Resting-State Network Functional Connectivity Analysis: A Multimodal Imaging and Computational Modeling Approach

Functional connectivity is referred to as the temporal interaction between different brain regions that are spatially distant from each other. It is commonly examined during resting states by classifying resting-state functional connectivity (rs-FC) patterns and their associated synchronized activity to understand the underlying neural mechanisms that support cognitive processes. With the advancement of cutting-edge neuronal systems, multimodal imaging, and computational advances have served as promising tools for rs-FC studies. Despite having reviewed resting-state networks and their influence on a range of health anomalies in the existing literature, there nevertheless remains a lack of reviews that delve into diverse imaging modalities and computational modeling approaches for rs-FC analysis. Therefore, this systematic review aims to provide an understanding of these technologies that analyze the resting-state network functional connectivity and their correlation to cognitive functions. Through a systematic approach to analyzing studies, the most frequently employed modality was functional magnetic resonance imaging (fMRI) due to its high spatial resolution. Key findings demonstrated that higher functional connectivity levels are often linked to greater Exin ratios, which subsequently affect large-scale functional brain synchronization. Across various computational models, a non-parametric classifier has shown promise for rs-FC analysis in various contexts due to its ability to handle multiple EEG features. Moreover, the multivariate model is identified to have superior performance with a predictive area under the curve (AUC) of ~0.77. Overall, the present study underscores the vitality of using imaging and computational tools in elucidating the intricacies of rs-FC and its effects on individual cognition.