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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
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How does sensorimotor mu rhythm activity impact corticospinal output during TMS delivery?

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Dr. Rishabh Pathak


Sensorimotor mu rhythm activity plays a significant role in influencing corticospinal output during transcranial magnetic stimulation (TMS) delivery. The sensorimotor mu rhythm is an oscillatory brain activity that occurs in the frequency range of 8-13 Hz and is predominantly observed over sensorimotor cortical regions. Here is how sensorimotor mu rhythm activity impacts corticospinal output during TMS delivery:


1.     Phase-Dependent Effects: Studies have shown that the phase of the sensorimotor mu rhythm can influence corticospinal excitability. Specifically, corticospinal output is modulated by the phase of the mu rhythm, with increased excitability observed during specific phases of the mu rhythm cycle. For example, corticospinal output tends to be higher during the trough (negative peak) phases of the mu rhythm compared to the peak (positive peak) phases.

2.     Enhancement vs. Suppression: When TMS is delivered to the primary motor cortex (M1) during the trough phases of the mu rhythm, it can lead to enhanced corticospinal transmission and improved motor learning. In contrast, TMS delivered during the peak phases of the mu rhythm may result in weaker corticospinal transmission and have less impact on motor learning. This phase-dependent effect highlights the importance of timing TMS interventions based on the ongoing sensorimotor mu rhythm activity.

3.     Interhemispheric Communication: In addition to influencing corticospinal output, sensorimotor mu rhythm activity also affects interhemispheric communication between homologous motor cortex regions. Studies have demonstrated that the phase synchronicity of the mu rhythm can determine the efficacy of communication between motor cortices, further emphasizing the role of mu rhythm activity in shaping neural interactions.

4.     Complex Interplay: The interplay between sensorimotor mu rhythm phase and power is complex and interdependent in shaping corticospinal tract activity. Both the phase and power of the mu rhythm contribute to modulating corticospinal output, highlighting the need to consider both aspects when utilizing TMS for measurement or interventional purposes.


In conclusion, sensorimotor mu rhythm activity exerts a significant influence on corticospinal output during TMS delivery, with phase-dependent effects playing a crucial role in modulating neural excitability and motor learning processes. Understanding and leveraging the impact of mu rhythm activity can enhance the efficacy of TMS interventions and provide insights into the mechanisms underlying motor control and plasticity in the human brain.

 

 

Hussain, S. J., Claudino, L., Bönstrup, M., Norato, G., Cruciani, G., Thompson, R., ... Cohen, L. G. (2019). Sensorimotor oscillatory phase–power interaction gates resting human corticospinal output. Cerebral Cortex, 29(9), 3766–3777. https://doi.org/10.1093/cercor/bhy255.

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