The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis.
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis.
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DOI：
10.3389/fninf.2012.00028
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发表时间：
2012
影响因子：
3.5
通讯作者：
Bookheimer SY
中科院分区：
文献类型：
--
作者：
Brown JA;Rudie JD;Bandrowski A;Van Horn JD;Bookheimer SY
脑连接组学研究已迅速扩大使用功能性MRI（fMRI）和弥散加权MRI（dwMRI）。这些不同分析的共同产物是连接矩阵（CM）。CM存储大脑网络中任何两个区域（“节点”）之间的连接强度。这种格式很有用，原因有几个：（1）它是高度提炼的，具有最小的数据大小和复杂性，（2）图论可以应用于表征网络的拓扑结构，以及（3）它保留了足够的信息来捕获个体差异，如年龄，性别，智商（IQ）或疾病状态。在这里，我们介绍了加州大学洛杉矶分校多模态连接数据库（http：umcd.humanconnectomeproject.org），一个开放的网站，用于大脑网络分析和数据共享。该网站是研究人员公开分享从他们的数据得出的CM的存储库。该网站还允许用户选择其他用户共享的任何CM，计算网站上的图论指标，可视化结果报告或下载原始CM。到目前为止，用户已经贡献了超过2000个单独的CM，跨越不同的成像模式（fMRI，dwMRI）和疾病（阿尔茨海默氏症，自闭症，注意力缺陷多动障碍）。为了证明该网站的功能，全脑功能和结构连接矩阵来自60名受试者（年龄26-45岁）的静息状态fMRI（rs-fMRI）和dwMRI数据，并上传到该网站。该网站被用来推导rs-fMRI和dwMRI网络的图论全球和区域措施。功能网络和结构网络之间的全局和节点图理论测量表现出较低的对应性。该示例演示了该工具如何增强来自不同成像模式和研究的大脑网络的可比性。这种基于连通性的存储库的存在应该促进更广泛的数据共享，并实现更大规模的荟萃分析，比较成像模式，年龄组和疾病状态的网络。
Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions (“nodes”) in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26–45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state.
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影响因子：
3
作者：
Gupta, Amarnath;Bug, William;Marenco, Luis;Qian, Xufei;Condit, Christopher;Rangarajan, Arun;Mueller, Hans Michael;Miller, Perry L.;Sanders, Brian;Grethe, Jeffrey S.;Astakhov, Vadim;Shepherd, Gordon;Sternberg, Paul W.;Martone, Maryann E.
通讯作者：
Martone, Maryann E.
影响因子：
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作者：
Imam FT;Larson SD;Bandrowski A;Grethe JS;Gupta A;Martone ME
通讯作者：
Martone ME
DOI：
10.1073/pnas.0811168106
发表时间：
2009-02-10
影响因子：
11.1
作者：
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通讯作者：
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DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
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通讯作者：
Grafton, Scott T.
DOI：
10.1073/pnas.1207523109
发表时间：
2012-10-09
影响因子：
11.1
作者：
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通讯作者：
Fiebach, Christian J.