Changes in structural network are associated with cortical demyelination in early multiple sclerosis

The aim of this study was to investigate the interplay between structural connectivity and cortical demyelination in early multiple sclerosis. About 27 multiple sclerosis patients and 18 age‐matched controls underwent two MRI scanning sessions. The first was done at 7T and involved acquiring quantitative T1 and T2* high‐resolution maps to estimate cortical myelination. The second was done on a Connectom scanner and consisted of acquiring high angular resolution diffusion‐weighted images to compute white matter structural connectivity metrics: strength, clustering and local efficiency. To further investigate the interplay between structural connectivity and cortical demyelination, patients were divided into four groups according to disease‐duration: 0–1 year, 1–2 years, 2–3 years, and >3 years. ANOVA and Spearman's correlations were used to highlight relations between metrics. ANOVA detected a significant effect between disease duration and both cortical myelin (p = 2 × 10−8) and connectivity metrics (p < 10−4). We observed significant cortical myelin loss in the shorter disease‐duration cohorts (0–1 year, p = .0015), and an increase in connectivity in the longer disease‐duration cohort (2–3 years, strength: p = .01, local efficiency: p = .002, clustering: p = .001). Moreover, significant covariations between myelin estimation and white matter connectivity metrics were observed: Spearman's Rho correlation coefficients of 0.52 (p = .0003), 0.55 (p = .0001), and 0.53 (p = .0001) for strength, local efficiency, and clustering, respectively. An association between cortical myelin loss and changes in white matter connectivity in early multiple sclerosis was detected. These changes in network organization might be the result of compensatory mechanisms in response to the ongoing cortical diffuse damage in the early stages of multiple sclerosis.

[1]  C. Stam.  Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[2]  B. Scheithauer,et al.  Inflammatory cortical demyelination in early multiple sclerosis. , 2011, The New England journal of medicine.

[3]  S. Golaszewski,et al.  Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task. , 2002, Brain : a journal of neurology.

[4]  F. Jacques.  Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[5]  S. T. Govindarajan,et al.  Beyond focal cortical lesions in MS , 2015, Neurology.

[6]  Phillip I. Good,et al.  Common Errors in Statistics (and How to Avoid Them) , 2003 .

[7]  G J Barker,et al.  Recovery from optic neuritis is associated with a change in the distribution of cerebral response to visual stimulation: a functional magnetic resonance imaging study , 2000, Journal of neurology, neurosurgery, and psychiatry.

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

[9]  David H. Miller,et al.  Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. , 2017, Functional neurology.

[10]  M. Fukunaga,et al.  Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast , 2010, Proceedings of the National Academy of Sciences.

[11]  Functional Plasticity of the Visual System in Multiple Sclerosis , 2015, Front. Neurol..

[12]  N. Lundbom,et al.  Relaxometry of brain: Why white matter appears bright in MRI , 1990, Magnetic resonance in medicine.

[13]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[14]  David H. Miller,et al.  Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation , 2008, Magnetic resonance in medicine.

[15]  B. Trapp,et al.  Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions , 2001, Annals of neurology.

[16]  D. V. van Essen,et al.  A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. , 2005, NeuroImage.

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

[18]  Jeffrey A. Cohen,et al.  Defining the clinical course of multiple sclerosis: the 2013 revisions. , 2014, Neurology.

[19]  J. Cohen-Adad,et al.  Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI , 2011, NeuroImage.

[20]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[21]  Hans Lassmann,et al.  Remyelination is extensive in a subset of multiple sclerosis patients. , 2006, Brain : a journal of neurology.

[22]  Fang-Cheng Yeh,et al.  Generalized ${ q}$-Sampling Imaging , 2010, IEEE Transactions on Medical Imaging.

[23]  R. Bowtell,et al.  Application of a Fourier‐based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility , 2005 .

[24]  G. Mangeat,et al.  Multivariate combination of magnetization transfer, T2* and B0 orientation to study the myelo-architecture of the in vivo human cortex , 2015, NeuroImage.

[25]  David C. Van Essen,et al.  A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex , 2005, NeuroImage.

[26]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[27]  P. M. Matthews,et al.  Evidence of early cortical atrophy in MS , 2003, Neurology.

[28]  B. Trapp,et al.  Cortical remyelination: A new target for repair therapies in multiple sclerosis , 2012, Annals of neurology.

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

[30]  Joaquín Goñi,et al.  Changes in structural and functional connectivity among resting-state networks across the human lifespan , 2014, NeuroImage.

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

[32]  Nicholas A. Bock,et al.  Visualizing the entire cortical myelination pattern in marmosets with magnetic resonance imaging , 2009, Journal of Neuroscience Methods.

[33]  V. Fleischer,et al.  Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis , 2017, Multiple sclerosis.

[34]  E. Bullmore,et al.  Functional magnetic resonance imaging of the cortical response to photic stimulation in humans following optic neuritis recovery , 2002, Neuroscience Letters.

[35]  S. Weisberg,et al.  Criticism and Influence Analysis in Regression , 1982 .

[36]  T. Brismar,et al.  P300 amplitude and response speed relate to preserved cognitive function in relapsing–remitting multiple sclerosis , 2015, Clinical Neurophysiology.

[37]  P. Matthews,et al.  Neocortical neuronal, synaptic, and glial loss in multiple sclerosis , 2006, Neurology.

[38]  David Pitt,et al.  Imaging cortical lesions in multiple sclerosis with ultra-high-field magnetic resonance imaging. , 2010, Archives of neurology.

[39]  F. Barkhof,et al.  Increased connectivity of hub networks and cognitive impairment in multiple sclerosis , 2017, Neurology.

[40]  Caterina Mainero,et al.  fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis , 2004, NeuroImage.

[41]  Robert Turner,et al.  Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2 ⁎-imaging at ultra-high magnetic field strength , 2013, NeuroImage.

[42]  Fabrice Bartolomei,et al.  Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[43]  Valeria Conte,et al.  Quantitative assessments of traumatic axonal injury in human brain: concordance of microdialysis and advanced MRI. , 2015, Brain : a journal of neurology.

[44]  Alan C. Evans,et al.  Multi-level bootstrap analysis of stable clusters in resting-state fMRI , 2009, NeuroImage.

[45]  F. Dick,et al.  Mapping the Human Cortical Surface by Combining Quantitative T1 with Retinotopy† , 2012, Cerebral cortex.

[46]  Robert Turner,et al.  Myelin and iron concentration in the human brain: A quantitative study of MRI contrast , 2014, NeuroImage.

[47]  S. T. Govindarajan,et al.  A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging. , 2015, Brain : a journal of neurology.

[48]  K. Trinkaus,et al.  Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis. , 2015, Brain : a journal of neurology.

[49]  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.

[50]  André J. W. van der Kouwe,et al.  Brain morphometry with multiecho MPRAGE , 2008, NeuroImage.

[51]  R. Nieuwenhuys.  The myeloarchitectonic studies on the human cerebral cortex of the Vogt–Vogt school, and their significance for the interpretation of functional neuroimaging data , 2013, Brain Structure and Function.

[52]  R. Rudick,et al.  Axonal transection in the lesions of multiple sclerosis. , 1998, The New England journal of medicine.

[53]  Peter R Luijten,et al.  Increased cortical grey matter lesion detection in multiple sclerosis with 7 T MRI: a post-mortem verification study. , 2016, Brain : a journal of neurology.

[54]  Ann Shimakawa,et al.  R  2* mapping in the presence of macroscopic B0 field variations , 2012, Magnetic resonance in medicine.

[55]  Pierre-Louis Bazin,et al.  A Histology-Based Model of Quantitative T1 Contrast for In-vivo Cortical Parcellation of High-Resolution 7 Tesla Brain MR Images , 2013, MICCAI.

[56]  Gary King,et al.  Comparative Effectiveness of Matching Methods for Causal Inference , 2011 .

[57]  Sara Llufriu,et al.  Structural networks involved in attention and executive functions in multiple sclerosis , 2016, NeuroImage: Clinical.

[58]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[59]  Pierre Bellec,et al.  A BOOTSTRAP TEST TO INVESTIGATE CHANGES IN BRAIN CONNECTIVITY FOR FUNCTIONAL MRI , 2008 .

[60]  Mark Jenkinson,et al.  Correspondences between retinotopic areas and myelin maps in human visual cortex , 2014, NeuroImage.

[61]  Christine Stadelmann,et al.  Remyelination in multiple sclerosis , 2003, Journal of the Neurological Sciences.

[62]  Michael P. Milham,et al.  A convergent functional architecture of the insula emerges across imaging modalities , 2012, NeuroImage.

[63]  M. Horsfield,et al.  Ultra-high-field MR imaging in multiple sclerosis , 2013, Journal of Neurology, Neurosurgery & Psychiatry.

[64]  Lloyd S. Nelson,et al.  Common Errors in Statistics (and How to Avoid Them) , 2005 .

[65]  R. Ordidge,et al.  High field MRI correlates of myelin content and axonal density in multiple sclerosis , 2003, Journal of Neurology.

[66]  Khader M Hasan,et al.  Diffusion-tensor MR imaging of cortical lesions in multiple sclerosis: initial findings. , 2008, Radiology.

[67]  Elizabeth A Stuart,et al.  Matching methods for causal inference: A review and a look forward. , 2010, Statistical science : a review journal of the Institute of Mathematical Statistics.

[68]  David H. Miller,et al.  Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain , 2004, Annals of neurology.

[69]  Anusha Mohan,et al.  Graph theoretical analysis of brain connectivity in phantom sound perception , 2016, Scientific Reports.

[70]  Julien Cohen-Adad,et al.  T2* mapping and B0 orientation-dependence at 7T reveal cyto- and myeloarchitecture organization of the human cortex , 2012, NeuroImage.

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

[72]  Nikolaus Weiskopf,et al.  Using high-resolution quantitative mapping of R1 as an index of cortical myelination , 2014, NeuroImage.

[73]  D. Chari,et al.  Remyelination In Multiple Sclerosis , 2007, International Review of Neurobiology.

[74]  S. T. Govindarajan,et al.  Reproducibility of T2* mapping in the human cerebral cortex in vivo at 7 tesla MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[75]  Caterina Mainero,et al.  In vivo imaging of cortical pathology in multiple sclerosis using ultra-high field MRI , 2009, Neurology.

[76]  Rohit Bakshi,et al.  Gray matter involvement in multiple sclerosis , 2007, Neurology.

[77]  J. Geurts,et al.  Cortical Remyelination Is Heterogeneous in Multiple Sclerosis , 2017, Journal of neuropathology and experimental neurology.

[78]  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.

[79]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[80]  Robert Turner,et al.  Voxel-based cortical thickness measurements in MRI , 2008, NeuroImage.