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

• 成果类型: Article
• 署名作者: Singh, Matthew F.; Braver, Todd S.; Cole, Michael; Ching, Shinung
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Washington University (WUSTL); Washington University (WUSTL); Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14583
• DOI: 10.1073/pnas.2409577121
• 发表日期: 2025-01-21
• 关键词: intraindividual variability; functional connectivity; alpha oscillations; eeg; state; frequency; ORGANIZATION; excitation; attention; FIELDS

摘要

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 timeseries 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.