The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Open accessAbstract
Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected—ideally functionally relevant—aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.
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A symmetric strategy that estimates a common set of unknown sources fitting both fMRI and EEG simultaneously is lacking, despite initial progress.
“Such a strategy is lacking despite initial progress (145), and awaits further quantitative understanding and modeling of the basis of and coupling between fMRI and EEG.”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [145]
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References 141 · Cited by 268
- Authors
- Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Bernstein Center for Computational Neuroscience Berlin (DE)
- University of Toronto (CA)
- Inserm (FR)
- Corresponding author
- Ritter, Petra
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2013-04
- Volume
- 3
- Issue
- 2
- Pages
- 121-145
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- PMC
- PMC3696923
- OpenAlex
- W1985220467
- Cited by
- 268
- References
- 141
- Citations by year
- 2026: 23 · 2025: 27 · 2024: 24 · 2023: 24 · 2022: 26 · 2021: 21 · 2020: 25 · 2019: 17 · 2018: 17 · 2017: 12 · 2016: 13 · 2015: 20 · 2014: 14 · 2013: 5
- FWCI
- 9.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain Connectivity (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- The Virtual Brain, brain simulation, computational neuroscience, neuron models, brain dynamics, multimodal neuroimaging, neurophysiological mechanisms, neuropathology, model-based simulation
- MeSH terms
- Brain, Computer Simulation, Electroencephalography, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Stochastic Processes, Time Factors, User-Computer Interface, Nonlinear Dynamics
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