Objective. Technical advances in deep brain stimulation (DBS) are crucial to improve therapeutic efficacy and battery life. We report the potentialities and pitfalls of one of the first commercially available devices capable of recording brain local field potentials (LFPs) from the implanted DBS leads, chronically and during stimulation. The aim was to provide clinicians with well-grounded tips on how to maximize the capabilities of this novel device, both in everyday practice and for research purposes. Approach. We collected clinical and neurophysiological data of the first 20 patients (14 with Parkinson’s disease (PD), five with dystonia, one with chronic pain) that received the Percept™ PC in our centres. We also performed tests in a saline bath to validate the recordings quality. Main results. The Percept PC reliably recorded the LFP of the implanted site, wirelessly and in real time. We recorded the most promising clinically useful biomarkers for PD and dystonia (beta and theta oscillations) with and without stimulation. Furthermore, we provide an open-source code to facilitate export and analysis of data. Critical aspects of the system are presently related to contact selection, artefact detection, data loss, and synchronization with other devices. Significance. New technologies will soon allow closed-loop neuromodulation therapies, capable of adapting stimulation based on real-time symptom-specific and task-dependent input signals. However, technical aspects need to be considered to ensure reliable recordings. The critical use by a growing number of DBS experts will alert new users about the currently observed shortcomings and inform on how to overcome them.

Auditory brainstem implants (ABIs) provide sound awareness to deaf individuals who are not candidates for the cochlear implant. The ABI electrode array rests on the surface of the cochlear nucleus (CN) in the brainstem and delivers multichannel electrical stimulation. The complex anatomy and physiology of the CN, together with poor spatial selectivity of electrical stimulation and inherent stiffness of contemporary multichannel arrays, leads to only modest auditory outcomes among ABI users. Here, we hypothesized that a soft ABI could enhance biomechanical compatibility with the curved CN surface. We developed implantable ABIs that are compatible with surgical handling, conform to the curvature of the CN after placement, and deliver efficient electrical stimulation. The soft ABI array design relies on precise microstructuring of plastic-metal-plastic multilayers to enable mechanical compliance, patterning, and electrical function. We fabricated soft ABIs to the scale of mouse and human CN and validated them in vitro. Experiments in mice demonstrated that these implants reliably evoked auditory neural activity over 1 month in vivo. Evaluation in human cadaveric models confirmed compatibility after insertion using an endoscopic-assisted craniotomy surgery, ease of array positioning, and robustness and reliability of the soft electrodes. This neurotechnology offers an opportunity to treat deafness in patients who are not candidates for the cochlear implant, and the design and manufacturing principles are broadly applicable to implantable soft bioelectronics throughout the central and peripheral nervous system.

Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.