Biomedical research aimed at understanding how drugs of abuse alter brain biology and function to engender a state of physical dependence and/or promote the compulsive behavior that characterizes addiction is generating a substantial amount of data of various types (imaging, genetic, physiological, electronic health records, etc.). These data need to be stored, managed, standardized, and published, and NIDA’s Strategic Plan outlines how big data science can be leveraged to reveal new aspects of addiction biology and is closely aligned with the NIH Strategic Plan for Data Science.
The interdisciplinary field of data science uses quantitative and analytical approaches, processes, and systems to extract knowledge and insights from increasingly large and/or complex datasets. Data science research is a cross-cutting program that spans all four branches of DNB, with a focus on:
- the integration of existing datasets and tools with those that are being newly developed
- making datasets and resources findable, accessible, interoperable, and reusable (FAIR)
- the development and/or improvement of statistical and analytical methods and tools
- data storage and management
- promoting stewardship and sustainability
Incorporating data science as a new tool for the study of substance use disorders will bring together researchers with expertise in a variety of disciplines, including computer science, bioinformatics, and mathematics; intra/inter-university and multi-disciplinary collaborations are encouraged. Integrating data of many different types will enable scientific discovery of the biological and behavioral complexity that underlies addiction.
Contact: Susan N. Wright, Ph.D.
- NIDA vision for big data science to understand the biological underpinnings of substance use disorders Wright, S.N., Little, A.R. NIDA vision for big data science to understand the biological underpinnings of substance use disorders. Neuropsychopharmacol. (2020). https://doi.org/10.1038/s41386-020-00850-1
- Notice of Special Interest (NOSI): The Application of Big Data Analytics to Drug Abuse Research Notice Number: NOT-DA-19-041
- Notice of Special Interest (NOSI): Modeling Social Contagion of Substance Use Epidemics Notice Number: NOT-DA-20-009
- Notice of Special Interest (NOSI): Advancing Research on SUD through Computational Neuroscience Notice Number: NOT-DA-20-022
Research Project Grants
- Biomedical Data Repository (U24 – Clinical Trials Not Allowed; PAR-20-089)
- Biomedical Knowledgebase (U24 – Clinical Trials Not Allowed; PAR-20-097)
- Collaborative Research in Computational Neuroscience (CRCNS) NSF Innovative Approaches to Science and Engineering Research on Brain Function
- Advancing HIV/AIDS Research through Computational Neuroscience FOA (R01 - Clinical Trial Optional; RFA-DA-21-013)
- Notice of Special Interest (NOSI): Administrative Supplements to Support Enhancement of Software Tools for Open Science Notice Number: NOT-OD-20-073
Research Project Grants
- Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R01) RFA-DA-20-006
- Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R21) RFA-DA-20-007
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R01) RFA-DA-20-008
- Leveraging Big Data Science to Elucidate the Mechanisms of HIV Activity and Interaction with Substance Use Disorder (R21) RFA-DA-20-009
- Single Cell Opioid Responses in the Context of HIV (SCORCH) Program: Data Coordination, Analysis, and Scientific Outreach (UM1) RFA-DA-19-038
- Reissue: Single Cell Opioid Responses in the Context of HIV (SCORCH) Program: Data Coordination, Analysis, and Scientific Outreach (UM1) RFA-DA-20-027
Meetings & Events
- January 7-8, 2021: 2020 NIDA-NIAAA Mini-Convention Frontiers in Addiction Research Virtual Meeting
- Session 1: Mapping the addiction neurocircuitry
- Session 2: AI-based approaches to addiction pathophysiology and novel therapeutics
- June 22-23, 2020: The CPDD 2020 Scientific Meeting Virtual Experience
- Mini-symposium: Artificial Intelligence Technologies to Enable Drug Development for Substance Use Disorders
Research Interests and Biographies
Susan N. Wright, Ph.D.– Program Director for Big Data and Computational Science
During her clinical postdoctoral fellowship at Maryland Psychiatric Research Center, University of Maryland School of Medicine, she studied white matter integrity, cognition, and aging in schizophrenic patients using various neuroimaging techniques, as well as imaging genetics. Her experience with experimental, theoretical/computational, and clinical research and multi-disciplinary approach make her highly qualified to oversee the program that advances NIDA’s Strategic Plan for Big Data Science. Other areas included in her portfolio are data curation, sharing, access, reproducibility, security, analysis, harmonization, quality metrics and standards, and visualization. She is a representative for NIDA on many NIH-wide and multi-agency committees, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, the Helping End Addiction Long-term (HEAL) initiative, the Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative, the Common Fund Acute to Chronic Pain Signatures (A2CPS) program, the Common Fund Artificial Intelligence for Biomedical Excellence (AIBLE) program, the Collaborative Research in Computational Neuroscience (CRCNS) program, and the Interagency Modeling and Analysis Group (IMAG). Susan also works closely with the Office of Data Science Strategy (ODSS) on implementation of the NIH Strategic Plan for Data Science.
Nina Bernick - Intern
Nina Bernick, a rising senior at Yale studying Applied Mathematics and Mechanical Engineering, is doing an internship in the Division of Neuroscience and Behavior as part of the Civic Digital Fellowship program. This fall, she’ll be working as a software engineer with Susan Wright, Roger Little, and members of the IT team with the goal of using AI algorithms to automate tasks for various NIDA staff to increase efficiency and productivity for grants management and administration. Nina is originally from the suburbs of Philadelphia but is currently living in New Haven. Her long-term interests are applying technology to issues in sustainability and healthcare. Outside of work and school, she loves to paint, read, cook, and spend time outdoors – she leads backpacking trips for incoming first-years at Yale and tries to get outdoors whenever she can. She’s looking forward to getting to know her colleagues at NIDA and learning more about the institute!