Division of Brain, Imaging, and Behaviour-Systems Neuroscience, Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Institute of Medical Science, University of Toronto, Toronto, Ontario M5S 2J7, Canada.
J Neurosci. 2018 Aug 15;38(33):7293-7302. doi: 10.1523/JNEUROSCI.0578-18.2018. Epub 2018 Jul 16.
Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a sensitive and reliable metric for studying pathological brain mechanisms across various chronic pain patient populations. However, the relationship between pathological brain activity and clinical symptoms are not well defined. Therefore, we used regional BOLD signal variability/amplitude of low-frequency oscillations (LFOs) to identify functional brain abnormalities in the dynamic pain connectome in chronic pain patients that are related to chronic pain characteristics (i.e., pain intensity). Moreover, we examined whether there were sex-specific attributes of these functional brain abnormalities and whether functional brain abnormalities in patients is related to pain intensity characteristics on different time scales. We acquired resting-state functional MRI and quantified frequency-specific regional LFOs in chronic pain patients with ankylosing spondylitis. We found that patients exhibit frequency-specific aberrations in LFOs. Specifically, lower-frequency (slow-5) abnormalities were restricted to the ascending pain pathway (thalamus and S1), whereas higher-frequency abnormalities also included the default mode (i.e., posterior cingulate cortex; slow-3, slow-4) and salience (i.e., mid-cingulate cortex) networks (slow-4). Using a machine learning approach, we found that these abnormalities, in particular within higher frequencies (slow-3), can be used to make generalizable inferences about patients' average pain ratings (trait-like pain) but not current (i.e., state-like) pain levels. Furthermore, we identified sex differences in LFOs in patients that were not present in healthy controls. These novel findings reveal mechanistic brain abnormalities underlying the longer-lasting symptoms (trait pain intensity) in chronic pain. Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a reliable metric for studying functional brain associated with chronic pain. The current results demonstrate that dysfunction in these intrinsic fluctuations/oscillations in the ascending pain pathway, default mode network, and salience network during resting state display sex differences and can be used to make inferences about trait-like pain intensity ratings in chronic pain patients. These results provide robust and generalizable implications for investigating brain mechanisms associated with longer-lasting/trait-like chronic pain symptoms.
个体静息时大脑活动的实时波动测量已被证明是研究各种慢性疼痛患者群体中病理性大脑机制的敏感和可靠指标。然而，病理性大脑活动与临床症状之间的关系尚不清楚。因此，我们使用局部血氧水平依赖信号变异性/低频振荡（LFO）幅度来识别慢性疼痛患者动态疼痛网络中的功能脑异常，这些异常与慢性疼痛特征（即疼痛强度）有关。此外，我们还研究了这些功能脑异常是否存在性别特异性特征，以及患者的功能脑异常是否与不同时间尺度上的疼痛强度特征有关。我们采集了强直性脊柱炎慢性疼痛患者的静息态功能磁共振成像，并量化了频率特异性局部 LFO。我们发现患者的 LFO 存在频率特异性异常。具体来说，较低频率（慢-5）异常局限于上行疼痛通路（丘脑和 S1），而较高频率异常还包括默认模式（即后扣带回皮层；慢-3、慢-4）和突显网络（即中扣带回皮层；慢-4）。使用机器学习方法，我们发现这些异常，尤其是较高频率（慢-3）中的异常，可以用来对患者的平均疼痛评分（特质样疼痛）进行可推广的推断，但不能对当前（即状态样）的疼痛水平进行推断。此外，我们在患者中发现了 LFO 中的性别差异，而在健康对照组中则没有。这些新发现揭示了慢性疼痛中较长时间持续的症状（特质疼痛强度）的潜在机制性大脑异常。个体静息时大脑活动的实时波动测量已被证明是研究与慢性疼痛相关的功能性大脑的可靠指标。目前的结果表明，在静息状态下，上行疼痛通路、默认模式网络和突显网络中这些内在波动/振荡的功能障碍存在性别差异，可以用来对慢性疼痛患者的特质样疼痛强度评分进行推断。这些结果为研究与更长时间持续/特质样慢性疼痛症状相关的大脑机制提供了有力且可推广的启示。
