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具有广泛的有害影响，其患病率和负担随着年龄的增长而增加。机器学习技术已应用于大脑图像，以生成大脑衰老的统计模型。具体来说，高斯过程回归在根据神经影像数据预测实际年龄方面特别有效，从而可以计算大脑年龄差距估计值（brain-AGE）。慢性疼痛等病理生物学过程可以影响大脑 AGE。由于慢性疼痛疾病的病因、严重程度、疼痛频率和性别相关患病率可能有所不同，因此我们假设脑 AGE 的表达可能具有疼痛特异性，并且在不同的慢性疼痛疾病之间存在差异。我们使用 812 名健康对照者的 T1 加权解剖 MRI 建立了一个机器学习模型，以提取 45 名三叉神经痛(TN)、52 名骨关节炎 (OA) 和 50 名慢性腰痛 (BP) 受试者的大脑 AGE。进行错误发现率校正韦尔奇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大脑 AGE 的显着差异。三叉神经痛和 OA 受试者的大脑 AGE 显着增大，但 BP 受试者则不然。在所有 3 个疼痛组中，我们观察到女性驱动的大脑 AGE 升高。此外，在 TN 中，显着较大的脑 AGE 与伽玛刀放射外科治疗 TN 疼痛的反应相关，并且与诊断时的年龄呈负相关。由于不同疼痛疾病的大脑 AGE 表达有所不同，因此对女性受试者具有明显的性别影响。因此，患有 TN 的年轻女性可能是需要快速慢性疼痛干预的弱势群体。为此，大脑 AGE 有望成为疼痛治疗反应的有效生物标志物。