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.

翻译：基于脑功能指标检测异质性精神障碍仍具有挑战，主要源于症状复杂性及可靠生物标志物的缺失。本文提出一种名为CAM（通过掩码图像建模实现皮层异常检测）的新型自监督框架，旨在利用皮层表面特征实现复杂脑部疾病的无监督检测。我们应用该框架检测精神病谱系个体，并与现有最优方法进行对比：在无需任何标签的情况下，该框架对分裂情感性障碍的AUC达到0.696，对精神分裂症样障碍的AUC达到0.769。此外，异常皮层区域的分析显示三角回及多个额叶区域（常与精神分裂症相关）被检出，进一步验证了方法的可靠性。综上，我们展示了一种基于皮层异常的可扩展方法，用于复杂脑部疾病的异常检测。