A methodology for empirical analysis of brain connectivity through graph mining

Graph theoretical analysis has been applied to both structural and functional brain connectivity networks and has helped researchers conceive the effects of neurological and neuropsychiatric diseases including Alzhemier and Schizophrenia. However, existing graph theoretical approaches to brain connectivity networks simply assume that temporal correlations between brain regions are stable during the entire timeseries under consideration, and only focus on high-level network topological characteristics such as degree distribution. To advance the understanding of brain connectivity networks at a fine granularity, we propose a new method that can help discover connectivity-oriented insights from a time series of brain connectivity networks. In particular, our method is capable of identifying (1) strong correlations, which are represented as frequent edges in brain connectivity networks, for each individual subject, and (2) frequent substructures, which are connected components appearing frequently in brain connectivity networks, for a group of subjects. We apply the method to a data set of 38 subjects that were involved in a study of early life stress on depression development. Our findings have been echoed by the domain experts in terms of their clinical implications.