ArticleProceedings of the National Academy of Sciences of the United States of America2025
Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment.Neural computation · 2026Article
- Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.bioRxiv : the preprint server for biology · 2026Article
- Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention.Nature communications · 2026Article
- Blind identification of state transitions and latent neural dynamics from electrophysiological recordings.Journal of neuroscience methods · 2026Article
- A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.Journal of neural engineering · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks.bioRxiv : the preprint server for biology · 2024Article
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Abstract
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG metrics. Last, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitatory-inhibitory balance, highlighting the explanatory power of our framework to generate mechanistic insights.
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