Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. Avants, B. B., Libon, D. J., Rascovsky, K., Boller, A., McMillan, C. T., Massimo, L., Coslett, H. B., Chatterjee, A., Gross, R. G., & Grossman, M. NeuroImage, 84:698–711, 2014. arXiv: NIHMS150003 Publisher: Elsevier Inc. ISBN: 1095-9572 (Electronic)\r1053-8119 (Linking)![Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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. © 2013 Elsevier Inc.
@article{avants_sparse_2014,
title = {Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population},
volume = {84},
issn = {10959572},
url = {http://dx.doi.org/10.1016/j.neuroimage.2013.09.048},
doi = {10.1016/j.neuroimage.2013.09.048},
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. © 2013 Elsevier Inc.},
journal = {NeuroImage},
author = {Avants, Brian B. and Libon, David J. and Rascovsky, Katya and Boller, Ashley and McMillan, Corey T. and Massimo, Lauren and Coslett, H. Branch and Chatterjee, Anjan and Gross, Rachel G. and Grossman, Murray},
year = {2014},
pmid = {24096125},
note = {arXiv: NIHMS150003
Publisher: Elsevier Inc.
ISBN: 1095-9572 (Electronic){\textbackslash}r1053-8119 (Linking)},
keywords = {Alzheimer disease, Frontotemporal lobar degeneration, MRI, PBAC, Philadelphia Brief Assessment of Cognition, Sparse canonical correlation analysis},
pages = {698--711},
}
Downloads: 0
{"_id":"5sJ3dsy6wiyjQGrZj","bibbaseid":"avants-libon-rascovsky-boller-mcmillan-massimo-coslett-chatterjee-etal-sparsecanonicalcorrelationanalysisrelatesnetworklevelatrophytomultivariatecognitivemeasuresinaneurodegenerativepopulation-2014","author_short":["Avants, B. B.","Libon, D. J.","Rascovsky, K.","Boller, A.","McMillan, C. T.","Massimo, L.","Coslett, H. B.","Chatterjee, A.","Gross, R. G.","Grossman, M."],"bibdata":{"bibtype":"article","type":"article","title":"Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population","volume":"84","issn":"10959572","url":"http://dx.doi.org/10.1016/j.neuroimage.2013.09.048","doi":"10.1016/j.neuroimage.2013.09.048","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. © 2013 Elsevier Inc.","journal":"NeuroImage","author":[{"propositions":[],"lastnames":["Avants"],"firstnames":["Brian","B."],"suffixes":[]},{"propositions":[],"lastnames":["Libon"],"firstnames":["David","J."],"suffixes":[]},{"propositions":[],"lastnames":["Rascovsky"],"firstnames":["Katya"],"suffixes":[]},{"propositions":[],"lastnames":["Boller"],"firstnames":["Ashley"],"suffixes":[]},{"propositions":[],"lastnames":["McMillan"],"firstnames":["Corey","T."],"suffixes":[]},{"propositions":[],"lastnames":["Massimo"],"firstnames":["Lauren"],"suffixes":[]},{"propositions":[],"lastnames":["Coslett"],"firstnames":["H.","Branch"],"suffixes":[]},{"propositions":[],"lastnames":["Chatterjee"],"firstnames":["Anjan"],"suffixes":[]},{"propositions":[],"lastnames":["Gross"],"firstnames":["Rachel","G."],"suffixes":[]},{"propositions":[],"lastnames":["Grossman"],"firstnames":["Murray"],"suffixes":[]}],"year":"2014","pmid":"24096125","note":"arXiv: NIHMS150003 Publisher: Elsevier Inc. ISBN: 1095-9572 (Electronic)\\r1053-8119 (Linking)","keywords":"Alzheimer disease, Frontotemporal lobar degeneration, MRI, PBAC, Philadelphia Brief Assessment of Cognition, Sparse canonical correlation analysis","pages":"698–711","bibtex":"@article{avants_sparse_2014,\n\ttitle = {Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population},\n\tvolume = {84},\n\tissn = {10959572},\n\turl = {http://dx.doi.org/10.1016/j.neuroimage.2013.09.048},\n\tdoi = {10.1016/j.neuroimage.2013.09.048},\n\tabstract = {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. © 2013 Elsevier Inc.},\n\tjournal = {NeuroImage},\n\tauthor = {Avants, Brian B. and Libon, David J. and Rascovsky, Katya and Boller, Ashley and McMillan, Corey T. and Massimo, Lauren and Coslett, H. Branch and Chatterjee, Anjan and Gross, Rachel G. and Grossman, Murray},\n\tyear = {2014},\n\tpmid = {24096125},\n\tnote = {arXiv: NIHMS150003\nPublisher: Elsevier Inc.\nISBN: 1095-9572 (Electronic){\\textbackslash}r1053-8119 (Linking)},\n\tkeywords = {Alzheimer disease, Frontotemporal lobar degeneration, MRI, PBAC, Philadelphia Brief Assessment of Cognition, Sparse canonical correlation analysis},\n\tpages = {698--711},\n}\n\n\n\n","author_short":["Avants, B. B.","Libon, D. J.","Rascovsky, K.","Boller, A.","McMillan, C. T.","Massimo, L.","Coslett, H. B.","Chatterjee, A.","Gross, R. G.","Grossman, M."],"key":"avants_sparse_2014","id":"avants_sparse_2014","bibbaseid":"avants-libon-rascovsky-boller-mcmillan-massimo-coslett-chatterjee-etal-sparsecanonicalcorrelationanalysisrelatesnetworklevelatrophytomultivariatecognitivemeasuresinaneurodegenerativepopulation-2014","role":"author","urls":{"Paper":"http://dx.doi.org/10.1016/j.neuroimage.2013.09.048"},"keyword":["Alzheimer disease","Frontotemporal lobar degeneration","MRI","PBAC","Philadelphia Brief Assessment of Cognition","Sparse canonical correlation analysis"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/peerherholz","dataSources":["JWysPm6bugYqREXu5"],"keywords":["alzheimer disease","frontotemporal lobar degeneration","mri","pbac","philadelphia brief assessment of cognition","sparse canonical correlation analysis"],"search_terms":["sparse","canonical","correlation","analysis","relates","network","level","atrophy","multivariate","cognitive","measures","neurodegenerative","population","avants","libon","rascovsky","boller","mcmillan","massimo","coslett","chatterjee","gross","grossman"],"title":"Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population","year":2014}