Novel Linear and Nonlinear Features for the Analysis of Dynamic Brain Functional Connectivity

Dynamic functional connectivity (dFC), estimating changes in the brain interaction, has been employed as a sensitive biomarker for identifying neurological and psychological disorders. Deep learning is frequently used method for the analysis of dFC, but it is unfortunately unexplainable and computational expensive. A root mean square (rms) value is also proposed but may be insufficient for a complete characterization of the dFC. This study aims at investigating the feasibility of several novel features for dFC analysis. Two public datasets were used, i.e., a rest-state functional magnetic resonance imaging (fMRI) dataset containing healthy controls (HCs) and Alzheimer’s disease (AD) patients and an electroencephalography (EEG) dataset containing two motor imagery tasks (cylindrical and lumbrical grasping). The dFC was estimated using the Pearson’s correlation with a sliding window. Ten features were extracted from pairwise Pearson’s correlation of the dFC. A Wilcoxon rank sum test was applied to each feature calculated from the two classes of each dataset. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the discrimination power of each feature. Our results show that 6014 out of 66700 features are significantly different between HC and AD, and 6670 out of 17700 features between cylindrical and lumbrical grasping. The maximum AUCs for individual features are 0.69 and 0.65 for the fMRI and EEG datasets, respectively. These results confirm that, in addition to rms, other categories of features contribute also significantly to the analysis of dFC.