CAJ | 학술논문

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.
위고찰대뇌재처리가공불동효개적정서도편시기뇌공능구역적련계여차이, 제출일충능경정학지제취출상대격활교약적공능련접구역적방법. 수선제출일충기우밀도사상적 K-means 산법병응용우뇌공능련접분석, 제취구유공능련접적뇌조직결구모식; 연후인입취합지수지표객관평판격활뇌구정위적준학도, 병여독립성분분석방법적처리결과진행대비; 최후종체소적격활강도화격활뇌구적정위정도등방면입수, 론증료기우밀도사상적 K-means 산법재뇌공능련접분석상적우세. 실험결과표명, 정서자격가공적과정중, 뇌구교위명현적격활구주요분포재전액협、구대회급하구뇌부근, 위후속림상관찰급진단제공료일충교위가고적방법화사로.
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.