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Related Experiment Video

Updated: Feb 13, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk:

Jiayu Chen1, Barnaly Rashid1,2, Qingbao Yu1

  • 1Mind Research Network, Albuquerque, NM, United States.

Frontiers in Neuroscience
|March 17, 2018
PubMed
Summary
This summary is machine-generated.

Variability in resting state networks (RSNs) and functional network connectivity (FNC) can distinguish schizophrenia patients from controls. These imaging features associate with genetic risk and network efficiency, showing promise for psychiatric disorder research.

Keywords:
PGCfunctional network connectivityparallel ICAresting state networkschizophreniavariability

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Area of Science:

  • Neuroimaging
  • Psychiatric Genetics
  • Computational Neuroscience

Background:

  • Imaging genetics aims to link genetic data with brain abnormalities in psychiatric disorders.
  • Analyzing 2D genetic data and 3D functional magnetic resonance imaging (fMRI) data is challenging due to dimensional asymmetry.
  • Summary features are needed for imaging data to enable subject-level association analysis.

Purpose of the Study:

  • To propose and evaluate variability in resting state networks (RSNs) and functional network connectivity (FNC) as imaging features for association analysis.
  • To investigate these features in a pilot study involving healthy controls and patients with schizophrenia (SZ).
  • To explore the association of these features with genetic risk factors and network efficiency.

Main Methods:

  • Utilized a group independent component analysis (ICA) framework to compute RSN and FNC variability.
  • Tested Euclidean distance, Pearson correlation, and Kullback-Leibler (KL) divergence as variability metrics.
  • Analyzed data from 171 healthy controls and 134 schizophrenia patients.

Main Results:

  • Euclidean distance and Pearson correlation metrics effectively discriminated controls from patients.
  • Patients showed greater deviation in RSN and FNC patterns compared to controls.
  • RSN and FNC variability correlated with network global efficiency and schizophrenia (SZ) risk single nucleotide polymorphisms (SNPs) and polygenic risk scores.

Conclusions:

  • Variability in RSN and FNC are promising imaging features for association studies in psychiatric disorders.
  • These features demonstrate potential as biomarkers for schizophrenia.
  • The findings support the utility of RSN and FNC variability in imaging genetics research.