Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

Human Brain Mapping

Volumn 36, Issue 12, 2015, Pages 4880-4896

  Jin, Yan ^a   Wee, Chong Yaw ^a   Shi, Feng ^a   Thung, Kim Han ^a   Ni, Dong ^b   Yap, Pew Thian ^a   Shen, Dinggang ^a,c  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b SHENZHEN UNIVERSITY   (China)

^c Korea University   (South Korea)

Author keywords

Autism spectrum disorder; Classification; Connectivity networks; Diffusion weighted imaging; Infant

Indexed keywords

ACCURACY; ARTICLE; AUTISM; CLINICAL ARTICLE; CONNECTOME; CONTROLLED STUDY; FEASIBILITY STUDY; FEMALE; FRACTIONAL ANISOTROPY; HIGH RISK INFANT; HUMAN; INFANT; MALE; PRIORITY JOURNAL; RISK ASSESSMENT; SUPPORT VECTOR MACHINE; WHITE MATTER; ALGORITHM; AUTISM SPECTRUM DISORDER; BRAIN; BRAIN MAPPING; DIFFUSION TENSOR IMAGING; FACTUAL DATABASE; IMAGE PROCESSING; MACHINE LEARNING; NERVE TRACT; PATHOLOGY; STATISTICS AND NUMERICAL DATA;

ALGORITHMS; AUTISM SPECTRUM DISORDER; BRAIN; BRAIN MAPPING; DATABASES, FACTUAL; DIFFUSION TENSOR IMAGING; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFANT; MACHINE LEARNING; MALE; NEURAL PATHWAYS; WHITE MATTER;

EID: 84956577012     PISSN: 10659471     EISSN: 10970193     Source Type: Journal    
DOI: 10.1002/hbm.22957     Document Type: Article

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