Wu Guo-Rong, Marinazzo Daniele
Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 1, 9000, Ghent, Belgium.
Brain Topogr. 2015 Jul;28(4):541-7. doi: 10.1007/s10548-014-0404-4. Epub 2014 Oct 4.
It is now recognized that important information can be extracted from the brain spontaneous activity, as exposed by recent analysis using a repertoire of computational methods. In this context a novel method, based on a blind deconvolution technique, is used to analyze potential changes due to chronic pain in the brain pain matrix's effective connectivity. The approach is able to deconvolve the hemodynamic response function from spontaneous neural events, i.e., in the absence of explicit onset timings, and to evaluate information transfer between two regions as a joint probability of the occurrence of such spontaneous events. The method revealed that the chronic pain patients exhibit important changes in the insula's effective connectivity which can be relevant to understand the overall impact of chronic pain on brain function.
现在人们认识到,可以从大脑自发活动中提取重要信息,正如最近使用一系列计算方法进行的分析所揭示的那样。在这种情况下,一种基于盲反卷积技术的新方法被用于分析慢性疼痛对大脑疼痛矩阵有效连接性的潜在影响。该方法能够从自发神经事件中反卷积出血流动力学响应函数,即在没有明确起始时间的情况下,并将两个区域之间的信息传递评估为这些自发事件发生的联合概率。该方法表明,慢性疼痛患者在脑岛的有效连接性方面表现出重要变化,这可能与理解慢性疼痛对脑功能的总体影响有关。