Revisiting brain asymmetry in schizophrenia: insights from machine learning subtypes from 1,761 patients

Brain asymmetry serves as a measure of typical neurodevelopment, and its disruption is implicated in the etiological framework of schizophrenia (SZ). While previous studies have reported significant alterations in brain structural asymmetry in SZ patients, their interpretations are often limited by relatively small sample sizes, varying approaches to process MRI data, and inconsistent findings. This study aims to identify robust brain asymmetry alterations in SZ using large-scale neuroimaging data, and to investigate how these alterations vary across neuroanatomical subtypes. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 1,761 individuals with SZ and 1,513 healthy controls (HC), primarily of East Asian ancestry. Regional asymmetry in cortical thickness, surface area, and subcortical volume was quantified using a standardized pipeline. We examined case-control differences in regional asymmetry and further compared two neuroanatomical subtypes previously derived using a machine learning algorithm (i.e., Subtype and Stage Inference). Compared with HC, SZ patients displayed significantly reduced leftward asymmetry in the rostral middle frontal gyrus, reduced rightward asymmetry in the medial orbitofrontal cortex, and enhanced rightward asymmetry in the amygdala ( p < 0.001, Bonferroni-corrected). Comparison with prior Western cohort findings over 5,000 SZ individuals revealed only moderate similarity in cortical surface area asymmetry ( r = 0.36, p = 0.02), with no significant correlations for cortical thickness or subcortical volume. Subtype differences emerged in temporal and parietal lobes, precentral gyrus, and caudate ( p < 0.05, Bonferroni-corrected). Furthermore, asymmetry in anterior cingulate volume correlated differently with negative symptoms across subtypes (permutation test, p < 0.05). This study confirms that while case-control asymmetry effect sizes are small, they are statistically significant in certain regions when SZ is stratified into neuroanatomical subtypes. These findings extend the long-standing hypothesis of abnormal cerebral asymmetry in SZ, and underscore the influence of subtype and ancestry on observed asymmetry alterations.

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Journal
BMC Medicine
Published
2026-10-15
DOI
https://doi.org/10.1186/s12916-026-05318-5
Primary Topic
Schizophrenia research and treatment
Type
article
Field-Weighted Citation Impact
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article

Revisiting brain asymmetry in schizophrenia: insights from machine learning subtypes from 1,761 patients

Cheng Luo, Qian Guo, Xinjia Lin, 金璐 et al.
BMC Medicine
Schizophrenia research and treatment
article

Revisiting brain asymmetry in schizophrenia: insights from machine learning subtypes from 1,761 patients

Cheng Luo, Qian Guo, Xinjia Lin, 金璐, Dezhong Yao, Long‐Biao Cui, Jingyu Zhou, Huan Huang, 伊琦忠, Tianhong Zhang, Jianfeng Feng, Yuchao Jiang, Zhenyu Huang, Hao Hu, Zhaoyun Liu, Hongxing Hu, Yingying Tang, Jijun Wang, Jie Zhang, Xiao Chang, Ruixue Yang
article en

Abstract

Brain asymmetry serves as a measure of typical neurodevelopment, and its disruption is implicated in the etiological framework of schizophrenia (SZ). While previous studies have reported significant alterations in brain structural asymmetry in SZ patients, their interpretations are often limited by relatively small sample sizes, varying approaches to process MRI data, and inconsistent findings. This study aims to identify robust brain asymmetry alterations in SZ using large-scale neuroimaging data, and to investigate how these alterations vary across neuroanatomical subtypes. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 1,761 individuals with SZ and 1,513 healthy controls (HC), primarily of East Asian ancestry. Regional asymmetry in cortical thickness, surface area, and subcortical volume was quantified using a standardized pipeline. We examined case-control differences in regional asymmetry and further compared two neuroanatomical subtypes previously derived using a machine learning algorithm (i.e., Subtype and Stage Inference). Compared with HC, SZ patients displayed significantly reduced leftward asymmetry in the rostral middle frontal gyrus, reduced rightward asymmetry in the medial orbitofrontal cortex, and enhanced rightward asymmetry in the amygdala ( p < 0.001, Bonferroni-corrected). Comparison with prior Western cohort findings over 5,000 SZ individuals revealed only moderate similarity in cortical surface area asymmetry ( r = 0.36, p = 0.02), with no significant correlations for cortical thickness or subcortical volume. Subtype differences emerged in temporal and parietal lobes, precentral gyrus, and caudate ( p < 0.05, Bonferroni-corrected). Furthermore, asymmetry in anterior cingulate volume correlated differently with negative symptoms across subtypes (permutation test, p < 0.05). This study confirms that while case-control asymmetry effect sizes are small, they are statistically significant in certain regions when SZ is stratified into neuroanatomical subtypes. These findings extend the long-standing hypothesis of abnormal cerebral asymmetry in SZ, and underscore the influence of subtype and ancestry on observed asymmetry alterations.

BMC Medicine
University of Electronic Science and Technology of China (CN), Xinjiang Medical University (CN), Shenzhen University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Fudan University (CN), Shanghai Mental Health Center (CN), First Affiliated Hospital of Xinjiang Medical University (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN), Shanghai Key Laboratory of Psychotic Disorders (CN), Xi'an Jiaotong University (CN), Air Force Medical University (CN)
Good health and well-being
Openalex Percentile: Top 13%
Schizophrenia research and treatment
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