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Related Concept Videos

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.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
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Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable

Yingying Xie1,2, Hao Ding1,2,3, Xiaotong Du1,2

  • 1Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.

Schizophrenia Bulletin
|August 4, 2022
PubMed
Summary
This summary is machine-generated.

A new Morphometric Integrated Classification Index (MICI) shows promise as a generalizable neuroimaging biomarker for schizophrenia diagnosis. This interpretable tool aids objective diagnosis and can be accessed online.

Keywords:
biomarkermachine learningmorphometric integrated classification indexmulti-siteschizophreniastructural magnetic resonance imaging

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Area of Science:

  • Neuroimaging
  • Psychiatric Disorders
  • Biomarker Development

Background:

  • Multisite neuroimaging data sharing is crucial for understanding schizophrenia pathophysiology and diagnosis.
  • Developing generalizable, interpretable, and evolvable neuroimaging biomarkers for schizophrenia remains a challenge.

Purpose of the Study:

  • To propose and validate a novel neuroimaging biomarker for objective schizophrenia diagnosis.
  • To ensure the biomarker is generalizable across different sites and interpretable.

Main Methods:

  • A Morphometric Integrated Classification Index (MICI) was developed using structural MRI data from 1270 subjects across 10 sites.
  • XGBoost classifier and SHapley Additive explanation algorithms were employed to construct the MICI measure.
  • Performance was evaluated against ensembling and single-site models, assessing generalizability and evolvability.

Main Results:

  • MICI demonstrated comparable performance to ensemble models and significantly outperformed single-site models.
  • The biomarker proved generalizable across independent datasets and improved with the inclusion of new sites (evolvable).
  • MICI showed strong associations with schizophrenia severity, symptom profiles, and genetic risk factors (interpretable).

Conclusions:

  • The MICI biomarker offers a simple, explainable method to support objective schizophrenia diagnosis.
  • An online platform was developed to enhance biomarker generalization and provide prediction services.