Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI.

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 a 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. PMID: 30320310 [PubMed - in process]
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research