NDLI: Evaluation of non-negative matrix factorization of grey matter in age prediction

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Evaluation of non-negative matrix factorization of grey matter in age prediction

Content Provider	Scilit
Author	Varikuti, Deepthi P. Genon, Sarah Sotiras, Aristeidis Schwender, Holger Hoffstaedter, Felix Patil, Kaustubh R. Jockwitz, Christiane Caspers, Svenja Moebus, Susanne Amunts, Katrin Davatzikos, Christos Eickhoff, Simon B.
Copyright Year	2018
Description	Journal: Neuroimage The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations.
Related Links	http://juser.fz-juelich.de/record/840630/files/OHBM_2017_Varikuti.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911196/pdf
Ending Page	410
Page Count	17
Starting Page	394
ISSN	10538119
DOI	10.1016/j.neuroimage.2018.03.007
Journal	Neuroimage
Volume Number	173
Language	English
Publisher	Elsevier BV
Publisher Date	2018-03-06
Access Restriction	Open
Subject Keyword	Journal: Neuroimage Non-negative Matrix Factorization Voxel-based Morphometry Dimensionality Reduction
Content Type	Text
Resource Type	Article
Subject	Neurology Cognitive Neuroscience

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