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Adaptive deep brain stimulation (aDBS) is a promising new technology with increasing use in experimental trials to treat a diverse array of indications such as movement disorders (Parkinson’s disease, essential tremor), psychiatric disorders (depression, OCD), chronic pain and epilepsy. In many aDBS trials, a neural biomarker of interest is compared with a predefined threshold and stimulation amplitude is adjusted accordingly. Across indications and implant locations, potential biomarkers are greatly influenced by sleep. Successful chronic embedded adaptive detectors must incorporate a strategy to account for sleep, to avoid unwanted or unexpected algorithm behavior. Here, we show a dual algorithm design with two independent detectors, one used to track sleep state (wake/sleep) and the other used to track parkinsonian motor state (medication-induced fluctuations). Across six hemispheres (four patients) and 47 days, our detector successfully transitioned to sleep mode while patients were sleeping, and resumed motor state tracking when patients were awake. Designing “sleep aware” aDBS algorithms may prove crucial for deployment of clinically effective fully embedded aDBS algorithms.

Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson’s disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation. An implanted device in the brain enables wireless neural monitoring and stimulation for up to 15 months following implantation.

Neural recordings in humans using invasive devices can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five patients with Parkinson’s disease for up to 15 months after implantation. Bilateral 4-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 hours were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated patient-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation. This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

Invasive neural recording in humans shows promise for understanding the circuit basis of brain disorders. Most recordings have been done for short durations from externalized brain leads in hospital settings, or from first-generation implantable sensing devices that offer only intermittent brief streaming of time series data. Here we report the first human use of an implantable neural interface for wireless multichannel streaming of field potentials over long periods, with and without simultaneous therapeutic neurostimulation, untethered to receiving devices. Four Parkinson's disease patients streamed bilateral 4-channel motor cortical and basal ganglia field potentials at home for over 500 hours, paired with wearable monitors that behaviorally categorize states of inadequate or excessive movement. Motor state during normal home activities was efficiently decoded using either supervised learning or unsupervised clustering algorithms. This platform supports adaptive deep brain stimulation, and may be widely applicable to brain disorders treatable by invasive neuromodulation.