Spatiotemporal histogenesis of the developing human cerebellum reveals dynamic layering of Bergmann glia.

Refractory neuropsychiatric and neurological disorders, such as severe to extreme obsessive-compulsive disorder and Parkinson's disease, are routinely managed with deep brain stimulation (DBS) []. Recent technological advances have made available both commercial and research-focused bidirectional stimulation and neural recordings from the implanted neural stimulator (INS) in the clinic and at home []. In particular, the Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and BRAIN Initiative-supported consortium, OpenMind, have developed software for decoding data streamed by the RC+S (Medtronic, Minneapolis, MN) using MATLAB, open-source waveform generator hardware, and a parameter assessment infrastructure (Anso et al., 2022) for using the RC+S application programming interface (API) [–]. The Summit system API is part of an investigational bi-directional neural interface that provides several tools to interface with the RC+S rechargeable INS system to enable streaming of data from implanted DBS leads, for the purpose of exploring potential disease- or symptom-specific biomarkers for use in closed-loop algorithms []. The application in this report leverages several of these tools while providing a framework for additional Summit system API tools; see . Of note, the Summit RC+S system is for investigational use only and is no longer available for implantation but is in use in active studies with patients in whom it was implanted; see . Some of these clinical research teams have explored sleep-aware algorithms in Parkinson's disease (PD), impedance rhythms in the limbic system of epilepsy subjects, and behavioral markers of obsessive-compulsive disorder (OCD) [–]. Modular solutions to expand the experimental options using the RC+S device are predominately designed in C# or Python programming languages. Even though the Summit API was also designed for use in MATLAB, no known publications reference its use with the RC+S device. Further, the idiosyncratic nature of the bespoke research platforms for in-home data collection often fails to account for challenges experienced by targeted patient populations, such as diminished motor ability (affecting Parkinson's disease, essential tremor, and dystonia patients) and anxiety (predominantly affecting Parkinson's disease and OCD patients) []. As PD progresses, motor symptoms—and anxiety if present—are exacerbated during diminished or off-medication states, leading to the need for in-home experimental solutions that provide a simple, easy-to-use interface []. Having a prominent tremor, for example, when interacting with a touchscreen, can incorrectly execute multiple finger-tapping inputs, hindering a subject's ability to engage with experimental tools (e.g., start a recording session). To improve accessibility and usability of the RC+S research platform in the context of in-home chronic recordings with real-time processing and investigational closed loop aDBS, we developed a series of applications using a benchtop setup followed by in-home recordings, expanding on the current ecosystem []. By developing this solution in MATLAB, we sought to address several limitations that hindered studies from chronic long-term data recording and collection, using a Bluetooth (BLE) connection alongside an established USB method with the clinical telemetry manager (CTM).