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Updated: Dec 23, 2025

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Decentralized dynamic functional network connectivity: State analysis in collaborative settings.

Bradley T Baker1,2,3, Eswar Damaraju1,2,3, Rogers F Silva1,2

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Summary
This summary is machine-generated.

Researchers developed decentralized dynamic functional network connectivity (ddFNC) for neuroimaging. This new method offers comparable performance to centralized approaches, enabling collaborative analysis with reduced privacy risks and communication overhead.

Keywords:
brain imagingdFNCdata sharingdecentralizeddecentralized algorithmdynamic functional network connectivity

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Area of Science:

  • Neuroscience
  • Data Science
  • Computational Biology

Background:

  • Neuroimaging data complexity is increasing, necessitating larger sample sizes through collaborative frameworks.
  • Centralized data collection has been dominant, but decentralized approaches are gaining traction due to privacy and communication concerns.

Purpose of the Study:

  • To develop and evaluate a decentralized version of dynamic functional network connectivity (dFNC).
  • To introduce decentralized dynamic functional network connectivity (ddFNC) by integrating decentralized group independent component analysis (dgICA) with decentralized k-means clustering.

Main Methods:

  • Developed a novel decentralized algorithm, ddFNC.
  • Integrated decentralized group independent component analysis (dgICA) with decentralized k-means clustering algorithms.
  • Evaluated ddFNC by comparing its performance, communication overhead, and convergence behavior against centralized counterparts.

Main Results:

  • ddFNC demonstrated comparable performance to centralized analysis pipelines.
  • Experiments evaluated communication overhead and convergence of various decentralization strategies.
  • The ddFNC algorithm is a viable tool for decentralized neuroimaging collaboration.

Conclusions:

  • ddFNC is a promising tool for facilitating decentralized collaboration in neuroimaging research.
  • The ddFNC framework is adaptable for incorporating privacy-enhancing techniques like differential privacy.