SINPASE fMRI course
Dr Cyril Pernet, University of Edinburgh
Dr Gordon Waiter, University of Aberdeen
Overview
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Matlab® environment: images are matrices
MRI and fMRI: image format and softwares
Computational Neuro-anatomy: theory
Computational Neuro-anatomy: pratice
Statistics: theory
fMRI Single level analysis in practice
fMRI Random effects analysis
Other software - visualization
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Data provided by the FIL: http://www.fil.ion.ucl.ac.uk/spm/
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Matlab® environment:
images are matrices
Read data and do basic stuffs
Matlab (1)
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Command
window:
A = 3+5
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Workspace: whos
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History
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Browser
Matlab (2)
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load MRI_3D  look in the workspace
size(MRI_3D)  returns the dimensions (here nb of voxels)
imagesc(MRI_3D(:,:,54))
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All rows All columns ‘column’ 54 on dim3
imagesc(MRI_3D(:,:,54)')
imagesc(flipud(MRI_3D(:,:,54)'))
colormap('gray')
Matlab (3)
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MRI images are matrices (tables) with 3 dimensions, but it can be
4 dimensions (fMRI) or more.
Operations on matrices
 A = [1 2 3 4 5]; B = A' (transpose)
 A*B → matrix multiplication sum of row*columns
 C = [1 1 1 1 1];
 A+C → addition / subtraction work by cell
 A.*C → multiplication by cell uses .* (and ./)
Exercise: subtract slice 54 from slice 55 and image it
imagesc(flipud(MRI_3D(:,:,55)'-MRI_3D(:,:,54)'))
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Matlab (4)
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Exercise: Make a script (Matlab Editor) to look at the all volume
using an axial view
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Possible functions to use: for, imagesc, squeeze, pause
the matlab help
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Loop For/End
for z = 1:108
do this and that for each z
end
… and
Matlab (5)
for z = 50:220
imagesc(squeeze(MRI_3D(:,z,:)))
colormap('gray')
title(['this is the slice ',num2str(z)])
pause(0.1)
end
MRI and fMRI:
image format and software
Image format
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DICOM format (.dcm)
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‘Standard’ format coming from all scanners
Stands for Digital Imaging and Communications in Medicine
Part 10 of the DICOM standard describes a file format for the
distribution of images
A single DICOM file contains both a header (which stores
information about the patient's name, the type of scan, image
dimensions, etc), as well as all of the image data (which can
contain information in three dimensions).
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 Manufacturers tend to output in DICOM but also put lots of
useful information in the ‘private’ part of the header
Image format
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Analyze format (.img .hdr)
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Analyze is an image processing program, written by The
Biomedical Imaging Resource at the Mayo Foundation.
There are two Analyze formats. One, by much the more
common, is Analyze 7.5 (this is one format used by SPM), the
other is Analyze AVW, the format used in the latest version of
the Analyze program
An Analyze (7.5) format image consists of two files, and image
(.img) and a header file (.hdr). The .img file contains the numbers
that make up the information in the image. The .hdr file contains
information about the img file, such as the volume represented
by each number in the image (voxel size) and the number of
pixels in the X, Y and Z directions. This header contains fields
of text, floating point, integer and other information.
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Image format
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NIfti format (.img .hdr or .nii)
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Stands for Neuroimaging Informatics Technology Initiative
(The National Institute of Mental Health and the National
Institute of Neurological Disorders and Stroke)
Facilitates inter-operation of functional MRI data analysis
software packages
Headers now include - affine coordinate definitions relating
voxel index (i,j,k) to spatial location (x,y,z); codes to indicate
spatio-temporal slice ordering for FMRI; - "Complete" set of 8128 bit data types; - Standardized way to store vector-valued
datasets over 1-4 dimensional domains; - etc
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1. Importing Data
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Matlab DICOM tools in the image processing toolbox, but also
plenty of free software including SPM
Type SPM  select fMRI
Import from the directory
3D_dicom
Save as one file
1. Importing Data
1. Importing Data
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Select the newly imported
image
Surf to the front, near the
eye
Check orientation
Check the voxel size!!
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edit spm_defaults
defaults.analyze.flip =
1; % input data left =
right
1. Importing Data
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Now simply double click on the nii file – this should bring
MRICron
Again surf to the front near the eyes   the white spot is at a
different location ?? Check outside the brain  MRICron is
telling you are on the right side
BE AWARE OF THE ORIENTATION
Computational Neuroanatomy: theory
fMRI time-series
kernel
Slice timing and
Realignment
smoothing
normalisation
Anatomical
reference
Statistics
Slice timing: Why?
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During the scanning, slices of the brain are acquired every TR (x
sec) and one wants to correct for this delay between slices.
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Data can be acquired in ascending/descending order, in this case
one realigns first, otherwise one would apply the same time
correction to voxels possibly coming from different slices, i.e.
acquired at different time.
Often one acquires first slices 1, 3, 5, 7, 9 … then 2, 4,6, 8, …
(interleaved mode), in this case slice timing is done first
otherwise the realignment would move voxels possibly coming
from different slices, i.e. acquired at different time (max TR/2),
onto the same plane and the slice timing would then be wrong.
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Realignment: Why?
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Subjects will always move in the scanner.
 movement may be related to the tasks performed.
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When identifying areas in the brain that appear activated due to
the subject performing a task, it may not be possible to discount
artefacts that have arisen due to motion.
The sensitivity of the analysis is determined by the amount of
residual noise in the image series, so movement that is unrelated
to the task will add to this noise and reduce the sensitivity.
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Normalization: Why?
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Inter-subject averaging
 extrapolate findings to the population as a whole
 increase activation signal above that obtained from single
subject
 increase number of possible degrees of freedom allowed in
statistical model
Enable reporting of activations as co-ordinates within a known
standard space
 e.g. MNI
Smoothing: Why?
Smoothing is used for 3 reasons:
 Potentially increase signal to noise (Depends on relative size of
smoothing kernel and effects to be detected - Matched filter
theorem: smoothing kernel = expected signal - Practically
FWHM 3· voxel size ; May consider varying kernel size if
interested in different brain regions (e.g. hippocampus -vsparietal cortex))
 Inter-subject averaging.
 Increase validity of SPM.
In SPM, smoothing is a convolution with a
Gaussian kernel, and the Kernel is defined in
terms of FWHM (full width at half maximum).
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Computational Neuroanatomy: practice
Single subject processing
Protocol – Imaging parameters
spm_data_set\Auditory_data_block_design
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Each acquisition consisted of 64 contiguous slices (3mm3).
Acquisition took 6.05s, with the scan to scan repeat time (TR) set
to 7s.
96 acquisitions were made (TR=7s) from a single subject, in
blocks of 6, giving 16 42s blocks.
The condition for successive blocks alternated between rest and
auditory stimulation, starting with rest. Auditory stimulation was
bi-syllabic words presented binaurally at a rate of 60 per minute.
The functional data starts at acquisition 4, image fM00223_004.
2. Slice timing
2. Slice timing
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Session – Specify files --> select all functional images -> 96 files
Number of slices? 64
TR? 7
TA? 6.05
Slice order? [64:-2:1, 64-1:-2:1]
Reference slice? 2
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OUTPUT: ‘a’ images .. afM00XX.img / .hdr
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3. Realignment
New session  Select the slice timed images afM00XX.img
For each session during the scanning, create a session !!
Creates the mean of all realigned EPI images – you do not have to write
the data, parameters (which comes from the ‘estimate’) are kept in the
header
created in your
directory
- the mean image
-a spm.ps
- .txt file
= translation and
rotation parameters
4. Normalize
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We could normalize on the EPI template but since we
have the subjects’ anatomical scan, we can use it to
normalize on the T1 template.
compute
T1
MNI T1 template
T1 like EPI
Mean image
Coregistration
apply
EPI
Realign and
normalize
4a. Coregister
4a. Coregister
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Target image?
mean EPI – this doesn’t change
Source image?
High resolution T1 (nsM00223_002.img)
We want this to be like the EPI
Other images?
nothing here
Reslice option  interpolation
trilinear is fine, could improve using b-spline
(come back to this latter)
4a. Coregistrer
Output:
rnsM00223_002.img
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4b. Normalization
4b. Normalize
Data  New Subject
Source Image  rns...img (compute from the coregistered T1)
Images to write  all the af…img (normalize all EPI images)
 we can add the mean.img
 we can add the rns…img (normalize the T1)
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Estimation options:
Template image  SPM5\Template\T1.nii
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Writing options: interpolation
4b. Normlization
Output:
 wa…img
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4c. Check the data
Check Reg
 Several images
at once
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4c. Check the data
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Select
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wmean…img
T1 template
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5. Smoothing
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Select the normalized images
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Smooth at 6 mm
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Output: s…img
A word on
interpolation
Writing down the images
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What is interpolation?
Interpolation is a process for estimating values that lie between
known data points
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exit spm and open/run the script called interp_ex.m
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z is a 2D matrix and one interpolates between the ‘points’ defined
by z – there are many options, here we use nearest neighbor,
bilinear, and bicubic interpolation methods – observe the
difference in the results
5. Writing down the images
Writing down the images
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Why is it better to compute (estimate) everything 1st and then
apply?
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While one can compute and write the realigned data and
compute and write the normalized one, I suggest here to
estimate the realignment parameters and then normalize. The
realignment only uses affine transformation (translations and
rotations in x, y ,z) and this info is stored in the header. Then we
compute how to normalize an image (translation, rotation,
zoom, shear and non-linear warping) and multiply the two
transformation matrix – as a result a voxel (and its’ contend) is
‘cut’ only 1 time vs. twice.
Statistical modelling:
theory
fMRI time-series
Design matrix
Parameter estimates
kernel
Slice timing and
Realignment
smoothing
General Linear Model
model fitting
statistic image
Multiple comparisons
correction
normalisation
Anatomical
reference
Statistical
Parametric Map
corrected p-values
General Linear Model
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Regression, t-tests, ANOVAs, AnCovas … are all instances of the
same linear modelling.
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Regression: - simple: searching to explain the data (y) by a single predictor
(x) such as y = βx + b – multiple: searching to explain the data (y) by
several predictors (x1, x2, …) such as y = β1x1 + β2x2 + b
Linear models can be solved by the
least squares method, i.e. one looks for
a coefficient (Beta) that minimizes the
error, i.e. the difference between the
model (βx+b) and the data (y)
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General Linear Model
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Dummy coding:
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Instead of a continuous variable, we have categorical variables
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for each data point (y) we have a 2 groups, i.e. 1 predictor (x)
regarding the group that we code like 1111-1-1-1-1 and we still
use the equation y = βx + b ( = t-test)
or we can have several groups/conditions (x1 = 1111-1-1-1-1,
x2 =11-1-111-1-1, x1x2=11-1-1-1-111) such as y = β1x1 + β2x2
+ β12x1x2 b (ANOVA)
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General Linear Model
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Using a matrix notation we can write any models like
Y = Xβ + e
Y a vector for each data point,
X the design matrix where each column is a vector
representing groups/conditions/continuous predictor
β a vector (length = nb of column of X) of the coefficients to
apply on X in order the minimize e the error (what is not
modeled/explained)
Matrix algebra offers a unique solution for all models:
β = (XTX)-1 XT Y
 using pseudoinverse in matlab: betas = pinv(X’*X)*X’*Y
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General Linear Model
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Exercise 1: multiple regression with 4 covariates
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Load the data called reg_eg.mat: load ('reg_eg')
Y = reg_eg(:,1); X = reg_eg(:,2:6); imagesc(X); X
Y=X*B+e  B = pinv(X'*X)*X'*Y;  Yhat = X*B;
plot(Y); hold on; plot(Yhat,'r')
ss_total = norm(Y - mean(Y)).^2; ss_effect = norm(Yhat mean(Yhat)).^2; ss_error = ss_total – ss_effect;
Rsquare = ss_effect/ss_total
f = (ss_effect/(rank(X)-1)) / (ss_error/(length(Y)-rank(X)))
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General Linear Model
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Exercise 2: ANOVA with 4 groups
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Clear all; close all; clc; load(‘anova_eg’);
Y = anova_eg(:,1); X = anova_eg(:,2:6); imagesc(X);
Y=X*B+e  B = pinv(X'*X)*X'*Y;  Yhat = X*B;
plot(Y); hold on; plot(Yhat,'r')
ss_total = norm(Y - mean(Y)).^2; ss_effect = norm(Yhat mean(Yhat)).^2; ss_error = ss_total – ss_effect;
Rsquare = ss_effect/ss_total
f = (ss_effect/(rank(X)-1)) / (ss_error/(length(Y)-rank(X)))
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EVERY ANALYSIS DEPENDS ON X AND THE DOF
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General Linear Model
Application to fMRI: massive univariate approach
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For each voxel of the brain we have a time series (data points Y)
and we know what happened during this time period
(experimental conditions X).
We also know that after a stimulus or a response, the blood flow
increases peaking at 5sec then decreases (hemodynamic
response)
We thus model the data such as Y(t) = [u(t)  h(τ)] β + e(t)
where u represent the occurance of a stimulus or a response and
h(τ) the hemodynamic response with τ the peristimulus time
General linear convolution model
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X(y) = u(t)  h(τ)
u = [0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 ];
h = spm_hrf(1)
X = conv(u,h);
General linear convolution model
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y(t) = X(t)β + e(t)
X as now two conditions u1 and u2 …
And we search the beta parameters to fit Y
Y
X
General linear convolution model
=
β1 +
β2 + u + e
General linear convolution model
=
β1
β2 + e
u
Single subject stat
modelling: practice
5. Single subject stats
5. Single subject stats
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Directory  select a directory to store your data
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Timing parameters
Unit for the design: scan
Interscan interval: 7 (TR)
Microtime onset: 3
(TA = 6.05 and we slice timed on the middle slice)
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Data and Design  ‘New Subject/Session’
Scans: images swaf…4.img to waf…99.img
5. Single subject stats
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Data and Design  ‘New Subject/Session’
Scans: start at 4 to avoid artefacts .. (you should always discard
initial scans) = 96 files
Conditions  New condition
 Name: ‘activation’
 Onsets: 6:12:84 (= every 12 scans from 6 to 84)
 Durations: 6
 Multiple regressors: select the realignment
parameters (.txt file)
 The
rest is implicitly modelled (goes into the mean)
5. Single subject stats
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Activations
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Movements
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Mean
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Model summary
5. Single subject stats
5. Single subject stats
5. Single subject stats
5. Single subject stats
5. Single subject stats
5. Single subject stats
5. Single subject stats
5. Single subject stats
Contrast estimate
(effect size)
Fitted response
(model or Y-error)
5. Single subject stats
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SPM outputs
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beta000X.img /hdr: betas values in each voxel
con000X.img /hdr: contrast (combination of betas)
spmT000X.ing /hdr: statistics image
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Also
mask.img / hdr: area analyzed by SPM (binary image)
RessMS: residual mean square
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RPV: Resels-Per-Voxel image; image of roughness
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Multiple comparisons
correction: theory
What Problem?
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4-Dimensional Data
 1,000 multivariate observations,
each with 100,000 elements
 100,000 time series, each
with 1,000 observations
Massively Univariate
Approach
1
 100,000 hypothesis
tests
Massive MCP!
1,000
3
2
Solutions for MCP
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Height Threshold
Familywise Error Rate (FWER)
 Chance of any false positives; Controlled by Bonferroni &
Random Field Methods
False Discovery Rate (FDR)
 Proportion of false positives among rejected tests
Set level statistic
Bayes Statistics
From single univariate to
massive univariate
Univariate stat
Functional neuroimaging
1 observed data
Many voxels
1 statistical value
Family of statistical values
Type 1 error rate (chance to
be wrong rejecting H0)
Null hypothesis
Family-wise error rate
Family-wise null hypothesis
Height Threshold
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Choose locations where a test statistic Z (T, 2, ...) is large to
threshold the image of Z at a height z
The problem is how to choose this threshold z to exclude false
positives with a high probability (e.g. 0.95)?
To control for family
wise error on must
take into account the
nb of tests
Bonferroni
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10000 Z-scores ; alpha = 5%
alpha corrected = .000005 ; z-score = 4.42
100 voxels
100 voxels
Bonferroni
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10000 Z-scores ; alpha = 5%
2D homogeneous smoothing – 100 independent observations
alpha corrected = .0005 ; z-score = 3.29
100 voxels
100 voxels
Random Field Theory
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10000 Z-scores ; alpha = 5%
Gaussian kernel smoothing –
How many independent observations ?
100 voxels
100 voxels
Random Field Theory
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RFT relies on theoretical results for smooth statistical maps
(hence the need for smoothing), allowing to find a threshold in a
set of data where it’s not easy to find the number of
independent variables. Uses the expected Euler characteristic
(EC density)
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1 Estimation of the smoothness = number of resel (resolution
element) = f(nb voxels, FWHM)
2 ! expected Euler characteristic = number of clusters above the
threshold
3 Calculation of the threshold
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Random Field Theory
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The Euler characterisitc can be seen as the number of blobs in
an image after thresholding
At high threshold, EC = 0 or 1 per resel: E[EC]  pFWE
2
E[EC] = R · (4 loge 2) · (2)−2/3 · Zt · e−1/2 Z t for a 2D image, more complicated in 3D
Random Field Theory
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For 100 resels, the equation gives E[EC] = 0.049 for a threshold
Z of 3.8, i.e. the probability of getting one or more blobs where
Z is greater than 3.8 is 0.049
100 voxels
100 voxels
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If the resel size is much larger than the voxel size then E[EC]
only depends on the nb of resels othersize it also depends on the
volume, surface and diameter of the search area (i.e. shape and
volume matter)
False discovery Rate
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Whereas family wise approach corrects for any false positive, the
FDR approach aim at correcting among positive results only.
1. Run an analysis with alpha = x%
2. Sort the resulting data
3. Threshold the resulting data to remove the false positives
(theoretical problem: threshold any voxels whatever their spatial
positions)
False discovery Rate
Signal+Noise
FEW correction
FDR correction
Levels of inference:
theory
Levels of inference
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-
3 levels of inference can be considered:
Voxel level (prob associated at each voxel)
Cluster level (prob associated to a set of voxels)
Set level (prob associated to a set of clusters)
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The 3 levels are nested and based on a single probability of
obtaining c or more clusters (set level) with k or more voxels
(cluster level) above a threshold u (voxel level): Pw(u,k,c)
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Both voxel and cluster levels need to address the multiple
comparison problem. If the activated region is predicted in
advance, the use of corrected P values is unnecessary and
inappropriately conservative – a correction for the number of
predicted regions (Bonferroni) is enough
Levels of inference
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Set level: we can reject H0 for an omnibus
test, i.e. there are some significant clusters of
activation in the brain.
Cluster level: we can reject H0 for an
area of a size k, i.e. a cluster of
‘activated’ voxels is likely to be true for a
given spatial extend.
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Voxel level: we can reject H0 at each
voxel, i.e. a voxel is ‘activated’ if
exceeding a given threshold
Inference in practice
Levels of inference and MCC
From single subjects to
random effects: theory
From single subjects to random effects
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Why random effect (also called pseudo-mixed)?
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Basic stats: compute the mean for each condition for each
subjects and do the stats on these means (inter-subject variance)
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Random effect: compute the beta parameters for each condition
(intra-subject variance) and do the stats on these beta parameter
(inter-subject variance)
From single subjects to
random effects: practice
Face data set
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Repetition priming experiment performed using event-related
fMRI
2x2 factorial study with factors `fame‘ and `repetition' where
famous and non-famous faces were presented twice against a
checkerboard baseline
Four event-types of interest; first and second presentations of
famous and non-famous faces, which are denoted N1, N2, F1
and F2
TR=2s
TA = 1.92s
24 descending slices (64x64 3x3mm2)
3mm thick with a 1.5mm gap
Single subject modelling
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Here we consider a more sophisticated model in which we use
the hrf and its derivatives.
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In the folder, spm_data_set\Face_data_event_related_design\
single_subject\Preprocessed, you can find the smoothed,
normalized, realigned, and slice timed images
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Try to model by yourself
Single subject modelling
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Stimulus Onsets Times
sots.mat
Cond 1 to 4 are F1, F2,
N1, N2 with the
respective onset time
sot{1} sot{2} sor{3}
and sot{4}
Single subject modelling
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Directory  stats
Timing parameters: Units (sec), Interscan interval (2),
Microtime resolution (24), Microtime onset (12)
Data and design  ‘New Subject/Session’: Scan (all EPI
swa…img), Condition  create 4 conditions, each time enter
name and sot (F1 sot{1} F2 sot {2} N1 sot{3} N2 sot{4}),
Multiple regressors: enter the .txt file from realignment
Factorial Design  create 2 factors (famous, level =2, and
repetition, level =2)
Basis function: Canonical HRF, model derivatives (time and
dispersion)
Single subject modelling
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Result  select the SPM.mat
Because we specified the factorial design, the variance has been
partitioned in a specific way and contrasts are already there
Select contrast nb 5
Single subject modelling
Single subject modelling
Single subject modelling
Define a new F contrast called ‘effect of interest’
(any effect of any regressor and combinations)  ok  done
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Single subject modelling
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Plot  contrast estimates and 90% CI  select effect of
interest
F1
F2
N1
N2
Single subject modelling
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Plot event-related response / fitted response
Single subject modelling
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In the matlab workspace
Y = fitted response, y = adjusted response
Exercise: plot for 4 fitted responses on 1 figure
Each time save using e.g. N1 = Y; then N2 = Y; .. then plot(N1);
hold on; plot(N2,’--’) …
Multi-subjects
All data were analyzed and are stored in the folder
Face_data_event_related_design\multisubjects\cons_informed
We have all the con images from 12 subjects, data were modelled
with Famous and Non famous faces (2 conditions only) with the hrf
and the two derivatives (12*6 files)
Random effects
Random effects
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Analyze Faces vs baseline
For each subject we have the con images for the hrf, the 1st
derivative and 2nd derivative
A full analysis will thus be a repeated measure ANOVA (we have
3 measures per subject)
SPM allows to correct for non-sphericity, i.e. it will take into
account the correlation between regressors – here regressors are
by inception correlated
Random effects







Design  Full factorial 
create a ‘new factor’
Name: Basis functions
Levels: 3
Independence: No
Specify cells  create 3 cells, 1
per level of the factor (cell 1 =
con 3 to 14 / cell 2 = 15 to 26
/ cell 3 = 27 to 38)
Run
Estimate
Random effects

In the contrast
manager, enter a
new F contrast:
effect of interest
(eye(3))

Evaluate
and
look at the result
with a correction
FWE @ .05
Random effects
Random effects

In the contrast
manager, enter a
new T contrast
for the hrf only:
100

Evaluate
and
look at the result
with a correction
FEW @ .05
Random effects
Select an image to
display (e.g. SPM
/canonical/T1)
Add blobs  select
the SPM.mat and
the 2 contrasts
Random effects



The display is
‘surfable’
MNI and voxel
coordinates available
Voxel value
}
Random effects
F Contrast [0 1 0]
 Time dispersion
(+/- 2 sec)

Earlier
response
(- = delayed)
F Contrast [0 0 1 ]
 Duration dispersion

Narrower
response
( - = wider)
Random effect

Paired t-test

12 pairs
3 vs 39
4 vs 40
…
Independence: No
Variance: equal





Visualizing the data
SPM and other software
Rendering with SPM
Segmentation
Segmentation

Input  coregistered image
in \spm_data_set\
Face_data_event_related_de
sign\single_subject\Structur
al  rs …img

Output
grey / white matter tissues
bias corrected image



affine normalization
parameters (and inverse)
Rendering with SPM
Select the grey and
white matter (c1 and
c2) images and save
the rendering
Rendering with SPM
Select the SPM.mat
from the individual
subject and then 2
sets / contrasts
Rendering in SPM

For random effects analysis you can use the render in
SPM/render which is in the MNI space

For individual subjects, best is to not normalize and make a
render .. Results are better with smaller voxel size (i.e. you could
interpolate the data @ 1mm3)

Overall, better to use slices / section – poor rendering
capabilities although new plug in are appearing ..
Surface visualization
with Caret
What does it do?

Surface visualization


Display experimental data (activation maps, connectivity patterns, etc)
View data in flexible combinations (cortical surface, flat maps, contours,
outlines, etc)

Surface manipulation and data analysis


generate inflated maps, spherical maps, and flat maps
probabilistic maps, surface based analysis

On-line search for fMRI maps, comparisons, etc ..
The Software





http://brainmap.wustl.edu/caret
my experience is that it works better with linux / mac but windows
does the job
From David Van Essen lab – Washingtom University in St Louis –
School of Medicine – Dpt of Anatomy & Neurobiology
Most famous paper from this guy?
Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical
processing in primate visual cortex, Cerebral Cortex, 1: 1-47.
Caret
File  Open Spec File …
 In Caret\
HUMAN.COLIN.ATLAS\
LEFT_HEM\ …spec

Caret
Caret
Caret

Attributes  Map
Volume(s) to Surface(s)
…  leave the Metric
option ticked and press
next
Caret

Add Volumes From
Disk  select the
spmT_0005.hdr file
from the single subject
analysis (Face data set)
Caret

Map to Caret
Caret

Chose your algorithm
 Average Voxel 
Neighbor Box Size 1 
Next ..
Caret
Caret
Caret
Individual visualization
with Anatomist
Brain Visa / Anatomist

Caret as well as BrainVisa / Anatomist works better with clean
data …
1 st – data are usual better handled with isotropic voxels (same
dimensions in x, y, z)  better acquire isotropic voxels, at least
for the T1
2 nd – there is usual a bias in one direction (often Z), i.e. for a
given concentration of tissue, the gray scale is slightly different
Solution: correct and resample

My favourite tools for this: FSL



Quick Tour into FSL



Depending on what you want to do
ApplyXFM  reinterpolation using the identity matrix
FSL  brings the interface  BET / SUSAN / FSL View
Back to BrainVisa / Anatomist
Back to Anatomist
Anatomist
Resources

IdoImaging: http://idoimaging.com/index.shtml

SPM: http://www.fil.ion.ucl.ac.uk/spm/
Caret: http://brainvis.wustl.edu/wiki/index.php/Caret:About
FSL: http://www.fmrib.ox.ac.uk/fsl/
BrainVisa / Anatomist: http://brainvisa.info/




Cambridge imaging wiki: http://imaging.mrccbu.cam.ac.uk/imaging/CbuImaging

Russ Poldracks’wiki on Matlab:
http://www.poldracklab.org/teaching/psych254

Digital signal processing in general:
http://www.dspguide.com/pdfbook.htm

Maths in general: http://mathworld.wolfram.com/