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Biological Causes of Schizophrenia

Biological Causes of Schizophrenia

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
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Updated: Sep 17, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

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Published on: November 27, 2019

Gray Matter Volume Abnormalities in Schizophrenia: Comparisons Between P-Value and Effect Size Inference Frameworks

Xin Li¹, Wenshuang Zhu¹, Zhen Zhao¹

¹Department of Radiology, Tianjin Key Lab of Functional Imaging, Tianjin Institute of Radiology and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China.

Schizophrenia Bulletin

|June 27, 2025

Summary

This summary is machine-generated.

Effect size (ES) inference reliably identifies brain abnormalities in schizophrenia, outperforming P-value methods. Image-based meta-analysis (IBMA) shows superior detection of structural changes in multi-center studies.

Keywords:

P-value big data effect size image-based meta-analysis mega-analysis schizophrenia

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Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

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Published on: September 8, 2021

Area of Science:

Neuroimaging
Psychiatric Disorders
Statistical Analysis

Background:

Identifying generalizable brain imaging markers for schizophrenia is challenging due to data aggregation methods and p-hacking.
Large multi-center datasets require robust statistical approaches to overcome variability.

Purpose of the Study:

To compare the efficacy of effect size (ES) inference versus P-value inference for identifying brain abnormalities in schizophrenia.
To evaluate different statistical aggregation methods: Mega-analysis (Mega), Image-Based Meta-analysis (IBMA), and Coordinate-Based Meta-analysis (CBMA).

Main Methods:

Voxel-wise gray matter volume (GMV) differences were analyzed using individual data from 976 schizophrenia patients and 801 controls across 16 datasets.
Published coordinates data from 103 studies (5151 patients, 5438 controls) were analyzed using Mega-analysis, IBMA, and CBMA under P-value and ES inference frameworks.

Main Results:

P-value Mega-analysis identified widespread GMV abnormalities (94.85% of voxels) highly sensitive to sample size.
Effect size Mega-analysis detected core abnormalities in fewer voxels (24.63%) but showed greater resistance to sample size.
ES-based IBMA and CBMA outperformed P-value methods, with IBMA demonstrating comparable performance to Mega-analysis and superior results to CBMAs.

Conclusions:

Effect size inference offers advantages for statistical aggregation in multi-center neuroimaging studies.
Image-Based Meta-analysis (IBMA) shows significant potential for reliably detecting brain structural abnormalities in schizophrenia.