Connectome-based Growth Models Reveal Neurophysiological Subtypes of Subthreshold Depression

Brisbane Convention & Exhibition Centre 

Subthreshold depression (StD) poses a high risk for major depressive disorder (MDD) and is characterized by significant clinical heterogeneity among individuals (Cuijpers and Smit, 2004; Eaton, et al., 1995). However, the neurobiological substrates of this heterogeneity remain largely unknown, posing substantial challenges for early detection and effective intervention. Previous studies using resting-state functional MRI (r-fMRI) have documented disruptions in the functional connectome in StD participants (Gao, et al., 2016; Hwang, et al., 2016; Yin, et al., 2024; Yokoyama, et al., 2018; Zhang, et al., 2021), advancing our understanding of its neurobiological basis. However, these studies primarily focused on group-averaged alterations, largely overlooking individual differences among StD participants. Here, we aimed to characterize the age-related trajectory of the functional connectome in a large healthy dataset using normative models, identifying clinically significant neurobiological subtypes based on each participant's deviations from this model. This exploration would deepen our understanding of the distinct neurobiological mechanism underlying clinical heterogeneity in StD and inspire imaging-derived candidate phenotypes for the guidance of precise diagnosis and treatment.