Stability of motor representations after paralysis
Abstract
Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain–computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure similar to fMRI recordings of able-bodied individuals' motor cortex, which is known to reflect able-bodied usage patterns. The finger representational structure was consistent throughout multiple sessions, even though the structure contributed to BCI decoding errors. Within individual BCI movements, the representational structure was dynamic, first resembling muscle activation patterns and then resembling the anticipated sensory consequences. Our results reveal that motor representations in PPC reflect able-bodied motor usage patterns even after paralysis, and BCIs can re-engage these stable representations to restore lost motor functions.
Additional Information
Funding: National Eye Institute (R01EY015545), Charles Guan, Richard A Andersen. National Eye Institute (UG1EY032039). Charles Guan. Richard A Andersen. Tianqiao and Chrissy Chen Brain-machine Interface Center at Caltech, Tyson Aflalo, Richard A Andersen. Boswell Foundation, Richard A Andersen. Amazon AI4Science Fellowship, Charles Guan. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. We thank NS for her dedicated participation in the study. Kelsie Pejsa and Viktor Scherbatyuk for administrative and technical assistance. Paulina Kieliba and Tamar Makin for sharing their fMRI data. Tamar Makin and Whitney Griggs for their helpful feedback on the manuscript. Jörn Diedrichsen and Spencer Arbuckle for sharing their fMRI data and models.Additional details
- PMCID
- PMC9555862
- Eprint ID
- 117534
- Resolver ID
- CaltechAUTHORS:20221024-114419100.1
- R01EY015545
- NIH
- UG1EY032039
- NIH
- Tianqiao and Chrissy Chen Institute for Neuroscience
- James G. Boswell Foundation
- Amazon AI4Science Fellowship
- Created
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2022-10-28Created from EPrint's datestamp field
- Updated
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2022-10-29Created from EPrint's last_modified field
- Caltech groups
- Tianqiao and Chrissy Chen Institute for Neuroscience, Division of Biology and Biological Engineering