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

Renee JohnstonMohamad AbbassBenjamin W CorriganRoberto A GulliJulio C Martinez-TrujilloAdam J Sachs
Published in: Journal of neural engineering (2023)
Objective . Decoding the intended trajectories from brain signals using a brain-computer interface system could be used to improve the mobility of patients with disabilities. Approach . Neuronal activity associated with spatial locations was examined while macaques performed a navigation task within a virtual environment. Main results. Here, we provide proof of principle that multi-unit spiking activity recorded from the lateral prefrontal cortex (LPFC) of non-human primates can be used to predict the location of a subject in a virtual maze during a navigation task. The spatial positions within the maze that require a choice or are associated with relevant task events can be better predicted than the locations where no relevant events occur. Importantly, within a task epoch of a single trial, multiple locations along the maze can be independently identified using a support vector machine model. Significance . Considering that the LPFC of macaques and humans share similar properties, our results suggest that this area could be a valuable implant location for an intracortical brain-computer interface system used for spatial navigation in patients with disabilities.
Keyphrases
  • prefrontal cortex
  • white matter
  • resting state
  • deep learning
  • cerebral ischemia
  • endothelial cells
  • minimally invasive
  • depressive symptoms
  • multiple sclerosis
  • machine learning
  • decision making
  • open label
  • neural network