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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB).

Arseny A Sokolov1,2, Peter Zeidman3, Michael Erb4

  • 1Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK. arseny.sokolov@chuv.ch.

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Structural and effective brain connectivity are linked. Using structural priors from diffusion imaging improved models of functional MRI data, confirming their interdependence for large-scale brain interactions.

Keywords:
Dynamic causal modelling (DCM)Effective connectivityFunctional MRIStructural connectivity

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Computational Neuroscience

Background:

  • Understanding the brain's functional neuroanatomy is challenging due to the complex interplay between structural and functional neuroimaging measures.
  • Task-related effective connectivity, detailing causal neuronal influences, is particularly difficult to analyze integratively.
  • Existing methods struggle to combine structural and functional connectivity data for large-scale brain network analysis.

Purpose of the Study:

  • To investigate if structural connectivity measures can enhance estimates of effective connectivity in large-scale brain networks.
  • To develop an integrative approach combining structural and functional neuroimaging data.
  • To leverage recent statistical advancements for improved brain connectivity modeling.

Main Methods:

  • Utilized Parametric Empirical Bayes for group-level effective connectivity estimation.
  • Employed Bayesian model reduction for efficient comparison of competing models.
  • Integrated structural priors from high angular resolution diffusion imaging (HARDI) into a dynamic causal model (DCM) of a 12-region network.

Main Results:

  • Structural priors derived from HARDI significantly improved the model evidence for the dynamic causal model (posterior probability of 1.00).
  • Demonstrated a crucial dependence of effective connectivity estimates on structural connectivity information.
  • Confirmed the interdependence of structural and effective connectivity in mediating large-scale brain interactions.

Conclusions:

  • Structural and effective connectivity are mutually informative and essential for understanding distributed, large-scale brain interactions.
  • The integrative approach provides a robust method for multimodal brain connectivity analysis.
  • Offers new insights into normal brain architecture and its disruption in clinical conditions.