This repository contains the code and analysis for our study on the whole-brain basis of reward-based motor learning, focusing on the coordinated activity of multiple neural systems across the cortex and subcortex. We utilized functional MRI to monitor human brain activity during a reward-based motor task and analyzed the patterns of functional connectivity projected onto a low-dimensional manifold space. Our findings offer a unique perspective on how higher-order brain systems interact with the sensorimotor cortex to facilitate learning, highlighting the dynamic changes in functional coupling between the medial prefrontal and sensorimotor cortex across different learning phases.
For more detailed insights into our findings, refer to our paper: Reconfigurations of cortical manifold structure during reward-based motor learning.
Authors: Qasem Nick, Daniel J. Gale, Corson Areshenkoff, Anouk De Brouwer, Joseph Nashed, Jeffrey Wammes, Tianyao Zhu, Randy Flanagan, Jonny Smallwood, Jason Gallivan
For further information or queries, feel free to contact us at jasongallivan@gmail.com or qniksefat@gmail.com.
Abstract: Adaptive motor behavior is crucially dependent on the coordinated activity of multiple neural systems distributed across the brain. While the sensorimotor cortex's role in motor learning is well-established, the interaction between higher-order brain systems and the sensorimotor cortex during learning is less understood. In our study, we used functional MRI to examine human brain activity during a reward-based motor task, focusing on how subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space, observing how regions expanded and contracted along the manifold during learning. Our results highlight the neural changes that underpin reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.
Epochs Defined in the Study:
- Rest: Subject is not performing the task. 297 trs, with the first 3 trs dismissed.
- Baseline: Subject performs the task without reward. 219 trs, with the first 3 trs dismissed.
- Learning: Subject begins receiving rewards, divided into early and late sections to differentiate the learning period.
- Early: Marks the onset of understanding task changes, with the first 3 trs dismissed (3:219 trs).
- Late: Indicates when subjects correctly perform the task, focusing on the last 216 trs.
The code used to generate figures from the paper is available in the file elife_submission.py.
- nilearn
- nibabel
- brainspace
- surfplot
- enigmatoolbox
- bct
- pyriemann
- pingouin (version 0.2.1)
- Data Directory: Contains subdirectories for different data sources and files related to the task.
- Notebooks Directory: Houses Jupyter notebooks for data analysis and visualization.
To run the notebooks: Navigate to the notebooks/ directory and launch Jupyter Notebook.