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-of-the-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, including Pars Triangularis and several frontal areas often implicated in schizophrenia, provides further confidence in our approach. Altogether, we demonstrate a scalable approach for anomaly detection of complex brain disorders based on cortical abnormalities. The code will be made available at https://github.com/chadHGY/CAM.

翻译：基于脑部读数检测异质性精神障碍仍具挑战性，原因在于症状的复杂性以及可靠生物标志物的缺失。本文提出CAM（基于掩码图像建模的皮层异常检测）——一种新颖的自监督框架，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，在分裂情感性障碍患者中达到0.696的AUC，在精神分裂症样障碍患者中达到0.769的AUC。此外，对非典型皮层区域（包括常与精神分裂症相关的三角部及多个额叶区域）的分析进一步验证了本方法的可靠性。总体而言，我们展示了一种基于皮层异常检测复杂脑部疾病的可扩展方法。代码将公开于https://github.com/chadHGY/CAM。