# Multi-Level Modeling of Addiction Comorbidity > **NIH NIH F30** · UNIV OF ARKANSAS FOR MED SCIS · 2020 · $16,736 ## Abstract PROJECT SUMMARY/ ABSTRACT This revised NIH Individual Fellowship Award (F30) proposal seeks to develop career-specific skills and knowledge of the behavioral contributions and underlying neural mechanisms associated with co-occurring drug use disorder (DUD) and other psychiatric illnesses. The diagnosis of DUD comorbidity is well established clinically, and is prone to more difficult and expensive treatment plans and worse treatment outcomes than either diagnosis alone. Despite the prevalence and clinical significance of DUD comorbidity, few studies have characterized the interactions between environment, behavior, and neural organizations that contribute to DUD comorbidity illness trajectories. Cognitions related to self-beliefs and self-directed behaviors are compromised in individuals with DUD, depression, or PTSD, yet the altered neural circuitry underlying such deficits in comorbid individuals has not been studied. The overall research goal of the proposed project is therefore to identify traits associated with functional neural networks underlying DUD comorbidity and determine how changes in network organization lead to deficits in self-related cognitions in comorbid individuals. Additionally, machine-learning computational models will be trained on Big Data to classify brain-wide patterns of network organization at two distinct stages of DUD comorbidity development. The proposal includes a rigorous training plan for the candidate to gain expertise in neuroimaging methodology and advanced computational approaches to neuroimaging data (e.g., structural equation modeling, machine learning, and multivariate pattern analysis (MVPA)). An increasingly large data sample (n=550+) of adults and adolescents 12-60 years old will be used to test the aims of this proposal. In Aim 1, controlling for age and sex, environmental variables and self-beliefs will be related to the expression of DUD comorbidity. Using whole-brain mediation analyses, significant traits will then be related to areas of brain activation, with focus on the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC). Aim 2 will use structural equation modeling to test how sex-specific changes in ACC networks are related to self-regulation in individuals with DUD and DUD comorbidity compared to healthy individuals. Aim 3 will use MVPA-derived computational models to classify whole-brain patterns of activity that characterize susceptibility to DUD comorbidity during adolescence and sustained comorbidity in adulthood. By classifying brain activity underlying DUD comorbidity at two separate stages of disorder development, this project will help pave the way for future research into more effective treatment methods and better preventative efforts to preclude DUD comorbidity. The career development milestones related to computational psychiatry – clinical medicine, responsible conduct of research, and computational neuroscience – will be attained via this mentored research... ## Key facts - **NIH application ID:** 9882986 - **Project number:** 5F30DA043928-03 - **Recipient organization:** UNIV OF ARKANSAS FOR MED SCIS - **Principal Investigator:** Bradford Martins - **Activity code:** F30 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $16,736 - **Award type:** 5 - **Project period:** 2018-03-30 → 2020-06-01 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9882986 ## Citation > US National Institutes of Health, RePORTER application 9882986, Multi-Level Modeling of Addiction Comorbidity (5F30DA043928-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9882986. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*