Tutorial 4

1. Tutorial 4 Single subject analysis

2. Tutorial 4 – topics • Computing t values • Contrasts • Multiple comparisons • Deconvolution analysis • Trigger average analysis

3. Computing t values Is beta significantly different from zero? t = b/sqrt(var(res)*pinv(model*model')) (P value will be computed according to the degrees of freedom)

4. Contrasts Assessing the difference between two conditions – Is beta1 significantly different from beta2? t = (C*b’) / sqrt(var(res)*C*pinv(model'*model)*C'); (where C = contrast vector, e.g. [1 0 -1])

5. Multiple comparisons • Increasing the likelihood of type I error (false positives), as we increase the number of comparisons. • Corrections for multiple comparisons: • Bonferroni – p/n, conservative • Random field theory – correction for number of independent tests, using FWHM • Cluster thresholding – less likely to find clusters of significant voxels • False discovery rate - Estimates the number of false positives (type I error) given the data

6. Deconvolution analysis • Frees us from assuming a certain hemodynamic response function. • Computing a beta value for each time point along a chosen time-window around each trial onset. (Which step are we skipping when building this model?)

7. Deconvolution analysis • We can then look for differences of beta values along this time window: • To compute betas of two (or more) different trials, we will need a very large model, which has n columns (n = number of time points in time-window) for each condition. Beta values

8. Trigger average analysis • Averaging the response of a defined time-window around the condition onset (after normalizing the trials, e.g. by normalizing each trial to its first two samples). • Works best if the experiment is conducted with a random / counter-balanced order of conditions, with jitter (different inter-trial intervals).