PT  - JOURNAL ARTICLE
AU  - Matthew F. Singh
AU  - Todd S. Braver
AU  - Michael W. Cole
AU  - ShiNung Ching
TI  - Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI
AID  - 10.1101/678243
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 678243
4099  - http://biorxiv.org/content/early/2019/10/17/678243.short
4100  - http://biorxiv.org/content/early/2019/10/17/678243.full
AB  - 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 minutes 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.