A network perspective on the neurodevelopment of emotion dysregulation in adolescence: insights into depression and anxiety

The National Institute of Mental Health awarded the University of Delaware $182,436 for a Project Grant (F31MH140551) to investigate neural networks underlying co-occurring depression and anxiety in adolescence through network-based neuroscience analysis, with an award date of October 12, 2025.

The research applies graph theory and person-centered network analysis approaches to identify disturbances in brain network organization shared by emerging depression and anxiety, and to isolate distinct subgroups of adolescents based on convergent neural patterns. The work aligns with NIMH's Strategic Plan Objective 1.3.A and addresses gaps in etiological and treatment models by examining how the brain functions as an integrated network rather than isolated regional disturbances, a shift from prior variable-centered research.

The University of Delaware, a land-grant research institution, is the recipient and prime performer. Work is performed in Newark, Delaware, with a performance period extending through May 31, 2027. This award falls under the Mental Health Research Grants program (CFDA 93.242), which supports basic and clinical research to transform understanding and treatment of mental illnesses.

Abstract

Adolescent depression and anxiety commonly CO-occur, and this CO-occurrence is associated with poorer health, psychological, and social outcomes than either presentation in isolation. Yet, little is known about the potential neural substrates driving the CO-occurrence of depression and anxiety in adolescence. Thus, an essential next step is to examine specific neural mechanisms underlying this CO-occurrence to provide critical insights into prevention and treatment targets. Although extant research in this area has identified disturbances in specific brain regions, this approach is inherently limited, as the brain functions as a network. The examination of disturbances in the organization of brain networks related to emerging, CO-occurring depression and anxiety may provide a more complete delineation of underlying etiological mechanisms. Moreover, the majority of research in this area has used variable-centered approaches, which fail to reflect the vast heterogeneity in network organization. Alternatively, person-centered approaches leverage the associations between individuals to identify subgroups based on (within-group) convergence and (between-group) differences in network organization. Identifying subgroups of individuals with similar network patterns allows US to examine whether such subgroups display distinct profiles of depression and anxiety, a critical next step in advancing our etiological and treatment models of depression and anxiety. These gaps in the literature could be addressed via the use of complex, cutting-edge network analyses, specifically graph theory and subgrouping-group iterative multiple model estimation. In line with nimh's strategic plan objective 1.3.a, the objective of the proposed research is to isolate disturbances in neural networks that are shared by emerging depression and anxiety (aim 1) and identify distinct subgroups of adolescents, based on patterns of underlying network connectivity, which may display differing symptom profiles (aim 2). We use a dimensional (VS. Diagnostic group) approach to fully capture the range of pathology, which is particularly relevant during adolescence, when pathology may still be emerging, and thus not at the level for a full diagnosis. The proposed research will use archival data collected from a community sample of adolescents (n=200). Functional magnetic resonance imaging (fmri) data were collected during an explicit emotion regulation task in which participants were instructed to either react naturally or utilize cognitive reappraisal in response to negative and neutral stimuli. The ultimate goal of this research is to refine etiological models of depression and anxiety in adolescence by identifying shared and unique network mechanisms. The proposed F31 will allow the applicant to gain expertise in two critical areas that are beyond the scope of the standard graduate curriculum: (i) adolescent depression and its shared mechanisms with anxiety and (II) complex statistical analysis of brain networks. The institutional environment and mentorship team will provide ample opportunities for the applicant's development towards an independent research career.