Individualized Dynamic Brain Models: Estimation and Validation with Resting-State fMRI

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 with a new approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling which enables networks of hundreds to thousands of nonlinear neural mass models to be fit directly to human brain data in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models generate individualized patterns of resting-state dynamics and that MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than correlative methods (functional connectivity).

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