5 hours– Pre-scan training. Post-scan tests.



• Critical issues– Poorly defined neuroanatomical hypothesis– Poorly controled baseline– Attentional effects– Learning effects– Stimulus habituation or sensitization– MR system and physiological drifts



• Manipulation of cognitive tasks:– Subtraction– Factorial– Parametric– Adaptation

• Presentation of cognitive tasks:– Blocked– Event-related

• Averaged / Single trial• Rapid / Spaced

– Mixed Blocked/Event-related



Blocked designs

• Efficient in terms of task effect relative to baseline• Many stimuli of same category presented during a block• Different blocks interleaved with baseline condition• Limitations:

• subjects may anticipate task (mix blocks)• only average effects are seen


Event Related Designs – Long ISI

• ISI: Inter Stimulus Interval• For full recovery of HRF need long ISI (>16s)• Poor efficiency: needs very long acquisitionsto collect enough trials (subject gets tired, motion, etc.)

ISI



Event Related Designs – Short ISI

Recommended Complementary Slides

• http://psychology.uwo.ca/fMRI4Newbies/Tutorials.html• http://web.mit.edu/hst.583/www

http://psychology.uwo.ca/fMRI4Newbies/Tutorials.html�

http://web.mit.edu/hst.583/www�

Documents

Introduction to Brain Science and fMRI · MR Lab Co-Director Center for Mind Brain Sciences University of Trento [email protected] Tokyo Institute of Technology February 2011