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Motor and Sensory Areas of the Cortex

Motor and Sensory Areas of the Cortex

The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....

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Related Experiment Video

Updated: Oct 23, 2025

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning.

Razieh Faghihpirayesh, Mathew Yarossi, Tales Imbiriba

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|August 18, 2021

Summary

This summary is machine-generated.

This study introduces an automated method for Transcranial Magnetic Stimulation (TMS) mapping, improving efficiency and accuracy. Active learning with Gaussian Processes optimizes cortical mapping, outperforming traditional and user-guided techniques.

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Published on: April 21, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Computational Neuroscience

Background:

Transcranial Magnetic Stimulation (TMS) is utilized for mapping cortical motor topography by stimulating the sensorimotor cortex and recording Motor Evoked Potentials (MEPs).
Traditional grid-based or randomized TMS mapping is inefficient due to sparse and spatially correlated responsive sites.
Current presurgical planning relies on operator expertise for guiding TMS stimulation, which is subjective and time-consuming.

Purpose of the Study:

To develop and evaluate an automated active learning strategy for optimizing TMS cortical mapping.
To replace subjective, user-guided TMS sampling with a data-driven, algorithmic approach.
To enhance the efficiency and accuracy of MEP-based cortical mapping.

Main Methods:

Comparison of traditional grid selection and randomized sampling with user-guided sampling (USRG).
Implementation and evaluation of novel active learning Gaussian Process (GP) strategies, including warped GP entropy and GP random-based sampling.
Experimental validation using real-world TMS data.

Main Results:

User-guided sampling (USRG) demonstrated superior time efficiency and MEP map accuracy compared to grid and random methods.
Novel active GP strategies, specifically warped GP entropy and GP random-based sampling, performed comparably or better than the USRG method.
Automated methods successfully identified and efficiently sampled regions with significant MEP response variations.

Conclusions:

Active learning GP-based sampling offers a fully automated and highly efficient alternative to current TMS mapping techniques.
These automated methods provide a foundation for precise and rapid cortical mapping, particularly when integrated with robotic coil operation.
The proposed approach significantly improves upon existing methods for both presurgical planning and general neuroscientific research.