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Concepts of SPM data analysis Marieke Schölvinck.

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1 Concepts of SPM data analysis Marieke Schölvinck

2 EPI structural

3 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

4 SPM user interface Preprocessing Analysis Extra functions ‘spm fmri’

5 PreprocessingPreprocessing

6 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 PreprocessingPreprocessing (making sure that all images look the same)

7 1. Realignment EPI (functional) images

8 1. 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 in a different place in the last image!… 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 accordinglyCorrect by estimating movement and reorienting images 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 in a different place in the last image!… 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 accordinglyCorrect by estimating movement and reorienting images accordingly Realignment involves two stages: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 Realignment involves two stages: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

9 It’s useful to display functional results (EPI) onto high resolution structural image (T1)…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.Therefore ‘warp’ functional images into the shape of the structural image. It’s useful to display functional results (EPI) onto high resolution structural image (T1)…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.Therefore ‘warp’ functional images into the shape of the structural image. 2. Coregistration

10 Each slice is typically acquired every 3 mm, requiring ~32 slices to cover cortexEach slice is typically acquired every 3 mm, requiring ~32 slices to cover cortex Each slice takes about ~60ms to acquire…Each slice takes about ~60ms to acquire… …entailing a typical TR for whole volume of 2-3s…entailing a typical TR for whole volume of 2-3s  2-3s between sampling the BOLD response in the first slice and the last slice Each slice is typically acquired every 3 mm, requiring ~32 slices to cover cortexEach slice is typically acquired every 3 mm, requiring ~32 slices to cover cortex Each slice takes about ~60ms to acquire…Each slice takes about ~60ms to acquire… …entailing a typical TR for whole volume of 2-3s…entailing a typical TR for whole volume of 2-3s  2-3s between sampling the BOLD response in the first slice and the last slice 3. Slice timing

11 MNI template brain 4. Normalisation

12 Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase statistical power Reporting of activations as co-ordinates within a standard stereotactic spaceReporting of activations as co-ordinates within a standard stereotactic space –e.g. Talairach & Tournoux, MNI Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase statistical power Reporting of activations as co-ordinates within a standard stereotactic spaceReporting of activations as co-ordinates within a standard stereotactic space –e.g. Talairach & Tournoux, MNI You do it by a 12 parameter transformation:You do it by a 12 parameter transformation: –3 translations –3 rotations –3 zooms –3 shears You do it by a 12 parameter transformation:You do it by a 12 parameter transformation: –3 translations –3 rotations –3 zooms –3 shears Rotation TranslationZoom Shear 4. Normalisation

13 Potentially increase signal to noisePotentially increase signal to noise Use a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mmUse a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mm Potentially increase signal to noisePotentially increase signal to noise Use a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mmUse a ‘kernel’ defined in terms of FWHM (full width at half maximum) - usually ~6-8mm Gaussian smoothing kernel FWHM 5. Smoothing

14 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: preprocessing MNI template brain

15 AnalysisAnalysis

16 AnalysisAnalysis 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)

17 ModelModel How well does the model fit the data?How well does the model fit the data? voxel timeseries model with 2 conditions

18 Design Matrix: several models at once 1 > 21 > 22 > 12 > 1other parameters (motion)

19 ContrastsContrasts T contrast: are the values for condition 1 in this voxel significantly higher than the values during condition 2?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?F contrast: are the values for both conditions significantly different from baseline? T contrast: are the values for condition 1 in this voxel significantly higher than the values during condition 2?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?F contrast: are the values for both conditions significantly different from baseline?

20 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’

21 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!)

22

23 The End


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