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Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural
Dhananjay Bhaskar1,2,3, Yanlei Zhang4, Jessica Moore5
1Kavli Institute for Neuroscience, Yale School of Medicine.
Biorxiv : the Preprint Server for Biology
|July 14, 2025
Summary
Neurospectrum, a novel framework, decodes complex neural signals into interpretable trajectories. This approach enhances understanding of brain activity for disease biomarker discovery and behavior prediction.
Area of Science:
- Computational Neuroscience
- Machine Learning
- Data Science
Background:
- Neural signals are inherently high-dimensional, noisy, and dynamic.
- Extracting interpretable features from neural data for behavioral or disease linkage is challenging.
Purpose of the Study:
- To introduce Neurospectrum, a framework for encoding neural activity into latent trajectories.
- To develop a method for uncovering meaningful neural dynamics and improving downstream predictions.
Main Methods:
- Representing neural signals on spatial graphs with attention mechanisms.
- Embedding signals using graph wavelets and manifold-regularized autoencoders.
- Summarizing latent trajectories with geometric, topological, and dynamical descriptors (e.g., curvature, persistent homology).
Main Results:
- Neurospectrum successfully tracks phase synchronization in simulations.
- It reconstructs visual stimuli from calcium imaging data.
- It identifies biomarkers for obsessive-compulsive disorder in fMRI data, outperforming traditional methods.
Conclusions:
- Neurospectrum provides a modular, interpretable, and end-to-end trainable framework for neural data analysis.
- The framework effectively uncovers meaningful neural dynamics across diverse datasets.
- It offers superior performance compared to traditional analysis techniques.
