Signal processing methods for interaction analysis of functional brain imaging data

Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recordings data. This is because most interaction measures are not robust to cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. We describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to evaluate the performance of our nulling beamforming method.Intracranial recordings have minimal cross-talk and therefore interactions can be measured more reliably. However, performing group level studies is a challenging task because of the sparsity and variable coverage of electrodes on each subjects' brain. We describe a set of group analysis procedures for intracranial EEG recordings, which include registration of MRI volumes and cortical surfaces, and parcellation of anatomical regions of interest. We use a parametric probability model to test for equality of phase synchrony, and use Fisher's combined p-value method to pool test results from electrodes on individual subjects into the parcellated regions of interest. We apply our group analysis procedure to intracranial EEG data recorded in a working memory experiment and find an interaction network that is modulated by memory load.