Langen Carolyn D, White Tonya, Ikram M Arfan, Vernooij Meike W, Niessen Wiro J
Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
PLoS One. 2015 Sep 2;10(9):e0137484. doi: 10.1371/journal.pone.0137484. eCollection 2015.
Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, "bi-modal comparison plots" show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using "worm plots". Group differences in connections are examined with an existing visualization, the "connectogram". These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the "Statistical Analysis of Minimum cost path based Structural Connectivity" method and the average fractional anisotropy along the fiber. The functional measures were Pearson's correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.
在脑部疾病研究中，结构和功能脑连接性越来越多地用于识别和分析组间差异。本研究提出了分析单模态和双模态脑连接性的方法，并评估了它们识别差异的能力。展示了比较多个指标的显著不同连接的新颖可视化方法。在全局层面，“双模态比较图”显示了单模态和双模态组间差异的分布以及结构与功能之间的关系。使用“蠕虫图”可视化脑叶之间的差异。连接性方面的组间差异通过现有的“连接图”可视化方法进行检查。这些可视化方法在两项概念验证研究中进行了评估：（1）中年人与老年人；（2）精神分裂症患者与对照组。每项研究都包括从扩散加权图像得出的两项测量指标和从功能磁共振图像得出的两项测量指标。结构测量指标是根据“基于最小成本路径的结构连接性统计分析”方法得出的两个解剖区域之间的最小成本路径以及沿纤维的平均分数各向异性。功能测量指标是平均区域时间序列的皮尔逊相关性和偏相关性。两项研究中结构与功能之间的关系相似。单模态组间差异在不同连接性类型之间差异很大。在两项研究中，在全局层面、脑叶内和区域之间都识别出了组间差异。在衰老研究中，最小成本路径在识别各级组间差异方面非常有效；分数各向异性和平均相关性在脑叶和区域层面显示出较小的差异。在精神分裂症研究中，最小成本路径和分数各向异性在全局层面和脑叶内显示出差异；平均相关性在脑叶层面显示出较小的差异。只有分数各向异性和平均相关性显示出区域差异。所展示的可视化方法有助于在两项研究的多个层面上比较和评估连接性测量指标。
