Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach

• Published in: NeuroImage (Orlando, Fla.) Vol. 75; pp. 58 - 67
• Main Authors: Mwangi, Benson, Hasan, Khader M., Soares, Jair C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.07.2013; Elsevier; Elsevier Limited
• Subjects: Adolescent; Adult; Age; Aged; Aged, 80 and over; Aging; Aging - pathology; Anisotropy; Artificial Intelligence; Biological and medical sciences; Brain; Brain - pathology; Cerebral ageing; Cerebral maturation; Child; Child, Preschool; Diffusion; Diffusion Tensor Imaging; Female; Fundamental and applied biological sciences. Psychology; Humans; Image Interpretation, Computer-Assisted; Infant; Infant, Newborn; Machine learning; Male; Middle Aged; Multivariate; Neuroimaging; NMR; Nuclear magnetic resonance; Relevance vector regression; Standard deviation; Studies; Success; Vertebrates: nervous system and sense organs; Young Adult; Neuroimaging; Cerebral ageing; Machine learning; Cerebral maturation; Relevance vector regression; Multivariate; Diffusion tensor imaging; Human; Senescence; Central nervous system; Encephalon; Learning; Acquisition process; Age

Diffusion tensor imaging has the potential to be used as a neuroimaging marker of natural ageing and assist in elucidating trajectories of cerebral maturation and ageing. In this study, we applied a multivariate technique relevance vector regression (RVR) to predict individual subject's age usi...