病人是否感到疼痛,医生只能通过询问才能知道。但对那些“疼也说不出口”的特殊病人,询问并不能解决问题。美国研究人员最新开发出一种方法,利用电脑便能检测出病人是否有疼痛感。
斯坦福大学医学院的研究人员报告说,他们利用电脑整理出人在感觉到疼痛时的大脑扫描图,让电脑“记住”这些图像的特征,从而通过这种神经成像技术检测人的疼痛感。
在试验中,研究人员让8名志愿者先后接触较热和滚烫的物体,并在他们接触这两类物体时分别进行大脑扫描,随后让电脑通过一种基于统计学习理论的模式识别方法来给大脑活动模式进行分类,从而确定志愿者是否正被疼痛折磨。结果显示,电脑测定志愿者疼痛感的准确率达80%以上。
参与这项研究的肖恩·麦基博士说,目前医生只能通过询问病人才知道他们是否感到疼痛,但过于年幼或年老的病人,以及患痴呆症、失去意识的病人,往往不能给出准确的答案。
为此,医学界一直在努力开发疼痛检测仪器,新技术有望最终解决疼痛检测问题,为治疗慢性疼痛疾病提供帮助。
新研究成果发表在新一期美国《科学公共图书馆—综合》。(生物谷 Bioon.com)
doi:10.1371/journal.pone.0024124
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Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation
Justin E. Brown, Neil Chatterjee, Jarred Younger, Sean Mackey
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.