Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology
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-...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 3; p. e2409577121 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
21.01.2025
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| Subjects | |
| Online Access | Get full text |
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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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| AbstractList | At rest, brain activity converges onto person-specific temporal patterns (dynamics). Signatures of these dynamics, such as synchronization and spectral-power, predict cognitive abilities but, at the person-level, have proven difficult to map onto biological models of the brain due to the many model parameters involved. Such models are critical to linking biological processes with dynamical outcomes and for personalized medicine. Our study develops and rigorously validates a data-driven approach to directly estimate individualized brain models containing hundreds of neural populations and thousands of model parameters from noninvasive brain recordings. By comparing models we identify a mathematical mechanism (attractor topology) that drives individual differences in spectral power and trace it back to individual differences in local inhibitory circuits.
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. 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. 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.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. |
| Author | Cole, Michael Braver, Todd S. Ching, ShiNung Singh, Matthew F. |
| Author_xml | – sequence: 1 givenname: Matthew F. orcidid: 0000-0003-0051-336X surname: Singh fullname: Singh, Matthew F. – sequence: 2 givenname: Todd S. orcidid: 0000-0002-2631-3393 surname: Braver fullname: Braver, Todd S. – sequence: 3 givenname: Michael orcidid: 0000-0003-4329-438X surname: Cole fullname: Cole, Michael – sequence: 4 givenname: ShiNung surname: Ching fullname: Ching, ShiNung |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39823302$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright National Academy of Sciences Jan 21, 2025 Copyright © 2025 the Author(s). Published by PNAS. 2025 |
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| Keywords | attractors MEG brain dynamics resting-state individual differences |
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| Snippet | Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of... At rest, brain activity converges onto person-specific temporal patterns (dynamics). Signatures of these dynamics, such as synchronization and spectral-power,... |
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| SubjectTerms | 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 |
| Title | Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology |
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