Preprocessing of FMRI Data f MRI Graduate Course

Preprocessing of FMRI Data f. MRI Graduate Course October 23, 2002

What is preprocessing? • Correcting for non-task-related variability in experimental data – Usually done without consideration of experimental design; thus, pre-analysis – Occasionally called post-processing, in reference to being after acquisition • Attempts to remove, rather than model, data variability

Signal, noise, and the General Linear Model Amplitude (solve for) Measured Data Noise Design Model Cf. Boynton et al. , 1996

Signal-Noise-Ratio (SNR) Task-Related Variability Non-task-related Variability

Preprocessing Steps • • Slice Timing Correction Motion Correction Coregistration Normalization Spatial Smoothing Segmentation Region of Interest Identification

Tools for Preprocessing • • • SPM Brain Voyager Vox. Bo AFNI Custom BIAC scripts (Favorini, Mc. Keown)

Slice Timing Correction

Why do we correct for slice timing? • Corrects for differences in acquisition time within a TR – Especially important for long TRs (where expected HDR amplitude may vary significantly) – Accuracy of interpolation also decreases with increasing TR • When should it be done? – Before motion correction: interpolates data from (potentially) different voxels • Better for interleaved acquisition – After motion correction: changes in slice of voxels results in changes in time within TR • Better for sequential acquisition

Effects of uncorrected slice timing • • Base Hemodynamic Response Base HDR + Noise Base HDR + Slice Timing Errors Base HDR + Noise + Slice Timing Errors

Base HDR: 2 s TR

Base HDR + Noise r = 0. 77 r = 0. 81 r = 0. 80

Base HDR + Slice Timing Errors r = 0. 92 r = 0. 85 r = 0. 62

HDR + Noise + Slice Timing r = 0. 65 r = 0. 67 r = 0. 19

Interpolation Strategies • Linear interpolation • Spline interpolation • Sinc interpolation

Motion Correction

Head Motion: Good and Bad

Correcting Head Motion • Rigid body transformation – 6 parameters: 3 translation, 3 rotation • Minimization of some cost function – E. g. , sum of squared differences

Simulated Head Motion

Severe Head Motion: Simulation Two 4 s movements of 8 mm in -Y direction (during task epochs) Motion

Severe Head Motion: Real Data Two 4 s movements of 8 mm in –Y direction (during task epochs) Motion

Effects of Head Motion Correction

Limitations of Motion Correction • Artifact-related limitations – Loss of data at edges of imaging volume – Ghosts in image do not change in same manner as real data • Distortions in f. MRI images – Distortions may be dependent on position in field, not position in head • Intrinsic problems with correction of both slice timing and head motion

Coregistration

Should you Coregister? • Advantages – – Aids in normalization Allows display of activation on anatomical images Allows comparison across modalities Necessary if no coplanar anatomical images • Disadvantages – May severely distort functional data – May reduce correspondence between functional and anatomical images

Normalization

Standardized Spaces • Talairach space (proportional grid system) – – From atlas of Talairach and Tournoux (1988) Based on single subject (60 y, Female, Cadaver) Single hemisphere Related to Brodmann coordinates • Montreal Neurological Institute (MNI) space – Combination of many MRI scans on normal controls • All right-handed subjects – Approximated to Talaraich space • Slightly larger • Taller from AC to top by 5 mm; deeper from AC to bottom by 10 mm – Used by SPM, National f. MRI Database, International Consortium for Brain Mapping

Normalization to Template Normalization Template Normalized Data

Anterior and Posterior Commissures Anterior Commissure Posterior Commissure

Should you normalize? • Advantages – – Allows generalization of results to larger population Improves comparison with other studies Provides coordinate space for reporting results Enables averaging across subjects • Disadvantages – Reduces spatial resolution – May reduce activation strength by subject averaging – Time consuming, potentially problematic • Doing bad normalization is much worse than not normalizing

Slice-Based Normalization Before Adjustment (15 Subjects) After Adjustment to Reference Image Registration courtesy Dr. Martin Mc. Keown (BIAC)

Spatial Smoothing

Techniques for Smoothing • Application of Gaussian kernel – Usually expressed in #mm FWHM – “Full Width – Half Maximum” – Typically ~2 times voxel size

Effects of Smoothing on Activity Unsmoothed Data Smoothed Data (kernel width 5 voxels)

Should you spatially smooth? • Advantages – Increases Signal to Noise Ratio (SNR) • Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal – Reduces number of comparisons • Allows application of Gaussian Field Theory – May improve comparisons across subjects • Signal may be spread widely across cortex, due to intersubject variability • Disadvantages – Reduces spatial resolution – Challenging to smooth accurately if size/shape of signal is not known

Segmentation • Classifies voxels within an image into different anatomical divisions – Gray Matter – White Matter – Cerebro-spinal Fluid (CSF) Image courtesy J. Bizzell & A. Belger

Histogram of Voxel Intensities

Region of Interest Drawing

Why use an ROI-based approach? • Allows direct, unbiased measurement of activity in an anatomical region – Assumes functional divisions tend to follow anatomical divisions • Improves ability to identify topographic changes – Motor mapping (central sulcus) – Social perception mapping (superior temporal sulcus) • Complements voxel-based analyses

Drawing ROIs • Drawing Tools – BIAC software (e. g. , Overlay 2) – Analyze – IRIS/SNAP (G. Gerig) • Reference Works – Print atlases – Online atlases • Analysis Tools – roi_analysis_script. m

ROI Examples

BIAC is studying biological motion and social perception – here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.

Additional Resources • SPM website – Course Notes • http: //www. fil. ion. ucl. ac. uk/spm/course/notes 01. ht ml – Instructions • Brain viewers – http: //www. bic. mni. mcgill. ca/cgi/icbm_view/