  There is increasing interest in identifying changes in the underlying states
of brain networks. The availability of large scale neuroimaging data creates a
strong need to develop fast, scalable methods for detecting and localizing in
time such changes and also identify their drivers, thus enabling
neuroscientists to hypothesize about potential mechanisms. This paper presents
a fast method for detecting break points in exceedingly long time series
neuroimaging data, based on vector autoregressive (Granger causal) models. It
uses a multi-step strategy based on a regularized objective function that leads
to fast identification of candidate break points, followed by clustering steps
to select the final set of break points and subsequent estimation with false
positives control of the underlying Granger causal networks. The latter
provides insights into key changes in network connectivity that led to the
presence of break points. The proposed methodology is illustrated on synthetic
data varying in their length, dimensionality, number of break points, strength
of the signal, and also applied to EEG data related to visual tasks.
