Decoding spatial locations from primate lateral prefrontal cortex neural activity during virtual navigation.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cognitive Science

Background:

• Brain-computer interfaces (BCIs) offer potential for restoring mobility in individuals with disabilities.
• Decoding intended movement trajectories from neural signals is a key challenge in BCI development.

Purpose of the Study:

• To investigate the feasibility of using neural activity from the lateral prefrontal cortex (LPFC) to predict spatial navigation trajectories.
• To establish proof of principle for LPFC as a target area for brain-computer interface systems.

Main Methods:

• Recorded multi-unit spiking activity from the LPFC of macaques during a virtual navigation task.
• Utilized a support vector machine model to decode spatial locations from neural data.

Main Results:

• Multi-unit spiking activity in the LPFC successfully predicted subject locations within a virtual maze.
• Predictive accuracy was higher for locations involving choices or task-relevant events.
• Multiple maze locations were independently identifiable within a single trial epoch.

Conclusions:

• The LPFC contains neural information predictive of spatial navigation.
• The LPFC's shared properties between macaques and humans suggest its potential as an implant site for spatial navigation BCIs.
• This research supports the development of BCIs for enhancing mobility in patients with disabilities.