Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis

Most of the existing dynamic functional connectivity (dFC) analytical methods compute the correlation between pairs of time courses with the sliding window. However, there is no clear indication on the standard window characteristics (length and shape) that best suit for all analyses, and it cannot pinpoint to compute the dynamic correlation of brain region for each time point. Besides, most of the current studies that utilize the dFC for MCI identification mainly relied on the local clustering coefficient for extracting dynamic features and the support vector machine (SVM) as a classifier. In this paper, we propose a novel adaptive dFC inference method and a deep learning classifier for MCI identification. Specifically, a group-constrained structure detection algorithm is first designed to identify the refined topology of the effective connectivity network, in which the individual information is preserved via different connectivity values. Second, based on the identified topology structure, the adaptive dFC network is then constructed by using the Kalman Filter algorithm to estimate the brain region connectivity strength for each time point. Finally, the adaptive dFC network is validated in MCI identification using a new Parallel Hierarchical Bidirectional Long Short-Term Memory (PH-BiLSTM) network, which extracts as much brain status change information as possible from both the past and future information. The results show that the proposed method achieves relatively high classification accuracy.