MRI phenotypes based on cerebral lesions and atrophy in patients with multiple sclerosis

BACKGROUND While disease categories (i.e. clinical phenotypes) of multiple sclerosis (MS) are established, there remains MRI heterogeneity among patients within those definitions. MRI-defined lesions and atrophy show only moderate inter-correlations, suggesting that they represent partly different processes in MS. We assessed the ability of MRI-based categorization of cerebral lesions and atrophy in individual patients to identify distinct phenotypes. METHODS We studied 175 patients with MS [age (mean ± SD) 42.7 ± 9.1 years, 124 (71%) women, Expanded Disability Status (EDSS) score 2.5 ± 2.3, n = 18 (10%) clinically isolated demyelinating syndrome (CIS), n=115 (66%) relapsing-remitting (RR), and n = 42 (24%) secondary progressive (SP)]. Brain MRI measures included T2 hyperintense lesion volume (T2LV) and brain parenchymal fraction (to assess whole brain atrophy). Medians were used to create bins for each parameter, with patients assigned a low or high severity score. RESULTS Four MRI phenotype categories emerged: Type I = low T2LV/mild atrophy [n = 67 (38%); CIS = 14, RR = 47, SP = 6]; Type II = high T2LV/mild atrophy [n = 21 (12%); RR = 19, SP = 2]; Type III = low T2LV/high atrophy [n = 21 (12%); CIS = 4, RR = 16, SP = 1]; and Type IV = high T2LV/high atrophy [n = 66 (38%); RR = 33, S P = 33]. Type IV was the most disabled and was the only group showing a correlation between T2LV vs. BPF and MRI vs. EDSS score (all p < 0.05). CONCLUSIONS We described MRI-categorization based on the relationship between lesions and atrophy in individual patients to identify four phenotypes in MS. Most patients have congruent extremes related to the degree of lesions and atrophy. However, many have a dissociation. Longitudinal studies will help define the stability of these patterns and their role in risk stratification.

[1]  D. Arnold,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[2]  J. Berkhof,et al.  Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics , 2006, Multiple sclerosis.

[3]  Rohit Bakshi,et al.  The Relationships among MRI‐Defined Spinal Cord Involvement, Brain Involvement, and Disability in Multiple Sclerosis , 2012, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[4]  A. Minagar,et al.  HLA-DRB1*1501, -DQB1*0301, -DQB1*0302, -DQB1*0602, and -DQB1*0603 alleles are associated with more severe disease outcome on MRI in patients with multiple sclerosis. , 2007, International review of neurobiology.

[5]  R. Reynolds,et al.  Meningeal inflammation is widespread and linked to cortical pathology in multiple sclerosis. , 2011, Brain : a journal of neurology.

[6]  Peter A. Calabresi,et al.  Revisiting Brain Atrophy and Its Relationship to Disability in Multiple Sclerosis , 2012, PloS one.

[7]  Rohit Bakshi,et al.  Prediction of longitudinal brain atrophy in multiple sclerosis by gray matter magnetic resonance imaging T2 hypointensity. , 2005, Archives of neurology.

[8]  M. A. Horsfield,et al.  Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis , 2010, NeuroImage.

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

[10]  H. Cate,et al.  Signalling Pathways that Inhibit the Capacity of Precursor Cells for Myelin Repair , 2013, International journal of molecular sciences.

[11]  Rohit Bakshi,et al.  Predicting clinical progression in multiple sclerosis with the magnetic resonance disease severity scale. , 2008, Archives of neurology.

[12]  R. Rudick,et al.  Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS , 1999, Neurology.

[13]  Rohit Bakshi,et al.  MRI in multiple sclerosis: current status and future prospects , 2008, The Lancet Neurology.

[14]  Massimo Filippi,et al.  Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. , 2009, Archives of neurology.

[15]  Olivier Gout,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[16]  Rohit Bakshi,et al.  The measurement and clinical relevance of brain atrophy in multiple sclerosis , 2006, The Lancet Neurology.

[17]  P. Matthews,et al.  Increased PK11195 PET binding in the cortex of patients with MS correlates with disability , 2012, Neurology.

[18]  Ponnada A Narayana,et al.  Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis , 2011, Multiple sclerosis.

[19]  Rohit Bakshi,et al.  The relationship between whole brain volume and disability in multiple sclerosis: A comparison of normalized gray vs. white matter with misclassification correction , 2005, NeuroImage.

[20]  Sara Llufriu,et al.  Cognitive functions in multiple sclerosis: impact of gray matter integrity , 2014, Multiple sclerosis.

[21]  N. De Stefano,et al.  Neocortical volume decrease in relapsing–remitting MS patients with mild cognitive impairment , 2004, Neurology.

[22]  Robert Zivadinov,et al.  Clinical–Magnetic Resonance Imaging Correlations in Multiple Sclerosis , 2005, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[23]  M. Pender,et al.  Correlation of Blood T Cell and Antibody Reactivity to Myelin Proteins with HLA Type and Lesion Localization in Multiple Sclerosis1 , 2008, The Journal of Immunology.

[24]  K. Zou,et al.  Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy , 2002, Journal of magnetic resonance imaging : JMRI.

[25]  D. Centonze,et al.  Disability in multiple sclerosis: When synaptic long-term potentiation fails , 2014, Neuroscience & Biobehavioral Reviews.

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

[27]  R. Bakshi,et al.  Corpus Callosum Atrophy Correlates with Gray Matter Atrophy in Patients with Multiple Sclerosis , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[28]  Arunesh Mittal,et al.  Casting light on multiple sclerosis heterogeneity: the role of HLA-DRB1 on spinal cord pathology. , 2013, Brain : a journal of neurology.

[29]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[30]  Angel Alberich-Bayarri,et al.  Brain atrophy and lesion load are related to CSF lipid-specific IgM oligoclonal bands in clinically isolated syndromes , 2011, Neuroradiology.

[31]  L. Jacobs,et al.  A profile of multiple sclerosis: The New York State Multiple Sclerosis Consortium , 1999, Multiple sclerosis.

[32]  G. Cutter,et al.  MRI as a marker for disease heterogeneity in multiple sclerosis , 2005, Neurology.

[33]  F. Barkhof,et al.  Multiple sclerosis , 2003, Neurology.

[34]  A. Tessitore,et al.  BDNF Val66Met polymorphism and brain volumes in multiple sclerosis , 2011, Neurological Sciences.

[35]  S. Weigand,et al.  Pathologic heterogeneity persists in early active multiple sclerosis lesions , 2014, Annals of neurology.

[36]  M. Calabrese,et al.  Longitudinal analysis of immune cell phenotypes in early stage multiple sclerosis: distinctive patterns characterize MRI-active patients. , 2006, Brain : a journal of neurology.

[37]  Rohit Bakshi,et al.  The Relationship between Normal Cerebral Perfusion Patterns and White Matter Lesion Distribution in 1,249 Patients with Multiple Sclerosis , 2012, Journal of neuroimaging : official journal of the American Society of Neuroimaging.