Title: From Meta-Analytic Findings to Developmental Predictions: Unraveling the Neural Architecture of Major Depressive Disorder 

Abstract: The heterogeneity of Major Depressive Disorder (MDD) poses a significant challenge for the identification of robust neural markers. To address this gap, the talk outlines a framework that moves from meta-analytic signatures of network dysfunctions to a developmental context, mapping how neurobehavioral deviations in youth diverge into pathways of risk or resilience.

I will begin by presenting a comprehensive meta-analysis of resting-state functional connectivity in MDD, which synthesizes data across the literature to identify robust and reproducible patterns of hyper- and hypo-connectivity that serve as the theoretical framework for understanding the disorder's neural architecture. Next, I will discuss recent findings on neurocognitive biotypes of risk and resilience in adolescents. Using a combination of cognitive measures and graph-theoretic network markers, we identified distinct profiles that characterize youth at risk for mood disorders, such as MDD. Finally, I will present a framework for optimizing models predicting risk and resilience for mood disorders by operating at the intersection of theory-driven and data-driven approaches. This hybrid approach aims to refine our understanding of how neural risk unfolds over time, ultimately moving us closer to clinically useful tools for early identification and intervention.

Now, as a postdoc at CU Boulder, she is tackling the complexity of adolescent mood disorders during a developmental window of heightened vulnerability. By mixing big data with clinical theory, she is working to create tools that can forecast mental health challenges before they become chronic. Ultimately, she wants to move the field toward a future where we can identify at-risk youth early and provide support that is tailored to their unique neural blueprint.