Journal Article

FZJ-2019-00224

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png What Can Computational Models Contribute to Neuroimaging Data Analytics?

Popovych, O. (Corresponding author)FZJ* ; Manos, T.FZJ* ; Hoffstaedter, F.FZJ* ; Eickhoff, S.FZJ*

2019
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: http://hdl.handle.net/2128/21486  doi:10.3389/fnsys.2018.00068

Abstract: 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.

Note: The authors gratefully acknowledge helpful discussions withViktor Jirsa and Gustavo Deco. This work was supportedby the Deutsche Forschungsgemeinschaft (DFG, EI 816/11-1),the National Institute of Mental Health (R01-MH074457), theHelmholtz Portfolio Theme Supercomputing and Modeling forthe Human Brain and the European Union’s Horizon 2020Research and Innovation Programme under Grant Agreement720270 (HBP SGA1) and 785907 (HBP SGA2).

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
2. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)

Appears in the scientific report 2019

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Web of Science Core Collection

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