Jody Culham Brain and Mind Institute Department of Psychology Western University http://www.fmri4newbies.com/ MVPA Tutorial Last Update: January 18, 2012 LastCourse: Update: March 10,9223, 2013W2010, University of Western Ontario Last Psychology Last Course: Psychology.
Download ReportTranscript Jody Culham Brain and Mind Institute Department of Psychology Western University http://www.fmri4newbies.com/ MVPA Tutorial Last Update: January 18, 2012 LastCourse: Update: March 10,9223, 2013W2010, University of Western Ontario Last Psychology Last Course: Psychology.
Jody Culham Brain and Mind Institute Department of Psychology Western University http://www.fmri4newbies.com/ MVPA Tutorial Last Update: January 18, 2012 LastCourse: Update: March 10,9223, 2013W2010, University of Western Ontario Last Psychology Last Course: Psychology 9223, W2013, Western University Test Data Set • Two runs: A and B (same protocol) • 5 trials per condition for 3 conditions Measures of Activity • β weights – z-normalized – %-transformed • t-values – β/error • % BOLD signal change – minus baseline low activity high activity low βz high βz low β% high β% low t high t Step 1: Trial Estimation • Just as in the Basic GLM, we are running one GLM per voxel • Now however, each GLM is estimating activation not across a whole condition but for each instance (trial or block) of a condition Three Predictors Per Instance 2-gamma constant linear within trial 5 instances of motor imagery 5 instances of mental calculation 5 instances of mental singing Step 1: Trial Estimation Dialog Step 1: Trial Estimation Output • Now for each instance of each condition in each run, for each voxel we have an estimate of activation Step 2: Support Vector Machine • SVMs are usually run in a subregion of the brain – e.g., a region of interest (= volume of interest) sample data: SMA ROI sample data: 3 Tasks ROI Step 2: Support Vector Machine • test data must be independent of training data – leave-one-run-out – leave-one-trial-out – leave-one-trial-set-out • often we will run a series of iterations to test multiple combinations of leave-X-out – e.g., with two runs, we can run two iterations of leave-one-run-out – e.g., with 10 trials per condition and 3 conditions, we could run up to 103 = 1000 iterations of leave-one-trial-set-out MVP file plots 98 functional voxels Run B = test set Run A = training set intensity = activation 15 trials SVM Output: Train Run A; Test Run B Guessed Condition Guessed Condition Actual Condition 15/15 correct Actual Condition 10/15 correct (chance = 5/15) SVM Output: Train Run B; Test Run A Permutation Testing • • • • • randomize all the condition labels run SVMs on the randomized data repeat this many times (e.g., 1000X) get a distribution of expected decoding accuracy test the null hypothesis (H0) that the decoding accuracy you found came from this permuted distribution Output from Permutation Testing our data reject H0 upper bound of 95% confidence limits on permuted distribution upper quartile of permuted distribution median of permuted distribution (should be 33.3%) lower quartile of permuted distribution Voxel Weight Maps • voxels with high weights contribute strongly to the classification of a trial to a given condition