Biology subjects

Seethapathi, N.

Publications and source records attributed to Seethapathi, N..

7 recordsLinked to original sources

bioRxiv2026

Neuromuscular impairments alter energetic cost landscape curvature and stride speed variability in post-stroke locomotion

Neuromuscular impairments induce compensatory effects which alter the dynamics of human movement, but the mechanism linking specific impairments to post-stroke locomotion remains poorly understood. Here, we combine a predictive neuromusculoskeletal simulation framework with experimental gait observations in stroke survivors to test how two hemiparetic impairments, reduced muscle strength and increased baseline muscle activity, reshape the energetic cost landscape. We then evaluate whether impairment-dependent changes in the cost landscape curvature are associated with stride speed variability, which is experimentally observed to be higher after stroke. Using neuromusculoskeletal simulations, we show that increased paretic muscle activity reduces local curvature near the cost-minimized speed more than reduced paretic muscle strength and find that this predicts increases in stride speed variability observed in hemiparetic locomotion. These results support a mechanistic hypothesis that flatter cost landscapes reduce the relative cost of suboptimal behavior and, therefore, may contribute to increased motor variability after stroke. Author summaryMotor disorders, such as those caused by stroke, change the way humans walk and interact with the world, often reducing quality of life. Stroke affects millions of people each year and commonly causes unilateral motor impairment that requires substantial rehabilitation. In this study, we use simulations to understand how different impairments, specifically reduced muscle strength and increased muscle activity, alter movement behavior. By varying the severity of the impairment and the speed of walking, we show how different impairments lead to compensation strategies and influence sensitivity to speed fluctuations. These findings suggest that the energetic penalty incurred from suboptimal behavior changes with neuromuscular impairment and are predicted to contribute to increased variability after stroke.

neuroscience↗

bioRxiv2026

Fall risk-aware adaptation explains suboptimal locomotor performance

AO_SCPLOWBSTRACTC_SCPLOWHuman locomotion requires balancing multiple biological objectives, such as metabolic energy efficiency, stability, and symmetry. While models based on optimization successfully predict how humans walk in familiar settings, they fail to explain why individuals adopt inefficient movement patterns in novel environments, even after extensive practice. Here, we show that such suboptimality in a novel environment arises from a fundamental prioritization of safety. We find that individuals do not simply fail to reach an optimal solution; instead, they navigate an environment-dependent risk landscape by mitigating the statistical probability of falling. We find that this risk-averse strategy is explained by adjusting internal learning parameters: specifically, the learning rate and the tradeoff between metabolic cost and symmetry, in a manner that lowers fall risk. To quantify this process, we developed an inverse adaptation modeling framework; this approach works backwards from locomotor performance data to mathematically infer the underlying internal learning parameters and how they vary with fall risk. Our analysis reveals that the observed motor performance is explained by a global probabilistic fall risk rather than a local step-based measure of instability. Ultimately, these findings reveal that fall risk-aware adaptation explains suboptimal locomotor behavior, providing a new data-driven framework to understand the drivers of motor performance.

neuroscience↗

bioRxiv2025

Magnetoelectric nanodiscs diminish motor deficits in a model of Parkinson's disease

Magnetoelectric nanodiscs (MENDs) offer a wireless, minimally invasive route to neuromodulation by converting weak magnetic fields into electric polarization, but their therapeutic applications have remained unrealized. Here, we report the therapeutic development of MENDs for deep brain stimulation (DBS) in the subthalamic nucleus (STN) to alleviate motor deficits in a mouse model of Parkinsons disease. To quantitatively assess therapeutic outcomes, we introduce GaitPattern, a computational pipeline that extracts DBS-induced alterations in salient gait features. Using GaitPattern, we demonstrate that MEND-mediated STN DBS induces motor improvements with precision comparable to clinical electrode-based DBS, while minimizing inflammatory responses associated with the implanted hardware. Notably, MEND-mediated DBS reduces oxidative stress in the brain, a key contributor to neurodegeneration. These findings position MEND-mediated STN DBS as an effective and minimally invasive neuromodulation strategy.

neuroscience↗

bioRxiv2024

Foot placement control underlies stable locomotion across species

Animals navigate their environment stably without inefficient course corrections despite unavoidable errors. In humans, this stability is achieved by varying the placement of the foot on each step such that recent movement errors are corrected. However, it is unknown how animals with diverse nervous systems and body mechanics use foot placement control: foot trajectories of many-legged animals are thought to be stereotypical velocity-driven patterns, as opposed to error-driven. Here, we posit a unified "feedforward-feedback" control structure for foot placement by combining velocity-driven and body state error-driven contributions. We provide empirical support for this control structure across flies, mice and humans by mining the variability in the foot placements and body states during natural locomotion. We find that a competing "feedforward-only" control structure with purely velocity-driven foot placement is not supported by the data. This work discovers shared behavioral signatures of foot placement control in flies, mice, and humans. The urgency and centralization of these control signatures vary with the animals neuromechanical embodiment; more inherently stable many-legged embodiment is associated with a lower control magnitude and timescale. Further, many-legged embodiment is accompanied by modular direction- and leg-specific signatures, which are centralized across both legs in humans. Taken together, our findings provide insight into stabilizing foot placement control across species, revealing how different neuromechanical embodiments achieve a shared functional goal.

animal behavior and cognition↗

bioRxiv2021

Exploration-based learning of a step to step controller predicts locomotor adaptation

Humans adapt their locomotion seamlessly in response to changes in the body or the environment. We do not understand how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controllers performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons -- capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.

neuroscience↗

bioRxiv2021

Moving outside the lab: markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump

Markerless motion capture using deep learning approaches have potential to revolutionize the field of biomechanics by allowing researchers to collect data outside of the laboratory environment, yet there remain questions regarding the accuracy and ease of use of these approaches. The purpose of this study was to apply a markerless motion capture approach to extract lower limb angles in the sagittal plane during the vertical jump and to evaluate agreement between the custom trained model and gold stand motion capture. We performed this study using a large open source data set (N=84) that included synchronized commercial video and gold standard motion capture. We split these data into a training set for model development (n=69) and test set to evaluate capture performance relative to gold standard motion capture using coefficient of multiple correlations (CMC) (n=15). We found very strong agreement between the custom trained markerless approach and marker-based motion capture within the test set across the entire movement (CMC>0.991, RMSE<3.22{degrees}), with at least strong CMC values across all trials for the hip (0.853 {+/-} 0.23), knee (0.963 {+/-} 0.471), and ankle (0.970 {+/-} 0.055). The strong agreement between markerless and marker-based motion capture provides evidence that markerless motion capture is a viable tool to extend data collection to outside of the laboratory. As biomechanical research struggles with representative sampling practices, markerless motion capture has potential to transform biomechanical research away from traditional laboratory settings into venues convenient to populations that are under sampled without sacrificing measurement fidelity.

bioengineering↗

bioRxiv2019

Computer vision to automatically assess infant neuromotor risk

An infants risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as video cameras. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N=19). For each infant, we calculate how much they deviate from a group of healthy infants (N=85 online videos) using Naive Gaussian Bayesian Surprise. After pre-registering our Bayesian Surprise calculations, we find that infants that are at higher risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.

animal behavior and cognition↗