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Gebodh, N.

Publications and source records attributed to Gebodh, N..

2 recordsLinked to original sources

Novel Evoked Synaptic Activity Potentials (ESAPs) elicited by Spinal Cord Stimulation

Spinal cord stimulation (SCS) evokes fast epidural Evoked Compound Action Potential (ECAPs) that represent activity of dorsal column axons, but not necessarily a spinal circuit response. Using a multimodal approach, we identified and characterized a delayed and slower potential evoked by SCS that reflects synaptic activity within the spinal cord. Anesthetized female Sprague Dawley rats were implanted with an epidural SCS lead, epidural motor cortex stimulation electrodes, an epidural spinal cord recoding lead, an intraspinal penetrating recording electrode array, and intramuscular electromyography (EMG) electrodes in the hindlimb and back. We stimulated the motor cortex or the epidural spinal cord and recorded epidural, intraspinal, and EMG responses. SCS pulses produced characteristic propagating ECAPs (composed of P1, N1, and P2 waves with latencies <2 ms) and an additional wave ("S1") starting after the N2. We verified the S1-wave was not a stimulation artifact and was not a reflection of hindlimb/back EMG. The S1-wave has a distinct stimulation-intensity dose response and spatial profile compared to ECAPs. CNQX (a selective competitive antagonist of AMPA receptors) significantly diminished the S1-wave, but not ECAPs. Furthermore, cortical stimulation, which did not evoke ECAPs, produced epidurally detectable and CNQX-sensitive responses at the same spinal sites, confirming epidural recording of an evoked synaptic response. Finally, applying 50 Hz SCS resulted in dampening of ESAPs, but not ECAPs. Therefore, we hypothesize that the S1-wave is synaptic in origin, and we term the S1-wave type responses: Evoked Synaptic Activity Potentials (ESAPs). The identification and characterization of epidurally recorded ESAPs from the dorsal horn may elucidate SCS mechanisms. Significance StatementSpinal cord stimulation (SCS) is an established treatment for chronic pain and has applications to other disorders and neurorehabilitation. Notwithstanding decades of trials and research, questions remain about SCS mechanisms of action - and indicators thereof. Recent technological developments have enabled the detection of Evoked Compound Action Potential (ECAPs) - reflecting synchronous activity of the dorsal column axons activated by SCS. However, ECAP is not a direct measure of sensory processing in the dorsal horn. Here, we identify and characterize a novel electrophysiological signal that is evoked and detectable by epidural SCS electrodes and reflects spinal synaptic currents. This new signal, termed an Evoked Synaptic Activity Potential (ESAP), is thus a novel means with which to interrogate spinal gray matter circuits during SCS.

neuroscience↗

A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning

Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our frameworks decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.

neuroscience↗