Partially Mixed Selectivity and Parietal Cortex

Brain-machine interfaces (BMIs) decode intention signals and other variables from the brain in order to control a computer, tablet, or prosthetic limb. In order to improve the technology, a better understanding of the representational mechanisms within the brain is necessary. Here we study how the anterior intraparietal area (AIP) of human posterior parietal cortex is able to represent many variables within a small patch of cortex. We record single unit activity using a 4 x 4 mm microelectrode array implanted in AIP of a human tetraplegic volunteer. Testing movements of different cognitive strategies, body parts, and body sides, we find that the neural population represents information in a high-dimensional way, termed "mixed selectivity", with individual units coding for idiosyncratic combinations of variables. Furthermore, we find that the variables are not randomly mixed but exhibited "partially mixed selectivity" with certain variables more randomly mixed than others. Representations were "functionally segregated", with representations of the hand and shoulder largely orthogonal despite the high degree of anatomical overlap; representations of body side and strategy were organized by body part. We also examine how the representations changed between BMI training and online BMI control. We find that the structure of the movement representations was preserved, with the different representations found during calibration maintained during online control. Finally, we study the sensory mirror system, a system that processes observed sensations similarly to experienced sensations. We once again find partially mixed selectivity and functional segregation by body parts, showing that this method of encoding information exists not just in the action intention domain but also in the sensory domain. Our results propose partially mixed selectivity as a general mechanism for encoding high dimensional in formation in a small neural population, while also advancing the possibility of limited electrode-array BMIs decoding movements of a large extent of the body.</p