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Related Concept Videos

Schizophrenia01:17

Schizophrenia

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Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
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Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

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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.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
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Psychological and Sociocultural Causes of Schizophrenia01:29

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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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Negative and Cognitive Symptoms of Schizophrenia01:30

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Negative symptoms of schizophrenia indicate a reduction or absence of typical behaviors and emotional responses found in healthy individuals, while positive symptoms reflect an excess or distortion of normal functioning.
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Schizophrenia is a complex psychiatric disorder characterized by a range of symptoms that significantly impact cognition, behavior, and emotional regulation. Among these, the positive symptoms stand out as they involve the addition or exaggeration of normal mental functions, deviating markedly from typical behavior and perception. Hallucinations and delusions are prominent positive symptoms, each profoundly affecting the individual's experience of reality.
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Related Experiment Video

Updated: Jan 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Dynamic thresholding networks for schizophrenia diagnosis.

Hongliang Zou1, Jian Yang1

  • 1PCA Lab, Key Lab of Intelligent Perception and systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.

Artificial Intelligence in Medicine
|June 6, 2019
PubMed
Summary

This study introduces a novel dynamic functional connectivity method for diagnosing schizophrenia, achieving high accuracy by analyzing time-varying brain networks. The approach effectively identifies neural alterations associated with schizophrenia, offering a promising tool for computer-aided diagnosis.

Keywords:
Dynamic time warpingOrthogonal minimum spanning treeSchizophreniaTime-varying window length DFCrs-fMRI

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Area of Science:

  • Neuroimaging
  • Brain Connectivity
  • Computational Psychiatry

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) reveals functional connectivity (FC) between brain regions.
  • Dynamic FC (DFC) analysis captures time-varying neural interactions, crucial for understanding cognition and brain disorders.
  • Conventional DFC methods face limitations like arbitrary window selection and noise-induced spurious connections.

Purpose of the Study:

  • To develop an effective dynamic thresholding brain network method for schizophrenia diagnosis.
  • To address limitations of existing DFC analysis in capturing time-varying brain connectivity.
  • To improve the accuracy and reliability of neuroimaging-based schizophrenia detection.

Main Methods:

  • Proposed a time-varying window length DFC method using dynamic time warping.
  • Applied orthogonal minimum spanning tree to eliminate spurious connections, creating time-varying window length dynamic thresholding FC (TVWDTFC) networks.
  • Validated the method on a dataset of 56 schizophrenia patients and 74 healthy controls.

Main Results:

  • Achieved a classification accuracy of 0.8077 (p < 0.001) using a support vector machine.
  • Demonstrated superior performance compared to several state-of-the-art approaches for schizophrenia diagnosis.
  • Identified discriminative features primarily located in frontal, parietal, and limbic brain areas.

Conclusions:

  • The proposed TVWDTFC method is effective for schizophrenia diagnosis.
  • The approach shows promise as a tool for computer-aided diagnosis of schizophrenia.
  • Dynamic brain network analysis offers valuable insights into neural alterations in schizophrenia.