MVPA Tutorial

http://www.fmri4newbies.com/

Last Update: January 18, 2012Last Course: Psychology 9223, W2010, University of Western OntarioLast Update: March 10, 2013

Last Course: Psychology 9223, W2013, Western University

Jody CulhamBrain and Mind Institute

Department of PsychologyWestern 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

lowactivity

highactivity

low t

low βz

low β%

high t

high βz

high β%

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 imagery5 instances of mental calculation5 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 plots98 functional voxels

15 trials

Run

A =

tra

inin

g se

tR

un B

= t

est

set

intensity = activation

SVM Output: Train Run A; Test Run B

GuessedCondition

ActualCondition

GuessedCondition

ActualCondition

15/15 correct

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

median of permuted distribution (should be 33.3%)

upper quartile of permuted distribution

lower quartile of permuted distribution

upper bound of 95% confidence limits on permuted distribution

our data reject H0

Voxel Weight Maps

• voxels with high weights contribute strongly to the classification of a trial to a given condition