幼儿小世界神经网络节点属性与影响因素的相关性分析  

Analysis of the Characteristics of Infantile Small World Neural Network Node Properties Correlated with the Influencing Factors

作　　者：曲海波[1] 吕粟[2] 张文静[2] 肖媛[2] 宁刚[1] 孙怀强[2] QU Haibo LU Su ZHANG Wenjing XIAO Yuan NING Gang SUN Huaiqiang(Department of Radiology, West China Second Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Ch ildren （ Sichuan University）, Ministry of education, Chengdu 610041, China Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China)

机构地区：[1]四川大学华西第二医院放射科,出生缺陷与相关妇儿疾病教育部重点实验室,成都610041 [2]四川大学华西医院放射科/磁共振研究中心,成都610041

出　　处：《生物医学工程学杂志》2016年第5期931-938,944,共9页Journal of Biomedical Engineering

基　　金：四川省医学科研青年创新课题资助项目(Q14037)

摘　　要：本文应用静息态功能磁共振成像(rfMRI)的图论数据分析方法,分析全脑共90个分区的幼儿小世界神经网络,试图明确:1幼儿小世界神经网络节点的属性参数与幼儿智力发育水平有无相关性。2幼儿小世界神经网络参数与儿童的基线参数,如性别、年龄,以及父母教育程度等人口学参数有无相关性。本研究共纳入12名健康幼儿,其中9名男性,3名女性,年龄(33.42±8.42)月。所有受试者的智力发育水平采用Gesell发育量表,并采集对血氧水平依赖(BOLD)信号敏感的静息态功能磁共振信号数据。采用Matlab环境下的SPM5软件包进行数据处理;进一步应用基于图论的分析方法得到全脑小世界属性及自动解剖标签(AAL)模板下的90个脑区的节点属性,并分别对上述属性与Gesell发育量表及人口学数据做了相关性分析。研究发现小世界神经网络诸多节点属性与Gesell发育量表参数相关,介数主要集中于丘脑、额上回及枕叶,大部分呈负相关,如量表中表示个人与社会相关的脑区枕上回的r值为-0.729(P=0.007);度主要集中于杏仁核、额上回、顶下回,大部分呈正相关,如量表中与大动作相关的顶下回的r值为0.725(P=0.008);效率集中于额下回、顶下回、岛叶,大部分呈正相关,如量表中语言相关的顶下回r值为0.738(P=0.006);节点聚集系数集中于额叶、顶下回、中央旁小叶,呈正相关;节点最短路径集中于额叶、顶下回、岛叶,呈负相关;左、右脑相关脑区分布不同。但关于小世界整体属性与Gesell发育量表的关系,我们未发现有统计学意义的相关。小世界网络节点属性指标与其他人口学指标有相关性的热点脑区位于颞叶、楔叶、扣带回、角回和中央旁小叶等区域,且大部分属于默认网络。本文研究结果说明,小世界神经网络节点属性与幼儿智力水平及人口学数据存在广泛的相关性,并且不同的脑区有其不同的分布特�We applied resting-state functional magnetic resonance imaging （rfMRI） combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain. We tried to get the following two points clear：① whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development; ② whether the parameters of the infantile small world neural network are correlated with the children＇s baseline parameters, i.e., the demographic parameters such as gender, age, parents＇ education level, etc. Twelve cases of healthy infants were included in the investigation （9 males and 3 females with the average age of 33.42±8.42 months.） We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test. We used a Siemens 3.0T Trio imaging system to perform resting-state （rs） EPI scans, and collected the BOLD functional Magnetic Resonance Imaging （fMRI） data. We performed the data processing with Statistical Parametric Mapping 5（SPM5） based on Matlab environment. Furthermore, we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling （ALL）. At last, we carried out correlation study between the above-mentioned attitudes, intelligence scale parameters and demographic data. The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters. Betweeness was mainly centered in thalamus, superior frontal gyrus, and occipital lobe （negative correlation）. The r value of superior occipital gyrus associated with the individual and social intelligent scale was -0.729 （P=0.007）; degree was mainly centered in amygdaloid nucleus, superior frontal gyrus, and inferior parietal gyrus （positive correlation）. The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.7

关 键 词：小世界 节点属性 幼儿 脑发育 

分 类 号：R445.2[医药卫生—影像医学与核医学] R35[医药卫生—诊断学]