Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation.
Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation.
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DOI：
10.1371/journal.pone.0024124
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Mackey S
中科院分区：
文献类型：
--
作者：
Brown JE;Chatterjee N;Younger J;Mackey S
疼痛通常在没有明显损伤的情况下存在；因此，疼痛评估的金标准一直是自我报告。由于无法进行语言交流会妨碍有效的疼痛管理，研究人员一直致力于开发一种工具，可以在不依赖自我报告的情况下准确评估疼痛。这些先前的努力在用临床有效的、基于生理的疼痛测量替代自我报告方面没有被证明是成功的。最近的神经影像学数据表明，功能磁共振成像（fMRI）和支持向量机（SVM）学习可以共同用于准确评估认知状态。因此，我们假设在fMRI数据上训练的SVM可以在没有自我报告的情况下评估疼痛。在功能磁共振成像（fMRI）实验中，24个个体分别受到疼痛和非疼痛的热刺激。使用8个人，我们训练了一个线性支持向量机，用全脑活动模式来区分这些刺激。我们通过对16个数据未用于训练的个体进行测试来评估该训练后的SVM模型的性能。全脑支持向量机在区分疼痛和非疼痛刺激方面准确率为81% （p<0.0000001）。使用与SVM超平面的距离作为置信度度量，准确率进一步提高到84%，尽管以排除15%最难分类的刺激为代价。支持向量机的整体性能主要受大脑疼痛处理区域活动的影响，包括初级体感皮层、次级体感皮层、岛状皮层、初级运动皮层和扣带皮层。感兴趣区域（ROI）分析显示，全脑活动模式比单个大脑区域的局部活动更准确地分类。我们的研究结果表明，支持向量机学习的功能磁共振成像可以评估疼痛，而不需要与被测试者进行任何交流。我们概述了应该完成的任务，以推进这种方法在临床环境中使用。
Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.
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影响因子：
5.7
作者：
Derbyshire, SWG;Whalley, MG;Oakley, DA
通讯作者：
Oakley, DA
DOI：
10.1073/pnas.0505210102
发表时间：
2005-12-20
影响因子：
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作者：
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通讯作者：
Mackey, SC
DOI：
10.1177/1073858409349902
发表时间：
2010-04
期刊：
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影响因子：
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作者：
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通讯作者：
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影响因子：
2.5
作者：
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影响因子：
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作者：
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