Personalized pathology maps to quantify diffuse and focal brain damage

Arbabshirani, 2017, Single subject prediction of brain disorders in neuroimaging: promises and pitfalls, NeuroImage, 145, 137, 10.1016/j.neuroimage.2016.02.079 Aubert-Broche, 2009, Clustering of atlas-defined cortical regions based on relaxation times and proton density, NeuroImage, 47, 523, 10.1016/j.neuroimage.2009.04.079 Barta, 2015, Modeling T(1) and T(2) relaxation in bovine white matter, J. Magn. Reson., 259, 56, 10.1016/j.jmr.2015.08.001 Beeson, 2011, Positive effects of language treatment for the logopenic variant of primary progressive aphasia, J. Mol. Neurosci., 45, 724, 10.1007/s12031-011-9579-2 Bilgic, 2015, Wave-CAIPI for highly accelerated 3D imaging, Magn. Reson. Med., 73, 2152, 10.1002/mrm.25347 Bonnier, 2014, Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis, Ann. Clin. Transl. Neurol., 1, 423, 10.1002/acn3.68 Bonnier, 2015, Multicontrast MRI quantification of focal inflammation and degeneration in Multiple Sclerosis, Biomed. Res. Int., 2015, 10.1155/2015/569123 Bonnier, 2017, The combined quantification and interpretation of multiple quantitative magnetic Resonance imaging metrics enlightens longitudinal changes compatible with brain repair in relapsing-remitting Multiple Sclerosis patients, Front. Neurol., 8, 506, 10.3389/fneur.2017.00506 Bookstein, 2001, "Voxel-based morphometry" should not be used with imperfectly registered images, NeuroImage, 14, 1454, 10.1006/nimg.2001.0770 Bruck, 2002, The pathology of primary progressive multiple sclerosis, Mult. Scler., 8, 93, 10.1191/1352458502ms785rr Colliot, 2006, Individual voxel-based analysis of gray matter in focal cortical dysplasia, NeuroImage, 29, 162, 10.1016/j.neuroimage.2005.07.021 Davies, 2007, Normal-appearing grey and white matter T1 abnormality in early relapsing-remitting multiple sclerosis: a longitudinal study, Mult. Scler., 13, 169, 10.1177/1352458506070726 Deoni, 2011, Magnetic resonance relaxation and quantitative measurement in the brain, Methods Mol. Biol., 711, 65, 10.1007/978-1-61737-992-5_4 Deoni, 2008, Standardized structural magnetic resonance imaging in multicentre studies using quantitative T1 and T2 imaging at 1.5 T, NeuroImage, 40, 662, 10.1016/j.neuroimage.2007.11.052 Droby, 2015, A human post-mortem brain model for the standardization of multi-centre MRI studies, NeuroImage, 110, 11, 10.1016/j.neuroimage.2015.01.028 Dusek, 2013, Imaging of iron, Int. Rev. Neurobiol., 110, 195, 10.1016/B978-0-12-410502-7.00010-7 Enzinger, 2015, Nonconventional MRI and microstructural cerebral changes in multiple sclerosis, Nat. Rev. Neurol., 11, 676, 10.1038/nrneurol.2015.194 Fartaria, 2017 Fartaria, 2017 Filippi, 2007, Magnetization transfer MRI in multiple sclerosis, J. Neuroimaging, 17, 22S, 10.1111/j.1552-6569.2007.00132.x Georgiades, 2001, MR imaging of the human brain at 1.5 T: regional variations in transverse relaxation rates in the cerebral cortex, AJNR Am. J. Neuroradiol., 22, 1732 Granziera, 2015, Brain Inflammation, degeneration and plasticity in Multiple Sclerosis, 917 Granziera, 2013, Micro-structural brain alterations in aviremic HIV+ patients with minor neurocognitive disorders: a multi-contrast study at high field, PLoS One, 8, 10.1371/journal.pone.0072547 Granziera, 2014, Structural abnormalities in the thalamus of migraineurs with aura: a multiparametric study at 3 T, Hum. Brain Mapp., 35, 1461, 10.1002/hbm.22266 Granziera, 2015, A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment, Neuroimage Clin., 8, 631, 10.1016/j.nicl.2015.06.003 Hasan, 2012, J. Neurol. Sci., 313, 99, 10.1016/j.jns.2011.09.015 Helms, 2015, Tissue properties from quantitative MRI, 287 Helms, 2009, In vivo quantification of the bound pool T1 in human white matter using the binary spin-bath model of progressive magnetization transfer saturation, Phys. Med. Biol., 54, N529, 10.1088/0031-9155/54/23/N01 Hilbert, 2018, Accelerated T2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI, J. Magn. Reson. Imaging., 48, 359, 10.1002/jmri.25972 Klaver, 2013, Grey matter damage in multiple sclerosis: a pathology perspective, Prion, 7, 66, 10.4161/pri.23499 Klein, 2010, Elastix: a toolbox for intensity-based medical image registration, IEEE Trans. Med. Imaging, 29, 196, 10.1109/TMI.2009.2035616 Kober, 2012, MP2RAGE multiple sclerosis magnetic resonance imaging at 3 T, Investig. Radiol., 47, 346, 10.1097/RLI.0b013e31824600e9 Kruggel, 2017, Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: an ADNI study, J. Neurosci. Methods, 278, 101, 10.1016/j.jneumeth.2016.12.020 Lassmann, 2001, Heterogeneity of multiple sclerosis pathogenesis: implications for diagnosis and therapy, Trends Mol. Med., 7, 115, 10.1016/S1471-4914(00)01909-2 Lucchinetti, 2004, The pathology of primary progressive multiple sclerosis, Mult. Scler., 10, S23, 10.1191/1352458504ms1027oa MacKay, 1994, In vivo visualization of myelin water in brain magnetic resonance, Magn. Reson. Med., 31, 673, 10.1002/mrm.1910310614 MacKay, 2006, Insights into brain microstructure from the T2 distribution, Magn. Reson. Imaging., 24, 515, 10.1016/j.mri.2005.12.037 Mallik, 2014, Imaging outcomes for trials of remyelination in multiple sclerosis, J. Neurol. Neurosurg. Psychiatry, 85, 1396, 10.1136/jnnp-2014-307650 Marques, 2010, MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field, NeuroImage, 49, 1271, 10.1016/j.neuroimage.2009.10.002 Metere, 2017, Simultaneous quantitative MRI mapping of T1, T2* and magnetic susceptibility with multi-echo MP2RAGE, PLoS One, 12, 10.1371/journal.pone.0169265 Metz, 2014, Pathologic heterogeneity persists in early active multiple sclerosis lesions, Ann. Neurol., 75, 728, 10.1002/ana.24163 Meyer, 2017, Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data, Neuroimage Clin., 14, 656, 10.1016/j.nicl.2017.02.001 Muhlau, 2009, Voxel-based morphometry in individual patients: a pilot study in early Huntington disease, AJNR Am. J. Neuroradiol., 30, 539, 10.3174/ajnr.A1390 Muhlau, 2013, White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI, Mult. Scler., 19, 1485, 10.1177/1352458513478673 Neema, 2007, T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis, J. Neuroimaging, 17, 16S, 10.1111/j.1552-6569.2007.00131.x Parry, 2002, White matter and lesion T1 relaxation times increase in parallel and correlate with disability in Multiple sclerosis, J. Neurol., 249, 1279, 10.1007/s00415-002-0837-7 Parry, 2003, MRI brain T1 relaxation time changes in MS patients increase over time in both the white matter and the cortex, J. Neuroimaging, 13, 234, 10.1111/j.1552-6569.2003.tb00184.x Polman, 2011, Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria, Ann. Neurol., 69, 292, 10.1002/ana.22366 Roche, 2014, Partial volume estimation in brain MRI revisited, Med. Image Comput. Comput. Assist. Interv., 17, 771 Romascano, 2015, Multicontrast connectometry: a new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis, Hum. Brain Mapp., 36, 1609, 10.1002/hbm.22698 Rovira, 2013, Magnetic resonance monitoring of lesion evolution in multiple sclerosis, Ther. Adv. Neurol. Disord., 6, 298, 10.1177/1756285613484079 Sajjadi, 2013, Diffusion tensor magnetic resonance imaging for single subject diagnosis in neurodegenerative diseases, Brain, 136, 2253, 10.1093/brain/awt118 Scarpazza, 2013, When the single matters more than the group: very high false positive rates in single case Voxel based Morphometry, NeuroImage, 70, 175, 10.1016/j.neuroimage.2012.12.045 Schmitter, 2015, An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease, Neuroimage Clin., 7, 7, 10.1016/j.nicl.2014.11.001 Seewann, 2009, Diffusely abnormal white matter in chronic Multiple sclerosis: imaging and histopathologic analysis, Arch. Neurol., 66, 601, 10.1001/archneurol.2009.57 Stephan, 2017, Computational neuroimaging strategies for single patient predictions, NeuroImage, 145, 180, 10.1016/j.neuroimage.2016.06.038 Sumpf, 2011, Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI, J. Magn. Reson. Imaging, 34, 420, 10.1002/jmri.22634 Vrenken, 2010, Diffusely abnormal white matter in progressive multiple sclerosis: in vivo quantitative MR imaging characterization and comparison between disease types, AJNR Am. J. Neuroradiol., 31, 541, 10.3174/ajnr.A1839 Wansapura, 1999, NMR relaxation times in the human brain at 30 tesla, J. Magn. Reson. Imaging, 9, 531, 10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L West, 2014, Normal appearing and diffusely abnormal white matter in patients with multiple sclerosis assessed with quantitative MR, PLoS One, 9, 10.1371/journal.pone.0095161