Keith Worsley Department of Mathematics and Statistics, and McConnell Brain Imaging Centre, Montreal...

Keith Worsley

Department of Mathematics and Statistics, andMcConnell Brain Imaging Centre,

Montreal Neurological Institute,McGill University

Correlation random fields, brain connectivity, and cosmology

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100CfA red shift survey, FWHM=13.3

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"Bubble"topology

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"Meat ball" topology

CfARandomExpected

Savic et al. (2005). Brain response to putative pheromones in homosexual men. Proceedings of the National Academy of Sciences, 102:7356-7361

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Highly significant effect, T=6.59

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Time, seconds

fMRI data: 120 scans, 3 scans each of hot, rest, warm, rest, hot, rest, …

T = (hot – warm effect) / S.d. ~ t110 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

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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”

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108mm and 150mm signals at the same location

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Scale space can even separate two signals at the same location!

Expressive or notexpressive (EXNEX)?

Male or female(GENDER)?

Correct bubbles

Image masked by bubblesas 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 1 Subject 2 Subject 3

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n=425 subjects, correlation = -0.56826

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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

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Subject id, block experiment Mixed effects

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Magnitude (%BOLD), diff - same sentence

Contour is: average anatomy > 2000

Random /fixed effects sdsmoothed

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P=0.05 threshold for peaks is +/- 5.1375

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Subject id, block experiment Mixed effects

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Contour is: magnitude, stimulus average, T statistic > 5

Random /fixed effects sdsmoothed

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P=0.05 threshold for peaks is +/- 4.0888

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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