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Neuroimage. 2014 Jan 1;84:698-711. doi: 10.1016/j.neuroimage.2013.09.048. Epub 2013 Oct 2.

Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population.

Author information

1
Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.

Abstract

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.

KEYWORDS:

Alzheimer disease; Frontotemporal lobar degeneration; MRI; PBAC; Philadelphia Brief Assessment of Cognition; Sparse canonical correlation analysis

PMID:
24096125
PMCID:
PMC3911786
DOI:
10.1016/j.neuroimage.2013.09.048
[Indexed for MEDLINE]
Free PMC Article

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