Cited by 268Neural dynamics and brain functionFunctional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

Open access
Ritter, Petra, Schirner, Michael, McIntosh, Anthony R., Jirsa, Viktor K.·2013·Brain Connectivity

Abstract

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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Crossref · 10.1089/brain.2012.0120

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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.”

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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
Published
2013-04
Volume
3
Issue
2
Pages
121-145
ISSN
  • 2158-0014 (ISSN-L)
  • 2158-0022
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
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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