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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
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Like autosomes, sex chromosomes contain a variety of genes necessary for normal body function. When a mutation in one of these genes results in biological deficits, the disorder is considered sex-linked.
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Normalized Dynamic Time Warping Increases Sensitivity in Differentiating Functional Network Connectivity in Schizophrenia.

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A multi-frequency ICA-based approach for estimating voxelwise frequency difference patterns in fMRI data.

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Related Experiment Video

Updated: Jul 21, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to

A Iraji1,2, J Chen1, N Lewis1,3

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.

Biorxiv : the Preprint Server for Biology
|July 28, 2023
PubMed
Summary

This study introduces a novel method to analyze dynamic brain networks in schizophrenia, revealing sex-specific alterations linked to genetic risk. The findings help unify existing models of brain dysconnectivity.

Keywords:
Polygenic Risk ScoreSchizophrenia (SZ)Sex DifferencesSingle Nucleotide Polymorphism (SNP)Spatial DynamicsSpatially Dynamic CovarianceTime-Resolved Referenced-Informed Network Estimation Techniques

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Genetics

Background:

  • Resting-state fMRI allows studying dynamic brain networks, but static nodes limit accuracy.
  • Time-resolved spatial properties are crucial for precise functional connectivity estimation.
  • Estimating dynamic networks faces challenges like low signal-to-noise and uncertain network identification.

Conclusions:

  • The method effectively captures dynamic networks and detects nuanced schizophrenia effects.
  • Revealed intricate relationships between dynamic brain information and genomic data.
  • Highlighted the potential of dynamic spatial dependence and weak connectivity in clinical applications.