Mapping Eye, Arm, and Reward Information in Frontal Motor Cortices Using Electrocorticography in Nonhuman Primates

Area of Science:

• Neuroscience
• Motor Control
• Computational Neuroscience

Background:

• Goal-directed movements involve complex, dynamic neural activity across multiple brain regions.
• Understanding the spatial and temporal distribution of neural computations is crucial for deciphering motor control.
• High spatiotemporal resolution mapping techniques are needed to investigate these distributed processes.

Purpose of the Study:

• To map the spatial and temporal distribution of neural information related to eye movements, arm movements, and reward processing.
• To investigate the overlapping neural computations underlying coordinated movements in frontal cortical areas.
• To demonstrate the utility of micro-electrocorticography (µECoG) for functional brain mapping.

Main Methods:

• Utilized micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches.
• Applied time-frequency and decoding analyses to map neural activity across primary motor cortex, premotor cortex, frontal eye field, and dorsolateral prefrontal cortex.
• Employed phase clustering analyses to resolve overlapping and distinct neural information streams for eye, arm, and reward signals.

Main Results:

• Neural information for eye and arm movements shifted across brain regions during reaches, suggesting transitions from planning to execution.
• Eye and arm movement information spatially overlapped within the motor cortex, with task-irrelevant eye movements impacting arm decoding.
• Reward-related activity was identified in the prefrontal and premotor cortex using phase clustering analyses.

Conclusions:

• Micro-electrocorticography (µECoG) is a powerful tool for high-resolution functional mapping of neural activity.
• Detailed the spatial distribution of eye, arm, and reward information processing across frontal cortices during reaching.
• Provided insights into overlapping neural computations for coordinated movements, with implications for brain-computer interface development.