Validity of modulation and optimal settings for advanced voxel-based morphometry

NeuroImage - Tập 86 - Trang 81-90 - 2014
Joaquim Radua1,2,3, Erick Jorge Canales-Rodríguez1,3, Edith Pomarol-Clotet1,3, Raymond Salvador1,3
1FIDMAG Research Unit, Barcelona, Spain
2Institute of Psychiatry, King's College London, London, UK
3Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain

Tài liệu tham khảo

Acosta-Cabronero, 2008, The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry, NeuroImage, 39, 1654, 10.1016/j.neuroimage.2007.10.051 Andersson, 2007, Non-linear registration, aka Spatial normalisation Ashburner, 2007, A fast diffeomorphic image registration algorithm, NeuroImage, 38, 95, 10.1016/j.neuroimage.2007.07.007 Ashburner, 2000, Voxel-based morphometry — the methods, NeuroImage, 11, 805, 10.1006/nimg.2000.0582 Ashburner, 2001, Why voxel-based morphometry should be used, NeuroImage, 14, 1238, 10.1006/nimg.2001.0961 Ashburner, 2003, Spatial normalization using basis functions Ashburner, 2011, Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation, NeuroImage, 55, 954, 10.1016/j.neuroimage.2010.12.049 Avants, 2008, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med. Image Anal., 12, 26, 10.1016/j.media.2007.06.004 Avants, 2010, The optimal template effect in hippocampus studies of diseased populations, NeuroImage, 49, 2457, 10.1016/j.neuroimage.2009.09.062 Bates, 2012, lme4: linear mixed-effects models using S4 classes Bora, 2011, Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis, Schizophr. Res., 127, 46, 10.1016/j.schres.2010.12.020 Borgwardt, 2012, Why are psychiatric imaging methods clinically unreliable? Conclusions and practical guidelines for authors, editors and reviewers, Behav. Brain Funct., 8, 46, 10.1186/1744-9081-8-46 Bullmore, 1999, Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain, IEEE Trans. Med. Imaging, 18, 32, 10.1109/42.750253 Canales-Rodriguez, 2013, Statistical analysis of brain tissue images in the wavelet domain: wavelet-based morphometry, NeuroImage, 72C, 214, 10.1016/j.neuroimage.2013.01.058 Cooper, 2014, Multimodal voxel-based meta-analysis of structural and functional magnetic resonance imaging studies in those at elevated genetic risk of developing schizophrenia, Psychiatry Res. Neuroimaging, 221, 69, 10.1016/j.pscychresns.2013.07.008 Dale, 1999, Cortical surface-based analysis. I. Segmentation and surface reconstruction, NeuroImage, 9, 179, 10.1006/nimg.1998.0395 Dashjamts, 2012, Alzheimer's disease: diagnosis by different methods of voxel-based morphometry, Fukuoka Igaku Zasshi, 103, 59 Dice, 1945, Measures of the amount of ecologic association between species, Ecology, 26, 297, 10.2307/1932409 Eckert, 2006, To modulate or not to modulate: differing results in uniquely shaped Williams syndrome brains, NeuroImage, 32, 1001, 10.1016/j.neuroimage.2006.05.014 Fein, 2006, Statistical parametric mapping of brain morphology: sensitivity is dramatically increased by using brain-extracted images as inputs, NeuroImage, 30, 1187, 10.1016/j.neuroimage.2005.10.054 Fischl, 2000, Measuring the thickness of the human cerebral cortex from magnetic resonance images, Proc. Natl. Acad. Sci. U. S. A., 97, 11050, 10.1073/pnas.200033797 Friston, 2007 Fusar-Poli, 2012, Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies, Schizophr. Bull., 38, 1297, 10.1093/schbul/sbr134 Good, 2001, Computational neuroanatomy: new perspectives for neuroradiology, Rev. Neurol. (Paris), 157, 797 Good, 2001, A voxel-based morphometric study of ageing in 465 normal adult human brains, NeuroImage, 14, 21, 10.1006/nimg.2001.0786 Hayasaka, 2004, Nonstationary cluster-size inference with random field and permutation methods, NeuroImage, 22, 676, 10.1016/j.neuroimage.2004.01.041 Henley, 2010, Pitfalls in the use of voxel-based morphometry as a biomarker: examples from huntington disease, AJNR Am. J. Neuroradiol., 31, 711, 10.3174/ajnr.A1939 Keller, 2004, Comparison of standard and optimized voxel-based morphometry for analysis of brain changes associated with temporal lobe epilepsy, NeuroImage, 23, 860, 10.1016/j.neuroimage.2004.07.030 Klein, 2009, Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration, NeuroImage, 46, 786, 10.1016/j.neuroimage.2008.12.037 Lansley, 2013, Localized grey matter atrophy in multiple sclerosis: a meta-analysis of voxel-based morphometry studies and associations with functional disability, Neurosci. Biobehav. Rev., 37, 819, 10.1016/j.neubiorev.2013.03.006 Matsunari, 2007, Comparison of 18F-FDG PET and optimized voxel-based morphometry for detection of Alzheimer's disease: aging effect on diagnostic performance, J. Nucl. Med., 48, 1961, 10.2967/jnumed.107.042820 Mechelli, 2005, Voxel-based morphometry of the human brain: methods and applications, Curr. Med. Imag. Rev., 1, 105, 10.2174/1573405054038726 Nakao, 2011, Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication, Am. J. Psychiatry, 168, 1154, 10.1176/appi.ajp.2011.11020281 Nelder, 1972, Generalized linear models, J. R. Stat. Soc. Ser. A Gen., 135, 370, 10.2307/2344614 Palaniyappan, 2012, Structural correlates of auditory hallucinations in schizophrenia: a meta-analysis, Schizophr. Res., 137, 169, 10.1016/j.schres.2012.01.038 R. Core Team, 2013 Radua, 2009, Voxel-wise meta-analysis of grey matter changes in obsessive–compulsive disorder, Br. J. Psychiatry, 195, 393, 10.1192/bjp.bp.108.055046 Radua, 2010, Meta-analytical comparison of voxel-based morphometry studies in obsessive–compulsive disorder vs other anxiety disorders, Arch. Gen. Psychiatry, 67, 701, 10.1001/archgenpsychiatry.2010.70 Radua, 2011, Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls, Psychol. Med., 41, 1539, 10.1017/S0033291710002187 Radua, 2012, Multimodal meta-analysis of structural and functional brain changes in first episode psychosis and the effects of antipsychotic medication, Neurosci. Biobehav. Rev., 36, 2325, 10.1016/j.neubiorev.2012.07.012 Radua, 2012, A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps, Eur. Psychiatry, 27, 605, 10.1016/j.eurpsy.2011.04.001 Salimi-Khorshidi, 2011, Adjusting the effect of nonstationarity in cluster-based and TFCE inference, NeuroImage, 54, 2006, 10.1016/j.neuroimage.2010.09.088 Salmond, 2002, Distributional assumptions in voxel-based morphometry, NeuroImage, 17, 1027, 10.1006/nimg.2002.1153 Smith, 2002, Fast robust automated brain extraction, Hum. Brain Mapp., 17, 143, 10.1002/hbm.10062 Smith, 2009, Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference, NeuroImage, 44, 83, 10.1016/j.neuroimage.2008.03.061 Via, 2011, Meta-analysis of gray matter abnormalities in autism spectrum disorder: should Asperger disorder be subsumed under a broader umbrella of autistic spectrum disorder?, Arch. Gen. Psychiatry, 68, 409, 10.1001/archgenpsychiatry.2011.27 Worsley, 1999, Detecting changes in nonisotropic images, Hum. Brain Mapp., 8, 98, 10.1002/(SICI)1097-0193(1999)8:2/3<98::AID-HBM5>3.0.CO;2-F Xu, 2009, Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia, Hum. Brain Mapp., 30, 711, 10.1002/hbm.20540 Zhang, 2001, Segmentation of brain MR images through a hidden Markov random field model and the expectation–maximization algorithm, IEEE Trans. Med. Imaging, 20, 45, 10.1109/42.906424