# Longitudinal neural fingerprinting of opioid-use trajectories

> **NIH NIH R21** · YALE UNIVERSITY · 2024 · $209,375

## Abstract

ABSTRACT
Opioid use disorder (OUD) is a significant public health problem in the United States, with overdoses and deaths
currently maintaining at epidemic levels. As risk of overdose is highest following relapse and treatment dropout,
improved mechanistic understanding of risk and protective factors in individuals currently receiving medications
for OUD (MOUD) is urgently needed. To address this, this Cutting-Edge, Basic Science Award (CEBRA)
application moves beyond the limitations of traditional case-control designs to rapidly advance our understanding
of the neural mechanisms of early MOUD treatment.
 For decades, clinical neuroimaging has relied on case-control designs in which individuals with a given
psychiatric disorder are compared to a group of matched healthy ‘control’ individuals, or a group of individuals
distinguished by another individual difference feature. While informative, these approaches by definition focus
on group average deviations from a presumed normative population and thus may have relatively limited real
world clinical utility. For example, recent findings from machine learning studies of addictions and other disorders
indicate that brain networks which distinguish patients from controls are often distinct from brain networks that
predict specific clinical outcomes within-group. This striking distinction suggests that person-specific
neurobiology is dissociable from group-specific patterns, and thus group-specific findings are unlikely to translate
to an improved understanding of person-specific pathophysiology or to treatment.
 Our innovation, the characterization of neural trajectories during MOUD treatment using dense sampling
(i.e., repeated longitudinal assessments of the same individual) will provide unprecedented mechanistic insight
into the neurobiological basis of OUD remission. Dense sampling is an emerging methodology that aims to
overcome limitations with cross-sectional research that inherently assumes the brain is static and unchanging.
This approach is particularly relevant to studying MOUD treatment: MOUD is multiphasic, comprised of
medication induction, stabilization, ongoing treatment and eventual discontinuation phases. However, with a few
small exceptions, existing neuroimaging efforts are almost exclusively single time-point assessments which, by
definition, fail to capture dynamic trajectories of individual risk and resilience that can be used to mechanistically
inform treatment advancements. This pilot project therefore applies dense sampling to characterize early
trajectories of MOUD recovery at unprecedented temporal resolution—i.e., bi-weekly over three months. To
maximize mechanistic insight, complementary clinical and behavioral computational data will also be acquired
longitudinally. Borrowing from basic human neuroscience, our dense sampling neuroimaging approach
represents a paradigm shift for psychiatry research in general, and holds enormous potential to inform
understanding of opioid...

## Key facts

- **NIH application ID:** 10934369
- **Project number:** 5R21DA058415-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Sarah Yip
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $209,375
- **Award type:** 5
- **Project period:** 2023-09-30 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934369

## Citation

> US National Institutes of Health, RePORTER application 10934369, Longitudinal neural fingerprinting of opioid-use trajectories (5R21DA058415-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10934369. Licensed CC0.

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