Volitional DBS (vDBS)--a paradigm in which patients use brain-computer interface (BCI) training to learn self-regulation of a neural signal that then controls closed-loop DBS)--is demonstrated, establishing that volitional control of intracranial neurostimulation is achievable without the need of an externalized manual controller.
Abstract
Deep brain stimulation (DBS) is transforming from a static therapy toward adaptive systems that adjust stimulation based on neural biomarkers. However, the detection of reliable biomarkers that capture the multi-dimensional nature of complex symptoms is often challenging. Here we demonstrate volitional DBS (vDBS)--a paradigm in which patients use brain-computer interface (BCI) training to learn self-regulation of a neural signal that then controls closed-loop DBS. Two patients with Parkinson's disease implanted with sensing-enabled neurostimulators completed chronic, at-home BCI training by playing an airplane simulation game. Through training, they were able to effectively down-regulate their cortical beta signal (p's < 1e-10), represented as the real-time position of a plane in the BCI game. Following training, this cortical beta signal served as the input to a closed-loop DBS algorithm. By modulating their beta signal to cross personalized thresholds, patients voluntarily increased or decreased neurostimulation amplitude at will, in the absence of physical movement (p's < 1e-10). This proof-of-principle demonstration establishes that volitional control of intracranial neurostimulation is achievable without the need of an externalized manual controller. BCI-vDBS could potentially be used for a range of neuropsychiatric conditions and brain rehabilitation to support personalized control of neurostimulation.
Thirty years on from the introduction of deep brain stimulation as a therapy for Parkinson's disease, adaptive deep brain stimulation (aDBS) is poised to transform neural stimulation for the treatment of motor disorders. Implementation of aDBS has been facilitated by the identification of biomarkers that reflect patients' clinical states and the introduction of hardware allowing simultaneous sensing and stimulation. However, the most appropriate algorithms for aDBS remain unclear. This review provides an overview of control algorithms used in computational, animal and human studies of aDBS in Parkinson's disease to date. Although shown to be effective and well-tolerated, only relatively simple aDBS algorithms have been used clinically. Computational studies have explored a wider range of more advanced algorithms which promise superior performance but need to be rigorously tested and compared against clinically-tested techniques before translating to patients. The need for comparative studies is further driven by advances in machine learning, which promise patient-specific programming solutions but require large datasets and have high computational requirements. Understanding the advantages and limitations of these different algorithms is critical for aDBS to reach its potential and enable personalized treatment to provide maximum patient benefit.
Unknown authors· IEEE Reviews in Biomedical E...· 0 citations
It is argued that the same handful of bottlenecks recur across all three technologies (long-term stability, neural coding, and equitable access) and the governance frameworks needed alongside continued engineering progress are outlined.
Volodymyr Mavrych, O. Bolgova, Leen Alhamd et al.· Frontiers in Neuroscience· 0 citations
A multicenter crossover study is proposed for adults with levodopa-responsive Parkinson’s disease and motor fluctuations despite optimized conventional stimulation, which combines blinded motor-state improvement with reduced troublesome dyskinesia and off time.
The neurophysiological basis of EEG-BCI and three major rehabilitation paradigms are outlined, including motor imagery with physical feedback, motor imagery with virtual/multisensory feedback, and the steady-state visual evoked potential (SSVEP)-driven paradigm.
Wang Peng, Yang Yang, Juehan Wang et al.· Topics in Stroke Rehabilitat...· 0 citations
Deep Brain Stimulation (DBS) is an implantable neuromodulation system that integrates electronic components, intracranial electrodes, and biological neural networks into a bioelectronic control system designed to therapeutically modulate brain activity. The development of DBS technology for treatment-resistant obsessive-compulsive disorder (TR-OCD) has evolved from a purely anatomical target-based stimulation approach toward the integration of system architecture, control strategies, and biomarker-driven adaptive neurostimulation. This narrative review examines the architecture of DBS systems, open-loop and closed-loop control strategies, brain-sensing technologies, neural biomarkers, and recent advances in adaptive neurostimulation for TR-OCD. The review was conducted through an appraisal of contemporary literature addressing the intersection of neuroscience, electrical engineering, and biomedical engineering in the implementation of DBS. The findings indicate that a DBS system comprises an implantable pulse generator (IPG), stimulation electrodes, signal transmission components, and target neural networks that collectively form a neuromodulation control system. Conventional DBS remains predominantly based on an open-loop paradigm, in which continuous stimulation is delivered according to predefined parameters without real-time neurophysiological feedback. In contrast, advances in brain-sensing technologies have enabled the recording of neural biomarkers, particularly local field potentials (LFPs), which serve as the foundation for the development of closed-loop DBS systems. These systems allow automatic adjustment of stimulation parameters through feedback-driven mechanisms, thereby offering the potential to enhance therapeutic efficacy, improve device energy efficiency, and facilitate personalized treatment. The integration of neural biomarkers, adaptive control algorithms, and connectomic DBS approaches is expected to establish the foundation for next-generation intelligent neuromodulation systems and support the implementation of precision psychiatry in the management of TR-OCD.
Hedya Nadhrati Surura, Rina Hastuti Lubis, Nashrul Fazli Mohd Nasir et al.· JET (Journal of Electrical T...· 0 citations
The study stresses the necessity of embedding relational autonomy and neural rights into BCI development, tying technological trajectories to governance demands in order to shape responsible paths for future neurotechnologies.
Yuzhang Wu· Theoretical and Natural Scie...· 0 citations
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