| I describe methods for the detection of brain activation and functional connectivity in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical analysis. I apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands, and I form observation matrices by putting together the power from all frequency bands and all trials. To detect changes in brain activity, I fit these observations into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables; the resulting Roy's maximum statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. As follow-up techniques to identify individual frequencies that contribute significantly to experimental effects, I further describe protected F-tests and linear discriminant analysis. To detect functional interactions in the brain, I take these observations and compute the canonical correlation between a chosen reference voxel and every other voxel in the brain. The canonical correlation maps are also thresholded for significance, but here I use parametric asymptotic approximations. Based on collinearity properties of the vectors associated to the canonical correlations, I implement procedures to discard voxels whose interaction with the reference is due to linear mixing, and describe approximate F-tests to identify individual frequencies that contribute significantly to detected interactions. I evaluate these multivariate approaches both on simulated data and experimental data from a MEG visuomotor task study. My results indicate that Roy's maximum root is more powerful than univariate approaches in detecting experimental effects when correlations exist between power across frequency bands; as for canonical correlation analysis, I find that it detects experimental effects, allowing the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single test. |
