Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population
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- 作者：Brian B. Avants ; David J. Libon ; Katya Rascovsky ; Ashley Boller ; Corey T. McMillan ; Lauren Massimo ; H. Branch Coslett ; Anjan Chatterjee ; Rachel G. Gross ; Murray Grossman
- 关键词：Alzheimer disease ; Frontotemporal lobar degeneration ; Philadelphia Brief Assessment of Cognition ; PBAC ; MRI ; Sparse canonical correlation analysis
- 刊名：NeuroImage
- 出版年：1 January, 2014
- 年：2014
- 卷：84
- 期：Complete
- 页码：698-711
- 全文大小：2199 K
文摘
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, non-fluent/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.