# Multivariate Machine Learning to Characterize Opioid-induced Alterations in the Brain in Chronic Pain

> **NIH NIH K25** · STANFORD UNIVERSITY · 2020 · $175,889

## Abstract

PROJECT SUMMARY/ABSTRACT
Prescription opioids are a potent class of drugs for treating pain. However, growing body of research has
described iatrogenic consequences of long-term (> 90 days) opioid use in patients with chronic pain including
hyperalgesia and impaired executive function. Dopamine is a critical modulator of executive function. While
changes in pain and behavior have been noted, little is known about the brain’s morphology, neural and
dopaminergic activity that change over time with long-term prescription opioid use. Consistent with the NIDA
Strategic Plan objective 1.3, this K25 proposal seeks to “establish the effects of drug use, addiction, and recovery
on brain circuits, behavior, and health” using neuroimaging-informed tools. Specifically, the present study
combines multiple levels of investigation, including structural and functional Magnetic Resonance Imaging (MRI),
Positron Emission Tomography (PET), Quantitative Sensory Testing (QST) and neuropsychological
assessments of executive function, and employ machine learning techniques for analysis to identify the effects
of long-term prescription opioid use on the brain in chronic pain patients. The applicant will use her advanced
quantitative skills in neuroimaging data analysis and modeling to training in QST, and experience in
cognitive neuropsychology, epidemiology of chronic pain and addiction to develop an independent research plan
in translational pain and successfully compete for future R01 funding. To achieve the training needed to facilitate
this investigation, the applicant has consulted with an expert in chronic pain research and opioid therapy, a
substance abuse specialist, a neuropsychologist, an epidemiologist, an imaging scientist, and a machine
learning leader in neuroimaging field to develop an innovative study and training plan. 40 patients with a
diagnostically homogeneous chronic pain condition (i.e., chronic low back pain; CLBP) on long-term opioid
therapy, as compared to 40 opioid-naïve CLBP patients, will be studied to achieve the following Aims: 1)
Measure pain, cognitive performance, neural and dopaminergic activity during concurrent pain and executive
function task fMRI-PET to characterize the effects of opioids on pain processing and executive function in CLBP;
2) measure intrinsic brain activity during resting state fMRI-PET to identify intrinsic brain alterations associated
with long-term opioid use in CLBP; and 3) apply high-resolution structural MRI to measure opioid-induced
morphological changes in CLBP. This research is innovative in its use of combined QST and neuro-
psychological measures with multimodal imaging and sophisticated statistical approaches. It is significant
because of its comprehensive approach towards addressing the NIDA Strategic Plan objective. Findings stand
to inform medical decision-making regarding pain care and opioid prescription, as well as risk mitigation
strategies. The research, training and results obtained will provi...

## Key facts

- **NIH application ID:** 9891124
- **Project number:** 1K25DA048179-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Behnaz Jarrahi
- **Activity code:** K25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $175,889
- **Award type:** 1
- **Project period:** 2020-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9891124

## Citation

> US National Institutes of Health, RePORTER application 9891124, Multivariate Machine Learning to Characterize Opioid-induced Alterations in the Brain in Chronic Pain (1K25DA048179-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9891124. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*