Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia.

Abstract

Disorders affecting the central nervous system have proven particularly hard to treat, and disappointingly few novel therapies have reached the clinics in recent decades. A better understanding of the physiological processes in the brain underlying various symptoms could therefore greatly improve the rate of progress in this field. We here show how systems-level descriptions of different brain states reliably can be obtained through a newly developed method based on large-scale recordings in distributed neural networks encompassing several different brain structures. Using this technology, we characterize the neurophysiological states associated with parkinsonism and levodopa-induced dyskinesia in a rodent model of Parkinson's disease together with pharmacological interventions aimed at reducing dyskinetic symptoms. Our results show that the obtained electrophysiological data add significant information to conventional behavioral evaluations and hereby elucidate the underlying effects of treatments in greater detail. Taken together, these results potentially open up for studies of neurophysiological mechanisms underlying symptoms in a wide range of neurological and psychiatric conditions that until now have been very hard to investigate in animal models of disease.