Biology subjects

Thakor, N. V.

Publications and source records attributed to Thakor, N. V..

3 recordsLinked to original sources

bioRxiv2026

Monitoring Autonomic Tone During Spinal Cord Neuromodulation Using Wearble AURIS Sensor

The transition of bioelectronic medicine to clinical use is currently limited by a lack of non-invasive sensors capable of measuring autonomic tone during active neuromodulation. Conventional monitoring modalities, such as mean arterial pressure (MAP) and Ag/AgCl chest electrodes, are often invasive, cumbersome, or susceptible to motion artifacts. Here, we present a novel framework employing an in-ear sensor (AURIS) to continuously monitor heart rate variability (HRV) during therapeutic neuromodulation. These sensors utilize a polydimethylsiloxane (PDMS) substrate to ensure biocompatibility and superior conformability. Experiments in a rodent model (n = 3) demonstrate that the AURIS platform achieves gold-standard fidelity, with mean heart rate differences of 6.03 BPM and mean RR interval deltas of 3.18 ms compared to chest electrodes. Sensor agreement was statistically validated using independent t-tests, showing no significant difference between modalities (all p > 0.46). While time-domain shifts trended toward significance, complexity metrics showed robust sequential responses with large effect sizes, including the SD1/SD2 ratio (d = 1.474) and the DFA ratio (d = 1.091). These findings validate a sensor architecture that is durable, accessible, and provides the necessary technical foundation for closed-loop feedback and non-invasive clinical trials.

bioengineering↗

bioRxiv2026

Reinnervation of Muscle Targets Enhances the Separability of Motor Unit Signals Following Peripheral Nerve Transfers

After amputation, advanced prosthetic limbs offer a promising means of restoring motor function. However, state-of-the-art prostheses often rely on aggregate electromyogram (EMG) signals to decode motor intention, which limits their ability to replicate natural limb movements. Decomposing EMG signals into individual motor unit components has shown potential for more natural control, but distinguishing between individual units can be challenging when nearby signals overlap. This study demonstrates that muscle target reinnervation surgeries can naturally increase physical separation between motor unit signals, thereby mitigating this overlap. Reinnervation of individual motor units is evaluated in a rodent hindlimb model after direct nerve-to-muscle implantation. Histological and electrophysiological analyses reveal that structural changes following reinnervation surgery result in beneficial motor unit signal changes, particularly improving spatial separation between motor unit signals compared to those in intact muscle. This spatial separation contributed to fewer instances of complex, overlapping signals in reinnervated muscle recordings. Motor unit signals were leveraged to provide a proof-of-concept of precise control of a virtual prosthesis for the first time after direct nerve-to-muscle implantation surgery. These findings highlight the potential of reinnervated muscle targets as key biological interfaces that facilitate motor unit separation, reducing the burden on decomposition algorithms and improving prosthetic control.

bioengineering↗

bioRxiv2025

Minimum biomechanical energy expenditure predicts upper-limb motor strategies in individuals with limb loss

Traditional models of upper-limb motion represent observed motor behaviors as the solution to an optimization problem defined over a cost function. However, these traditional formulations are computationally expensive and it is unclear if they extend to individuals with non-standard anatomy (such as those with upper-limb loss). Goal: We propose an optimal path planning framework that leverages musculoskeletal modeling to generate motor strategies during unconstrained, upper-limb movement. Methods: We validate this framework against upper-limb trajectories measured from a 3D target acquisition task and compare performance against multiple models of upper-limb motion previously presented in literature. Results: When compared to measured upper-limb trajectories, the proposed method generates upper-limb paths with significantly less geometric error than alternative methods (p < 0.001). Significance: Our approach provides a method for upper-limb motion planning that is easily adaptable to non-standard anatomies and computationally efficient enough for prosthesis control applications. Conclusions: The proposed path planning framework provides accurate motor strategy prediction for individuals both with and without upper-limb loss.

bioengineering↗