کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6028573 | 1580920 | 2014 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population
ترجمه فارسی عنوان
تجزیه و تحلیل همبستگی اسپیرس کانتونی، تنفس سطح شبکه را به اقدامات شناختی چند متغیره در یک جمعیت نابجا
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 84, 1 January 2014, Pages 698-711
Journal: NeuroImage - Volume 84, 1 January 2014, Pages 698-711
نویسندگان
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,