Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important, non-invasive data informing on brain function in human neuro-psychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatio-temporal dynamics that may be useful in diagnosis, may improve the understanding of the underlying neurophysiology, or that may improve treatment through identifying predictors and indicators of clinical outcomes. In this applied context, we discuss the development and application of novel, non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic-clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. EEG data routinely recorded during such ECT seizures have previously been explored in a number of studies that suggest deeper analysis of the spatio-temporal properties of multiple EEG traces have promise for improving our understanding of the remarkable but poorly understand therapeutic effects of ECT. In addition, there is evidence that such analyses may be helpful for improving the effectiveness of the treatment while decreasing side-effects. Two varieties of models -- dynamic latent factor models and dynamic regression models -- are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo (MCMC) techniques. In an application to the analysis of a typical set of nineteen EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the multiple series that are strongly related to electrode placement around the scalp. In this application we illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and discuss the potential uses of such methods as a clinical aid in ECT work and in basic EEG research in general.

Keywords: ECT, EEG, rTMS, Major Depression

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