Biology subjects

Rakhshan, M.

Publications and source records attributed to Rakhshan, M..

6 recordsLinked to original sources

bioRxiv2026

Consistency Analyses of Open-source Software for Motor Unit Decomposition Using High-density Electromyography Signal

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.

bioengineering↗

bioRxiv2025

Limb Position Effect in Myoelectric Control: Strategies for Optimisation and Standardisation

ObjectiveMyoelectric control uses electromyography (EMG) signals for muscle-machine interfacing with applications in prostheses, augmented/virtual reality, and consumer electronics. However, factors such as changes in the limbs position during activities of daily living reduce the controllers reliability. Therefore, there is a need to develop techniques that reduce this limb position effect to increase the widespread adoption of these technologies. ApproachWe developed an open-source device to standardise myoelectric control experiments. The device has sixteen locations for automatically positioning the participants arms to perform hand gestures or grasp objects, with lights and sensors for guidance and timekeeping. We used this device to collect data from eighteen healthy participants in a five-session study under three modalities: performing five hand gestures with a static or dynamic limb and moving three objects. We recorded forearm electromyography and kinematics of the upper limb and trained a linear discriminant analysis model to assess the classifiers accuracy across different modalities and arm positions. Main resultsThe classifiers accuracy with a static limb was decreased when tested on untrained positions, confirming the limb position effect. More training positions improved accuracy, with four optimally balancing the training burden and classifier accuracy. Classifiers trained with data from dynamic movements outperformed when tested on dynamic data. Furthermore, adding kinematic data to the classifier increased accuracy yet significantly reduced learning rates. However, training with a dynamic limb improved this learning rate. SignificanceThe limb position effect can be countered by training with multiple positions and including kinematic data. Classifiers with EMG and kinematic data should be trained using a dynamic limb to achieve high accuracy with reasonable amounts of training data. Our open-source, automated device will help standardise datasets between laboratories, aiding the further development of robust and widespread myoelectric control.

bioengineering↗

bioRxiv2024

Neural mechanisms underlying robust target selection in response to microstimulation of the oculomotor system

Despite its prevalence in studying the causal roles of different brain circuits in cognitive processes, electrical microstimulation often results in inconsistent behavioral effects. These inconsistencies are assumed to be due to multiple mechanisms, including habituation, compensation by other brain circuits, and contralateral suppression. Considering the presence of reinforcement in most experimental paradigms, we hypothesized that interactions between reward feedback and microstimulation could contribute to inconsistencies in behavioral effects of microstimulation. To test this, we analyzed data from electrical microstimulation of the frontal eye field of male macaques during a value-based decision-making task and constructed network models to capture choice behavior. We found evidence for microstimulation-dependent adaptation in saccadic choice, such that in stimulated trials, monkeys choices were biased toward the target in the response field of the microstimulated site (Tin). In contrast, monkeys showed a bias away from Tin in non-stimulated trials following microstimulation. Critically, this bias slowly decreased as a function of the time since the last stimulation. Moreover, microstimulation-dependent adaptation was influenced by reward outcomes in preceding trials. Despite these local effects, we found no evidence for the global effects of microstimulation on learning and sensitivity to the reward schedule. By simulating choice behavior across various network models, we found a model in which microstimulation and reward-value signals interact competitively through reward-dependent plasticity can best account for our observations. Our findings indicate a reward-dependent compensatory mechanism that enhances robustness to perturbations within the oculomotor system and could explain the inconsistent outcomes observed in previous microstimulation studies.

neuroscience↗

bioRxiv2020

Separable influences of reward value on visual processing and choice

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remains unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the endpoints of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion in order to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, even though the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for integration of reward outcomes.

neuroscience↗

bioRxiv2019

Dissociable roles for Anterior Cingulate Cortex and Basolateral Amygdala in Decision Confidence and Learning under Uncertainty

It has been suggested the subjective sense of certainty, or confidence, in ambiguous sensory cues can alter the interpretation of reward feedback and facilitate learning. We trained rats to report the orientation of ambiguous visual stimuli according to a spatial stimulus-response rule. Following choice, rats could wait a self-timed delay for reward or initiate a new trial. Waiting times increased with discrimination accuracy, demonstrating that this measure could be used as a proxy for confidence. Chemogenetic silencing of BLA shortened waiting times overall whereas ACC inhibition rendered waiting times insensitive to confidence-modulating attributes of visual stimuli, suggesting contribution of ACC but not BLA to confidence computations. Subsequent reversal learning was enhanced by confidence. Both ACC and BLA inhibition blocked this enhancement but via differential modulation of learning strategies and consistency in using learned rules. Altogether, we demonstrate dissociable roles for ACC and BLA in transmitting confidence and learning under uncertainty.

neuroscience↗

bioRxiv2018

Influence of expected reward on perceptual decision making

Perceptual decision making is influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing and/or later stages of decision making. To address this question, we conducted two experiments in which human subjects made saccades to what they perceived to be the first or second of two visually identical but asynchronously presented targets, while we manipulated expected reward from correct and incorrect responses on each trial. We found that unequal reward caused similar shifts in target selection (reward bias) between the two experiments. Moreover, observed reward biases were independent of the individuals sensitivity to sensory signals. These findings suggest that the observed reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing and thus, are more compatible with response bias rather than perceptual bias. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that during a temporal judgment task, the influence of reward information on perceptual choice is more compatible with changing later stages of decision making rather than early sensory processing.

neuroscience↗