# Assessing chronic pain using brain entropy mapping

> **NIH NIH R21** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $441,886

## Abstract

Chronic pain is one of the most prevalent health problems in the world but remains poorly understood and
challenging to treat or manage. Two major barriers to progress are the unclear brain mechanism of chronic pain
and the complexity to model the multifaceted individual differences in pain experience, making it difficult to
accurately diagnose pain or monitor pain progression or treatment effects. To solve this problem, we need big
data and sophistic models. In this novel project, we will use the large (n~100000) UK Biobank (UKB) data and
deep machine learning (DL) to address the two critical problems in chronic pain research. We propose to use
resting state fMRI (rsfMRI) because pain perception and processing are ongoing in the brain which can be
characterized by the spontaneous brain activity measured by rsfMRI. We will focus on temporal coherence (TC)
of rsfMRI given its fundamental role in brain functions and our leading expertise in this research topic. We initiated
the concept of brain entropy (BEN) mapping as a tool to measure regional brain TC and our systematic work
has demonstrated the high test-retest stability, sensitivity to causal effects, specificity to focal stimulation,
different diseases, and drug states, as well as the potential as an intervention target through neuromodulations
or medication. We also showed that TC/BEN contains unique information that can not be characterized by other
neuroimaging measures. Aim 1 of this project will use TC/BEN mapping to find a potential chronic pain brain
circuit where the resting TC is positively correlated with chronic pain and individual differences of pain experience.
Aim 2 will use resting TC to build a multi-task DL-based pain prediction model. This project represents the first-
of-its-kind to study TC in chronic pain. It will bring new knowledge about chronic pain brain mechanisms (resting
TC alterations and associations) and a DL-based quantitative pain prediction model. Research rigor and
method/finding generalizability will be ensured by the use of by far the largest rsfMRI data. These high
innovations may lead to intervention targets for pain treatment or intervention development and a quantitative
tool to evaluate individual differences in pain or pain progression. Feasibility of this project is guaranteed by the
existing large data from UKB, our years of work experience in related research fields, the strong pilot data, and
the strong team expertise. Success of this pilot project will immediately lead to large size future important studies
in this new research direction.

## Key facts

- **NIH application ID:** 10598873
- **Project number:** 1R21AG082345-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Ze Wang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $441,886
- **Award type:** 1
- **Project period:** 2022-09-30 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598873

## Citation

> US National Institutes of Health, RePORTER application 10598873, Assessing chronic pain using brain entropy mapping (1R21AG082345-01). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10598873. Licensed CC0.

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