Preservation of partially mixed selectivity in human posterior parietal cortex across changes in task context
Abstract
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 × 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.
Additional Information
© 2020 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. Received June 10, 2019; Accepted December 17, 2019; Published online January 22, 2020. We thank subject NS for participating in the studies, and Kelsie Pejsa, Tessa Yao, and Viktor Scherbatyuk for technical and administrative assistance. The authors declare no competing financial interests. This work was supported by the National Institutes of Health (Grant R01-EY-015545), the T&C Chen Brain-Machine Interface Center at Caltech, the Della Martin Foundation, the Conte Center for Social Decision Making at Caltech (Grant P50MH094258), and the Boswell Foundation.Attached Files
Published - ENEURO.0222-19.2019.full.pdf
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Additional details
- PMCID
- PMC7070450
- Eprint ID
- 100859
- Resolver ID
- CaltechAUTHORS:20200122-161431225
- R01-EY-015545
- NIH
- Tianqiao and Chrissy Chen Institute for Neuroscience
- Della Martin Foundation
- P50MH094258
- NIH
- James G. Boswell Foundation
- Created
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2020-01-23Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Tianqiao and Chrissy Chen Institute for Neuroscience, Division of Biology and Biological Engineering