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Poster A62 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Learning along the manifold of human brain activity via real-time neurofeedback
Erica L. Busch1 (), E. Chandra Fincke1, Guillaume Lajoie2,3, Smita Krishnaswamy1,4, Nicholas B. Turk-Browne1,4; 1Yale University, 2Mila - Quebec AI Institute, 3University of Montreal, 4Wu Tsai Institute
Learning to perform a new behavior is constrained by the geometry, or intrinsic manifold, of the neural population activity supporting that behavior. Recent work highlights the importance of manifolds capturing low-dimensional neural dynamics for learning to control brain-computer interfaces (BCIs). In non-human primate studies, BCI learning has been expedited and stabilized by mapping neural recordings from motor cortex through a low-dimensional manifold and then to a feedback display. In macaque motor cortex, the manifold uncovers more concise and plastic neural signals. Here, we investigate the manifold constraints on human learning in brain regions associated with higher-order cognitive processes using a non-invasive BCI. Using a custom neural manifold learning framework for real-time fMRI neurofeedback and a virtual reality stimulus, we trained participants in a multi-session study to perform a navigation task using their brain activity. Task performance was significantly improved by feedback based on the brain's intrinsic relative to lower-ranked ("off") manifold activity. Neural activity was modulated along the manifold over the course of neurofeedback training, such that neural activity became better aligned with the components of the manifold determining the feedback as performance improved.
Keywords: Brain-computer interface learning manifold learning fMRI