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Estimation and validation of individualized dynamic brain models with resting state fMRI
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-dri...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875185/ https://www.ncbi.nlm.nih.gov/pubmed/32603858 http://dx.doi.org/10.1016/j.neuroimage.2020.117046 |
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author | Singh, Matthew F. Braver, Todd S. Cole, Michael W. Ching, ShiNung |
author_facet | Singh, Matthew F. Braver, Todd S. Cole, Michael W. Ching, ShiNung |
author_sort | Singh, Matthew F. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7875185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-78751852021-02-10 Estimation and validation of individualized dynamic brain models with resting state fMRI Singh, Matthew F. Braver, Todd S. Cole, Michael W. Ching, ShiNung Neuroimage Article 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. 2020-06-27 2020-11-01 /pmc/articles/PMC7875185/ /pubmed/32603858 http://dx.doi.org/10.1016/j.neuroimage.2020.117046 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Singh, Matthew F. Braver, Todd S. Cole, Michael W. Ching, ShiNung Estimation and validation of individualized dynamic brain models with resting state fMRI |
title | Estimation and validation of individualized dynamic brain models with resting state fMRI |
title_full | Estimation and validation of individualized dynamic brain models with resting state fMRI |
title_fullStr | Estimation and validation of individualized dynamic brain models with resting state fMRI |
title_full_unstemmed | Estimation and validation of individualized dynamic brain models with resting state fMRI |
title_short | Estimation and validation of individualized dynamic brain models with resting state fMRI |
title_sort | estimation and validation of individualized dynamic brain models with resting state fmri |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875185/ https://www.ncbi.nlm.nih.gov/pubmed/32603858 http://dx.doi.org/10.1016/j.neuroimage.2020.117046 |
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