%A Popovych,Oleksandr V.
%A Manos,Thanos
%A Hoffstaedter,Felix
%A Eickhoff,Simon B.
%D 2019
%J Frontiers in Systems Neuroscience
%C
%F
%G English
%K Neuroimaging,resting state,Mathematical Models,Brain Dynamics,functional connectivity,simulation,high-performance computing
%Q
%R 10.3389/fnsys.2018.00068
%W
%L
%N 68
%M
%P
%7
%8 2019-January-10
%9 Perspective
%#
%! Models and Neuroimaging Data Analytics
%*
%<
%T What Can Computational Models Contribute to Neuroimaging Data Analytics?
%U https://www.frontiersin.org/article/10.3389/fnsys.2018.00068
%V 12
%0 JOURNAL ARTICLE
%@ 1662-5137
%X 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.