Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population.
Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population.
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DOI：
10.1016/j.neuroimage.2013.09.048
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发表时间：
2014-01-01
期刊：
影响因子：
5.7
通讯作者：
Grossman M
中科院分区：
文献类型：
--
作者：
Avants BB;Libon DJ;Rascovsky K;Boller A;McMillan CT;Massimo L;Coslett HB;Chatterjee A;Gross RG;Grossman M
这项研究确立了稀疏典型相关分析(SCCAN)确定了与五种不同的认知类别相关的可概括的、结构性的MRI派生的皮质网络。我们从费城认知简明评估(PBAC)的领域特定子量表获得多变量心理测量学。通过训练和单独测试阶段，我们发现PBAC定义的语言、视觉空间功能、情景记忆、执行控制和社会功能的认知域与灰质(GM)的独特和分布区域相关。相比之下，并行单变量框架无法从训练数据中识别出在遗漏的测试数据集中也很重要的区域。队列包括164名阿尔茨海默病、行为变异型额颞叶痴呆、语义变异型原发进行性失语、非流利/无语法原发进行性失语或皮质-基底综合征患者。分析是用开源软件实现的，我们在正文中提供了例子。总而言之，我们表明，多变量技术可以识别支持特定认知域的生物学上看似合理的大脑区域。这些发现在训练数据中得到确认，并在测试数据中得到证实。
This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer’s disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, nonfluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data.
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