Concepts of SPM data analysis Marieke Schölvinck

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 ‘spm fmri’ Preprocessing Analysis Extra functions

5. Preprocessing

6. 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

7. 1. Realignment EPI (functional) images

8. 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

9. 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.

10. 3. Slice timing • Each slice is typically acquired every 3 mm, requiring ~32 slices to cover cortex • Each slice takes about ~60ms to acquire… • …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

11. 4. Normalisation MNI template brain

12. Rotation Shear Translation Zoom 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

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

14. 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 5. Smoothing MNI template brain

15. Analysis

16. 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

17. Model • How well does the model fit the data? voxel timeseries model with 2 conditions

18. Design Matrix: several models at once 1>2 2>1 other parameters (motion)

19. Contrasts • 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 bothconditions significantly different from baseline? 1 -1 -1 1

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!)

23. The End