Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States; Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Ave. S. AA 1105 MCN, Nashville, TN 37232-2310, United States; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China.
Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States; Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Ave. S. AA 1105 MCN, Nashville, TN 37232-2310, United States.
Neuroimage. 2022 Aug 15;257:119244. doi: 10.1016/j.neuroimage.2022.119244. Epub 2022 May 6.
Pain perception involves multiple brain regions and networks. Understanding how these brain networks work together is fundamental for appreciating network-wise changes reported in patients with chronic pain disorders. Parcellating pain related networks and understanding their causal relationships is the first step to understand how painful information is processed, integrated, and modulated, and it requires direct manipulation of specific brain regions. Nonhuman primates (NHP) offer an ideal model system to achieve these goals because cortical and subcortical regions in the NHP brain are established based on a variety of different types of data collected in a way that is not feasible or, at least, extremely difficult in humans (i.e., histology data, tract-tracing, intracerebral recordings). In addition, different methodological techniques can also help characterize and further understand these brain cortical and subcortical regions over the course of development. Here we used a heat nociceptive stimulation that is proven to elicit activity of nociceptive neurons in the cortex to refine and parcellate the whole brain nociceptive functional networks, to identify key network hubs, and to characterize network-wise temporal dynamic signatures using high-resolution fMRI. We first functionally localized 24 cortical and subcortical regions that responded to heat nociceptive stimuli (somatosensory area 1/2, area 3a/3b, S2, posterior insula (pIns), anterior insula, area 7b, posterior parietal cortex, anterior cingulate cortex (ACC), prefrontal cortex, caudate, and mediodorsal (MD) and ventral posterior lateral (VPL) thalamic nuclei) and used them as seeds in resting state fMRI (rsfMRI) data analysis. We applied both hierarchical clustering and graph-theory analyses of the pairwise rsfMRI correlation metrics and identified five cortical and one subcortical sub-networks: strong resting state functional connectivity (rsFC) between ACC and prefrontal regions, parietal cortex and area 7b, S2 and posterior insula, areas 3a/3b and 1/2 within the S1 cortex, and thalamic MD and caudate nuclei. The rsFC strengths between cortical areas within each subnetwork were significantly stronger than those between subcortical regions. Regions within each sub-network also exhibited highly correlated temporal dynamics at rest, but the overall dynamic patterns varied drastically across sub-networks. Graph-theory analysis identified the MD nucleus as a hub that connects subcortical and cortical nociceptive sub-networks. The S2-pIns connection joins the sensory and affective/cognitive sub-networks.
疼痛感知涉及多个大脑区域和网络。了解这些大脑网络如何协同工作对于理解慢性疼痛障碍患者报告的网络变化至关重要。分割与疼痛相关的网络并理解它们的因果关系是理解疼痛信息如何被处理、整合和调节的第一步,这需要直接操纵特定的大脑区域。非人类灵长类动物(NHP)提供了实现这些目标的理想模型系统,因为 NHP 大脑中的皮质和皮质下区域是基于各种不同类型的数据建立的,这些数据的收集方式在人类中是不可行的,或者至少是极其困难的(即组织学数据、追踪、颅内记录)。此外,不同的方法学技术也可以帮助在发育过程中描述和进一步理解这些大脑皮质和皮质下区域。在这里,我们使用热伤害感受刺激来精确分割和分割整个大脑伤害感受功能网络,以识别关键网络枢纽,并使用高分辨率 fMRI 来描述网络时间动态特征。我们首先功能定位了 24 个皮质和皮质下区域,这些区域对热伤害感受刺激有反应(体感区 1/2、区 3a/3b、S2、后岛叶(pIns)、前岛叶、区 7b、后顶叶皮层、前扣带皮层(ACC)、前额叶皮层、尾状核和中背侧(MD)和腹后外侧(VPL)丘脑核),并将它们用作静息状态 fMRI(rsfMRI)数据分析中的种子。我们应用了层次聚类和图论分析成对 rsfMRI 相关度量,并确定了五个皮质和一个皮质下亚网络:ACC 和前额叶区域之间、顶叶皮层和区 7b 之间、S2 和后岛叶之间、S1 皮层内的区 3a/3b 和 1/2 之间以及丘脑 MD 和尾状核之间的静息状态功能连接(rsFC)较强。每个亚网络内皮质区域之间的 rsFC 强度明显强于皮质下区域之间的 rsFC 强度。每个亚网络内的区域在休息时也表现出高度相关的时间动态,但整体动态模式在亚网络之间有很大差异。图论分析确定 MD 核作为连接皮质下和皮质伤害感受亚网络的枢纽。S2-pIns 连接将感觉和情感/认知亚网络连接起来。