Exploring brain effective connectivity of early MCI with GRU_GC model on resting-state fMRI

Abstract

Background Investigating the functional interactions between different brain regions and revealing the transmission of information by computing brain connectivity have great potential and significance in the diagnosis of early Mild Cognitive Impairment (EMCI). Methods The Granger causality with Gate Recurrent Unit (GRU_GC) model is a suitable method that allows the detection of a nonlinear causal relationship and solves the limitation of fixed time lag, which cannot be detected by the classical Granger method. The model can transmit time series signals with any transmission delay length, and the time series can be screened and learned through the gate model. Results The classification experiment of 89 EMCI and 73 neurologically healthy controls (HC) shows that the accuracy reached 87.88%. Compared with multivariate variables GC (MVGC) and Long Short-Term Memory-based GC (LSTM_GC), the GRU_GC significantly improved the estimation of brain connectivity communication. Constructing a difference network to explore the brain effective connectivity between EMCI and HC. Conclusions The GRU_GC can discover the abnormal brain regions, including the parahippocampal gyrus, the posterior cingulate gyrus. The method can be used in clinical applications as an effective brain connectivity analysis tool and provides auxiliary means for the medical diagnosis of EMCI.