Biology subjects

Krishnan, C.

Publications and source records attributed to Krishnan, C..

3 recordsLinked to original sources

bioRxiv2025

Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance

Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements--something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications. Significance StatementMany researchers believe that brain and muscle synergies represent a fundamental control strategy and could enhance brain-machine interface (BMI) decoding performance. These synergies, extracted through dimensionality reduction techniques, are thought to simplify complex neural data, improving the efficiency and accuracy of BMI systems. In our study, we evaluated brain and muscle synergies in a dexterous finger task. We found that while these synergies effectively compressed high-dimensional data, they did not improve performance through denoising or generalize well across different contexts. Instead, the highest performance was achieved when using all available data, suggesting that synergies, although useful for data compression, may not provide the "true" control space needed to enhance decoder robustness or adaptability in implanted BMI systems.

neuroscience↗

bioRxiv2023

Muscle Coordination Matters: Insights into Motor Planning using Corticospinal Responses during Functional Reaching

The central nervous system (CNS) moves the human body by forming a plan in the primary motor cortex and then executing this plan by activating the relevant muscles. It is possible to study motor planning by using noninvasive brain stimulation techniques to stimulate the motor cortex prior to a movement and examine the evoked responses. Studying the motor planning process can reveal useful information about the CNS, but previous studies have generally been limited to single degree of freedom movements (e.g., wrist flexion). It is currently unclear if findings in these studies generalize to multi-joint movements, which may be influenced by kinematic redundancy and muscle synergies. Here, our objective was to characterize motor planning in the cortex prior to a functional reach involving the upper extremity. We asked participants to reach for a cup placed in front of them when presented with a visual "Go Cue". Following the go cue, but prior to movement onset, we used transcranial magnetic stimulation (TMS) to stimulate the motor cortex and measured the changes in the magnitudes of evoked responses in several upper extremity muscles (MEPs). We varied each participants initial arm posture to examine the effect of muscle coordination on MEPs. Additionally, we varied the timing of the stimulation between the go cue and movement onset to examine the time course of changes in the MEPs. We found that the MEPs in all proximal (shoulder and elbow) muscles increased as the stimulation was delivered closer to movement onset, regardless of arm posture, but MEPs in the distal (wrist and finger) muscles were not facilitated or even inhibited. We also found that facilitation varied with arm posture in a manner that reflected the coordination of the subsequent reach. We believe that these findings provide useful insight into the way the CNS plans motor skills.

neuroscience↗

bioRxiv2020

Motor variability prior to learning is a poor predictor of the ability to adopt new movement solutions

Many contexts in motor learning require a learner to change from an existing movement solution to a novel movement solution to perform the same task. Recent evidence has pointed to motor variability prior to learning as a potential marker for predicting individual differences in motor learning. However, it is not known if this variability is predictive of the ability to adopt a new movement solution for the same task. Here, we examined this question in the context of a redundant precision task requiring control of motor variability. Fifty young adults learned a precision task that involved throwing a virtual puck toward a target using both hands. Because the speed of the puck depended on the sum of speeds of both hands, this task could be achieved using multiple solutions. Participants initially performed a baseline task where there was no constraint on the movement solution, and then performed a novel task where they were constrained to adopt a specific movement solution requiring asymmetric left and right hand speeds. Results showed that participants were able to learn the new solution, and this change was associated with changes in both the amount and structure of variability. However, individual differences in baseline motor variability were only weakly correlated with initial and final task performance when using the new solution, with greater variability being associated with higher errors. We also found a strong specificity component - initial variability when using the new solution was highly correlated with final task performance with the new solution, but once again, higher variability was associated with greater errors. These results suggest that motor variability is not necessarily indicative of flexibility and highlight the need to consider the task context in determining the relation between motor variability and learning.

neuroscience↗