fMRI Preprocessing Essentials: Motion, Slice Timing, and Spatial Normalization

1. Basics of fMRI Preprocessing Douglas N. Grevegreve@nmr.mgh.harvard.edu

2. fMRI Analysis Overview Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization X X X X X C C C C C Subject 1 First Level GLM Analysis Raw Data Subject 2 First Level GLM Analysis Raw Data Higher Level GLM Subject 3 First Level GLM Analysis Raw Data Subject 4 First Level GLM Analysis Raw Data

3. Preprocessing • Assures that assumptions of the analysis are met • Time course comes from a single location • Uniformly spaced in time • Spatial “smoothness” • Analysis – separating signal from noise

4. Input 4D Volume 64x64x35 85x1

5. Preprocessing • Start with a 4D data set • Motion Correction • Slice-Timing Correction • B0 Distortion Correction • Spatial Normalization • Spatial Smoothing • End with a 4D data set • Can be done in other orders • Not everything is always done • Note absence of temporal filtering

6. Motion • Analysis assumes that time course represents a value from a single location • Subjects move • Shifts can cause noise, uncertainty • Edge of the brain and tissue boundaries

7. Motion • Sometimes effect on signal is positive, sometimes negative

8. One Effect of Uncorrected Motion on fMRI Analysis • Ring-around-the-brain From Huettel, Song, McCarthy

9. Motion Correction Template Target Reference Input Time Point Difference (Error) • Adjust translation and rotation of input time point to reduce absolute difference. • Requires spatial interpolation

10. Motion Correction Raw Corrected • Motion correction reduces motion • Not perfect

11. Motion Correction • Motion correction parameters • Six for each time point • Sometimes used as nuisance regressors • How much motion is too much?

12. Slice Timing Ascending Interleaved • Volume not acquired all at one time • Acquired slice-by-slice • Each slice has a different delay

13. Effect of Slice Delay on Time Course • Volume = 30 slices • TR = 2 sec • Time for each slice = 2/30 = 66.7 ms 2s 0s

14. Slice Timing Correction Slice TR1 TR2 TR3 X • Temporal interpolation of adjacent time points • Usually sinc interpolation • Each slice gets a different interpolation • Some slices might not have any interpolation • Can also be done in the GLM • You must know the slice order!

15. Motion, Slice Timing, Spin History • Every other slice is bright • Interleaved slice acquisition • Happens with sequential, harder to see

16. Motion, Slice Timing, Spin History Slice TR1 TR2 TR3 • In TR3 subject moved head up • Red is now the first slice • Pink is now not in the FoV • Red has not seen an RF pulse before (TR=infinite) and so will be very bright • Other slices have to wait longer to see an RF pulse, ie, the TR increases slightly, causes brightening. • Direction of intensity change depends on a lot of things

17. B0 Distortion Stretch Dropout • Metric (stretching or compressing) • Intensity Dropout • A result of a long readout needed to get an entire slice in a single shot. • Caused by B0 Inhomogeneity

18. B0 Map Phase Magnitude Voxel Shift Map Echo 1 TE1 Echo 2 TE2

19. Voxel Shift Map • Units are voxels (3.5mm) • Shift is in-plane • Blue = PA, Red AP • Regions affected near air/tissue boundaries • sinuses

20. B0 Distortion Correction • Can only fix metric distortion • Dropout is lost forever

21. B0 Distortion Correction • Can only fix metric distortion • Dropout is lost forever • Interpolation • Need: • “Echo spacing” – readout time • Phase encode direction • More important for surface than for volume • Important when combining from different scanners

22. Spatial Normalization • Transform volume into another volume • Re-slicing, re-gridding • New volume is an “atlas” space • Align brains of different subjects so that a given voxel represents the “same” location. • Similar to motion correction • Preparation for comparing across subjects • Volume-based • Surface-based • Combined Volume-surface-based (CVS)

23. Spatial Normalization Native Space MNI305 Space Subject 1 Subject 1 MNI305 MNI152 Subject 2 Subject 2 Affine (12 DOF) Registration

24. Problems with Affine (12 DOF) Registration Subject 2 aligned with Subject 1 (Subject 1’s Surface) Subject 1

25. Surface Registration Subject 1 Subject 2 (Before) Subject 2 (After)

26. Surface Registration Subject 1 Subject 2 (Before) Subject 2 (After) • Shift, Rotate, Stretch • High dimensional (~500k) • Preserve metric properties • Take variance into account • Common space for group analysis (like Talairach)

27. Spatial Smoothing • Replace voxel value with a weighted average of nearby voxels (spatial convolution) • Weighting is usually Gaussian • 3D (volume) • 2D (surface) • Do after all interpolation, before computing a standard deviation • Similarity to interpolation • Improve SNR • Improve Intersubject registration • Can have a dramatic effect on your results

28. Full Max 2mm FWHM Half Max 5mm FWHM 10mm FWHM Spatial Smoothing • Spatially convolve image with Gaussian kernel. • Kernel sums to 1 • Full-Width/Half-max:FWHM = s/sqrt(log(256)) • s = standard deviation of the Gaussian Full-Width/Half-max 0 FWHM 5 FWHM 10 FWHM

29. Spatial Smoothing

30. Effect of Smoothing on Activation • Working memory paradigm • FWHM: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20

31. Volume- vs Surface-based Smoothing 14mm FWHM • 5 mm apart in 3D • 25 mm apart on surface • Averaging with other tissue types (WM, CSF) • Averaging with other functional areas

32. Choosing the FWHM • No hard and fast rules • Matched filter theorem – set equal to activation size • How big is that? • Changes with brain location • Changes with contrast • May change with subject, population, etc • Two voxels – to meet the assumptions of Gaussian Random Fields (GRF) for correction of multiple comparisons

33. Temporal filtering • Replace value at a given time point with a weighted average of its neighbors • Should/Is NOT be done as a preprocessing step – contrary to what many people think. • Belongs in analysis.

34. fMRI Analysis Overview Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization Preprocessing MC, STC, B0 Smoothing Normalization X X X X X C C C C C Subject 1 First Level GLM Analysis Raw Data Subject 2 First Level GLM Analysis Raw Data Higher Level GLM Subject 3 First Level GLM Analysis Raw Data Subject 4 First Level GLM Analysis Raw Data

35. Preprocessing • Start with a 4D data set • Motion Correction - Interpolation • Slice-Timing Correction • B0 Distortion Correction - Interpolation • Spatial Normalization - Interpolation • Spatial Smoothing – Interpolation-like • End with a 4D data set • Can be done in other orders • Note absence of temporal filtering