Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task

Area of Science:

• Neuroscience
• Motor Control Research
• Computational Neuroscience

Background:

• Primary motor cortex (M1) activity is complex, correlating with multiple motor variables, which complicates understanding its precise role in motor control.
• Disentangling M1's contribution to classifying motor fields versus predicting spatiotemporal muscle activity patterns is challenging.

Purpose of the Study:

• To develop and validate a novel two-stage analytical process to separate the classification of M1 neuron motor fields from the prediction of their spatiotemporal activity patterns during reaching.
• To investigate the relationship between neural network connectivity, neural activity, and motor task performance.

Main Methods:

• A two-stage analytical process was developed to classify M1 neuron motor fields and predict their spatiotemporal activity.
• A neural network model controlling a two-joint arm was used to test the approach.
• Analysis was conducted using neural recordings from rhesus monkeys during reaching tasks.

Main Results:

• M1 neurons, classified using the new method, exhibited preferred reaching directions consistent with their associated muscle groups.
• Neural population signals successfully predicted the spatiotemporal dynamics of associated muscle groups.
• A subset of M1 neurons showed reversed directional preferences, suggesting a role in controlling antagonist muscles.

Conclusions:

• The developed method effectively separates M1 neuron classification from activity prediction, providing a clearer understanding of M1's role in motor control.
• M1 encodes detailed spatiotemporal patterns of muscle activity crucial for motor skills like reaching.
• The findings suggest a selective role for certain M1 neurons in antagonist muscle control.