Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label...

Anxiety and depression share many symptoms, frequently leading to diagnostic uncertainty. They are also the most common co-occurring psychiatric disorders. Standard clinical evaluations typically diagnose each condition categorically rather than assessing their relative severity when both are present. Prior imaging studies largely concentrated on delineating similarities and distinctions between the two.

To address this gap, we propose a novel noisy label learning method to investigate the biotypes and relevant neural characteristics among anxiety, depression, and their comorbidity. Our approach constructs a robust classification model to address potential diagnostic confusion among anxiety, depression, and comorbidity using a noisy label learning strategy, and employs the trained model to identify the anxiety and depression biotypes, as well as comorbidity biotypes characterized by differing degrees of anxiety and depression predominance.

Using brain functional network connectivity (FNC) from 502 depression patients, 245 anxiety patients, 177 comorbid patients with both anxiety and depression, and 500 healthy controls, we not only identify four distinct biotypes, but also reveal neural linkages between comorbid biotypes and depression and anxiety biotypes. Compared to the depression biotype (Biotype 1), the anxiety biotype (Biotype 2), and the comorbid biotype with an anxiety predominance (Biotype 3-2) demonstrate comparable alterations across seven FNCs, mainly including the connections between the cognitive control and visual domains. Relative to the anxiety biotype (Biotype 2), the depression biotype (Biotype 1), and the comorbid biotype with a depression predominance (Biotype 3-1) exhibit similar connectivity profiles across 16 FNCs, primarily involving connections within the cognitive control domain, as well as between the sensorimotor and visual domains. Furthermore, biotypes are characterized by distinct patterns of behavioral symptoms that are consistent with their underlying neural relationships.

In summary, we propose a neuroimage-based model that addresses the diagnostic ambiguity between anxiety and depression and derive data-driven subtypes for promoting the precise diagnosis.