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What Can Computational Models Contribute to Neuroimaging Data Analytics?
Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynami...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338060/ https://www.ncbi.nlm.nih.gov/pubmed/30687028 http://dx.doi.org/10.3389/fnsys.2018.00068 |
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author | Popovych, Oleksandr V. Manos, Thanos Hoffstaedter, Felix Eickhoff, Simon B. |
author_facet | Popovych, Oleksandr V. Manos, Thanos Hoffstaedter, Felix Eickhoff, Simon B. |
author_sort | Popovych, Oleksandr V. |
collection | PubMed |
description | Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields. |
format | Online Article Text |
id | pubmed-6338060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63380602019-01-25 What Can Computational Models Contribute to Neuroimaging Data Analytics? Popovych, Oleksandr V. Manos, Thanos Hoffstaedter, Felix Eickhoff, Simon B. Front Syst Neurosci Neuroscience Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields. Frontiers Media S.A. 2019-01-10 /pmc/articles/PMC6338060/ /pubmed/30687028 http://dx.doi.org/10.3389/fnsys.2018.00068 Text en Copyright © 2019 Popovych, Manos, Hoffstaedter and Eickhoff. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Popovych, Oleksandr V. Manos, Thanos Hoffstaedter, Felix Eickhoff, Simon B. What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title | What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title_full | What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title_fullStr | What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title_full_unstemmed | What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title_short | What Can Computational Models Contribute to Neuroimaging Data Analytics? |
title_sort | what can computational models contribute to neuroimaging data analytics? |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338060/ https://www.ncbi.nlm.nih.gov/pubmed/30687028 http://dx.doi.org/10.3389/fnsys.2018.00068 |
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