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Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations.

Gustavo Deco1, Adrián Ponce-Alvarez, Dante Mantini

  • 1Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.

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Resting-state functional connectivity (FC) emerges from brain structure via a simplified dynamic mean field model. This model reveals FC arises from noise propagation and slowed neural dynamics, linking brain anatomy to function.

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Area of Science:

  • Computational Neuroscience
  • Neuroimaging
  • Network Science

Background:

  • Resting-state functional connectivity (FC) shows structured spatial patterns, but its origin from anatomical connections is unclear.
  • Existing large-scale models are too complex to fully elucidate the structure-function relationship in brain dynamics.
  • A simplified yet realistic model is needed to understand how brain anatomy shapes resting-state activity.

Purpose of the Study:

  • To derive a simplified dynamic mean field model of brain activity constrained by diffusion imaging data.
  • To investigate the mechanistic origin of resting-state functional connectivity (FC) from anatomical structure.
  • To establish an analytical link between neural network dynamics, anatomical connectivity, and emergent FC.

Main Methods:

  • Developed a dynamic mean field model from a detailed spiking network constrained by human diffusion imaging data.
  • Analyzed the emergent ensemble dynamics, focusing on the system's longest time scale.
  • Simplified the model further into equations for statistical moments to establish analytical links.

Main Results:

  • Demonstrated that FC emerges as structured linear fluctuations near a stable, low-activity state.
  • Showed that the simplified model analytically links anatomical structure, neural dynamics, and FC.
  • Identified noise propagation and dynamical slowing down as key mechanisms for FC emergence.

Conclusions:

  • Resting-state functional connectivity arises from noise propagation and dynamical slowing in anatomically constrained neural systems.
  • The derived model provides a framework for understanding FC development from neuronal dynamics and anatomical underpinnings.
  • This approach can inform future task-evoked studies and clinical applications by clarifying structure-function relationships.