Tutorial 4 Single subject analysis

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

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)

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])

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

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?)

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

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).