Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations.
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations.
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DOI：
10.3389/neuro.09.032.2009
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发表时间：
2009
影响因子：
2.9
通讯作者：
Hoeft F
中科院分区：
文献类型：
--
作者：
Bray S;Chang C;Hoeft F
对功能和结构成像数据的分析通常涉及对大脑中每个体素的假设进行测试。然而，通常情况下，分布式空间模式可能是区分条件或组的更合适的度量。多变量模式分析已经在成人、健康和临床人群的神经成像中获得了吸引力；研究表明，神经成像数据中存在的信息可以用来解码意图和感知状态，以及区分健康和患病的大脑。虽然到目前为止很少有研究将这些方法应用于儿科人群，但在这篇综述中，我们讨论了在研究健康和异常的大脑发育方面令人兴奋的潜在应用。我们包括对方法的概述以及对挑战和限制的讨论。
Analyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been gaining traction in neuroimaging of adult healthy and clinical populations; studies have shown that information present in neuroimaging data can be used to decode intentions and perceptual states, as well as discriminate between healthy and diseased brains. While few studies to date have applied these methods in pediatric populations, in this review we discuss exciting potential applications for studying both healthy, and aberrant, brain development. We include an overview of methods and discussion of challenges and limitations.
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DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
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通讯作者：
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