Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada.
BC Mental Health & Addictions Research Institute, BC Children's Hospital Research Institute, 938 West 28th Ave, Vancouver, BC, V5Z 4H4, Canada.
Neuroinformatics. 2022 Jan;20(1):155-172. doi: 10.1007/s12021-021-09527-6. Epub 2021 Jun 8.
The rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of "functional networks", often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique-Constrained Principal Component Analysis for fMRI (fMRI-CPCA)-that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data.
功能磁共振成像（fMRI）的兴起使我们对大脑皮质处理疼痛的方式有了更深入的了解。这些进展的核心是“功能网络”的识别和分析，这些网络通常源自预先选定的疼痛区域组。在这项研究中，我们的主要目标是通过检查全脑激活来识别与疼痛感知相关的功能脑网络，而无需事先选择区域。我们应用了一种数据驱动的技术——功能磁共振成像约束主成分分析（fMRI-CPCA），该技术可在不假设网络解剖或时间属性的情况下识别网络。对来自热痛任务的开源 fMRI 数据（33 名健康参与者）进行 fMRI-CPCA 以提取网络，然后通过将主观疼痛评分建模为网络激活强度的函数，将网络与疼痛感知相关联。出现了三个功能网络：感觉运动反应网络、突显介导的注意力网络和默认模式网络。这些网络共同构成了一种大脑状态，该状态解释了个体内和个体间疼痛感知的可变性，证明了数据驱动的、全脑功能网络技术在疼痛成像数据分析中的潜力。
