Abstract

<h3>Objective:</h3> In this study, we aim to 1) utilize Arterial Spin Labeling (ASL) to develop neurophysiological characterization of the subjective chronic pain experience, and 2) explore the feasibility of incorporating ASL in routine clinical scans. <h3>Background:</h3> Over 100 million people in the US suffer from chronic pain. This leads to tremendous burden on individuals and society. However, as the mechanisms for chronic pain are unclear, robust diagnostic and prognostic methods remain limited. Our previous research using resting-state fMRI revealed changes in low-frequency fluctuations in nucleus accumbens (NAc) when low back pain became chronic. We hypothesize that physiological changes in NAc in chronic pain also affect local blood perfusion. <h3>Design/Methods:</h3> ASL is a non-invasive perfusion-based MRI technique that can quantify cerebral blood flow (CBF). We collected resting-state ASL data from 43 patients with chronic low back pain (CLBP) and 33 healthy controls (HC) on a research scanner, as well as 11 CLBP patients and 10 HC on a clinical scanner. Brain perfusion maps were generated to quantify the CBF and relative CBF (rCBF). <h3>Results:</h3> Compared to HC, CLBP patients had significantly higher rCBF in the NAc (<i>p</i>=0.046). However, multivariate analysis revealed sex and age as main contributors to this difference in our preliminary clinical dataset. NAc rCBF increased with age and decreased with female sex. We also observed linear correlation between CBF from the clinical scanner and the research scanner in grey matter and white matter (<i>p</i>=0.0008 and 0.001; <i>r</i><sub>2</sub>=0.57 and 0.55, respectively). <h3>Conclusions:</h3> Our preliminary ASL results show consistency with prior fMRI findings in identifying NAc as a biomarker for chronic pain. Altered NAc activity in chronic pain may be involved in the motivational and hedonic aspect of chronic pain. This pilot study also supports the potential of utilizing ASL in clinical care for chronic pain patients. <b>Disclosure:</b> Ms. Yuan has nothing to disclose. Madalina Tivarus has nothing to disclose. Miss Tahsin has nothing to disclose. Mr. Alldritt has nothing to disclose. Dr. Geha has nothing to disclose.

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