Spatiotemporal dynamics of the human effective connectome at rest

<p>Radu Mutihac</p>

Spatiotemporal dynamics of the human effective connectome at rest

Radu Mutihac

University of Bucharest, Romania

: J Spine Neurosurg

Abstract

Recent studies of the human brain connectivity alterations using resting-state/sleep functional Magnetic Resonance Imaging (rsfMRI), Diffusion Tensor Imaging (DTI) and more recently Diffusion Spectroscopic Imaging (DSI) data have advanced and enlarged our knowledge on the organization of large-scale structural and functional brain networks, which consist of spatially distributed, but functionally linked regions that continuously share information. Energy of the human brain is largely consumed at rest during spontaneous neuronal activity (~20%), while task-related increases in metabolism energy are minor (<5%). Spontaneous ultralow-frequency fluctuations in BOLD-based rsfMRI signals (<0.01 Hz) at the level of large-scale neural systems are not noise, but orderly and organized in a series of functional networks that permanently maintain a high level of temporal coherence among brain areas that are structurally segregated and functionally linked in Resting State Networks (RSNs). Some RSNs are functionally organized as dynamically competing systems both at rest and while performing various tasks. The Default Mode Network (DMN), the most important RSN, is even more active during rest and involved in realization of tasks like memory retrieval, emotional process and social cognition. Cortical connectivity at rest is altered in several neurological and psychiatric disorders. Most recently, human brain function has been imaged in rsfMRI and thereby accessing both sides of human mind-brain interface (subjective experience and objective observations) has simultaneously been performed. As such, functional neuroimaging moves onto new potential applications like reading the brain states, discriminate neurological dysfunctions, brain-computer interfaces, lie detection and alike. The present report aims to review and evaluate the most current approaches for early detection and classification of neurological disorders, particularly among syndromes with relatively similar behavioral effects, based on alterations of the human effective connectome at rest explored by rsfMRI, DTI and DSI.