Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

NeuroImage

Volumn 163, Issue , 2017, Pages 115-124

  Cole, James H ^a   Poudel, Rudra P K ^b   Tsagkrasoulis, Dimosthenis ^a   Caan, Matthan W A ^c   Steves, Claire ^b   Spector, Tim D ^b   Montana, Giovanni ^a,b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c ACADEMIC MEDICAL CENTER   (Netherlands)

Author keywords

Biomarker; Brain ageing; Convolutional neural networks; Deep learning; Gaussian processes; Heritability; Neuroimaging; Reliability

Indexed keywords

ADULT; AGE DETERMINATION; AGED; AGING; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN; BRAIN AGING; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; DIZYGOTIC TWINS; FEMALE; GRAY MATTER; HERITABILITY; HUMAN; MACHINE LEARNING; MALE; MONOZYGOTIC TWINS; NEUROIMAGING; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; TEST RETEST RELIABILITY; WHITE MATTER; ADOLESCENT; DIAGNOSTIC IMAGING; IMAGE PROCESSING; MIDDLE AGED; PATHOLOGY; PHENOTYPE; PROCEDURES; VERY ELDERLY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; AGING; BRAIN; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PHENOTYPE; YOUNG ADULT;

EID: 85029673464     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2017.07.059     Document Type: Article

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