Skip to main content Accessibility help
×
Home
  • Print publication year: 2011
  • Online publication date: December 2011

15 - Diffusion imaging in multiple sclerosis

from Section II - Clinical trial methodology

Summary

As the prevalence and functional consequences of multiple sclerosis (MS)-related cognitive dysfunction became more widely recognized, several definitive trials of disease-modifying medications for relapsing remitting MS and progressive MS incorporated neuropsychological (NP) outcome measures. This chapter lists clinical trials designed to assess the efficacy of medications as symptomatic treatment for cognitive impairment. Several factors complicate the assessment of NP outcomes in MS trials, although none is insurmountable. With the recent development of functional magnetic resonance imaging (fMRI), it has been possible to image MS patients while they perform cognitive tests in the scanner. In general, these fMRI studies have demonstrated that, even when cognitive testing is comparable to healthy controls, MS patients exhibit a larger number of activated regions, an increase in MR signal change and spatial extent in regions also activated by controls, and a decrease in laterality indices.

References

1. Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001; 13:534–46.
2. Castriota Scanderbeg A, Tomaiuolo F, Sabatini U, Nocentini U, Grasso MG, Caltagirone C. Demyelinating plaques in relapsing–remitting and secondary-progressive multiple sclerosis: assessment with diffusion MR imaging. Am J Neuroradiol 2000; 21:862–68.
3. Cercignani M, Iannucci G, Rocca MA, Comi G, Horsfield MA, Filippi M. Pathologic damage in MS assessed by diffusion-weighted and magnetization transfer MRI. Neurology 2000; 54:1139–44.
4. Christiansen P, Gideon P, Thomsen C, Stubgaard M, Henriksen O, Larsson HB. Increased water self-diffusion in chronic plaques and in apparently normal white matter in patients with multiple sclerosis. Acta Neurol Scand 1993; 87:195–9.
5. Filippi M, Iannucci G, Cercignani M, Assunta Rocca M, Pratesi A, Comi G. A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging. Arch Neurol 2000; 57:1017–21.
6. Nusbaum AO, Lu D, Tang CY, Atlas SW. Quantitative diffusion measurements in focal multiple sclerosis lesions: correlations with appearance on TI-weighted MR images. Am J Roentgenol 2000; 175:821–5.
7. Nusbaum AO, Tang CY, Wei T, Buchsbaum MS, Atlas SW. Whole-brain diffusion MR histograms differ between MS subtypes. Neurology 2000; 54:1421–7.
8. Rocca MA, Cercignani M, Iannucci G, Comi G, Filippi M. Weekly diffusion-weighted imaging of normal-appearing white matter in MS. Neurology 2000; 55:882–4.
9. Roychowdhury S, Maldjian JA, Grossman RI. Multiple sclerosis: comparison of trace apparent diffusion coefficients with MR enhancement pattern of lesions. Am J Neuroradiol 2000; 21:869–74.
10. Tievsky AL, Ptak T, Farkas J. Investigation of apparent diffusion coefficient and diffusion tensor anisotrophy in acute and chronic multiple sclerosis lesions. Am J Neuroradiol 1999; 20:1491–9.
11. Rocca MA, Mastronardo G, Rodegher M, Comi G, Filippi M. Long-term changes of magnetization transfer-derived measures from patients with relapsing-remitting and secondary progressive multiple sclerosis. Am J Neuroradiol 1999; 20:821–7.
12. Werring DJ, Brassat D, Droogan AG, et al. The pathogenesis of lesions and normal-appearing white matter changes in multiple sclerosis: a serial diffusion MRI study. Brain 2000; 123 (8):1667–76.
13. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med 2000; 44:625–32.
14. Conturo TE, Lori NF, Cull TS, et al. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci USA 1999; 96:10422–7.
15. Jones DK, Simmons A, Williams SC, Horsfield MA. Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn Reson Med 1999; 42:37–41.
16. Poupon C, Clark CA, Frouin V, et al. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. Neuroimage 2000; 12:184–95.
17. Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med 1999; 42:1123–7.
18. Bammer R, Augustin M, Strasser-Fuchs S, et al. Magnetic resonance diffusion tensor imaging for characterizing diffuse and focal white matter abnormalities in multiple sclerosis. Magn Reson Med 2000; 44:583–91.
19. Ciccarelli O, Werring DJ, Wheeler-Kingshott CA, et al. Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations. Neurology 2001; 56:926–33.
20. Filippi M, Cercignani M, Inglese M, Horsfield MA, Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 2001; 56:304–11.
21. Guo AC, MacFall JR, Provenzale JM. Multiple sclerosis: diffusion tensor MR imaging for evaluation of normal-appearing white matter. Radiology 2002; 222:729–36.
22. Werring DJ, Clark CA, Barker GJ, Thompson AJ, Miller DH. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 1999; 52:1626–32.
23. Fox RJ, Cronin T, Lin J, et al. Measuring myelin repair and axonal loss with diffusion tensor imaging. Am J Neuroradiol; 2011; 32:85–91.
24. Naismith RT, Xu J, Tutlam NT, et al. Increased diffusivity in acute multiple sclerosis lesions predicts risk of black hole. Neurology 2010; 74:1694–701.
25. Coombs BD, Best A, Brown MS, et al. Multiple sclerosis pathology in the normal and abnormal appearing white matter of the corpus callosum by diffusion tensor imaging. Mult Scler 2004; 10:392–7.
26. Ge Y, Law M, Johnson G, et al. Preferential occult injury of corpus callosum in multiple sclerosis measured by diffusion tensor imaging. J Magn Reson Imaging 2004; 20:1–7.
27. Guo AC, Jewells VL, Provenzale JM. Analysis of normal-appearing white matter in multiple sclerosis: comparison of diffusion tensor MR imaging and magnetization transfer imaging. Am J Neuroradiol 2001; 22:1893–900.
28. Kim JH, Budde MD, Liang HF, et al. Detecting axon damage in spinal cord from a mouse model of multiple sclerosis. Neurobiol Dis 2006; 21:626–32.
29. Rocca MA, Iannucci G, Rovaris M, Comi G, Filippi M. Occult tissue damage in patients with primary progressive multiple sclerosis is independent of T2-visible lesions–a diffusion tensor MR study. J Neurol 2003; 250:456–60.
30. Rovaris M, Bozzali M, Iannucci G, et al. Assessment of normal-appearing white and gray matter in patients with primary progressive multiple sclerosis: a diffusion-tensor magnetic resonance imaging study. Arch Neurol 2002; 59:1406–412.
31. Caramia MD, Palmieri MG, Desiato MT, et al. Brain excitability changes in the relapsing and remitting phases of multiple sclerosis: a study with transcranial magnetic stimulation. Clin Neurophysiol 2004; 115:956–65.
32. Rovaris M, Iannucci G, Falautano M, et al. Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis: an exploratory study with diffusion tensor MR imaging. J Neurol Sci 2002; 195:103–9.
33. Absinta M, Rocca MA, Moiola L, et al. Brain macro- and microscopic damage in patients with paediatric MS. J Neurol Neurosurg Psychiatry 2010; 81:1357–62.
34. Raz E, Cercignani M, Sbardella E, et al. Gray- and white-matter changes 1 year after first clinical episode of multiple sclerosis: MR imaging. Radiology 2010; 257:448–54.
35. Raz E, Cercignani M, Sbardella E, et al. Clinically isolated syndrome suggestive of multiple sclerosis: voxelwise regional investigation of white and gray matter. Radiology 2010; 254:227–34.
36. Caramia F, Pantano P, Di Legge S, et al. A longitudinal study of MR diffusion changes in normal appearing white matter of patients with early multiple sclerosis. Magn Reson Imaging 2002; 20:383–8.
37. Ranjeva JP, Pelletier J, Confort-Gouny S, et al. MRI/MRS of corpus callosum in patients with clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler 2003; 9:554–65.
38. Bester M, Heesen C, Schippling S, et al. Early anisotropy changes in the corpus callosum of patients with optic neuritis. Neuroradiology 2008; 50:549–57.
39. Rovaris M, Judica E, Ceccarelli A, et al. A 3-year diffusion tensor MRI study of grey matter damage progression during the earliest clinical stage of MS. J Neurol 2008; 255:1209–14.
40. Yu CS, Lin FC, Liu Y, Duan Y, Lei H, Li KC. Histogram analysis of diffusion measures in clinically isolated syndromes and relapsing-remitting multiple sclerosis. Eur J Radiol 2008; 68:328–34.
41. Henry RG, Shieh M, Amirbekian B, Chung S, Okuda DT, Pelletier D. Connecting white matter injury and thalamic atrophy in clinically isolated syndromes. J Neurol Sci 2009; 282:61–6.
42. Rocca MA, Ceccarelli A, Rodegher M, et al. Preserved brain adaptive properties in patients with benign multiple sclerosis. Neurology 2010; 74:142–9.
43. Ceccarelli A, Rocca MA, Pagani E, et al. The topographical distribution of tissue injury in benign MS: a 3T multiparametric MRI study. Neuroimage 2008; 39:1499–509.
44. Rovaris M, Riccitelli G, Judica E, et al. Cognitive impairment and structural brain damage in benign multiple sclerosis. Neurology 2008; 71:1521–6.
45. Ceccarelli A, Filippi M, Neema M, et al. T2 hypointensity in the deep gray matter of patients with benign multiple sclerosis. Mult Scler 2009; 15:678–86.
46. Cercignani M, Inglese M, Pagani E, Comi G, Filippi M. Mean diffusivity and fractional anisotropy histograms of patients with multiple sclerosis. Am J Neuroradiol 2001; 22:952–8.
47. Rashid W, Hadjiprocopis A, Griffin CM, et al. Diffusion tensor imaging of early relapsing–remitting multiple sclerosis with histogram analysis using automated segmentation and brain volume correction. Mult Scler 2004; 10:9–15.
48. Rocca MA, Falini A, Colombo B, Scotti G, Comi G, Filippi M. Adaptive functional changes in the cerebral cortex of patients with nondisabling multiple sclerosis correlate with the extent of brain structural damage. Ann Neurol 2002; 51:330–9.
49. Poonawalla AH, Hasan KM, Gupta RK, et al. Diffusion-tensor MR imaging of cortical lesions in multiple sclerosis: initial findings. Radiology 2008; 246:880–6.
50. Pulizzi A, Rovaris M, Judica E, et al. Determinants of disability in multiple sclerosis at various disease stages: a multiparametric magnetic resonance study. Arch Neurol 2007; 64:1163–8.
51. Vrenken H, Pouwels PJ, Geurts JJ, et al. Altered diffusion tensor in multiple sclerosis normal-appearing brain tissue: cortical diffusion changes seem related to clinical deterioration. J Magn Reson Imaging 2006; 23:628–36.
52. Rovaris M, Judica E, Gallo A, et al. Grey matter damage predicts the evolution of primary progressive multiple sclerosis at 5 years. Brain 2006; 129:2628–34.
53. Rovaris M, Gallo A, Valsasina P, et al. Short-term accrual of gray matter pathology in patients with progressive multiple sclerosis: an in vivo study using diffusion tensor MRI. Neuroimage 2005; 24:1139–46.
54. Oreja-Guevara C, Rovaris M, Iannucci G, et al. Progressive gray matter damage in patients with relapsing-remitting multiple sclerosis: a longitudinal diffusion tensor magnetic resonance imaging study. Arch Neurol 2005; 62:578–84.
55. Rocca MA, Pagani E, Ghezzi A, et al. Functional cortical changes in patients with multiple sclerosis and nonspecific findings on conventional magnetic resonance imaging scans of the brain. Neuroimage 2003; 19:826–36.
56. Cercignani M, Bammer R, Sormani MP, Fazekas F, Filippi M. Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers. Am J Neuroradiol 2003; 24:638–43.
57. Bozzali M, Cercignani M, Sormani MP, Comi G, Filippi M. Quantification of brain gray matter damage in different MS phenotypes by use of diffusion tensor MR imaging. Am J Neuroradiol 2002; 23:985–8.
58. Zhou F, Zee CS, Gong H, Shiroishi M, Li J. Differential changes in deep and cortical gray matters of patients with multiple sclerosis: a quantitative magnetic resonance imaging study. J Comput Assist Tomogr 2010 34:431–6.
59. Hecke WV, Nagels G, Leemans A, Vandervliet E, Sijbers J, Parizel PM. Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis. J Magn Reson Imaging; 31:1492–98.
60. Bodini B, Khaleeli Z, Cercignani M, Miller DH, Thompson AJ, Ciccarelli O. Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: an in vivo study with TBSS and VBM. Hum Brain Mapp 2009; 30:2852–61.
61. 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.
62. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 2005; 26:546–54.
63. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010; 23:803–20.
64. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006; 31:1487–505.
65. Roosendaal SD, Schoonheim MM, Hulst HE, et al. Resting state networks change in clinically isolated syndrome. Brain 2010; 133:1612–21.
66. Cader S, Johansen-Berg H, Wylezinska M, et al. Discordant white matter N-acetylasparate and diffusion MRI measures suggest that chronic metabolic dysfunction contributes to axonal pathology in multiple sclerosis. Neuroimage 2007; 36:19–27.
67. Dineen RA, Vilisaar J, Hlinka J, et al. Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain 2009; 132:239–49.
68. Schmierer K, Wheeler-Kingshott CA, Boulby PA, et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 2007; 35:467–77.
69. Drobyshevsky A, Song SK, Gamkrelidze G, et al. Developmental changes in diffusion anisotropy coincide with immature oligodendrocyte progression and maturation of compound action potential. J Neurosci 2005; 25:5988–97.
70. Song SK, Kim JH, Lin SJ, Brendza RP, Holtzman DM. Diffusion tensor imaging detects age-dependent white matter changes in a transgenic mouse model with amyloid deposition. Neurobiol Dis 2004; 15:640–7.
71. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 2003; 20:1714–22.
72. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 2002; 17:1429–36.
73. Song SK, Yoshino J, Le TQ, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005; 26:132–40.
74. Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK. Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn Reson Med 2006; 55:302–8.
75. Sun SW, Neil JJ, Song SK. Relative indices of water diffusion anisotropy are equivalent in live and formalin-fixed mouse brains. Magn Reson Med 2003; 50:743–8.
76. Concha L, Gross DW, Wheatley BM, Beaulieu C. Diffusion tensor imaging of time-dependent axonal and myelin degradation after corpus callosotomy in epilepsy patients. Neuroimage 2006; 32:1090–9.
77. Beaulieu C, Does MD, Snyder RE, Allen PS. Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med 1996; 36:627–31.
78. Pierpaoli C, Barnett A, Pajevic S, et al. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage 2001; 13:1174–85.
79. Henry RG, Oh J, Nelson SJ, Pelletier D. Directional diffusion in relapsing-remitting multiple sclerosis: a possible in vivo signature of Wallerian degeneration. J Magn Reson Imaging 2003; 18:420–6.
80. Oh J, Henry RG, Genain C, Nelson SJ, Pelletier D. Mechanisms of normal appearing corpus callosum injury related to pericallosal T1 lesions in multiple sclerosis using directional diffusion tensor and 1H MRS imaging. J Neurol Neurosurg Psychiatry 2004; 75:1281–6.
81. Assaf Y, Chapman J, Ben-Bashat D, et al. White matter changes in multiple sclerosis: correlation of q-space diffusion MRI and (1)H MRS. Magn Reson Imaging 2005; 23:703–10.
82. Sijens PE, Irwan R, Potze JH, Mostert JP, De Keyser J, Oudkerk M. Analysis of the human brain in primary progressive multiple sclerosis with mapping of the spatial distributions using 1H MR spectroscopy and diffusion tensor imaging. Eur Radiol 2005; 15:1686–93.
83. Hickman SJ, Wheeler-Kingshott CA, Jones SJ, et al. Optic nerve diffusion measurement from diffusion-weighted imaging in optic neuritis. Am J Neuroradiol 2005; 26:951–6.
84. Trip SA, Wheeler-Kingshott C, Jones SJ, et al. Optic nerve diffusion tensor imaging in optic neuritis. Neuroimage 2006; 30:498–505.
85. Wheeler-Kingshott CA, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med 2009; 61:1255–60.
86. Lowe MJ, Beall EB, Sakaie KE, et al. Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum Brain Mapp 2008; 29:818–27.
87. Lowe MJ, Horenstein C, Hirsch JG, et al. Functional pathway-defined MRI diffusion measures reveal increased transverse diffusivity of water in multiple sclerosis. Neuroimage 2006; 32:1127–33.
88. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999; 45:265–9.
89. Mori S, van Zijl PC. Fiber tracking: principles and strategies – a technical review. NMR Biomed 2002; 15:468–80.
90. Tench CR, Morgan PS, Wilson M, Blumhardt LD. White matter mapping using diffusion tensor MRI. Magn Reson Med 2002; 47:967–72.
91. Vaithianathar L, Tench CR, Morgan PS, Wilson M, Blumhardt LD. T1 relaxation time mapping of white matter tracts in multiple sclerosis defined by diffusion tensor imaging. J Neurol 2002; 249:1272–8.
92. Pagani E, Bammer R, Horsfield MA, et al. Diffusion MR imaging in multiple sclerosis: technical aspects and challenges. Am J Neuroradiol 2007; 28:411–20.
93. Pagani E, Filippi M, Rocca MA, Horsfield MA. A method for obtaining tract-specific diffusion tensor MRI measurements in the presence of disease: application to patients with clinically isolated syndromes suggestive of multiple sclerosis. Neuroimage 2005; 26:258–65.
94. Lin F, Yu C, Jiang T, Li K, Chan P. Diffusion tensor tractography-based group mapping of the pyramidal tract in relapsing-remitting multiple sclerosis patients. Am J Neuroradiol 2007; 28:278–82.
95. Rocca MA, Absinta M, Valsasina P, et al. Abnormal connectivity of the sensorimotor network in patients with MS: a multicenter fMRI study. Hum Brain Mapp 2009; 30:2412–25.
96. Rocca MA, Pagani E, Absinta M, et al. Altered functional and structural connectivities in patients with MS: a 3-T study. Neurology 2007; 69:2136–45.
97. Rocca MA, Valsasina P, Ceccarelli A, et al. Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Mapp 2009; 30:276–90.
98. Pine AB, Jones S, Lowe MJ, Sakaie K, Phillips MD. Fiber-tracking through multiple sclerosis lesions using probabilistic tracking. In 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine. Honolulu, 2009.
99. Wilson M, Tench CR, Morgan PS, Blumhardt LD. Pyramidal tract mapping by diffusion tensor magnetic resonance imaging in multiple sclerosis: improving correlations with disability. J Neurol Neurosurg Psychiatry 2003; 74:203–7.
100. Audoin B, Guye M, Reuter F, et al. Structure of WM bundles constituting the working memory system in early multiple sclerosis: a quantitative DTI tractography study. Neuroimage 2007; 36:1324–30.
101. Bonzano L, Tacchino A, Roccatagliata L, Abbruzzese G, Mancardi GL, Bove M. Callosal contributions to simultaneous bimanual finger movements. J Neurosci 2008; 28:3227–33.
102. Bonzano L, Tacchino A, Roccatagliata L, Mancardi GL, Abbruzzese G, Bove M. Structural integrity of callosal midbody influences intermanual transfer in a motor reaction-time task. Hum Brain Mapp 2011; 32:218–28.
103. Fox RJ, McColl RW, Lee JC, Frohman T, Sakaie K, Frohman E. A preliminary validation study of diffusion tensor imaging as a measure of functional brain injury. Arch Neurol 2008; 65:1179–84.
104. Freund P, Wheeler-Kingshott C, Jackson J, Miller D, Thompson A, Ciccarelli O. Recovery after spinal cord relapse in multiple sclerosis is predicted by radial diffusivity. Mult Scler 2010; 16:1193–202.
105. Kolbe S, Chapman C, Nguyen T, et al. Optic nerve diffusion changes and atrophy jointly predict visual dysfunction after optic neuritis. Neuroimage 2009; 45:679–86.
106. Lenzi D, Conte A, Mainero C, et al. Effect of corpus callosum damage on ipsilateral motor activation in patients with multiple sclerosis: a functional and anatomical study. Hum Brain Mapp 2007; 28:636–44.
107. Roca M, Torralva T, Meli F, et al. Cognitive deficits in multiple sclerosis correlate with changes in fronto-subcortical tracts. Mult Scler 2008; 14:364–9.
108. Warlop NP, Achten E, Debruyne J, Vingerhoets G. Diffusion weighted callosal integrity reflects interhemispheric communication efficiency in multiple sclerosis. Neuropsychologia 2008; 46:2258–64.
109. Fink F, Eling P, Rischkau E, et al. The association between California Verbal Learning Test performance and fibre impairment in multiple sclerosis: evidence from diffusion tensor imaging. Mult Scler 2010; 16:332–41.
110. Ozturk A, Smith SA, Gordon-Lipkin EM, et al. MRI of the corpus callosum in multiple sclerosis: association with disability. Mult Scler 2010; 16:166–77.
111. Lin X, Tench CR, Morgan PS, Constantinescu CS. Use of combined conventional and quantitative MRI to quantify pathology related to cognitive impairment in multiple sclerosis. J Neurol Neurosurg Psychiatry 2008; 79:437–41.
112. Bonzano L, Pardini M, Mancardi GL, Pizzorno M, Roccatagliata L. Structural connectivity influences brain activation during PVSAT in Multiple Sclerosis. Neuroimage 2009; 44:9–15.
113. Ceccarelli A, Rocca MA, Valsasina P, et al. Structural and functional magnetic resonance imaging correlates of motor network dysfunction in primary progressive multiple sclerosis. Eur J Neurosci 2010; 31:1273–80.
114. Rocca MA, Valsasina P, Absinta M, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010; 74:1252–9.
115. Altmann DR, Jasperse B, Barkhof F, et al. Sample sizes for brain atrophy outcomes in trials for secondary progressive multiple sclerosis. Neurology 2009; 72:595–601.
116. Anderson VM, Bartlett JW, Fox NC, Fisniku L, Miller DH. Detecting treatment effects on brain atrophy in relapsing remitting multiple sclerosis: sample size estimates. J Neurol 2007; 254:1588–94.
117. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002; 17:479–89.
118. Pelletier D, Garrison K, Henry R. Measurement of whole-brain atrophy in multiple sclerosis. J Neuroimaging 2004; 14:11S–9S.
119. Jasperse B, Valsasina P, Neacsu V, et al. Intercenter agreement of brain atrophy measurement in multiple sclerosis patients using manually-edited SIENA and SIENAX. J Magn Reson Imaging 2007; 26:881–5.
120. Sharma S, Noblet V, Rousseau F, Heitz F, Rumbach L, Armspach JP. Evaluation of brain atrophy estimation algorithms using simulated ground-truth data. Med Image Anal 2010; 14:373–89.
121. Neacsu V, Jasperse B, Korteweg T, et al. Agreement between different input image types in brain atrophy measurement in multiple sclerosis using SIENAX and SIENA. J Magn Reson Imaging 2008; 28:559–65.
122. Bisdas S, Bohning DE, Besenski N, Nicholas JS, Rumboldt Z. Reproducibility, interrater agreement, and age-related changes of fractional anisotropy measures at 3T in healthy subjects: effect of the applied b-value. Am J Neuroradiol 2008; 29:1128–33.
123. Bonekamp D, Nagae LM, Degaonkar M, et al. Diffusion tensor imaging in children and adolescents: reproducibility, hemispheric, and age-related differences. Neuroimage 2007; 34:733–42.
124. Ciccarelli O, Parker GJ, Toosy AT, et al. From diffusion tractography to quantitative white matter tract measures: a reproducibility study. Neuroimage 2003; 18:348–59.
125. Fox RJ, Sakaie K, Lee JC, et al. A validation study of multi-centre diffusion tensor imaging. Mult Scler 2007; 13:S76.
126. Heiervang E, Behrens TE, Mackay CE, Robson MD, Johansen-Berg H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage 2006; 33:867–77.
127. Jansen JF, Kooi ME, Kessels AG, Nicolay K, Backes WH. Reproducibility of quantitative cerebral T2 relaxometry, diffusion tensor imaging, and 1H magnetic resonance spectroscopy at 3.0 Tesla. Invest Radiol 2007; 42:327–37.
128. Pfefferbaum A, Adalsteinsson E, Sullivan EV. Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain. J Magn Reson Imaging 2003; 18:427–33.
129. Vollmar C, O’Muircheartaigh J, Barker GJ, et al. Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners. Neuroimage 2010; 51:1384–94.
130. Pagani E, Hirsch JG, Pouwels PJ, et al. Intercenter differences in diffusion tensor MRI acquisition. J Magn Reson Imaging 2010; 31:1458–68.
131. Harrison DM, Caffo BS, Shiee N, et al. Longitudinal changes in diffusion tensor-based quantitative MRI in multiple sclerosis. Neurology 2011; 76:179–86.