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Related Concept Videos

Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

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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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Schizophrenia is a complex mental health disorder that can manifest with various positive symptoms, including thought, movement, and behavior disorders. These symptoms significantly disrupt cognitive and motor functions, leading to profound effects on an individual's ability to engage with the world.
Thought Disorders
Disorganized and unusual thought processes mark thought disorders in schizophrenia. One key feature is disorganized speech, where an individual's conversation includes...
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Negative and Cognitive Symptoms of Schizophrenia01:30

Negative and Cognitive Symptoms of Schizophrenia

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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.
Negative Symptoms
Negative symptoms of schizophrenia manifest as deficits in normal emotional and behavioral functioning, profoundly impacting daily life. Individuals with schizophrenia often display a flat affect, characterized by a near-total absence of emotional expression,...
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Biological Causes of Schizophrenia01:29

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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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Related Experiment Video

Updated: Jun 22, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients.

Natalia Maksymchuk1, Juan R Bustillo2, Daniel H Mathalon3,4

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

Biorxiv : the Preprint Server for Biology
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

Schizophrenia patients show altered brain connectivity patterns. A new method, inter-network connectivity entropy (ICE), reveals higher randomness in SZ patients

Keywords:
biomarkersbrain statesdynamic functional connectivityentropyfMRIfunctional connectivity patternsimage data analysismental healthschizophreniastatic functional connectivity

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • Schizophrenia (SZ) is characterized by abnormal static and dynamic functional connectivity in the brain.
  • Understanding brain network alterations is crucial for diagnosing and treating SZ.
  • Existing methods may not fully capture the complex, dynamic nature of brain connectivity.

Purpose of the Study:

  • To introduce and validate a novel approach, inter-network connectivity entropy (ICE), for assessing brain connectivity.
  • To investigate differences in static and dynamic ICE between SZ patients and healthy controls (HC).
  • To explore the potential of ICE as a diagnostic tool for mental health conditions.

Main Methods:

  • Analysis of fMRI data from 151 SZ patients and 160 HC.
  • Calculation of static and dynamic inter-network connectivity entropy (ICE) across multiple brain networks.
  • Application of C-means fuzzy clustering and k-means clustering to analyze ICE patterns.

Main Results:

  • Significant differences in connectivity heterogeneity were found between SZ patients and HC across various brain networks (SC, AUD, SM, VIS, CC, DMN, CB).
  • SZ patients exhibited elevated ICE, indicating higher randomness in time-varying connectivity compared to HC.
  • Clustering analyses revealed distinct patterns of functional entropy correlation and occupancy rates between SZ patients and HC.

Conclusions:

  • The proposed ICE measure offers a novel framework for understanding brain states in health and disease.
  • Dynamic ICE analysis suggests SZ patients struggle to transition into more focused brain connectivity patterns.
  • ICE holds promise as an advanced method for the diagnostics of mental health conditions.