Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach
Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach
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DOI：
10.1523/jneurosci.0578-18.2018
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发表时间：
2018-08-15
影响因子：
5.3
通讯作者：
Davis, Karen D.
中科院分区：
文献类型：
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作者：
Rogachov, Anton;Cheng, Joshua C.;Davis, Karen D.
测量个体在休息时大脑活动的瞬间波动已被证明是研究各种慢性疼痛患者人群的病理性大脑机制的敏感和可靠的度量。然而，病理性脑活动和临床症状之间的关系还没有很好的定义。因此，我们使用低频振荡（LFO）的局部BOLD信号变异性/振幅来识别慢性疼痛患者中与慢性疼痛特征相关的动态疼痛连接体中的功能性脑异常（即，疼痛强度）。此外，我们研究了这些功能性脑异常是否有性别特异性属性，以及患者的功能性脑异常是否与不同时间尺度上的疼痛强度特征相关。我们获得了强直性脊柱炎慢性疼痛患者的静息态功能MRI和量化频率特异性区域LFO。我们发现患者在LFO中表现出频率特异性畸变。具体而言，低频（慢-5）异常仅限于上行疼痛通路（丘脑和SI），而高频异常还包括默认模式（即，后扣带皮层;慢-3，慢-4）和显著性（即，中扣带皮层）网络（慢-4）。使用机器学习方法，我们发现这些异常，特别是在较高频率（慢-3）内，可以用于对患者的平均疼痛评级（特质样疼痛）进行可推广的推断，但不能用于当前（即，类状态）疼痛水平。此外，我们确定了患者LFO的性别差异，而健康对照组中不存在这种差异。这些新的发现揭示了慢性疼痛中持久症状（特质疼痛强度）的机制性大脑异常。
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 SI ), 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.