Summary To clarify the organization of motor representations in posterior parietal cortex, we test how three motor variables (body side, body part, cognitive strategy) are coded in the human anterior intraparietal cortex. All tested movements were encoded, arguing against strict anatomical segregation of effectors. Single units coded for diverse conjunctions of variables, with different dimensions anatomically overlapping. Consistent with recent studies, neurons encoding body parts exhibited mixed selectivity. This mixed selectivity resulted in largely orthogonal coding of body parts, which “functionally segregate” the effector responses despite the high degree of anatomical overlap. Body side and strategy were not coded in a mixed manner as effector determined their organization. Mixed-coding of some variables over others, what we term “partially mixed coding”, argues that the type of functional encoding depends on the compared dimensions. This structure is advantageous for neuroprosthetics, allowing a single array to decode movements of a large extent of the body.

Highlights d AIP neurons learn to modulate their activity to compensate for errors in BMI tasks d Changes in the neural activity reflect a cognitive readaptation mechanism d AIP fails to compensate for errors when novel neural activity patterns are required d Learning in AIP is constrained by the pre-existing neuronal structure

By using outcome data at the time of admission, a discharge destination can be predicted for stroke patients with significant sensitivity and specificity.

Recent studies in posterior parietal cortex (PPC) have found multiple effectors and cognitive strategies represented within a shared neural substrate in a structure termed "partially mixed selectivity" (Zhang et al., 2017). In this study, we examine whether the structure of these representations is preserved across changes in task context and is thus a robust and generalizable property of the neural population. Specifically, we test whether the structure is conserved from an open-loop motor imagery task (training) to a closed-loop cortical control task (online), a change that has led to substantial changes in neural behavior in prior studies in motor cortex. Recording from a 4 ϫ 4 mm electrode array implanted in PPC of a human tetraplegic patient participating in a brain-machine interface (BMI) clinical trial, we studied the representations of imagined/attempted movements of the left/right hand and compare their individual BMI control performance using a one-dimensional cursor control task. We found that the structure of the representations is largely maintained between training and online control. Our results demonstrate for the first time that the structure observed in the context of an open-loop motor imagery task is maintained and accessible in the context of closed-loop BMI control. These results indicate that it is possible to decode the mixed variables found from a small patch of cortex in PPC and use them individually for BMI control. Furthermore, they show that the structure of the mixed representations is maintained and robust across changes in task context.

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