  Dynamic functional connectivity analysis provides valuable information for
understanding brain functional activity underlying different cognitive
processes. Besides sliding window based approaches, a variety of methods have
been developed to automatically split the entire functional MRI scan into
segments by detecting change points of functional signals to facilitate better
characterization of temporally dynamic functional connectivity patterns.
However, these methods are based on certain assumptions for the functional
signals, such as Gaussian distribution, which are not necessarily suitable for
the fMRI data. In this study, we develop a deep learning based framework for
adaptively detecting temporally dynamic functional state transitions in a
data-driven way without any explicit modeling assumptions, by leveraging recent
advances in recurrent neural networks (RNNs) for sequence modeling.
Particularly, we solve this problem in an anomaly detection framework with an
assumption that the functional profile of one single time point could be
reliably predicted based on its preceding profiles within stable functional
state, while large prediction errors would occur around change points of
functional states. We evaluate the proposed method using both task and
resting-state fMRI data obtained from the human connectome project and
experimental results have demonstrated that the proposed change point detection
method could effectively identify change points between different task events
and split the resting-state fMRI into segments with distinct functional
connectivity patterns.
