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Related Concept Videos

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

173
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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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep

Yue Cui1, Chao Li1, Bing Liu2

  • 1Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China.

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Deep learning accurately identifies schizophrenia by analyzing brain structure, aiding earlier diagnosis. This approach detects grey matter abnormalities, improving clinical outcomes for patients with schizophrenia.

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Area of Science:

  • Neuroimaging
  • Psychiatry
  • Machine Learning

Background:

  • Schizophrenia is linked to structural brain changes in grey and white matter.
  • Deep learning offers a data-driven method to analyze complex neuroanatomical patterns for improved schizophrenia diagnosis.

Approach:

  • A meta-analysis of voxel-based morphometry was conducted on 662 schizophrenia patients and 613 controls across eight Chinese centers.
  • Deep neural networks classified schizophrenia using grey matter, white matter, and cerebrospinal fluid volumes, with validation on independent datasets.

Key Points:

  • Consistent grey matter abnormalities were found in the superior temporal gyrus, temporal pole, insula, frontal cortices, cingulum, and thalamus.
  • Classification models achieved 77.19-85.74% accuracy and 0.797-0.909 AUC across independent sites.

Conclusions:

  • Deep learning effectively discriminates schizophrenia patients from healthy controls using multidimensional neuroanatomical data.
  • These findings support the potential of deep learning for enhancing clinical diagnosis and treatment strategies in schizophrenia.