Keith Worsley Department of Mathematics and Statistics, and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Correlation random fields, brain connectivity, and cosmology

Savic et al. (2005). Brain response to putative pheromones in homosexual men. Proceedings of the National Academy of Sciences, 102:

fMRI data: 120 scans, 3 scans each of hot, rest, warm, rest, hot, rest, … T = (hot – warm effect) / S.d. ~ t 110 if no effect

Scale space : smooth X(t) with a range of filter widths, s = continuous wavelet transform adds an extra dimension to the random field: X(t, s) 15mm signal best detected with a ~15mm smoothing filter Scale space, no signal One 15mm signal S = FWHM (mm, on log scale) t (mm)

mm and 23mm signals Two 10mm signals 20mm apart S = FWHM (mm, on log scale) t (mm) But if the signals are too close together they are detected as a single signal half way between them Matched Filter Theorem (= Gauss-Markov Theorem): “to best detect a signal + white noise, filter should match signal”

mm and 150mm signals at the same location S = FWHM (mm, on log scale) t (mm) Scale space can even separate two signals at the same location!

Expressive or not expressive (EXNEX)? Male or female (GENDER)? Correct bubbles Image masked by bubbles as presented to the subject All bubbles Correct / all bubbles

Fig. 1. Results of Experiment 1. (a) the raw classification images, (b) the classification images filtered with a smooth low-pass (Butterworth) filter with a cutoff at 3 cycles per letter, and (c) the best matches between the filtered classification images and 11,284 letters, each resized and cut to fill a square window in the two possible ways. For (b), we squeezed pixel intensities within 2 standard deviations from the mean. Subject 1Subject 2Subject 3

Average lesion volume Average cortical thickness n=425 subjects, correlation =

threshold

BrainStat - the details Jonathan Taylor, Stanford Keith Worsley, McGill

What is BrainStat? Based on FMRISTAT (Matlab) Written in Python (open source) Part of BrainPy (Poster 763 T-AM) Concentrates on statistics Analyses both magnitudes and delays (latencies) P-values for peaks and clusters uses latest random field theory

Details Input data is motion corrected and preferably slice timing corrected Output is complete hierarchical mixed effects ReML analysis (local AR(p) errors at first stage) Spatial regularization of (co)variance ratios chosen to target 100 df (Poster 610 M-PM) P-values for peaks and clusters are best of Bonferroni random field theory discrete local maxima (Poster 539 T-AM)

Methods Slice timing and motion correction by FSL AR(1) errors on each run For each subject, 2 runs combined using fixed effects analysis Spatial registration to 152 MNI by FSL Subjects combined using mixed effects analysis Repeated for all contrasts of both magnitudes and delays

Subject id, block experiment Mixed effects Ef Sd T df Magnitude (%BOLD), diff - same sentence Contour is: average anatomy > 2000 Random /fixed effects sd smoothed mm FWHM (mm) P=0.05 threshold for peaks is +/ y (mm) x (mm)

Conclusions Strong overall %BOLD increase of 3±0.5% Substantial subject variability (sd ratio ~8) Evidence for greater %BOLD response for different sentences (0.5±0.1%) Evidence for greater latency for different sentences (0.16±0.04 secs) Event design is better for delays Block design is better for overall magnitude