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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population
NeuroImage (Orlando, Fla.), v 84, pp 698-711
01 Jan 2014
PMID: 24096125
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
•Identify the neural substrate supporting cognitive domains measured by the PBAC.•Contribute sparse canonical correlation analysis for neuroimaging (SCCAN) software.•Show regions uncovered by SCCAN in training data generalize to test data.•SCCAN improves generalizability over a statistical ROI defined by univariate methods.
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Details
- Title
- Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population
- Creators
- Brian B. Avants - University of PennsylvaniaDavid J. Libon - Drexel UniversityKatya Rascovsky - University of PennsylvaniaAshley Boller - University of PennsylvaniaCorey T. McMillan - University of PennsylvaniaLauren Massimo - University of PennsylvaniaH. Branch Coslett - University of PennsylvaniaAnjan Chatterjee - University of PennsylvaniaRachel G. Gross - University of PennsylvaniaMurray Grossman - University of Pennsylvania
- Publication Details
- NeuroImage (Orlando, Fla.), v 84, pp 698-711
- Publisher
- Elsevier
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:000328868600064
- Scopus ID
- 2-s2.0-84885721540
- Other Identifier
- 991021901314604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging