ArticleFrontiers in systems neuroscience2018
What Can Computational Models Contribute to Neuroimaging Data Analytics?
Article in Frontiers in systems neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia.Scientific reports · 2026Article
- Stable individualized brain computing model informed by spatiotemporal co-activity patterns.PLoS computational biology · 2025Article
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- Effective workflow from multimodal MRI data to model-based prediction.Scientific reports · 2025Article
- Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling.Science advances · 2025Article
- Article
- Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns.Frontiers in computational neuroscience · 2025Article
- Arbor-TVB: a novel multi-scale co-simulation framework with a case study on neural-level seizure generation and whole-brain propagation.Frontiers in computational neuroscience · 2025Article
- Modeling brain network flexibility in networks of coupled oscillators: a feasibility study.Scientific reports · 2024Article
- An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control.Frontiers in computational neuroscience · 2024Article
- Simulated brain networks reflecting progression of Parkinson's disease.Network neuroscience (Cambridge, Mass.) · 2024Article
- Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.Brain sciences · 2023Review
- Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes.Frontiers in computational neuroscience · 2023Article
- Whole-brain dynamical modelling for classification of Parkinson's disease.Brain communications · 2023Article
- Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.Network neuroscience (Cambridge, Mass.) · 2023Article
- Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.Brain sciences · 2022Review
- Towards an efficient validation of dynamical whole-brain models.Scientific reports · 2022Article
- Editorial: From Structure to Function in Neuronal Networks: Effects of Adaptation, Time-Delays, and Noise.Frontiers in systems neuroscience · 2022Article
- BOLD Monitoring in the Neural Simulator ANNarchy.Frontiers in neuroinformatics · 2022Article
- Whole-Brain Network Models: From Physics to Bedside.Frontiers in computational neuroscience · 2022Review
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4 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.