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Mathematical framework for large-scale brain network modeling in The Virtual Brain.

Paula Sanz-Leon1, Stuart A Knock2, Andreas Spiegler1

  • 1Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université, Marseille, France.

Neuroimage
|January 17, 2015
PubMed
Summary
This summary is machine-generated.

The Virtual Brain (TVB) offers a computational model for simulating whole-brain dynamics, linking mathematical frameworks to numerical implementations for exploring brain network models and their outputs.

Keywords:
BOLDBrain network modelsBrain simulatorCortical surfaceEEGForward solutionsMEGNeural fieldNeural massWhole brain dynamicsfMRI

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Area of Science:

  • Computational neuroscience
  • Neuroimaging
  • Systems neuroscience

Background:

  • The Virtual Brain (TVB) is a powerful tool for simulating large-scale brain network dynamics.
  • Understanding the mathematical underpinnings is crucial for its effective application.

Purpose of the Study:

  • To detail the mathematical framework of The Virtual Brain (TVB) computational model.
  • To establish a clear link between mathematical descriptions and numerical implementations of brain network models (BNMs).
  • To summarize forward models for mapping simulated neural activity to various experimental modalities.

Main Methods:

  • Described the mathematical framework for simulating collective whole-brain dynamics.
  • Detailed the implementation allowing manipulation of BNM components (e.g., neural mass models, structural connectivity).
  • Provided a consistent notation for generalized BNMs.

Main Results:

  • Presented a unified mathematical notation for generalized BNMs.
  • Demonstrated the direct link between mathematical BNM descriptions and TVB's numerical components.
  • Summarized forward models for electroencephalography (EEG), magnetoencephalography (MEG), sterotactic electroencephalogram (sEEG), and functional Magnetic Resonance Imaging (fMRI).

Conclusions:

  • The mathematical framework of TVB enables systematic exploration of large-scale brain network models.
  • The presented notation facilitates understanding and application of TVB's computational model.
  • An overview of forward models with their respective advantages and limitations is provided for simulated neural activity mapping.