Major depressive disorder (MDD) is a severe mental health condition affecting over 246 million people globally. Despite its widespread impact, identifying consistent brain-based markers to improve diagnosis and treatment has remained challenging. Neuroimaging studies of MDD have often yielded conflicting results, largely due to variations in research methodologies and analytical approaches, making it difficult to establish reliable neurobiological signatures of depression.
Addressing this challenge, a new study led by Research Fellow Siti Nurul Zhahara and Professor Yoshiyuki Hirano from Chiba University, in collaboration with Osaka University, introduces a novel approach focused on individual-specific brain connectivity patterns, referred to as functional connectome (FC) uniqueness. This method emphasizes the distinctiveness of each person’s brain connectivity and offers new potential for identifying biomarkers in MDD. The study was co-authored by researchers from Chiba and Hiroshima Universities and will appear in The Journal of Affective Disorders in April 2026.
FC uniqueness, also known as brain fingerprinting, reflects how distinct an individual’s functional brain network organization is. Prior studies have demonstrated that these connectivity patterns remain stable over time, making them a promising and reproducible measure for studying mental health conditions.
Using resting-state fMRI data from young adults with and without MDD across multiple research sites, the study confirmed that healthy individuals exhibit highly identifiable connectivity signatures. In contrast, patients with MDD showed significantly reduced FC uniqueness, particularly within frontoparietal and sensorimotor networks. Moreover, reduced FC uniqueness was associated with greater depression severity, as indicated by higher PHQ-9 and BDI-II scores.
“These results suggest that MDD is characterized by a less distinctive functional brain organization,” Prof. Hirano noted. Overall, the findings position FC uniqueness as a robust and clinically relevant biomarker with potential applications in improving diagnosis and guiding personalized treatment strategies for depression.
