Concepts of SPM data analysis

Marieke Schölvinck

EPI

structural

Basic ideaBasic ideaBasic ideaBasic 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 interfaceSPM user interfaceSPM user interfaceSPM user interface

Preprocessing

Analysis

Extra functions

‘spm fmri’

PreprocessingPreprocessingPreprocessingPreprocessing

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: 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

PreprocessingPreprocessingPreprocessingPreprocessing

(making sure that all images look the same)

1. Realignment1. Realignment1. Realignment1. Realignment

EPI (functional) images

1. Realignment1. Realignment1. Realignment1. Realignment

• Subjects will always move in the scanner…Subjects will always move in the scanner…

• … … therefore the same voxel in the first image will be therefore the same voxel in the first image will be in a different place in the last image!in a different place in the last image!

• Correct by estimating movement and reorienting Correct by estimating movement and reorienting images accordinglyimages accordingly

• Subjects will always move in the scanner…Subjects will always move in the scanner…

• … … therefore the same voxel in the first image will be therefore the same voxel in the first image will be in a different place in the last image!in a different place in the last image!

• Correct by estimating movement and reorienting Correct by estimating movement and reorienting images accordinglyimages accordingly

• Realignment involves two stages:Realignment involves two stages:

– 1. Registration1. Registration - - estimateestimate the 6 movement parameters that describe the the 6 movement parameters that describe the transformation between each image and a reference image (usually the first transformation between each image and a reference image (usually the first scan)scan)

– 2. Reslicing 2. Reslicing - - re-samplere-sample each image according to the determined each image according to the determined transformation parameterstransformation parameters

• Realignment involves two stages:Realignment involves two stages:

– 1. Registration1. Registration - - estimateestimate the 6 movement parameters that describe the the 6 movement parameters that describe the transformation between each image and a reference image (usually the first transformation between each image and a reference image (usually the first scan)scan)

– 2. Reslicing 2. Reslicing - - re-samplere-sample each image according to the determined each image according to the determined transformation parameterstransformation parameters

• It’s useful to display functional results (EPI) onto high resolution It’s useful to display functional results (EPI) onto high resolution structural image (T1)…structural image (T1)…

• Therefore ‘warp’ functional images into the shape of the structural Therefore ‘warp’ functional images into the shape of the structural image. image.

• It’s useful to display functional results (EPI) onto high resolution It’s useful to display functional results (EPI) onto high resolution structural image (T1)…structural image (T1)…

• Therefore ‘warp’ functional images into the shape of the structural Therefore ‘warp’ functional images into the shape of the structural image. image.

2. Coregistration2. Coregistration2. Coregistration2. Coregistration

• Each slice is typically acquired Each slice is typically acquired every 3 mm, requiring ~32 slices every 3 mm, requiring ~32 slices to cover cortexto cover cortex

• Each slice takes about ~60ms to Each slice takes about ~60ms to acquire…acquire…

• ……entailing a typical TR for entailing a typical TR for whole volume of 2-3s whole volume of 2-3s

2-3s between sampling the 2-3s between sampling the BOLD response in the first slice BOLD response in the first slice and the last slice and the last slice

• Each slice is typically acquired Each slice is typically acquired every 3 mm, requiring ~32 slices every 3 mm, requiring ~32 slices to cover cortexto cover cortex

• Each slice takes about ~60ms to Each slice takes about ~60ms to acquire…acquire…

• ……entailing a typical TR for entailing a typical TR for whole volume of 2-3s whole volume of 2-3s

2-3s between sampling the 2-3s between sampling the BOLD response in the first slice BOLD response in the first slice and the last slice and the last slice

3. Slice timing3. Slice timing3. Slice timing3. Slice timing

Slic

e no

0 1 2 3 4 5 6 7 8 9

2468

10

0 1 2 3 4 5 6 7 8 9

-2

0

2

Slic

e 1

0 1 2 3 4 5 6 7 8 9

-2

0

2

Slic

e 5

0 1 2 3 4 5 6 7 8 9

-2

0

2

Slic

e 5

0 1 2 3 4 5 6 7 8 9

-2

0

2S

lice

5

Time (in TRs)

MNI template brain

4. Normalisation4. Normalisation4. Normalisation4. Normalisation

• Inter-subject averagingInter-subject averaging– extrapolate findings to the population as a wholeextrapolate findings to the population as a whole

– increase statistical power increase statistical power

• Reporting of activations as co-ordinates within a Reporting of activations as co-ordinates within a standard stereotactic spacestandard stereotactic space

– e.g. e.g. Talairach & Tournoux, MNITalairach & Tournoux, MNI

• Inter-subject averagingInter-subject averaging– extrapolate findings to the population as a wholeextrapolate findings to the population as a whole

– increase statistical power increase statistical power

• Reporting of activations as co-ordinates within a Reporting of activations as co-ordinates within a standard stereotactic spacestandard stereotactic space

– e.g. e.g. Talairach & Tournoux, MNITalairach & Tournoux, MNI

• You do it by a 12 parameter transformation:You do it by a 12 parameter transformation:

– 3 translations3 translations– 3 rotations3 rotations– 3 zooms3 zooms– 3 shears3 shears

Rotation

Translation Zoom

Shear

4. Normalisation4. Normalisation4. Normalisation4. Normalisation

• Potentially increase signal to noisePotentially increase signal to noise

• Use a ‘kernel’ defined in terms of FWHM (full width at Use a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mmhalf maximum) - usually ~6-8mm

• Potentially increase signal to noisePotentially increase signal to noise

• Use a ‘kernel’ defined in terms of FWHM (full width at Use a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mmhalf maximum) - usually ~6-8mm

Gaussian smoothing kernel

FWHM

5. Smoothing5. Smoothing5. Smoothing5. Smoothing

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: 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

Wrapping up: preprocessingWrapping up: preprocessingWrapping up: preprocessingWrapping up: preprocessing

MNI template brain

AnalysisAnalysisAnalysisAnalysis

AnalysisAnalysisAnalysisAnalysis

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

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

(fitting model to data and seeing whether this fit is statistically significant)

ModelModelModelModel

• How well does the model fit the data?How well does the model fit the data?• How well does the model fit the data?How well does the model fit the data?

voxel timeseries

model with 2 conditions

Design Matrix: several models at onceDesign Matrix: several models at onceDesign Matrix: several models at onceDesign Matrix: several models at once

1 > 2 2 > 1 other parameters (motion)

ContrastsContrastsContrastsContrasts

• T contrast: are the values for T contrast: are the values for condition 1condition 1 in this voxel significantly higher in this voxel significantly higher than the values during than the values during condition 2condition 2? ?

• F contrast: are the values for F contrast: are the values for bbootthh ccoonnddiittiioonnss significantly different from significantly different from baseline? baseline?

• T contrast: are the values for T contrast: are the values for condition 1condition 1 in this voxel significantly higher in this voxel significantly higher than the values during than the values during condition 2condition 2? ?

• F contrast: are the values for F contrast: are the values for bbootthh ccoonnddiittiioonnss significantly different from significantly different from baseline? baseline?

1 -1

-1 1

Test every model for every voxelTest every model for every voxelTest every model for every voxelTest 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…A word on multiple comparisons…A word on multiple comparisons…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!)

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