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Clarifying the DMN-Depression Connectivity Paradox: Functional Connectivity Associations between Specialized DMN Subnetworks and Depression Severity

Poster Session B - Sunday, March 8, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballroom

Joanna Ladopoulou¹ (), Mackenzie Tremaine¹, Sarah Modesitt¹, Niceidy Green¹, Estrella Calvo Jung¹, Jonas Kaplan¹, Bruna Martins-Klein¹; ¹University of Southern California

Depression is a pervasive and debilitating mood disorder that disrupts multiple aspects of cognition, including episodic memory and perspective-taking processes. Neural evidence suggests that depression is associated with changes in functional connectivity of the Default Mode Network (DMN), a neural network that supports self-generated thought processes such as autobiographical memory, self-referential processing, and social cognition. However, findings are inconsistent, with some studies supporting DMN hyper-connectivity, and others DMN hypo-connectivity as a risk factor for depression, introducing a DMN-Depression Connectivity Paradox. Recent precision functional brain mapping findings reveal high inter-individual variability of DMN organization and suggest the DMN is composed of two distinct, functionally dissociable subnetworks, involved in episodic memory and perspective taking, respectively. In this study, Aim 1 will validate a data-efficient within-individual brain mapping pipeline to discriminate between the two DMN subnetworks using resting-state neuroimaging data from the Human Connectome Project, and verify its mapping accuracy with fMRI localizer tasks. Aim 2 will test associations between the two DMN subnetworks' resting-state functional connectivity and depression severity, in contrast to whole-DMN associations. We predict that a model including each DMN subnetwork’s connectivity separately will better predict depressive symptomatology than whole-DMN connectivity, given that these two subnetworks uniquely contribute to the cognitive deficits present in depression. This study has the potential to clarify mixed findings in prior literature and pave the way for identifying personalized neurobiological markers of depression. Clinical implications of this work include furthering neuroscientific models of depression, as well as precision psychiatry applications such as connectivity-based treatment stratification.

Topic Area: EMOTION & SOCIAL: Emotion-cognition interactions