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Advanced Techniques in Single Subject Analysis: T-values, Contrasts, and Deconvolution

This tutorial delves into key concepts in single subject analysis, including the computation of t-values to determine if beta values significantly differ from zero or between conditions. It addresses contrasts for assessing differences, methods to handle multiple comparisons, and the risks of type I errors. We explore deconvolution analysis to free assumptions about hemodynamic response functions and how to compute beta values effectively during task onset. Trigger average analysis is also discussed, emphasizing random condition order and jitter for optimal results.

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Advanced Techniques in Single Subject Analysis: T-values, Contrasts, and Deconvolution

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  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).

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