Despite their proven efficacy in treating neurological disorders, especially Parkinson's disease, deep brain stimulation (DBS) systems could be further optimized to maximize treatment benefits. In particular, because current open-loop DBS strategies based on fixed stimulation settings leave the typical parkinsonian motor fluctuations and rapid symptom variations partly uncontrolled, research has for several years focused on developing novel "closed-loop" or "adaptive" DBS (aDBS) systems. aDBS consists of a simple closed-loop model designed to measure and analyze a control variable reflecting the patient's clinical condition to elaborate new stimulation settings and send them to an "intelligent" implanted stimulator. The major problem in developing an aDBS system is choosing the ideal control variable for feedback. Here we review current evidence on the advantages of neurosignal-controlled aDBS that uses local field potentials (LFPs) as a control variable, and describe the technology already available to create new aDBS systems, and the potential benefits of aDBS for patients with Parkinson's disease.
Copyright © 2012. Published by Elsevier Inc.