Division of Brain, Imaging & Behaviour Systems Neuroscience, Krembil Brain Institute, University Health Network, Toronto, Canada.
Institute of Medical Science, University of Toronto, Toronto, Canada.
Pain. 2022 Aug 1;163(8):1468-1478. doi: 10.1097/j.pain.0000000000002613. Epub 2022 Feb 23.
Chronic pain has widespread, detrimental effects on the human nervous system and its prevalence and burden increase with age. Machine learning techniques have been applied on brain images to produce statistical models of brain aging. Specifically, the Gaussian process regression is particularly effective at predicting chronological age from neuroimaging data which permits the calculation of a brain age gap estimate (brain-AGE). Pathological biological processes such as chronic pain can influence brain-AGE. Because chronic pain disorders can differ in etiology, severity, pain frequency, and sex-linked prevalence, we hypothesize that the expression of brain-AGE may be pain specific and differ between discrete chronic pain disorders. We built a machine learning model using T1-weighted anatomical MRI from 812 healthy controls to extract brain-AGE for 45 trigeminal neuralgia (TN), 52 osteoarthritis (OA), and 50 chronic low back pain (BP) subjects. False discovery rate corrected Welch t tests were conducted to detect significant differences in brain-AGE between each discrete pain cohort and age-matched and sex-matched controls. Trigeminal neuralgia and OA, but not BP subjects, have significantly larger brain-AGE. Across all 3 pain groups, we observed female-driven elevation in brain-AGE. Furthermore, in TN, a significantly larger brain-AGE is associated with response to Gamma Knife radiosurgery for TN pain and is inversely correlated with the age at diagnosis. As brain-AGE expression differs across distinct pain disorders with a pronounced sex effect for female subjects. Younger women with TN may therefore represent a vulnerable subpopulation requiring expedited chronic pain intervention. To this end, brain-AGE holds promise as an effective biomarker of pain treatment response.
慢性疼痛对人类神经系统有广泛的、有害的影响，其患病率和负担随着年龄的增长而增加。机器学习技术已被应用于脑图像，以生成大脑衰老的统计模型。具体来说，高斯过程回归特别有效地从神经影像学数据中预测年龄，从而可以计算大脑年龄差距估计值（brain-AGE）。病理性生物过程，如慢性疼痛，可以影响 brain-AGE。由于慢性疼痛障碍在病因、严重程度、疼痛频率和性别相关性患病率方面可能存在差异，我们假设 brain-AGE 的表达可能是疼痛特异性的，并且在不同的慢性疼痛障碍之间存在差异。我们使用来自 812 名健康对照者的 T1 加权解剖 MRI 构建了一个机器学习模型，以提取 45 例三叉神经痛（TN）、52 例骨关节炎（OA）和 50 例慢性腰痛（BP）患者的 brain-AGE。进行了经假发现率校正的 Welch t 检验，以检测每个离散疼痛队列与年龄匹配和性别匹配对照之间 brain-AGE 的显著差异。三叉神经痛和骨关节炎，但不是腰痛患者，具有明显更大的 brain-AGE。在所有 3 个疼痛组中，我们观察到女性驱动的 brain-AGE 升高。此外，在 TN 中，与 TN 疼痛的伽玛刀放射手术反应相关的更大的 brain-AGE 与诊断时的年龄呈负相关。由于 brain-AGE 的表达在不同的疼痛障碍中存在差异，并且女性患者的性别影响明显，因此年轻的 TN 女性可能代表一个脆弱的亚人群，需要加快慢性疼痛干预。为此，brain-AGE 有望成为疼痛治疗反应的有效生物标志物。
