Objective: Brain–computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.Methods: We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.Results: Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.Interpretation BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD. Ann Neurol 2013;74:449–457

See Moll and Engel (doi:) for a scientific commentary on this article.During pathologies like tremor, neural populations become locked into temporal configurations that reinforce neural synchrony to the point that motor function is compromised. Cagnan et al. present a novel stimulation approach to selectively control neural synchrony, and demonstrate that deep brain stimulation can be precisely timed to disrupt disease pathophysiology.

Microelectrode arrays (MEAs) are designed to monitor and/or stimulate extracellularly neuronal activity. However, the biomechanical and structural mismatch between current MEAs and neural tissues remains a challenge for neural interfaces. This article describes a material strategy to prepare neural electrodes with improved mechanical compliance that relies on thin metal film electrodes embedded in polymeric substrates. The electrode impedance of micro-electrodes on polymer is comparable to that of MEA on glass substrates. Furthermore, MEAs on plastic can be flexed and rolled offering improved structural interface with brain and nerves in vivo.MEAs on elastomer can be stretched reversibly and provide in vitro unique platforms to simultaneously investigate the electrophysiological of neural cells and tissues to mechanical stimulation. Adding mechanical compliance to MEAs is a promising vehicle for robust and reliable neural interfaces.

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