STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data

NeuroImage

Volumn 134, Issue , 2016, Pages 550-562

  Hyun, Jung Won ^a   Li, Yimei ^a   Huang, Chao ^b   Styner, Martin ^c   Lin, Weili ^c   Zhu, Hongtu ^b,c  

^a ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

^b University of North Carolina at Chapel Hill   (United States)

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

Author keywords

Functional principal component analysis; Kriging; Neuroimaging; Prediction; Spatio temporal modeling

Indexed keywords

ACCURACY; AGED; ALZHEIMER DISEASE; ARTICLE; BRAIN FUNCTION; BRAIN STRUCTURE; COMPARATIVE STUDY; COMPUTER SIMULATION; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); FEMALE; FUNCTIONAL PRINCIPAL COMPONENT MODEL; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; LATERAL BRAIN VENTRICLE; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; NERVOUS SYSTEM PARAMETERS; NEUROIMAGING; PARTITION PARAMETRIC SPACE TIME COVARIANCE MODEL; POSITRON EMISSION TOMOGRAPHY; PREDICTIVE VALUE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SIGNAL NOISE RATIO; SPATIOTEMPORAL GAUSSIAN PROCESS MODEL; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; TIME SERIES ANALYSIS; VALIDATION STUDY; WHITE NOISE; BRAIN; DIAGNOSTIC IMAGING; GROWTH, DEVELOPMENT AND AGING; NORMAL DISTRIBUTION; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; SIGNAL PROCESSING;

AGED; ALZHEIMER DISEASE; BRAIN; COMPUTER SIMULATION; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; NEUROIMAGING; NORMAL DISTRIBUTION; POSITRON-EMISSION TOMOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84964421394     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2016.04.023     Document Type: Article

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