Responsive Versus Continuous Deep Brain Stimulation for Speech in Essential Tremor: A Pilot Study.

BACKGROUND: Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET). OBJECTIVE: To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression. METHODS: Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS). RESULTS: During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF. CONCLUSIONS: rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. Teaser: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.

Wearable sensor-driven responsive deep brain stimulation for essential tremor.

BACKGROUND: Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET. OBJECTIVE: The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals. METHODS: Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states. RESULTS: Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS. CONCLUSIONS: The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.

Florida research open-source synchronization tool (FROST) for electrophysiology experiments.

BACKGROUND: Accurate interpretation of electrophysiological data in cognitive and behavioral experiments requires the acquisition of time labels, such as marking the exact start of a condition or moment a stimulus is presented to a research subject. NEW METHOD: Here we present an inexpensive (∼30 USD) device used as a central relay for multiple peripheral devices, such as a computer screen presenting an experiment, a pressure-sensor push button, a multi-button responder, a pulse oximeter sensor, a light-emitting diode trigger for camera synchronization, and more. We refer to this device as the Florida Research Open-source Synchronization Tool (FROST). FROST allows for easy hardware and Arduino-based firmware modifications that enable a standard platform for the integration of novel peripheral sensors. RESULTS: With two examples, we demonstrate the application of this device during human research experiments: intracranial-electroencephalography (EEG) recordings in a patient with epilepsy and surface-EEG recordings in a healthy participant. We provide an example setup for a rodent experiment as well. We also demonstrate the timing delays of our device. COMPARISON WITH EXISTING METHODS: There is currently very few existing open-source synchronization tools for electrophysiological research that enable customization with new device compatibility. We developed this tool to enable widespread replication for many applications through an open-source platform. CONCLUSIONS: FROST can be easily adapted for research experiments beyond the included example cases. All materials are open-source at github.com/Brain-Mapping-Lab/FROST.