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Assessment and modulation of resting-state neural networks after stroke

Rick M. Dijkhuizen, Greg Zaharchuk, Willem M. Otte

发表年份
2014
引用次数
47

摘要

PURPOSE OF REVIEW: Stroke is a major cause of disability; however, most patients experience spontaneous partial recovery of functions in subacute to chronic phases. Poststroke loss and recovery of functions have been increasingly correlated with brain-wide alterations in the connectivity of neural networks, which is described in this review. Elucidation of the mechanisms of functional brain remodeling could reveal targets and strategies for more effective neurorehabilitation. RECENT FINDINGS: Data from recent resting-state functional MRI, electroencephalography, magnetoencephalography, and optical imaging studies in patients and animal models have demonstrated that loss of function after stroke is closely associated with disrupted connectivity in large-scale networks beyond the lesion territory. Restoration of functional connectivity in the surviving networks appears to be critical for functional recovery, and this may be promoted with specific therapeutic strategies, such as robot-assisted training and noninvasive brain stimulation. The adaptability of functional networks relies on the structural integrity of neuronal pathways, but the relationship between the two remains incompletely understood. Furthermore, disturbed neurovascular coupling after stroke can confound hemodynamically based measurements of functional connectivity. SUMMARY: Identification of key network processes in adaptive brain plasticity can aid in the prediction of functional outcome and the development of therapeutic interventions to support and promote recovery after stroke.

关键词

Resting state fMRIStroke (engine)Artificial neural networkNeuroscienceMedicinePsychologyPhysical medicine and rehabilitationComputer scienceArtificial intelligencePhysics

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