Improved Estimation of Effective Brain Connectivity in Functional Neuroimaging Through Higher Order Fuzzy Cognitive Maps

In this paper, a novel technique for the computation of effective brain connectivity in functional Near-Infrared Spectroscopy (fNIRS) data is presented. The estimation of effective brain connectivity using the proposed approach of higher order Fuzzy Cognitive Maps (FCMs), used in conjunction with Genetic Algorithm (GA), is shown to be more accurate. Owing to lack of dependency on human knowledge, the FCM-GA model becomes more robust to subjective beliefs of experts from various domains when establishing connectivity matrix. Furthermore, higher order FCMs are capable of assessing causal relations in historical data with variable time lag, g, therefore generating more accurate predictions for complex causal data such as fNIRS where the causality may not necessarily follow a first order dynamics. The computation model of higher order FCM-GA is shown to perform better than Granger Causality (GC) for estimating effective brain connectivity in synthetic fNIRS data at 95% significance level. The proposed approach is also tested on real fNIRS data, and shown to estimate the causal structure amongst region of interests (ROIs) with improved accuracy.