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Related Concept Videos

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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The human brain, a complex organ, is functionally divided into two cerebral hemispheres—left and right. These hemispheres are interconnected by a structure of paramount importance, the corpus callosum. This substantial bundle of neural fibers is not just a bridge between the hemispheres but a crucial element for the brain's comprehensive functioning. It enables efficient communication between the two hemispheres, allowing each side of the brain to control and receive sensory and motor...
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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.
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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 29, 2025

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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Characterizing cognitive subtypes in schizophrenia using cortical curvature.

Irina Papazova1, Stephan Wunderlich2, Boris Papazov3

  • 1Psychiatry and Psychotherapy, Faculty of Medicine, University of Augsburg, Geschwister-Schönert-Straße 1, 86156, Augsburg, Germany; Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; DZPG (German Center for Mental Health), partner site München, Augsburg, Germany.

Journal of Psychiatric Research
|March 26, 2024
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Summary

Mean curvature (MC) effectively differentiates schizophrenia patients with high and low cognitive performance. This neuroanatomical parameter shows promise for identifying distinct cognitive subtypes in schizophrenia research.

Keywords:
Cognitive subtypesCortical curvatureSchizophrenia

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Neuroimaging

Background:

  • Cognitive deficits are a primary symptom of schizophrenia.
  • Patient heterogeneity in deficit severity complicates research into neural underpinnings.
  • Identifying neuroanatomical correlates of cognitive profiles is crucial.

Purpose of the Study:

  • To classify patients with high (HighCog) versus low (LowCog) cognitive performance using neuroimaging features.
  • To determine which cortical feature (grey matter volume, cortical thickness, or mean curvature) best distinguishes cognitive profiles.
  • To identify key brain regions associated with cognitive performance differences.

Main Methods:

  • Utilized logistic regression and random forest machine learning models.
  • Analyzed cortical features: grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC).
  • Employed two independent patient samples (N=57 discovery, N=52 validation).

Main Results:

  • The classification model based on mean curvature (MC) achieved the highest performance (AUC 76% and 73%).
  • Fronto-temporal and occipital brain regions were identified as most important for classification.
  • Significant differences in MC were found in specific brain regions between HighCog and LowCog groups.

Conclusions:

  • Mean curvature (MC) is a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.
  • MC-based analysis can help differentiate cognitive profiles in schizophrenia.
  • This approach aids in understanding the neural heterogeneity of cognitive deficits in schizophrenia.