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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
Gaussian threshold
Eul
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(E
C)
"Bubble"topology
"Sponge"topology
"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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T statistic for hot - warm effect
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Highly significant effect, T=6.59
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No significant effect, T=-0.74
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Drift
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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Scale space, no signal
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One 15mm signal
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10mm and 23mm signals
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Two 10mm signals 20mm apart
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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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Average lesion volume
Ave
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n=425 subjects, correlation = -0.56826
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Correlation = 0.091943
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Correlation = -0.1257
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threshold
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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
11.5625mm
FWHM (mm)
P=0.05 threshold for peaks is +/- 5.1375
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Subject id, block experiment Mixed effects
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Delay shift (secs), diff - same sentence
Contour is: magnitude, stimulus average, T statistic > 5
Random /fixed effects sdsmoothed
14.3802mm
FWHM (mm)
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