MR g-ratio-weighted connectome analysis in patients with multiple sclerosis

[1]  M. Barnett,et al.  MRI biomarkers of disease progression in multiple sclerosis: old dog, new tricks? , 2020, Quantitative imaging in medicine and surgery.

[2]  Matteo Mancini,et al.  Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects , 2018, NeuroImage.

[3]  Kotagiri Ramamohanarao,et al.  Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? , 2018, Magnetic resonance in medicine.

[4]  D. Feinstein,et al.  Influence of diet on axonal damage in the EAE mouse model of multiple sclerosis , 2018, Journal of Neuroimmunology.

[5]  Ludwig Kappos,et al.  Clinical Correlations of Brain Lesion Location in Multiple Sclerosis: Voxel-Based Analysis of a Large Clinical Trial Dataset , 2018, Brain Topography.

[6]  Yunyun Duan,et al.  Progressive brain rich-club network disruption from clinically isolated syndrome towards multiple sclerosis , 2018, NeuroImage: Clinical.

[7]  S. Aoki,et al.  Application of Quantitative Microstructural MR Imaging with Atlas-based Analysis for the Spinal Cord in Cervical Spondylotic Myelopathy , 2018, Scientific Reports.

[8]  S. Aoki,et al.  The Advantage of Synthetic MRI for the Visualization of Anterior Temporal Pole Lesions on Double Inversion Recovery (DIR), Phase-sensitive Inversion Recovery (PSIR), and Myelin Images in a Patient with CADASIL , 2017, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[9]  D. Yablonskiy,et al.  Limbic system damage in MS: MRI assessment and correlations with clinical testing , 2017, PloS one.

[10]  O. Abe,et al.  Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio , 2017, American Journal of Neuroradiology.

[11]  Andrew Zalesky,et al.  Building connectomes using diffusion MRI: why, how and but , 2017, NMR in biomedicine.

[12]  O. Abe,et al.  SyMRI of the Brain , 2017, Investigative radiology.

[13]  B. Uitdehaag,et al.  Outcome Measures in Clinical Trials for Multiple Sclerosis , 2017, CNS Drugs.

[14]  K. Kumamaru,et al.  Synthetic MRI in the Detection of Multiple Sclerosis Plaques , 2017, American Journal of Neuroradiology.

[15]  Julien Cohen-Adad,et al.  Promise and pitfalls of g-ratio estimation with MRI , 2017, NeuroImage.

[16]  Roland Opfer,et al.  MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation? , 2016, NeuroImage: Clinical.

[17]  Leonardo L. Gollo,et al.  Connectome sensitivity or specificity: which is more important? , 2016, NeuroImage.

[18]  Stamatios N. Sotiropoulos,et al.  Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images , 2016, NeuroImage.

[19]  Yunyun Duan,et al.  Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis , 2016, Scientific Reports.

[20]  Andrew L. Alexander,et al.  Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging , 2016, NeuroImage.

[21]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[22]  Ken E Sakaie,et al.  The relationship between cognitive function and high-resolution diffusion tensor MRI of the cingulum bundle in multiple sclerosis , 2015, Multiple sclerosis.

[23]  F. Dick,et al.  Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers , 2015, Front. Neurosci..

[24]  F. Barkhof,et al.  Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—establishing disease prognosis and monitoring patients , 2015, Nature Reviews Neurology.

[25]  Julien Cohen-Adad,et al.  In vivo histology of the myelin g-ratio with magnetic resonance imaging , 2015, NeuroImage.

[26]  R. Reynolds,et al.  Regional Distribution and Evolution of Gray Matter Damage in Different Populations of Multiple Sclerosis Patients , 2015, PloS one.

[27]  F. Zhou,et al.  Disconnection of the hippocampus and amygdala associated with lesion load in relapsing–remitting multiple sclerosis: a structural and functional connectivity study , 2015, Neuropsychiatric disease and treatment.

[28]  N. Toschi,et al.  Structural ‘connectomic’ alterations in the limbic system of multiple sclerosis patients with major depression , 2015, Multiple sclerosis.

[29]  G. Johnson,et al.  A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data , 2015, Cerebral cortex.

[30]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[31]  Jean-Philippe Thiran,et al.  Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data , 2015, NeuroImage.

[32]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

[33]  T. Paus,et al.  White matter as a transport system , 2014, Neuroscience.

[34]  Yong He,et al.  Assessment of system dysfunction in the brain through MRI-based connectomics , 2013, The Lancet Neurology.

[35]  Yasheng Chen,et al.  Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing‐remitting multiple sclerosis , 2013, Human brain mapping.

[36]  Thomas Kohlmann,et al.  Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis , 2013, BMC Neurology.

[37]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[38]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

[39]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..

[40]  Alan Connelly,et al.  SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.

[41]  Edward T. Bullmore,et al.  Schizophrenia, neuroimaging and connectomics , 2012, NeuroImage.

[42]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[43]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[44]  Massimo Filippi,et al.  Association between pathological and MRI findings in multiple sclerosis , 2012, The Lancet Neurology.

[45]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[46]  Yong He,et al.  Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. , 2011, Cerebral cortex.

[47]  Xiaoqi Huang,et al.  Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder , 2011, Biological Psychiatry.

[48]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[49]  Daniel S Reich,et al.  Diffusion Tensor Imaging of the Optic Tracts in Multiple Sclerosis: Association with Retinal Thinning and Visual Disability , 2011, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[50]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[51]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[52]  Glen R Morrell,et al.  An analysis of the accuracy of magnetic resonance flip angle measurement methods , 2010, Physics in medicine and biology.

[53]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[54]  Peter Dechent,et al.  Modeling the influence of TR and excitation flip angle on the magnetization transfer ratio (MTR) in human brain obtained from 3D spoiled gradient echo MRI , 2010, Magnetic resonance in medicine.

[55]  M. Filippi,et al.  Default-mode network dysfunction and cognitive impairment in progressive MS , 2010, Neurology.

[56]  J. Ranjeva,et al.  Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

[57]  L. Passamonti,et al.  Neurobiological mechanisms underlying emotional processing in relapsing-remitting multiple sclerosis. , 2009, Brain : a journal of neurology.

[58]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[59]  P. Dechent,et al.  High‐resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI , 2008, Magnetic resonance in medicine.

[60]  Massimo Filippi,et al.  A voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes , 2008, NeuroImage.

[61]  P. Lundberg,et al.  Rapid magnetic resonance quantification on the brain: Optimization for clinical usage , 2008, Magnetic resonance in medicine.

[62]  D. Reich,et al.  Corticospinal Tract Abnormalities Are Associated with Weakness in Multiple Sclerosis , 2008, American Journal of Neuroradiology.

[63]  Christine Stadelmann,et al.  Extensive Cortical Remyelination in Patients with Chronic Multiple Sclerosis , 2007, Brain pathology.

[64]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[65]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[66]  M. Rovaris,et al.  A new method for analyzing histograms of brain magnetization transfer ratios: comparison with existing techniques. , 2004, AJNR. American journal of neuroradiology.

[67]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[68]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[69]  Muge M. Bakircioglu,et al.  Mapping visual cortex in monkeys and humans using surface-based atlases , 2001, Vision Research.

[70]  C. Poser,et al.  Diagnostic criteria for multiple sclerosis , 2001, Clinical Neurology and Neurosurgery.

[71]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[72]  G. Barker,et al.  The effect of section thickness on MR lesion detection and quantification in multiple sclerosis. , 1998, AJNR. American journal of neuroradiology.

[73]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[74]  R. Friede,et al.  Combined scatter diagrams of sheath thickness and fibre calibre in human sural nerves: changes with age and neuropathy. , 1985, Journal of neurology, neurosurgery, and psychiatry.

[75]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[76]  S. Waxman Determinants of conduction velocity in myelinated nerve fibers , 1980, Muscle & nerve.

[77]  C. Hildebrand,et al.  Relation between myelin sheath thickness and axon size in spinal cord white matter of some vertebrate species , 1978, Journal of the Neurological Sciences.

[78]  W. Rushton A theory of the effects of fibre size in medullated nerve , 1951, The Journal of physiology.

[79]  Frederik Barkhof,et al.  Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. , 2016, Brain : a journal of neurology.

[80]  Masaaki Hori,et al.  Diffusional kurtosis imaging of normal-appearing white matter in multiple sclerosis: preliminary clinical experience , 2012, Japanese Journal of Radiology.

[81]  A. Connelly,et al.  Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions , 2009 .

[82]  J. M. Schröder,et al.  Changes of the ratio between myelin thickness and axon diameter in human developing sural, femoral, ulnar, facial, and trochlear nerves , 2004, Acta Neuropathologica.

[83]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[84]  W. L. Benedict,et al.  Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.