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

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 perf...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 173; S. 394 - 410
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.06.2018 Elsevier Limited Elsevier
Schlagworte:	Adult Adults Age Age prediction Aged Aged, 80 and over Aging Algorithms Alzheimer's disease Brain - growth & development Brain Mapping - methods Brain research Cognitive ability Compression Data compression Data processing Datasets Datasets as Topic Decomposition Dimensionality reduction Engineering, computing & technology Female Gray Matter - growth & development Humans Image processing Image Processing, Computer-Assisted - methods Ingénierie, informatique & technologie LASSO regression Machine Learning Magnetic Resonance Imaging - methods Male Medical imaging Middle Aged Multivariate analysis Neurodegenerative diseases Neuroimaging Non-negative matrix factorization Segmentation Structural MRI Studies Substantia grisea Voxel-based morphometry Young Adult Dimensionality reduction LASSO regression Structural MRI Age prediction Non-negative matrix factorization Voxel-based morphometry
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
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