Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
This provides a scalable method for detecting complex brain disorders like schizophrenia, addressing the lack of reliable biomarkers, though it appears incremental as it builds on masked image modeling techniques.
The paper tackles the challenge of detecting heterogeneous mental disorders from brain readouts by introducing CAM, a self-supervised framework for unsupervised anomaly detection, achieving AUCs of 0.696 for Schizoaffective and 0.769 for Schizophreniform without labels.
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.