Reconstructions and Representations of Cerebral Cortex

项目摘要 该项目将产生关于大脑皮质组织和连接性的广泛新发现 人类和非人类灵长类动物使用由我们的 合作者和青壮年人类连接组项目(YA-HCP)。它将加速 通过与科学工作者免费共享所产生的工具和实验数据来取得进展 社区。第一个目标将提供急需的非侵入性连接评估 猕猴体内侵入性解剖示踪剂的相关措施。不同的方法 将评估功能磁共振成像派生的功能连接性，以确定 这种方法与基于示踪剂的“地面真实”解剖连接关系最好。这一目标将 还通过比较以下区域组织来生成关于大脑皮层进化的新见解 人类、猕猴和绒猴使用一种新的方法，其中区域特征(髓鞘) 地图、休眠状态网络和已识别的同源区域)限制注册 在物种之间。第二个目标将集中在个别受试者的大脑皮质组织上 改进的HCP风格的分析工具。它将优化和评估主题间对齐(使用 新近发展的多模面匹配法)和个体-主体分割 (使用基于机器学习的区域分类器)。一个重要的结果将是 为其他获得的功能磁共振数据少于YA的项目提供最佳实践建议-- HCP。这些数据也将被用来描述人类大脑皮层区域的个体变异性。 对于180个区域中的每个区域，大小和拓扑结构的个体差异(邻里关系) 将检查遗传力和两个半球的对称性。其他内容 分析将揭示某些区域是否在某些个体和 在某些科目中是否出现了“小说”区域。第三个目标是增强能力 Connectome工作台可视化和分析平台和BALSA数据库 这是在之前的赠款期间推出的。Connectome工作台的增强功能 将：(I)实现非侵入性电生理(MEG/EEG)和侵袭性神经生理学 将与MRI数据和基于图谱的连接数据集成的数据(II)促进 不同地图集之间的互操作性，(3)改善互动的“HCP式”分析能力， 以及(Iv)实现基于大脑皮层的分析。对BALSA数据库的改进包括： (I)基于WebGL的网络查看器，用于交互式在线可视化，特别注重 该项目产生的独特数据，(2)一个在线空间定位工具和(3)支持 从除工作台之外的其他5个软件平台将场景文件上传到BALSA。

Project Summary This project will generate extensive new findings about cortical organization and connectivity in humans and nonhuman primates using high quality, multimodal datasets provided by our collaborators and by the young adult Human Connectome Project (YA-HCP). It will accelerate progress by freely sharing the resulting tools and experimental data with the scientific community. The first aim will provide a critically needed evaluation of non-invasive connectivity measures in relation to invasive anatomical tracers in macaque monkeys. Different methods for estimating fMRI-derived functional connectivity will be evaluated in order to determine which approach best correlates with `ground truth' tracer-based anatomical connectivity. This aim will also generate new insights about cortical evolution by comparing areal organization across humans, macaques, and marmosets using a novel approach in which areal features (myelin maps, resting-state networks, and identified homologous areas) constrain the registration between species. The second aim will focus on cortical organization in individual subjects using refined HCP-style analysis tools. It will optimize and evaluate intersubject alignment (using a recently developed Multimodal Surface Matching method) and individual-subject parcellation (using a machine learning based areal classifier). An important outcome will be recommendations of `best practice' for other projects that acquire less fMRI data than in the YA- HCP. These data will also be used to characterize individual variability of human cortical areas. For each of 180 areas, individual differences in size and topology (neighborhood relationships) will be examined for heritability and for symmetry across the two hemispheres. Additional analyses will reveal whether some areas are reproducibly absent in some individuals and whether `novel' areas are present in some subjects. The third aim is to enhance the capabilities of the Connectome Workbench visualization and analysis platform and the BALSA database that were introduced during previous grant periods. Enhancements to Connectome Workbench will: (i) enable non-invasive electrophysiological (MEG/EEG) and invasive neurophysiological data to be integrated with MRI data and atlas-based connectivity data (ii) facilitate interoperability across different atlases, (iii) improve interactive `HCP-style' analysis capabilities, and (iv) enable cortical layer-based analyses. Enhancements to the BALSA database include: (i) a WebGL-based web-viewer for interactive online visualization with special focus on the unique data generated by this project, (ii) an online spatial localization tool and (iii) support for uploading scene files to BALSA from 5 other software platforms besides Workbench.