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.

功能性磁共振成像 (fMRI) 的兴起使人们对疼痛的皮质处理有了更深入的了解。这些进步的核心是识别和分析“功能网络”，这些网络通常来自预先选择的疼痛区域组。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性大脑网络，避免需要先验选择区域。我们应用了一种数据驱动技术——fMRI 的约束主成分分析 (fMRI-CPCA)——在不假设网络的解剖学或时间属性的情况下识别网络。在热痛任务（33 名健康参与者）中收集的开源 fMRI 数据经过 fMRI-CPCA 进行网络提取，通过将主观疼痛评级建模为网络激活强度的函数，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、显着性介导的注意网络和默认模式网络。这些网络共同构成了一种大脑状态，解释了个体内部和个体之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分析疼痛成像数据方面的潜力。