Innovations In Clinical Neuroscience

HOTTOP Multiple Sclerosis MAR 2018

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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R E V I E W 19 Hot Topics in Multiple Sclerosis [March 2018] Neurology. 2007;69:1213–23. 27. Mesaros S, Rocca MA, Absinta M, et al. Evidence of thalamic gray matter loss in pediatric multiple sclerosis. Neurology. 2008;70:1107–12. 28. Rocca MA, Mesaros S, Pagani E, et al. Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology. 2010;257:463– 9. 29. Chen JT, Narayanan S, Collins DL, et al. Relating neocortical pathology to disability progression in multiple sclerosis using MRI. NeuroImage. 2004;23:1168–75. 30. Pagani E, Rocca MA, Gallo A, et al. Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. AJNR Am J Neuroradiol. 2005;26:341–6. 31. Tedeschi G, Lavorgna L, Russo P, et al. Brain atrophy and lesion load in a large population of patients with multiple sclerosis. Neurology. 2005;65:280–5. 32. Benedict RH, Bruce JM, Dwyer MG, et al. Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Arch Neurol. 2006;63:1301–6. 33. Benedict RH, Bruce JM, Dwyer MG, et al. Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis—a longitudinal voxel-based morphometry study. NeuroImage. 2009;45:60–7. 34. Riccitelli G, Rocca MA, Pagani E, et al. Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp. 2011;32:1535–43. 35. Roosendaal SD, Bendfeldt K, Vrenken H, et al. Gray matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult Scler. 2011;17:1098– 106. 36. Fisher E, Lee JC, Nakamura K, Rudick RA. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol. 2008;64:255–65. 37. Ceccarelli A, Rocca MA, Valsasina P, et al. A multiparametric evaluation of regional brain damage in patients with primary progressive multiple sclerosis. Hum Brain Mapp. 2009;30:3009–19. 38. Steenwijk MD, Daams M, Pouwels PJ, et al. What explains gray matter atrophy in long-standing multiple sclerosis? Radiology. 2014;272:832–42. 39. Smith S, Zhang Y, Jenkinson M, et al. SIENA: Single and multiple time point brain atrophy analysis. NeuroImage. 2001;13:250. 40. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage. 2002;17:479–89. 41. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage. 2004;23:S208–19. 42. Jenkinson M, Beckmann CF, Behrens TE, et al. FSL. NeuroImage. 2012;62:782–90. 43. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation- maximization algorithm. IEEE Trans Med Imaging. 2001;20:45–57. 44. Smith SM, De Stefano N, Jenkinson M, Matthews PM. Normalized accurate measurement of longitudinal brain change. J Comput Assist Tomogr. 2001;25:466–75. 45. Rudick RA, Fisher E, Lee JC, et al. Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Multiple Sclerosis Collaborative Research Group. Neurology. 1999;53:1698–170. 46. Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol. 2014;75:43–9. 47. Ashburner J, Friston KJ. Voxel- based morphometry-the methods. NeuroImage. 2000;11(6 Pt 1):805– 21. 48. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA. 2000;97:11050–5. 49. Jasperse B, Valsasina P, Neacsu V, et al. Magnetic imaging in multiple sclerosis (MAGNIMS) study group. Intercenter agreement of brain atrophy measurement in multiple sclerosis patients using manually- edited SIENA and SIENAX. J Magn Reson Imaging. 2007;26:881–5. 50. Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005;26:839–51. 51. Fischl B. FreeSurfer. NeuroImage. 2012;62:774–81. 52. Das SR, Avants BB, Grossman M, Gee JC. Registration based cortical thickness measurement. NeuroImage. 2009;45:867–79. 53. Tustison NJ, Cook PA, Klein A, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. NeuroImage. 2014;99:166–79. 54. Dalton CM, Chard DT, Davies GR, et al. Early development of multiple sclerosis is associated with progressive gray matter atrophy in patients presenting with clinically isolated syndromes. Brain. 2004;127:1101–7. 55. Fisniku LK, Chard DT, Jackson JS, et al. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol. 2008;64:247– 54. 56. Calabrese M, Romualdi C, Poretto V, et al. The changing clinical course of multiple sclerosis: a matter of gray matter. Ann Neurol. 2013;74:76–83. 57. Pérez-Miralles F, Sastre-Garriga J, Tintoré M, et al. Clinical impact of early brain atrophy in clinically isolated syndromes. Mult Scler. 2013;19:1878–86. 58. De Stefano N, Matthews PM, Filippi M, et al. Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology. 2003;60:1157–62. 59. Sanfilipo MP, Benedict RH, Sharma J, et al. The relationship betweenwhole brain volumeand disability in multiple sclerosis: a comparison of normalized gray vs. white matter with misclassification correction. NeuroImage. 2005;26:1068–77. 60. Anderson VM, Fisniku LK, Altmann DR, et al. MRI measures show significant cerebellar gray matter volume loss in multiple sclerosis and are associated with cerebellar dysfunction. Mult Scler. 2009;15:811–7.

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