Division of Brain, Imaging, and Behaviour-Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
Pain. 2018 Sep;159(9):1764-1776. doi: 10.1097/j.pain.0000000000001264.
Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various timescales (eg, short-term state vs long-term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting-state dynamic functional connectivity (dFC) and static functional connectivity in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than static functional connectivity were better associated with patients' pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder NP, but greater dynamic connectivity with limbic networks was associated with greater NP. Compared with healthy individuals, the dFC features most highly related to trait NP were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients' overall pain condition (ie, trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.
大脑内部的交流是动态的。慢性疼痛也可能是动态的，随着时间的推移，其强度会发生变化。目前尚不清楚慢性疼痛中大脑动力学是如何被打乱的，或者与患者在不同时间尺度上（例如短期状态与长期特征）评估的疼痛有何关系。患者会经历疼痛的“特征”，这些特征反映了他们的一般状况，但也会经历每天都在变化的疼痛“状态”。在这里，我们使用基于网络的多元机器学习来确定动态和静态大脑交流模式与慢性疼痛的不同特征和时间尺度有何关系。我们的模型基于慢性神经性疼痛（NP）或非 NP 患者的静息态动态功能连接（dFC）和静息态功能连接。模型中最突出的网络是默认模式、突显和执行控制网络。我们还发现，dFC 的跨网络测量值而不是静态功能连接与患者的疼痛更好相关，但仅在具有 NP 特征的患者中相关。这些关联与特征疼痛的测量值比状态疼痛的测量值相关性更高、更广泛。此外，与执行控制网络的动态连通性增加与 NP 程度较轻相关，而与边缘网络的动态连通性增加与 NP 程度较重相关。与健康个体相比，与特征性 NP 相关性最高的 dFC 特征在疼痛程度较大的患者中也更异常。我们的研究结果表明，dFC 反映了患者的整体疼痛状况（即特征性疼痛），而不仅仅是他们当前的状态，并且受到疼痛特征复杂性的影响，这些特征超出了强度。
