Yang Zhi, Qiu Jiang, Wang Peipei, Liu Rui, Zuo Xi-Nian
Key Laboratory of Behavioral Science, Laboratory for Functional Connectome and Development and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, China.
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Beijing, 100101, China.
Brain Struct Funct. 2016 Dec;221(9):4459-4474. doi: 10.1007/s00429-015-1177-6. Epub 2016 Jan 9.
The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure-function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.
人类大脑结构与功能指标之间的关系在很大程度上仍不为人知。我们关于结构 - 功能关联的有限知识大多是通过对特定患者群体与健康对照进行比较而获得的。遗憾的是，正常人群中多个结构和功能指标之间关联的直接且完整的视图尚付阙如。我们通过使用大规模神经影像数据集来学习结构和功能指标之间的个体间协方差，填补了这一空白。一种发现 - 验证方案应用于两个独立的多模态神经影像数据集样本（N = 184 和 N = 340）。在发现阶段，使用数据挖掘工具gRAICAR生成六个功能和六个结构影像指标之间协方差的定量且无偏假设。这些假设在验证阶段使用独立数据集进行验证。代表12个指标之间不同协方差关系的15个多指标协方差单元在两组神经影像数据集中是可靠的。这些可靠的协方差单元被汇总到一个数据库中，用户可以在任何指标的皮质图上选择任何位置，以检查与其他11个指标的协变图。该数据库根据与结构指标的协方差对六个功能指标进行了表征，并提供了详细的参考，以连接先前使用不同指标的研究结果并预测未检查指标的图谱。性别、年龄和利手与协方差单元相关，一项关于精神分裂症的子研究证明了协方差数据库的实用性。
