Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study

• Type of Material: Article
• Authors: Eric Ichesco, University of Michigan, Ann Arbor; Scott J. Peltier, University of Michigan, Ann Arbor; Ishtiaq Mawla, University of Michigan, Ann Arbor; Daniel Harper, Emory University; Lynne Pauer, Pfizer Inc.; Steven E. Harte, University of Michigan, Ann Arbor; Daniel J. Clauw, University of Michigan, Ann Arbor; Richard E. Harris, University of Michigan, Ann Arbor
• Language: English
• Date: 2021-11-01
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Pfizer. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/art.41781
• Title of Journal or Parent Work: Arthritis and Rheumatology
• Volume: 73
• Issue: 11
• Start Page: 2127
• End Page: 2137
• Keywords: Female; Pregabalin; Brain; Chronic Pain; Cross-Over Studies; Magnetic Resonance Imaging; Adult; Treatment Outcome; Middle Aged; Milnacipran; Double-Blind Method; Humans; Support Vector Machine; Neuroimaging; Analgesics; Biomarkers; Fibromyalgia; Young Adult
• Research Categories: Biology, Neuroscience

Abstract

Objective: There is increasing demand for prediction of chronic pain treatment outcomes using machine-learning models, in order to improve suboptimal pain management. In this exploratory study, we used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM. Methods: FM patients participated in 2 separate double-blind, placebo-controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin. Results: Connectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies. Conclusion: Our findings indicate that brain functional connectivity patterns used in a machine-learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.