Functional Neural Network Connectivity in Myalgic Encephalomyelitis

Abstract

Myalgic Encephalomyelitis (ME) is a chronic illness with debilitating neurocognitive impairment that remains poorly understood. Previous studies have characterized cognitive deficits as a process by which brain abnormalities are inferred from pre-established testing paradigms using neuroimaging with low temporal resolution. Unfortunateily, this approach has been shown to provide limited predictive power, rendering it inadequate for the study of neuronal communication between synchronized regions. More recent developments have highlighted the importance of modeling spatiotemporal dynamic interactions within and between large-scale and small-scale neural networks on a millisecond time scale. Here, we focus on recent emergent principles of complex cortical systems, suggesting how subtle disruptions of network properties could be related to significant disruptions in cognition and behavior found in ME. This review, therefore, discusses how electrical neuroimaging methods with time-dependent metrics (e.g., coherence, phase, cross-frequency coupling) can be a useful approach for the understanding of the cognitive symptoms in ME. By providing a platform for utilizing real-time alterations of the perpetual signals as an outcome, the disruptions to higher-level cognition typically seen in ME can be readily identified, creating new opportunities for better diagnosis and targeted treatments.