Abstract

Identifying neuroimaging-based biomarkers is greatly needed to boost the progress of mental disorder diagnosis. However, it has been well acknowledged that inaccurate diagnosis on mental disorders may in turn raise unreliable biomarkers. In this paper, we propose a new method that can detect typical patients with specific mental disorders, which is beneficial to further biomarker identification. In our method, we extend an advanced sample noise detection technology based on random forest to identify typical patients, and apply it to identify typical subjects from schizophrenia (SZ) and bipolar disorder (BP) patients with neuroimaging features estimated from resting fMRI data. To evaluate the capacity of our method, we investigate the typical subjects and whole subjects with respect to group differences, classification accuracy, clustering, and projection performance based on the identified typical subjects. Our results supported that the typical subjects showed greater group differences between SZ and BP, higher classification accuracy, more compact clusters in both clustering and projection. In short, our work presents a novel method to explore discriminative and typical subjects for different mental disorders, which is promising for identifying reliable biomarkers.