Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 3; p. e2409577121
• Main Authors: Singh, Matthew F., Braver, Todd S., Cole, Michael, Ching, ShiNung
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 21.01.2025
• Subjects: Algorithms; Alpha Rhythm - physiology; Beta Rhythm - physiology; Biological Sciences; Brain; Brain - physiology; Cerebral Cortex - physiology; EEG; Electroencephalography; Electrophysiology; Frequency variation; Humans; Models, Neurological; Nerve Net - physiology; Oscillations; Predictions; Topology; attractors; MEG; brain dynamics; resting-state; individual differences
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2409577121

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.