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Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors.

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Summary

This study introduces a novel adaptive filter to enhance the estimation of dynamic and directed functional connectivity (FC) by integrating structural connectivity (SC) priors. The filter refines FC estimates, particularly in low signal-to-noise conditions, creating sparser networks that merge structural and functional brain data.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging

Background:

  • Brain's functional networks dynamically change but are constrained by underlying structural connections.
  • Integrating structural connectivity (SC) and functional connectivity (FC) using multimodal approaches remains a challenge.
  • Existing methods have limitations in accurately estimating dynamic and directed FC.

Purpose of the Study:

  • To develop and validate a new adaptive filter for estimating dynamic and directed functional connectivity (FC).
  • To leverage structural connectivity (SC) information as priors to improve FC estimation.
  • To enhance multimodal integration of brain structure and function data.

Main Methods:

  • Proposed a novel adaptive filter incorporating structural connectivity (SC) information as priors.
  • Tested the filter using rat epicranial recordings and human event-related EEG data.
  • Utilized SC priors derived from tracer studies and diffusion tensor imaging metrics.

Main Results:

  • The SC-informed filter refined estimates of directed FC, especially in low signal-to-noise ratio conditions.
  • The filter promoted sparse functional networks that integrated information from both structure and function.
  • Demonstrated intrinsic protection against SC-related false negatives and robustness against false positives.

Conclusions:

  • The developed adaptive filter is a valuable tool for multimodal neuroimaging analysis.
  • It improves the estimation of dynamic and directed functional connectivity by integrating structural priors.
  • Offers enhanced accuracy and robustness for brain network analysis.