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Related Experiment Video

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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Quantifying Similarity of Dynamic Brain Networks: Two Novel Indices for Structural Change and Temporal Evolution.

Xiaocheng Wang1, Yongquan He2, Tian Zhou1

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.

Bioengineering (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

New indices Dynamic Network Similarity (DNS) and Dynamic Network Evolution Similarity (DNES) effectively analyze dynamic brain networks. These tools offer a dynamic alternative to static methods for brain connectivity research.

Keywords:
braindynamic networkindexstructural changestemporal evolutiontime points

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Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging

Background:

  • Brain functional connectivity is dynamic, changing with development, aging, illness, and cognition.
  • Traditional static network analysis fails to capture these crucial brain dynamics.

Purpose of the Study:

  • Introduce two novel indices: Dynamic Network Similarity (DNS) and Dynamic Network Evolution Similarity (DNES).
  • Evaluate the efficacy of DNS and DNES in analyzing dynamic brain networks using simulations and real-world fMRI data.

Main Methods:

  • Developed DNS to measure temporal and structural dynamic similarity.
  • Developed DNES to specifically assess the temporal evolution of dynamic networks.
  • Validated indices with simulated data (varying Δφ, λ, α, β) and fMRI data from stroke patients undergoing transcranial direct current stimulation (tDCS).

Main Results:

  • DNS demonstrated sensitivity to all dynamic features, while DNES was sensitive to phase (Δφ) and relative amplitude (λ) variations.
  • Both DNS and DNES successfully detected overall differences in brain network dynamics.
  • Indices revealed significantly higher similarity in groups receiving the same therapy (ST) compared to different therapies (DT).

Conclusions:

  • DNS and DNES are effective tools for studying dynamically evolving brain networks.
  • These indices provide a valuable alternative to traditional static methods for analyzing brain connectivity.
  • The methods are particularly useful for longitudinal neuroimaging studies in neurodevelopment, aging, and disease recovery.