![]() While major advancements have been made in the development of rigorous analytical and computational methods, their application in substance use disorders (SUD) research remains an area for further growth. The second shift comes from significant advances in explanatory modeling that seek to uncover neural computational mechanisms underlying cognition, behavior, and associated dysfunction (e.g., reinforcement learning models applied towards dopamine’s role in reward learning and motivation). Researchers will also need to be familiar with existing large-scale analysis tools, and/or learn to develop new ones. Responsible use of large and complex datasets therefore requires knowledge of analytical and statistical considerations specific to large datasets. Thus, the nature of the study requires extraordinary caution in conducting analyses and interpreting results. Further, because of the large sample sizes, there is a high likelihood that analyses will produce statistically significant effects with small effect sizes that may not be clinically or biologically meaningful. Increased access to large-scale, open data has led to a large number of analyses being conducted on such data, and subsequently to a significant number of false positives being reported. This shift has accelerated artificial intelligence-based efforts towards prediction of substance use and psychiatric outcomes. The first change has been a shift in the field towards pooling together smaller datasets (e.g., ENIGMA) or creating larger ones that are widely disseminated (e.g., Human Connectome Project, the Adolescent Brain Cognitive Development SM Study, the Healthy Brain and Cognitive Development Study, the All of Us Research Program). Neurocognitive studies have undergone transformative changes over the last decade. Kirschstein National Research Service Award (NRSA) programs. R25 programs may augment institutional research training programs (e.g., T32, T90) but cannot be used to replace or circumvent Ruth L. Research education programs may complement ongoing research training and education occurring at the applicant institution, but the proposed educational experiences must be distinct from those training and education programs currently receiving Federal support. ![]() Particular topics of interest include biostatistics, machine learning, artificial intelligence, and explanatory modeling, enhanced rigor and reproducibility in big data analyses. Courses for Skills Development: Activities may include short-term workshops or seminars for undergraduate, graduate/medical students, postdoctorates, medical residents, and faculty that will emphasize computational and analytical research methods.To accomplish the stated over-arching goal, this FOA will support creative educational activities with a primary focus on: The overarching goal of this R25 program is to support educational activities that complement and/or enhance the training of a workforce to meet the nation’s biomedical, behavioral and clinical research needs. The NIH Research Education Program (R25) supports research education activities in the mission areas of the NIH.
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