Identification of temporal transition of functional states using recurrent neural networks from functional MRI

TL;DR

This paper introduces a deep learning framework using recurrent neural networks to detect dynamic functional state transitions in fMRI data without relying on traditional assumptions, improving the understanding of brain activity.

Contribution

It presents a novel RNN-based anomaly detection approach for identifying change points in fMRI data, avoiding explicit modeling assumptions of previous methods.

Findings

Effectively detects change points in task fMRI data.

Successfully segments resting-state fMRI into distinct functional connectivity states.

Outperforms traditional methods in change point detection accuracy.

Abstract

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.