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Related Experiment Video

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How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Timothée Proix1, Andreas Spiegler1, Michael Schirner2

  • 1Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.

Neuroimage
|August 3, 2016
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Summary

Large-scale brain models using diffusion MRI (dMRI) benefit from including both major white matter tracts and local connections. This improves the accuracy of modeling brain activity across different dynamic scales, from slow fMRI signals to faster neural oscillations.

Keywords:
DiffusionFunctional and structural MRILarge-scale brain network modelsParcellationsSCRIPTSShort-range connectivityThe virtual brain

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging

Background:

  • Whole-brain models rely on diffusion magnetic resonance imaging (dMRI) for connectivity estimates.
  • Short-range cortico-cortical white-matter connections are often underrepresented in large-scale brain models.
  • Optimal representation scale for white matter fibers and the role of short-range connections in brain activity remain unclear.

Purpose of the Study:

  • To quantify the impact of connectivity variations on large-scale brain network dynamics.
  • To investigate the optimal scale of white matter fiber representation for brain activity modeling.
  • To assess the influence of including generic short-range connections in brain network simulations.

Main Methods:

  • Systematically varied the number of brain regions to compute connectivity matrices.
  • Incorporated generic short-range connections into network models.
  • Utilized Human Connectome Project dMRI data and the SCRIPTS preprocessing suite for The Virtual Brain platform.
  • Performed simulations and analyzed spatiotemporal dynamics using Shannon Entropy, dwell time, and Principal Component Analysis.

Main Results:

  • Major white matter fiber bundles significantly influence slow dynamics in large-scale brain networks, as observed in fMRI data.
  • Faster dynamics, such as gamma oscillations, are sensitive to short-range connectivity when transmission delays are considered.
  • The scale of connectivity representation impacts the characterization of brain network dynamics.

Conclusions:

  • Accurate large-scale brain modeling requires careful consideration of both long-range white matter tracts and local connections.
  • The inclusion of short-range connections is crucial for capturing faster neural dynamics.
  • This study provides insights into optimizing brain network models for improved simulation accuracy.