# Brain connectivity and genetics as predictors of opioid abuse treatment outcomes

> **NIH VA I01** · MICHAEL E DEBAKEY VA MEDICAL CENTER · 2022 · —

## Abstract

Opioid use disorder (OUD) is a major problem in America, currently reaching epidemic levels.
Unfortunately, OUD is especially prevalent among Veterans, as it is common that Veterans need pain
treatment and the liberal use of opioids in medicine is one of the major reasons why the OUD problem
keeps growing.
There are good treatment options for OUD: Both buprenorphine and methadone can be used in
maintenance therapies in which, as long as the patient stays in treatment, they will not likely truly abuse
opioids. This is extremely important as one major reason for death in OUD is death by fentanyl
overdose, and a patient in maintenance therapies will likely avoid that fate. However, it is very common
that patients discontinue treatment.
An important gap in knowledge arises from the fact that we have no means to predict which patients
are more likely to drop from treatment. Such prediction would be of great interest as limited resources
could be optimally allocated. In addition, an understanding of the brain circuitry behind both OUD and
OUD treatment outcomes is necessary for the rational design of the next wave of therapeutic
approaches.
Big data approaches to scientific questions are increasingly common, however in psychiatry advances
are (as usual, psychiatry likely being the most complex field in medicine) lagging. We have shown that
using a machine learning approach to human brain imaging analysis, we can classify psychiatric
patients according to past suicide attempt and high suicide ideation. We propose to use a similar (albeit
improved) approach to the prediction of buprenorphine treatment in Veterans with OUD.
We propose to use different MRI modalities (structure, white matter, resting state functional
connectivity) and limited genotyping (two single nucleotide polymorphisms in the µ opioid receptor and
the α 5 nicotinic acetylcholine receptor subunit known to be associated with OUD risk) in machine
learning algorithms to predict OUD treatment outcomes. MRI and genetics will be collected before
treatment and MRI again within 10 days of treatment initiation (with a smaller group imaged at 6 months
also), and Veterans will be followed up to study outcomes.
If successful, this proposal would provide both mechanistic data (brain circuitry and function, including
a genetic component) about OUD and OUD treatment outcome, and an unbiased approach to OUD
treatment prediction.

## Key facts

- **NIH application ID:** 10316149
- **Project number:** 5I01CX001937-02
- **Recipient organization:** MICHAEL E DEBAKEY VA MEDICAL CENTER
- **Principal Investigator:** RAMIRO SALAS
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-10-01 → 2024-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316149

## Citation

> US National Institutes of Health, RePORTER application 10316149, Brain connectivity and genetics as predictors of opioid abuse treatment outcomes (5I01CX001937-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10316149. Licensed CC0.

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