A spatio-temporal reference model of the aging brain
Tài liệu tham khảo
Achterberg, 2010, Prediction of dementia by hippocampal shape analysis, 23
Balci, 2007, Free-form B-spline deformation model for groupwise registration, 23
Baloch, 2009, Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis, NeuroImage, 45, S73, 10.1016/j.neuroimage.2008.10.048
Bhatia, 2004, Consistent groupwise non-rigid registration for atlas construction, 908
Brewer, 2009, Fully-automated volumetric MRI with normative ranges: translation to clinical practice, Behav. Neurol., 21, 21, 10.1155/2009/616581
Bron, 2014, Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia, Hum. Brain Mapp., 35, 4916, 10.1002/hbm.22522
Carpenter, 2000, Bootstrap confidence intervals: when, which, what?, Statistics Med., 19, 1141, 10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F
Cole, 1991, Smoothing reference centile curves: the LMS method and penalized likelihood, Stat. Med., 11, 1305, 10.1002/sim.4780111005
Costafreda, 2011, Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment, Neuroimage, 56, 212, 10.1016/j.neuroimage.2011.01.050
Cuingnet, 2011, Automatic classification of patients with Alzheimers disease from structural MRI: a comparison of ten methods using the ADNI database, NeuroImage, 56, 10.1016/j.neuroimage.2010.06.013
Davis, 2010, Population shape regression from random design data, Int. J. Comput. Vis., 90, 255, 10.1007/s11263-010-0367-1
Dittrich, 2014, A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation, Med. Image Anal., 18, 9, 10.1016/j.media.2013.08.004
Fishbaugh, 2017, Geodesic shape regression with multiple geometries and sparse parameters, Med. Image Anal., 39, 1, 10.1016/j.media.2017.03.008
Folgoc, 2016, Quantifying registration uncertainty with sparse Bayesian modelling, IEEE Trans. Med. Imaging, 36, 607, 10.1109/TMI.2016.2623608
Gousias, 2008, Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest, Neuroimage, 40, 672, 10.1016/j.neuroimage.2007.11.034
Hammers, 2003, Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe, Hum. Brain Mapp., 19, 224, 10.1002/hbm.10123
Höskuldsson, 1988, PLS regression methods, J. Chemom., 2, 211, 10.1002/cem.1180020306
Huizinga, 2016, PCA-based groupwise image registration for quantitative MRI, Med. Image Anal., 29, 65, 10.1016/j.media.2015.12.004
Huizinga, 2016, Modeling the brain morphology distribution in the general aging population
Ikram, 2015, The Rotterdam Scan Study: design update 2016 and main findings, Eur. J. Epidemiol., 30, 1299, 10.1007/s10654-015-0105-7
Jack, 2008, The Alzheimerś disease neuroimaging initiative (ADNI): MRI methods, J. Magnetic Reson. Imaging, 27, 685, 10.1002/jmri.21049
de Jong, 1992, SIMPLS: an alternative approach to partial least squares regression, Chemom. Intelligent Laboratory Syst., 18, 251, 10.1016/0169-7439(93)85002-X
Klein, 2010, elastix: a toolbox for intensity based medical image registration, IEEE Trans. Med. Imaging, 29, 196, 10.1109/TMI.2009.2035616
Krishnan, 2011, Partial least squares (PLS) methods for neuroimaging: a tutorial and review, NeuroImage, 56, 455, 10.1016/j.neuroimage.2010.07.034
Kybic, 2010, Bootstrap resampling for image registration uncertainty estimation without ground truth, IEEE Trans. Image Process., 19, 64, 10.1109/TIP.2009.2030955
Marquand, 2016, Understanding heterogeneity in clinical cohort using normative models: beyond case-control studies, Biol. Psychiatry, 80, 552, 10.1016/j.biopsych.2015.12.023
Mazziotta, 2001, A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM), Philosofical Trans. R. Soc. Lond., 356, 1293, 10.1098/rstb.2001.0915
Metz, 2011, Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach, Med. Image Anal., 15, 238, 10.1016/j.media.2010.10.003
de Onis, 2006
Rueckert, 1999, Nonrigid registration using free-form deformations: application to breast MR images, IEEE Trans. Med. Imaging, 18, 712, 10.1109/42.796284
Schrijvers, 2012, Is dementia incidence declining?: Trends in dementia incidence since 1990 in the Rotterdam Study, Neurology, 78, 10.1212/WNL.0b013e3182553be6
Serag, 2012, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NeuroImage, 59, 2255, 10.1016/j.neuroimage.2011.09.062
Singh, 2014, Quantifying anatomical shape variations in neurological disorders, Med. Image Anal., 18, 616, 10.1016/j.media.2014.01.001
Sokooti, 2016, Accuracy estimation for medical image registration using regression forests, 107
Tustison, 2010, N4ITK: improved N3 bias correction, IEEE Trans. Med. Imaging, 29, 1310, 10.1109/TMI.2010.2046908
Vernooij, 2012, Structural neuroimaging in aging and Alzheimer's disease, Neuroimaging Clin. N. Am., 22, 33, 10.1016/j.nic.2011.11.007
Wiklund, 2007, A randomization test for PLS component selection, J. Chemom., 21, 427, 10.1002/cem.1086
Wold, 2001, PLS-regression: a basic tool of chemometrics, Chemom. Intelligent Laboratory Syst., 58, 109, 10.1016/S0169-7439(01)00155-1
Yee, 2010, The VGAM package for categorical data analysis, J. Stat. Softw., 32, 1
Yeo, 2000, A new family of power transformations to improve normality or symmetry, Biometrika, 87, 954, 10.1093/biomet/87.4.954
Ziegler, 2013, Partial least squares correlation of multivariate cognitive abilities and local brain structure in children and adolescents, NeuroImage, 82, 284, 10.1016/j.neuroimage.2013.05.088
Ziegler, 2012, Models of the aging brain structure and individual decline, Front. Neuroinformatics, 6, 1, 10.3389/fninf.2012.00003
Ziegler, 2014, Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects, NeuroImage, 97, 333, 10.1016/j.neuroimage.2014.04.018