ArticleNeuroImage2020
Estimation and validation of individualized dynamic brain models with resting state fMRI.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 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
- On the control of recurrent neural networks using constant inputs.IEEE transactions on automatic control · 2026Article
- Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention.Nature communications · 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
- Blind identification of state transitions and latent neural dynamics from electrophysiological recordings.Journal of neuroscience methods · 2026Article
- The turbulent brain: Modeling vortex interactions for understanding human cognition.Network neuroscience (Cambridge, Mass.) · 2026Article
- Stable individualized brain computing model informed by spatiotemporal co-activity patterns.PLoS computational biology · 2025Article
- Two views of the brain are reconciled by a unifying principle of maximal information processing.bioRxiv : the preprint server for biology · 2025Article
- Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.Journal of neural engineering · 2025Article
- Article
- Mapping effective connectivity by virtually perturbing a surrogate brain.Nature methods · 2025Article
- Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Revealing excitation-inhibition imbalance in Alzheimer's disease using multiscale neural model inversion of resting-state functional MRI.Communications medicine · 2025Article
- Dynamical models reveal anatomically reliable attractor landscapes embedded in resting-state brain networks.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Quantifying brain-functional dynamics using deep dynamical systems: Technical considerations.iScience · 2024Article
- Data-driven modeling of neural dynamics from EEG to track physiological changes.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks.bioRxiv : the preprint server for biology · 2024Article
- Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations.bioRxiv : the preprint server for biology · 2023Article
- Bayesian inference of a spectral graph model for brain oscillations.NeuroImage · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.