Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson’s Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, p < 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic.

The rise of adaptive stimulation approaches has shown great therapeutic promise in the growing field of neuromodulation. The discovery and growth of these novel adaptive stimulation paradigms has been largely concentrated around several implantable devices with research application programming interfaces (APIs) that allow for custom applications to be created for clinical neuromodulation studies. However, the sunsetting of devices and ongoing development of new platforms is leading to an increased fragmentation in the research environment- resulting in the reinvention of system features and the inability to leverage previous development efforts for future studies. The Open Mind Neuromodulation Interface (OMNI) is a previously proposed solution to address the weaknesses of the DLL-driven API approach of past neuromodulation research by utilizing an alternative gRPC-enabled microservice framework. Here, we introduce OMNI-BIC, an implementation of the OMNI framework to the CorTec Brain Interchange system. This paper describes the design and implementation of the OMNI-BIC software tools and demonstrates the framework’s capabilities for implementing customized neuromodulation therapies for clinical investigations. Through the development and deployment of the OMNI-BIC system, we hope to improve future clinical studies with the Brain Interchange system and aid in continuing the growth and momentum of the exciting field of adaptive neuromodulation.

Virtual reality (VR) offers a robust platform for  human behavioral neuroscience, granting unprecedented  experimental control over every aspect of an immersive and  interactive visual environment. VR experiments have already  integrated non-invasive neural recording modalities such as  EEG and functional MRI to explore the neural correlates of  human behavior and cognition. Integration with implanted  electrodes would enable significant increase in spatial and  temporal resolution of recorded neural signals and the option of  direct brain stimulation for neurofeedback. In this paper, we  discuss the first such implementation of a VR platform with  implanted electrocorticography (ECoG) and stereo?electroencephalography (sEEG) electrodes in human, in-patient  subjects. Noise analyses were performed to evaluate the effect of  the VR headset on neural data collected in two VR-naïve  subjects, one child and one adult, including both ECOG and  sEEG electrodes. Results demonstrate an increase in line noise  power (57-63Hz) while wearing the VR headset that is mitigated  effectively by common average referencing (CAR), and no  significant change in the noise floor bandpower (125-240Hz). To  our knowledge, this study represents first demonstrations of VR  immersion during invasive neural recording with in-patient  human subjects.