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Functional Connectivity's Degenerate View of Brain Computation.

Guillaume Marrelec1, Arnaud Messé2, Alain Giron1

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Summary

Functional connectivity (FC) measures may offer a limited view of brain interactions, reflecting static structural connectivity (SC) more than complex neural dynamics. Variability in FC is largely explained by a simple linear subspace, suggesting a degenerate representation.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Brain computation involves complex neural interactions quantified by functional connectivity (FC).
  • The relationship between FC, static structural connectivity (SC), and neurophysiological dynamics remains debated.
  • Empirical findings on FC variability and its cognitive correlates are inconsistent.

Purpose of the Study:

  • To characterize the variety of patterns in FC and SC using a unified computational approach.
  • To investigate the extent to which FC reflects underlying neurophysiological dynamics versus static SC.
  • To assess the dimensionality and complexity of brain interaction patterns captured by FC.

Main Methods:

  • Combined multivariate analysis, bootstrap methods, and computational modeling.
  • Generated simulations from models with varying dynamical behaviors.
  • Analyzed empirical neuroimaging data (BOLD signals) and structural connectivity (SC) estimates.

Main Results:

  • Variability across FC patterns was largely explained by a low-dimensional linear subspace (1-2 dimensions).
  • BOLD signal variability could not be similarly reduced, indicating higher complexity.
  • FC strongly reflected SC and was influenced by a Gaussian process, with limitations in SC estimation impacting results.
  • FC measures may represent brain interactions degenerately, with a common core reflecting SC and limited residual variability.

Conclusions:

  • FC measures may provide a simplified or degenerate representation of complex brain interactions.
  • The limited dynamical range of the BOLD signal and SC estimation methods constrain the information captured by FC.
  • FC's common core reflects network capacity constrained by SC, with residual variability potentially holding meaningful information.