Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
March 2023
in “
bioRxiv (Cold Spring Harbor Laboratory)
”
Studysummary This study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.
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Neurospectrum is a novel deep learning framework designed to analyze complex neural signals by encoding them as latent trajectories that reflect spatial and temporal structures. It utilizes graph-based representations and a manifold-regularized autoencoder to preserve temporal geometry, allowing for the extraction of multiscale geometric, topological, and dynamical features. These features are used for downstream predictions in a modular and interpretable manner. Neurospectrum has been tested on both simulated and experimental datasets, demonstrating its ability to track phase synchronization, reconstruct visual stimuli, and identify biomarkers of obsessive-compulsive disorder, outperforming traditional analysis methods. The study was supported by the National Institutes of Health and the National Science Foundation, among others.
