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Related Experiment Video

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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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A Method for Estimating Dynamic Functional Network Connectivity Gradients (dFNGs) From ICA Captures Smooth

Najme Soleimani1, Armin Iraji1, Theo G M van Erp2

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.

Human Brain Mapping
|July 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new dynamic functional network connectivity gradient (dFNG) analysis to better understand brain function over time. The method reveals distinct brain network patterns in schizophrenia patients compared to healthy controls, offering new insights into functional brain dysconnectivity.

Keywords:
dynamic functional network connectivity (dFNC)dynamic functional network connectivity gradient (dFNG)gradientindependent component analysis (ICA)schizophrenia

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Analysis

Background:

  • Dynamic functional network connectivity (dFNC) analysis is crucial for understanding brain function over time.
  • Current dFNC methods often use fixed spatial maps, limiting the capture of dynamic brain network changes.
  • Functional dysconnectivity is observed in schizophrenia (SZ), but its neural underpinnings require further elucidation.

Purpose of the Study:

  • To present a novel dFNC approach that dynamically reorders spatial components to optimize for smooth functional connectivity gradients (dFNGs).
  • To apply this dFNG analysis to resting-state fMRI data from individuals with schizophrenia (SZ) and healthy controls (HCs).
  • To identify group differences in static and dynamic functional connectivity gradients.

Main Methods:

  • Developed a method for dynamic functional network connectivity gradient (dFNG) analysis, including static (sFNG) and dynamic summaries.
  • Utilized resting-state fMRI data from 151 SZ patients and 160 HCs.
  • Applied spatially constrained independent component analysis (ICA) to extract 53 intrinsic connectivity networks (ICNs) and computed gradient analyses.

Main Results:

  • Static analysis showed altered connectivity in subcortical, auditory, and visual networks in SZ patients.
  • sFNG analysis revealed distinct clustering patterns and group differences in subcortical and cerebellar domains.
  • dFNG analysis indicated that SZ patients favor specific network states (e.g., SC/CB) and exhibit altered gradient synchrony compared to HCs.

Conclusions:

  • The dFNG approach provides a more comprehensive spatiotemporal summary of brain activity than traditional dFNC.
  • Results highlight unique large-scale brain network dynamics in schizophrenia patients.
  • This method offers a novel perspective for studying functional brain dysconnectivity and brain network modulation.