为考察大脑在处理加工不同效价的情绪图片时其脑功能区域的联系与差异,提出一种能更精确地提取出相对激活较弱的功能连接区域的方法。首先提出一种基于密度思想的 K-means 算法并应用于脑功能连接分析,提取具有功能连接的脑组织结构模式;然后引入聚合指数指标客观评判激活脑区定位的准确度,并与独立成分分析方法的处理结果进行对比;最后从体素的激活强度和激活脑区的定位精度等方面入手,论证了基于密度思想的 K-means 算法在脑功能连接分析上的优势。实验结果表明,情绪刺激加工的过程中,脑区较为明显的激活区主要分布在前额叶、扣带回及下丘脑附近,为后续临床观察及诊断提供了一种较为可靠的方法和思路。
In order to investigate the relationship and difference in brain functional connectivity when subjects process different valence of emotional images, a new method is employed to extract the relatively weak connected regions more accurately. First, aK-means algorithm based on density is proposed to analyze the brain functional connectivity and extract the brain structure model which has the functional connectivity. Then, aggregation index is introduced to evaluate the positioning accuracy of activated brain regions. The above results are also compared with the results using independent component analysis (ICA) algorithm; Finally, the advantage ofK-means algorithm based on density in the field of brain functional connectivity analysis is demonstrated in terms of the intensity of voxel activation and the position precision of activated brain regions. The experimental results show that relatively obvious activity areas mainly distributed in the frontal lobe, cingulum and hypothalamus in the process of emotional stimulation processing, which provides a more reliable method for subsequent clinical observation and diagnosis.