concepts of SPM

Report
Concepts of SPM data analysis
Marieke Schölvinck
EPI
structural
Basic idea
Make sure all images look the same
Make model of what you think brain activity in your
experiment should look like…
And fit this model to the data; see whether this fit is
statistically significant
… within a single subject, and then over the whole group
SPM user interface
‘spm fmri’
Preprocessing
Analysis
Extra functions
Preprocessing
Preprocessing
(making sure that all images look the same)
1. Realignment: align scans to each other
2. Coregistration: align scans to structural scan
3. Slice timing: make up for differences in
acquisition time
4. Normalisation: to a standard brain
5. Smoothing
1. Realignment
EPI (functional) images
1. Realignment
• Subjects will always move in the scanner…
• … therefore the same voxel in the first image will be
in a different place in the last image!
• Correct by estimating movement and reorienting
images accordingly
• Realignment involves two stages:
– 1. Registration - estimate the 6 movement parameters that describe the
transformation between each image and a reference image (usually the first
scan)
– 2. Reslicing - re-sample each image according to the determined
transformation parameters
2. Coregistration
• It’s useful to display functional results (EPI) onto high resolution
structural image (T1)…
• Therefore ‘warp’ functional images into the shape of the structural
image.
 2-3s between sampling the
BOLD response in the first slice
and the last slice
Slice no
…entailing a typical TR for
whole volume of 2-3s
Slice 1
•
Each slice takes about ~60ms to
acquire…
Slice 5
•
Each slice is typically acquired
every 3 mm, requiring ~32 slices
to cover cortex
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3. Slice timing
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4. Normalisation
MNI template brain
4. Normalisation
• Inter-subject averaging
– extrapolate findings to the population as a whole
– increase statistical power
• Reporting of activations as co-ordinates within a
standard stereotactic space
– e.g. Talairach & Tournoux, MNI
• You do it by a 12 parameter transformation:
–
–
–
–
3 translations
3 rotations
3 zooms
3 shears
Rotation
Shear
Translation
Zoom
5. Smoothing
• Potentially increase signal to noise
• Use a ‘kernel’ defined in terms of FWHM (full width at
half maximum) - usually ~6-8mm
FWHM
Gaussian smoothing kernel
Wrapping up: preprocessing
1. Realignment: align scans to each other
2. Coregistration: align scans to structural scan
3. Slice timing: make up for differences in acquisition time
4. Normalisation: to a standard brain
MNI template brain
5. Smoothing
Analysis
Analysis
(fitting model to data and seeing whether this fit is statistically significant)
SOME TERMS
• SPM is a massively univariate approach - meaning
that the timecourse for every voxel is analysed
separately
• The experiment is specified in a model called a
design matrix. This model is fit to each voxel to
see how well it agrees with the data
• Hypotheses (contrasts) are tested to make
statistical statements (p-values), using the General
Linear Model
Model
voxel timeseries
model with 2 conditions
• How well does the model fit the data?
Design Matrix: several models at once
1>2
2>1
other parameters (motion)
Contrasts
1 -1
-1 1
• T contrast: are the values for condition 1 in this voxel significantly higher than
the values during condition 2?
• F contrast: are the values for both conditions significantly different from
baseline?
Test every model for every voxel
‘1 -1’
‘give me all the voxels for which this model (condition 1 makes the voxel more
active than condition 2) fits the data significantly’
A word on multiple comparisons…
Because you’re looking at thousands of voxels, some will
give a positive result just by chance. You need to correct for
this ‘multiple comparison’ problem using one of several
options in SPM:
FWE (family-wise error), FDR (false discovery rate), or
uncorrected
(and say which one you used!)
The End

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