Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure

Multiple time series of scalp electrical potential activity are generated r outinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their s patiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment th rough identifying predictors and indicators of clinical outcomes. We discus s the development and application of nonstationary time series models for m ultiple EEG series generated from individual subjects in a clinical neurops ychiatric setting. The subjects are depressed patients experiencing general ized tonic-clonic seizures elicited by electroconvulsive therapy (ECT) as a ntidepressant treatment. Two varieties of models-dynamic latent factor mode ls and dynamic regression models-are introduced and studied. We discuss mod el motivation acid form, and aspects of statistical analysis including para meter identifiability, posterior inference and implementation of these mode ls via Markov chain Monte Carte techniques. In an application to the analys is of a typical set of 19 EEG series recorded during an ECT seizure at diff erent locations over a patient's scalp, these models reveal time-varying fe atures across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such ti me-varying spatial structure and its relevance in the ECT study, and in bas ic EEG research in general.