The detection of heterogeneous mental disorders based on brain readouts remains challenging due to the complexity of symptoms and the absence of reliable biomarkers. This paper introduces CAM (Cortical Anomaly Detection through Masked Image Modeling), a novel self-supervised framework designed for the unsupervised detection of complex brain disorders using cortical surface features. We employ this framework for the detection of individuals on the psychotic spectrum and demonstrate its capabilities compared to state-ofthe-art methods, achieving an AUC of 0.696 for Schizoaffective and 0.769 for Schizophreniform, without the need for any labels. Furthermore, the analysis of atypical cortical regions includes Pars Triangularis and several frontal areas, often implicated in schizophrenia, provide further confidence in our approach. Altogether, we demonstrate a scalable approach for anomaly detection of complex brain disorders based on cortical abnormalities.

通过大脑皮层异常特征的自我监督检测框架CAM（Cortical Anomaly Detection through Masked Image Modeling），本研究在未标记数据下探索复杂脑部疾病的无监督检测方法，具体应用于精神病谱系个体的检测，结果表明相比先进方法，在Schizoaffective型和Schizophreniform型病例上获得了0.696和0.769的AUC值，无需任何标签，同时对与精神分裂症相关的非典型皮层区域例如Pars Triangularis和若干额叶区域的分析也进一步验证了该方法的可靠性。总体来说，本研究展示了一种基于皮层异常的复杂脑部疾病异常检测的可扩展方法。

通过屏蔽编码实现对未监督异质性映射的皮层异常学习