Human neuroimaging as a "Big Data" science.
Human neuroimaging as a "Big Data" science.
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DOI：
10.1007/s11682-013-9255-y
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发表时间：
2014-06
影响因子：
3.2
通讯作者：
Toga, Arthur W.
中科院分区：
文献类型：
--
作者：
Van Horn, John Darrell;Toga, Arthur W.
关键词：
体内神经影像学的成熟带来了令人难以置信的大量关于人脑的数字信息。尽管科学界的数据洪流已被人们广泛关注，但神经影像学代表了“大数据”冲击的前沿。一系列神经影像数据库方法简化了此类脑成像研究数据的传输、存储和传播。然而，支持神经影像科学的通用解决方案（如果有的话）却很少。在本文中，我们讨论现代神经影像学研究如何代表多因素和广泛的数据挑战，涉及所获取的数据规模不断增长；社会学和后勤共享问题；多站点、多数据类型归档的基础设施挑战；以及探索和挖掘这些数据的方法。随着神经影像学的进一步发展，例如衰老、遗传学和与年龄相关的疾病，需要新的视野来管理和处理这些信息，同时将这些资源整理成新的结果。因此，“大数据”可以成为“大”脑科学。
The maturation of in vivo neuroimaging has lead to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of “big data”. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a mutifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociologial and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, “big data” can become “big” brain science.
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影响因子：
3.5
作者：
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通讯作者：
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3.1
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