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Yadav, A. P.

Publications and source records attributed to Yadav, A. P..

6 recordsLinked to original sources

Axonal spike-count regimes link spinal cord stimulation periodicity to artificial sensory detection and discrimination in rodents

Electrical stimulation is widely used to evoke artificial sensation, yet how temporal stimulation patterns are transformed into neural activity and perception remains poorly understood. Recent behavioral studies have shown that increasing the aperiodicity of spinal cord stimulation pulse trains alters both detection thresholds and discrimination performance despite constant pulse counts, suggesting a role for temporal structure beyond rate alone. However, the neural mechanisms underlying these effects remain unresolved. We developed a biophysically grounded computational framework linking a finite-element model of the rodent spinal cord, conductance-based axon simulations, and observer decision models to investigate how stimulation periodicity shapes neural responses and resulting behavior. By systematically varying stimulation amplitude, frequency, and inter-pulse interval variability, we identified distinct spike-count regimes arising from interactions between stimulation timing and axonal membrane dynamics. These regimes ranged from single spikes and small volleys to sustained spike trains, and they exhibited diverse frequency- and variability-dependent trends. No single regime was able to sufficiently explain experimentally observed detection threshold trends; instead, mixtures of regimes accurately reproduced both frequency-dependent threshold behavior and trial-level variability. Extending this framework to periodicity discrimination, we show that features derived from regime mixtures contain sufficient information to recover behavioral psychometric curves. Furthermore, observer model results provide a mechanistic account of behavioral asymmetries depending on the periodicity of the reference stimulus: discrimination relative to periodic inputs relied on combined rate and timing evidence, whereas discrimination relative to aperiodic inputs was dominated by timing irregularity. These results establish a mechanistic link between stimulation temporal structure, axonal spike generation, and perceptual behavior. This framework suggests that spinal cord stimulation does not operate within a single fixed neural regime but instead engages a spectrum of spike-count regimes whose mixtures shape perception. These findings have important implications for the design of biomimetic stimulation strategies, highlighting temporal patterning as a key dimension for controlling sensory outcomes.

neuroscience↗

OP-GLX: A MATLAB toolbox for online processing and plotting of Neuropixels data acquired with SpikeGLX

Online processing and visualization of large-scale neural data is critical for neuroscientific discovery and advancements in neural engineering. However, with the development of technologies like Neuropixels (NP) probes, which enable simultaneous streaming from hundreds of recording electrodes, handling such data in real-time has become an ongoing challenge. Moreover, keeping pace with recording hardware has required most existing software, such as SpikeGLX for NP probes, to prioritize acquisition stability, leaving data processing and visualization to primarily be performed offline. Thus, we created OP-GLX, a MATLAB-based toolbox designed to operate in tandem with SpikeGLX to enhance the fetching, processing, and visualization of incoming neural data. The OP-GLX toolbox features several processing capabilities, including spike detection, computing time-binned firing rates, plotting spike waveforms, and conducting principal component analysis (PCA). The processed neural data is displayed on a native graphical user interface (GUI) for intuitive and customizable interaction with the experiment. The performance testing of OP-GLX showed that it supports real-time operation, confirmed by the absence of SpikeGLX stream buffer fetch errors across multiple acquisition settings. By complementing current neural data acquisition methods and providing stable online functionality, we envision that OP-GLX will enable researchers to visualize and interpret their data more effectively during ongoing neuroscience experiments.

neuroscience↗

The role of stimulus periodicity on spinal cord stimulation-induced artificial sensations in rodents

Sensory feedback is critical for effectively controlling brain-machine interfaces (BMIs) and neuroprosthetic devices. Spinal cord stimulation (SCS) is proposed as a technique to induce artificial sensory perceptions in rodents, monkeys, and humans. However, to realize the full potential of SCS as a sensory neuroprosthetic technology, a better understanding of the effect of SCS pulse train parameter changes on sensory detection and discrimination thresholds is necessary. Here we investigated whether stimulation periodicity impacts rats ability to detect and discriminate SCS-induced perceptions at different frequencies. By varying the coefficient of variation (CV) of interstimulus pulse interval, we showed that at lower frequencies, rats could detect highly aperiodic SCS pulse trains at lower amplitudes (i.e., decreased detection thresholds). Furthermore, rats learned to discriminate stimuli with subtle differences in periodicity, and the just-noticeable differences (JNDs) from a highly aperiodic stimulus were smaller than those from a periodic stimulus. These results demonstrate that the temporal structure of an SCS pulse train is an integral parameter for modulating sensory feedback in neuroprosthetic applications.

neuroscience↗

Closed-loop spinal cord stimulation is superior in restoring locomotion in rodent models of Parkinson's Disease

Dorsal column stimulation (DCS) of the spinal cord is emerging as a promising new technology to treat Parkinsons disease (PD). However, optimal stimulation settings that maximize its therapeutic effect on PD symptoms are yet to be determined. Here we demonstrate a closed-loop DCS (CLDCS) paradigm - a substantial advancement from previously tested continuous high-frequency DCS - in a bilateral intrastriatal 6-hydroxydopamine (6-OHDA) rodent model of PD. Firstly, CLDCS, triggered by corticostriatal beta frequency oscillations facilitated a pro-locomotion brain state that restored locomotion and reduced akinesia. Secondly, CLDCS was better at disrupting ongoing beta oscillations and achieved it with lesser overall charge delivery than continuous open-loop stimulation. These results indicate that CLDCS is markedly better than traditional spinal cord stimulation methods and can potentially be highly effective in treating PD symptoms. We envision that the CLDCS approach can be beneficial in the treatment of other neurological disorders which showcase similar pathological neuronal oscillations.

neuroscience↗

Generating Artificial Sensations with Spinal Cord Stimulation in Primates and Rodents

For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animals ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Webers law of just noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.

neuroscience↗

A Brain to Spine Interface for Transferring Artificial Sensory Information

Lack of sensory feedback is a major obstacle in the rapid absorption of prosthetic devices by the brain. While electrical stimulation of cortical and subcortical structures provides unique means to deliver sensory information to higher brain structures, these approaches require highly invasive surgery and are dependent on accurate targeting of brain structures. Here, we propose a semi-invasive method, Dorsal Column Stimulation (DCS) as a tool for transferring sensory information to the brain. Using this new approach, we show that rats can learn to discriminate artificial sensations generated by DCS and that DCS-induced learning results in corticostriatal plasticity. We also demonstrate a proof of concept brain-to-spine interface (BTSI), whereby tactile and artificial sensory information are decoded from the brain of an \"encoder\" rat, transformed into DCS pulses, and delivered to the spinal cord of a second \"decoder\" rat while the latter performs an analog-to-digital conversion during a tactile discrimination task. These results suggest that DCS can be used as an effective sensory channel to transmit prosthetic information to the brain or between brains, and could be developed as a novel platform for delivering tactile and proprioceptive feedback in clinical applications of brain-machine interfaces.

neuroscience↗