Brain tissue volumes and relaxation rates in multiple sclerosis: implications for cognitive impairment

ObjectiveBoth normal gray matter atrophy and brain tissue relaxation rates, in addition to total lesion volume, have shown significant correlations with cognitive test scores in multiple sclerosis (MS). Aim of the study was to assess the relative contributions of macro- and microstructural changes of both normal and abnormal brain tissues, probed, respectively, by their volumes and relaxation rates, to the cognitive status and physical disability of MS patients.MethodsMRI studies from 241 patients with relapsing–remitting MS were retrospectively analyzed by fully automated multiparametric relaxometric segmentation. Ordinal backward regression analysis was applied to the resulting volumes and relaxation rates of both normal (gray matter, normal-appearing white matter and CSF) and abnormal (T2-weighted lesions) brain tissues, controlling for age, sex and disease duration, to identify the main independent contributors to the cognitive status, as measured by the percentage of failed tests at a cognitive test battery (Rao’s Brief Repeatable Battery and Stroop test, available in 186 patients), and to the physical disability, as assessed by the Expanded Disability Status Scale (EDSS).ResultsThe R1 relaxation rate (a putative marker of tissue disruption) of the MS lesions appeared the single most significant contributor to cognitive impairment (p < 0.001). On the contrary, the EDSS appeared mainly affected by the decrease in R2 of the gray matter (p < 0.0001), (possibly influenced by cortical plaques, edema and inflammation).ConclusionsIn RR-MS the tissue damage in white matter lesions appears the single main determinant of the cognitive status of patients, likely through disconnection phenomena, while the physical disability appears related to the involvement of gray matter.

[1]  Mark D. Does,et al.  Inferring brain tissue composition and microstructure via MR relaxometry , 2018, NeuroImage.

[2]  David H. Miller,et al.  High field (9.4 Tesla) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis. , 2010, Brain : a journal of neurology.

[3]  Mário João Fartaria,et al.  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 , 2017, Front. Neurol..

[4]  Oliver Bieri,et al.  Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI , 2016, PloS one.

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

[6]  F. Barkhof,et al.  High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis , 2016, Multiple sclerosis.

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

[8]  J. Thiran,et al.  Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis , 2014, Annals of Clinical and Translational Neurology.

[9]  J. DeLuca,et al.  Cognitive impairment in multiple sclerosis , 2008, The Lancet Neurology.

[10]  Massimo Filippi,et al.  Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis , 2015, The Lancet Neurology.

[11]  C. Pozzilli,et al.  No evidence for an effect on brain atrophy rate of atorvastatin add-on to interferon β1b therapy in relapsing–remitting multiple sclerosis (the ARIANNA study) , 2016, Multiple sclerosis.

[12]  C. Laule,et al.  Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology , 2006, Multiple sclerosis.

[13]  Rohit Bakshi,et al.  Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. , 2004, Archives of neurology.

[14]  Hui Jing Yu,et al.  Multiple white matter tract abnormalities underlie cognitive impairment in RRMS , 2012, NeuroImage.

[15]  Bruno Alfano,et al.  A Novel Multiparametric Approach to 3D Quantitative MRI of the Brain , 2015, PloS one.

[16]  J. Thiran,et al.  Multicontrast MRI Quantification of Focal Inflammation and Degeneration in Multiple Sclerosis , 2015, BioMed research international.

[17]  M Quarantelli,et al.  A voxel-based morphometry study of disease severity correlates in relapsing–remitting multiple sclerosis , 2010, Multiple sclerosis.

[18]  Rajiv Midha,et al.  MR properties of excised neural tissue following experimentally induced inflammation , 2004, Magnetic resonance in medicine.

[19]  Bruno Alfano,et al.  Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis , 2000 .

[20]  G. Siracusa,et al.  Cognitive assessment and quantitative magnetic resonance metrics can help to identify benign multiple sclerosis , 2008, Neurology.

[21]  T. Ptak,et al.  Investigation of apparent diffusion coefficient and diffusion tensor anisotrophy in acute and chronic multiple sclerosis lesions. , 1999, AJNR. American journal of neuroradiology.

[22]  S. Sorbi,et al.  The Rao’s Brief Repeatable Battery and Stroop test: normative values with age, education and gender corrections in an Italian population , 2006, Multiple sclerosis.

[23]  E. Capitani,et al.  A normative study on visual reaction times and two Stroop colour-word tests , 1998, The Italian Journal of Neurological Sciences.

[24]  A. Compston,et al.  Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis , 2001, Annals of neurology.

[25]  J. Thiran,et al.  MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing-remitting multiple sclerosis , 2014, Journal of Neurology.

[26]  M. Ron,et al.  Cognitive impairment in multiple sclerosis can be predicted by imaging early in the disease , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[27]  Bruno Alfano,et al.  Grey matter loss in relapsing–remitting multiple sclerosis: A voxel-based morphometry study , 2006, NeuroImage.

[28]  G. Tedeschi,et al.  Atorvastatin Combined To Interferon to Verify the Efficacy (ACTIVE) in relapsing— remitting active multiple sclerosis patients: a longitudinal controlled trial of combination therapy , 2010, Multiple sclerosis.

[29]  D. Louis Collins,et al.  Quantitative Magnetic Resonance Imaging of Cortical Multiple Sclerosis Pathology , 2012, Multiple sclerosis international.

[30]  M Filippi,et al.  Magnetisation transfer imaging: theory and application to multiple sclerosis. , 1998, Journal of neurology, neurosurgery, and psychiatry.

[31]  Rohit Bakshi,et al.  Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis , 2006, Neurology.

[32]  A. Lutti,et al.  Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. , 2015, Current opinion in neurology.

[33]  Hans Lassmann,et al.  Inflammatory central nervous system demyelination: Correlation of magnetic resonance imaging findings with lesion pathology , 1997, Annals of neurology.

[34]  Christian Enzinger,et al.  Predictive value of different conventional and non-conventional MRI-parameters for specific domains of cognitive function in multiple sclerosis , 2015, NeuroImage: Clinical.

[35]  C. Jacobsen,et al.  MRI evaluation of grey matter atrophy and disease course in multiple sclerosis: an overview of current knowledge , 2014, Acta neurologica Scandinavica. Supplementum.

[36]  Jie Luo,et al.  Detection and quantification of regional cortical gray matter damage in multiple sclerosis utilizing gradient echo MRI , 2015, NeuroImage: Clinical.

[37]  L. Leocani,et al.  Clinical and MRI assessment of brain damage in MS , 2001, Neurological Sciences.

[38]  B. Mädler,et al.  MR relaxation in multiple sclerosis. , 2009, Neuroimaging clinics of North America.

[39]  M. Calabrese,et al.  Widespread cortical thinning characterizes patients with MS with mild cognitive impairment , 2010, Neurology.

[40]  F. Jolesz,et al.  In vivo characterization of cytotoxic intracellular edema by multicomponent analysis of transverse magnetization decay curves. , 1995, Academic radiology.

[41]  Bruno Alfano,et al.  Simultaneous Display of Multiple MR Parameters with “Quantitative Magnetic Color Imaging” , 1992, Journal of computer assisted tomography.