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...
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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