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  • Review Article
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Mind the gap: from neurons to networks to outcomes in multiple sclerosis

Abstract

MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.

Key points

  • Multiple sclerosis (MS) pathology affects neuroaxonal structure (for example, via axonal transection) and function (for example, via demyelination), and does so in both lesions and extra-lesional tissues.

  • Multiple pathological processes can combine to affect neural network function.

  • In individuals with MS, white matter tracts are disrupted by lesions, the cortex is relatively disconnected and the grey matter network topology is more random than in healthy controls.

  • Increases and decreases in connectivity, centred around hubs such as the thalamus and default-mode network, are seen; abnormal connectivity seems to evolve rather than simply progress over time, for example, early increases can be followed by later decreases.

  • Both structural and functional network changes are associated with clinical outcomes, and treatments that modify neural network function — not structure — might still have a beneficial effect.

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Fig. 1: From neurons to clinical outcomes in MS.

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References

  1. Fornito, A. & Bullmore, E. T. Connectomics – a new paradigm for understanding brain disease. Eur. Neuropsychopharmacol. 25, 733–748 (2015).

    CAS  PubMed  Google Scholar 

  2. Lassmann, H. Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8, a028936 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. Correale, J., Marrodan, M. & Benarroch, E. E. What is the role of axonal ion channels in multiple sclerosis? Neurology 95, 120–123 (2020).

    PubMed  Google Scholar 

  4. Campbell, G., Licht-Mayer, S. & Mahad, D. Targeting mitochondria to protect axons in progressive MS. Neurosci. Lett. 710, 134258 (2019).

    CAS  PubMed  Google Scholar 

  5. Lapointe, E., Li, D. K. B., Traboulsee, A. L. & Rauscher, A. What have we learned from perfusion MRI in multiple sclerosis? AJNR Am. J. Neuroradiol. 39, 994–1000 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Deuschl, G. et al. The burden of neurological diseases in Europe: an analysis for the Global Burden of Disease Study 2017. Lancet Public Health 5, e551–e567 (2020).

    PubMed  Google Scholar 

  7. Barkhof, F. The clinico-radiological paradox in multiple sclerosis revisited. Curr. Opin. Neurol. 15, 239–245 (2002).

    PubMed  Google Scholar 

  8. Fox, R. J. et al. Phase 2 trial of ibudilast in progressive multiple sclerosis. N. Engl. J. Med. 379, 846–855 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Sumowski, J. F. et al. Cognition in multiple sclerosis. Neurology 90, 278–288 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. McDonald, W. I., Miller, D. H. & Thompson, A. J. Are magnetic resonance findings predictive of clinical outcome in therapeutic trials in multiple sclerosis? The dilemma of interferon-beta. Ann. Neurol. 36, 14–18 (1994).

    CAS  PubMed  Google Scholar 

  11. Goodin, D. S. Magnetic resonance imaging as a surrogate outcome measure of disability in multiple sclerosis: have we been overly harsh in our assessment? Ann. Neurol. 59, 597–605 (2006).

    PubMed  Google Scholar 

  12. Kurtzke, J. F. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1452 (1983).

    CAS  Google Scholar 

  13. Tintore, M. et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain 138, 1863–1874 (2015).

    PubMed  Google Scholar 

  14. Ayache, S. S. & Chalah, M. A. Fatigue in multiple sclerosis – insights into evaluation and management. Neurophysiol. Clin. 47, 139–171 (2017).

    PubMed  Google Scholar 

  15. Manjaly, Z. M. et al. Pathophysiological and cognitive mechanisms of fatigue in multiplesclerosis. J. Neurol. Neurosurg. Psychiatry 90, 642–651 (2019).

    PubMed  PubMed Central  Google Scholar 

  16. Bertoli, M. & Tecchio, F. Fatigue in multiple sclerosis: does the functional or structural damage prevail? Mult. Scler. https://doi.org/10.1177/1352458520912175 (2020).

    Article  PubMed  Google Scholar 

  17. Martínez-Lapiscina, E. H. et al. The visual pathway as a model to understand brain damage in multiple sclerosis. Mult. Scler. 20, 1678–1685 (2014).

    PubMed  Google Scholar 

  18. Trapp, B. D. et al. Axonal transection in the lesions of multiple sclerosis. N. Engl. J. Med. 338, 278–285 (1998).

    CAS  PubMed  Google Scholar 

  19. Bodini, B. et al. White and gray matter damage in primary progressive MS: the chicken or the egg? Neurology 86, 170–176 (2015).

    PubMed  Google Scholar 

  20. Audoin, B. et al. Selective magnetization transfer ratio decrease in the visual cortex following optic neuritis. Brain 129, 1031–1039 (2006).

    PubMed  Google Scholar 

  21. Singh, S. et al. Relationship of acute axonal damage, Wallerian degeneration, and clinical disability in multiple sclerosis. J. Neuroinflammation 4, 57 (2017).

    Google Scholar 

  22. Allen, I. V., McQuaid, S., Mirakhur, M. & Nevin, G. Pathological abnormalities in the normal-appearing white matter in multiple sclerosis. Neurol. Sci. 22, 141–144 (2001).

    CAS  PubMed  Google Scholar 

  23. Bø, L., Vedeler, C. A., Nyland, H. I., Trapp, B. D. & Mørk, S. J. Subpial demyelination in the cerebral cortex of multiple sclerosis patients. J. Neuropathol. Exp. Neurol. 62, 723–732 (2003).

    PubMed  Google Scholar 

  24. Jürgens, T. et al. Reconstruction of single cortical projection neurons reveals primary spine loss in multiple sclerosis. Brain 139, 39–46 (2016).

    PubMed  Google Scholar 

  25. Magliozzi, R. et al. A Gradient of neuronal loss and meningeal inflammation in multiple sclerosis. Ann. Neurol. 68, 477–493 (2010).

    CAS  PubMed  Google Scholar 

  26. Henry, R. G. et al. Regional grey matter atrophy in clinically isolated syndromes at presentation. J. Neurol. Neurosurg. Psychiatry 79, 1236–1244 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Cifelli, A. et al. Thalamic neurodegeneration in multiple sclerosis. Ann. Neurol. 52, 650–653 (2002).

    PubMed  Google Scholar 

  28. Bell, P. T. & Shine, J. M. Subcortical contributions to large-scale network communication. Neurosci. Biobehav. Rev. 71, 313–322 (2016).

    PubMed  Google Scholar 

  29. Lin, F. et al. Altered nuclei-specific thalamic functional connectivity patterns in multiple sclerosis and their associations with fatigue and cognition. Mult. Scler. 25, 1243–1254 (2019).

    PubMed  Google Scholar 

  30. Paling, D. et al. Cerebral arterial bolus arrival time is prolonged in multiple sclerosis and associated with disability. J. Cereb. Blood Flow. Metab. 34, 34–42 (2013).

    PubMed  PubMed Central  Google Scholar 

  31. Roostaei, T. et al. Channelopathy-related SCN10A gene variants predict cerebellar dysfunction in multiple sclerosis. Neurology 86, 410–417 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Desai, R. A. et al. Cause and prevention of demyelination in a model multiple sclerosis lesion. Ann. Neurol. 79, 591–604 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Fan, A. P. et al. Quantitative oxygen extraction fraction from 7-Tesla MRI phase: reproducibility and application in multiple sclerosis. J. Cereb. Blood Flow. Metab. 35, 131–139 (2014).

    PubMed  PubMed Central  Google Scholar 

  34. Rocca, M. A. et al. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol. 14, 302–317 (2015).

    PubMed  Google Scholar 

  35. Roosendaal, S. D. et al. Grey matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult. Scler. 17, 1098–1106 (2011).

    PubMed  Google Scholar 

  36. Fisher, E., Lee, J.-C., Nakamura, K. & Rudick, R. A. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann. Neurol. 64, 255–265 (2008).

    PubMed  Google Scholar 

  37. Filippi, M. et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology 81, 1759–1767 (2013).

    PubMed  Google Scholar 

  38. Eijlers, A. J. C. et al. Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study. Brain 141, 2605–2618 (2018).

    PubMed  Google Scholar 

  39. Barkhof, F., Haller, S. & Rombouts, S. A. Resting-state functional MR imaging: a new window to the brain. Radiology 272, 29–49 (2014).

    PubMed  Google Scholar 

  40. Alonso-Nanclares, L., Gonzalez-Soriano, J., Rodriguez, J. R. & DeFelipe, J. Gender differences in human cortical synaptic density. Proc. Natl Acad. Sci. USA 105, 14615–14619 (2008).

    CAS  PubMed  Google Scholar 

  41. Carp, J. The secret lives of experiments: methods reporting in the fMRI literature. Neuroimage 63, 289–300 (2012).

    PubMed  Google Scholar 

  42. Puce, A. & Hämäläinen, M. A review of issues related to data acquisition and analysis in EEG/MEG studies. Brain Sci. 7, 58 (2017).

    PubMed Central  Google Scholar 

  43. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).

    PubMed  Google Scholar 

  44. Schmierer, K. et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 35, 467–477 (2007).

    PubMed  PubMed Central  Google Scholar 

  45. Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J. & Miller, D. H. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415 (2004).

    PubMed  Google Scholar 

  46. Thiebaut de Schotten, M. et al. From Phineas Gage and Monsieur Leborgne to H.M.: revisiting disconnection syndromes. Cereb. Cortex 25, 4812–4827 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Friston, K. J. et al. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage 6, 218–229 (1997).

    CAS  PubMed  Google Scholar 

  48. McIntosh, A. R. & Gonzalez-Lima, F. Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system. Brain Res. 547, 295–302 (1991).

    CAS  PubMed  Google Scholar 

  49. McIntosh, A. R. & Gonzalez-Lima, F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2, 2–22 (1994).

    Google Scholar 

  50. Buchel, C. & Friston, K. J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7, 768–778 (1997).

    CAS  PubMed  Google Scholar 

  51. Penny, W. D., Stephan, K. E., Mechelli, A. & Friston, K. J. Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23 (Suppl. 1), S264–S274 (2004).

    PubMed  Google Scholar 

  52. Tijms, B. M., Series, P., Willshaw, D. J. & Lawrie, S. M. Similarity-based extraction of individual networks from gray matter MRI scans. Cereb. Cortex 22, 1530–1541 (2012).

    PubMed  Google Scholar 

  53. Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17–33 (2018).

    CAS  Google Scholar 

  54. Enzinger, C. et al. Longitudinal fMRI studies: exploring brain plasticity and repair in MS. Mult. Scler. 22, 269–278 (2016).

    CAS  PubMed  Google Scholar 

  55. Fleischer, V. et al. Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis. Mult. Scler. 23, 432–441 (2017).

    PubMed  Google Scholar 

  56. Cope, E. C. & Gould, E. Adult neurogenesis, glia, and the extracellular matrix. Cell Stem Cell 24, 690–705 (2019).

    CAS  PubMed  Google Scholar 

  57. Pardini, M. et al. Motor network efficiency and disability in multiple sclerosis. Neurology 85, 1115–1122 (2015).

    PubMed  PubMed Central  Google Scholar 

  58. Steenwijk, M. D. et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 139, 115–126 (2016).

    PubMed  Google Scholar 

  59. Cercignani, M. & Gandini Wheeler-Kingshott, C. From micro- to macro-structures in multiple sclerosis: what is the added value of diffusion imaging. NMR Biomed. 32, e3888 (2019).

    PubMed  Google Scholar 

  60. Chen, J. E., Rubinov, M. & Chang, C. Methods and considerations for dynamic analysis of functional MR imaging data. Neuroimaging Clin. N. Am. 27, 547–560 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. Tewarie, P. et al. Disruption of structural and functional networks in long-standing multiple sclerosis. Hum. Brain Mapp. 35, 5946–5961 (2014).

    PubMed  PubMed Central  Google Scholar 

  62. Pantano, P., Petsas, N., Tona, F. & Sbardella, E. The role of fMRI to assess plasticity of the motor system in MS. Front. Neurol. 6, 55 (2015).

    PubMed  PubMed Central  Google Scholar 

  63. Roosendaal, S. D. et al. Resting state networks change in clinically isolated syndrome. Brain 133, 1612–1621 (2010).

    PubMed  Google Scholar 

  64. Faivre, A. et al. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult. Scler. 18, 1251–1258 (2012).

    PubMed  Google Scholar 

  65. Rocca, M. A. et al. Functional and structural connectivity of the motor network in pediatric and adult-onset relapsing-remitting multiple sclerosis. Radiology 254, 541–550 (2010).

    PubMed  Google Scholar 

  66. Rocca, M. A. et al. Functional network connectivity abnormalities in multiple sclerosis: Correlations with disability and cognitive impairment. Mult. Scler. 24, 459–471 (2018).

    PubMed  Google Scholar 

  67. Liu, Y. et al. Functional brain network alterations in clinically isolated syndrome and multiple sclerosis: a graph-based connectome study. Radiology 282, 534–541 (2017).

    PubMed  Google Scholar 

  68. Faivre, A. et al. Depletion of brain functional connectivity enhancement leads to disability progression in multiple sclerosis: a longitudinal resting-state fMRI study. Mult. Scler. 22, 1695–1708 (2016).

    PubMed  Google Scholar 

  69. Eijlers, A. J. C. et al. Reduced network dynamics on functional mri signals cognitive impairment in multiple sclerosis. Radiology 292, 449–457 (2019).

    PubMed  Google Scholar 

  70. Bisecco, A. et al. Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization. Mult. Scler. 24, 1696–1705 (2018).

    PubMed  Google Scholar 

  71. Schoonheim, M. M., Meijer, K. A. & Geurts, J. J. G. Network collapse and cognitive impairment in multiple sclerosis. Front. Neurol. 6, 82 (2015).

    PubMed  PubMed Central  Google Scholar 

  72. Kipp, M. et al. Thalamus pathology in multiple sclerosis: from biology to clinical application. Cell Mol. Life Sci. 72, 1127–1147 (2014).

    PubMed  Google Scholar 

  73. Castellazzi, G. et al. Functional connectivity alterations reveal complex mechanisms based on clinical and radiological status in mild relapsing remitting multiple sclerosis. Front. Neurol. 9, 690 (2018).

    PubMed  PubMed Central  Google Scholar 

  74. Schoonheim, M. M. et al. Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis. Mult. Scler. 20, 1058–1065 (2014).

    CAS  PubMed  Google Scholar 

  75. Schoonheim, M. M. et al. Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 84, 776–783 (2015).

    PubMed  Google Scholar 

  76. Tona, F. et al. Multiple sclerosis: altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology 271, 814–821 (2014).

    PubMed  Google Scholar 

  77. Hidalgo de la Cruz, M. et al. Abnormal functional connectivity of thalamic sub-regions contributes to fatigue in multiple sclerosis. Mult. Scler. 24, 1183–1195 (2018).

    PubMed  Google Scholar 

  78. d’Ambrosio, A. et al. Structural connectivity-defined thalamic subregions have different functional connectivity abnormalities in multiple sclerosis patients: Implications for clinical correlations. Hum. Brain Mapp. 38, 6005–6018 (2017).

    PubMed  PubMed Central  Google Scholar 

  79. Jaeger, S. et al. Multiple sclerosis-related fatigue: altered resting-state functional connectivity of the ventral striatum and dorsolateral prefrontal cortex. Mult. Scler. 25, 554–564 (2019).

    PubMed  Google Scholar 

  80. Meijer, K. A., Eijlers, A. J. C., Geurts, J. J. G. & Schoonheim, M. M. Staging of cortical and deep grey matter functional connectivity changes in multiple sclerosis. J. Neurol. Neurosurg. Psychiatr. 89, 205–210 (2018).

    Google Scholar 

  81. Lansley, J., Mataix-Cols, D., Grau, M., Radua, J. & Sastre-Garriga, J. Localized grey matter atrophy in multiple sclerosis: A meta-analysis of voxel-based morphometry studies and associations with functional disability. Neurosci. Biobehav. Rev. 37, 819–830 (2013).

    CAS  PubMed  Google Scholar 

  82. Eshaghi, A. et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 141, 1665–1677 (2018).

    PubMed  PubMed Central  Google Scholar 

  83. Rimkus, C. M. et al. Gray matter networks and cognitive impairment in multiple sclerosis. Mult. Scler. 25, 382–391 (2019).

    PubMed  Google Scholar 

  84. Tur, C. et al. Clinical relevance of cortical network dynamics in early primary progressive MS. Mult. Scler. 26, 442–456 (2020).

    PubMed  Google Scholar 

  85. Shu, N. et al. Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cereb. Cortex 21, 2565–2577 (2011).

    PubMed  Google Scholar 

  86. Pardini, M. et al. Cingulum bundle alterations underlie subjective fatigue in multiple sclerosis. Mult. Scler. 21, 442–447 (2015).

    PubMed  Google Scholar 

  87. Ciccarelli, O. et al. Optic radiation changes after optic neuritis detected by tractography-based group mapping. Hum. Brain Mapp. 25, 308–316 (2005).

    PubMed  PubMed Central  Google Scholar 

  88. Gabilondo, I. et al. Retrograde retinal damage after acute optic tract lesion in MS. J. Neurol. Neurosurg. Psychiatry 84, 824–826 (2013).

    PubMed  Google Scholar 

  89. Rombouts, S. A. et al. Visual activation patterns in patients with optic neuritis: an fMRI pilot study. Neurology 50, 1896–1899 (1998).

    CAS  PubMed  Google Scholar 

  90. Gareau, P. J. et al. Reduced visual evoked responses in multiple sclerosis patients with optic neuritis: comparison of functional magnetic resonance imaging and visual evoked potentials. Mult. Scler. 5, 161–164 (1999).

    CAS  PubMed  Google Scholar 

  91. Toosy, A. T. et al. Adaptive cortical plasticity in higher visual areas after acute optic neuritis. Ann. Neurol. 57, 622–633 (2005).

    PubMed  Google Scholar 

  92. Korsholm, K. et al. Recovery from optic neuritis: an ROI-based analysis of LGN and visual cortical areas. Brain 130, 1244–1253 (2007).

    PubMed  Google Scholar 

  93. Jenkins, T. et al. Dissecting structure–function interactions in acute optic neuritis to investigate neuroplasticity. Hum. Brain Mapp. 31, 276–286 (2010).

    PubMed  Google Scholar 

  94. Backner, Y. et al. Anatomical wiring and functional networking changes in the visual system following optic neuritis. JAMA Neurol. 75, 287–295 (2018).

    PubMed  PubMed Central  Google Scholar 

  95. Gallo, A. et al. Visual resting-state network in relapsing-remitting MS with and without previous optic neuritis. Neurology 79, 1458–1465 (2012).

    PubMed  Google Scholar 

  96. Koini, M. et al. Correlates of executive functions in multiple sclerosis based on structural and functional MR imaging: insights from a multicenter study. Radiology 280, 869–879 (2016).

    PubMed  Google Scholar 

  97. Meijer, K. A. et al. Is impaired information processing speed a matter of structural or functional damage in MS? Neuroimage Clin. 20, 844–850 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Liu, Y. et al. Disrupted module efficiency of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Front. Hum. Neurosci. 12, 138 (2018).

    PubMed  PubMed Central  Google Scholar 

  99. Dineen, R. A. et al. Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain 132, 239–249 (2009).

    CAS  PubMed  Google Scholar 

  100. Mesaros, S. et al. Diffusion tensor MRI tractography and cognitive impairment in multiple sclerosis. Neurology 78, 969–975 (2012).

    CAS  PubMed  Google Scholar 

  101. Tewarie, P. et al. Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: an empirically informed modeling study. Hum. Brain Mapp. 39, 2541–2548 (2018).

    PubMed  PubMed Central  Google Scholar 

  102. Rocca, M. A. et al. Abnormal connectivity of the sensorimotor network in patients with MS: a multicenter fMRI study. Hum. Brain Mapp. 30, 2412–2425 (2009).

    PubMed  Google Scholar 

  103. Sumowski, J. F. et al. Brain reserve and cognitive reserve protect against cognitive decline over 4.5 years in MS. Neurology 82, 1776–1783 (2014).

    PubMed  PubMed Central  Google Scholar 

  104. Cordani, C. et al. Imaging correlates of hand motor performance in multiple sclerosis: a multiparametric structural and functional MRI study. Mult. Scler. 26, 233–244 (2020).

    PubMed  Google Scholar 

  105. Goodman, A. D. et al. Sustained-release oral fampridine in multiple sclerosis: a randomised, double-blind, controlled trial. Lancet 373, 732–738 (2009).

    CAS  PubMed  Google Scholar 

  106. Mainero, C. et al. Enhanced brain motor activity in patients with MS after a single dose of 3,4-diaminopyridine. Neurology 62, 2044–2050 (2004).

    CAS  PubMed  Google Scholar 

  107. Cader, S., Palace, J. & Matthews, P. M. Cholinergic agonism alters cognitive processing and enhances brain functional connectivity in patients with multiple sclerosis. J. Psychopharmacol. 23, 686–696 (2009).

    CAS  PubMed  Google Scholar 

  108. Fuchs, T. A. et al. Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis. Hum. Brain Mapp. 40, 5231–5241 (2019).

    PubMed  PubMed Central  Google Scholar 

  109. van Geest, Q. et al. The importance of hippocampal dynamic connectivity in explaining memory function in multiple sclerosis. Brain Behav. 8, e00954 (2018).

    PubMed  PubMed Central  Google Scholar 

  110. van Geest, Q. et al. Information processing speed in multiple sclerosis: Relevance of default mode network dynamics. Neuroimage Clin. 19, 507–515 (2018).

    PubMed  PubMed Central  Google Scholar 

  111. Lin, S.-J. et al. Education, and the balance between dynamic and stationary functional connectivity jointly support executive functions in relapsing-remitting multiple sclerosis. Hum. Brain Mapp. 39, 5039–5049 (2018).

    PubMed  PubMed Central  Google Scholar 

  112. Bosma, R. L. et al. Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain. Pain 159, 2267–2276 (2018).

    PubMed  Google Scholar 

  113. Llufriu, S. et al. Structural networks involved in attention and executive functions in multiple sclerosis. Neuroimage Clin. 13, 288–296 (2017).

    PubMed  Google Scholar 

  114. Pagani, E. et al. Structural connectivity in multiple sclerosis and modeling of disconnection. Mult. Scler. 26, 220–232 (2020).

    PubMed  Google Scholar 

  115. Prosperini, L., Piattella, M. C., Giannì, C. & Pantano, P. Functional and structural brain plasticity enhanced by motor and cognitive rehabilitation in multiple sclerosis. Neural Plast. 2015, 481574 (2015).

    PubMed  PubMed Central  Google Scholar 

  116. Filippi, M. et al. Multiple sclerosis: effects of cognitive rehabilitation on structural and functional MR imaging measures–an explorative study. Radiology 262, 932–940 (2012).

    PubMed  Google Scholar 

  117. Gaede, G. et al. Safety and preliminary efficacy of deep transcranial magnetic stimulation in MS-related fatigue. Neurol. Neuroimmunol. Neuroinflamm. 5, e423 (2017).

    PubMed  PubMed Central  Google Scholar 

  118. Hulst, H. E. et al. rTMS affects working memory performance, brain activation and functional connectivity in patients with multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 88, 386–394 (2017).

    CAS  PubMed  Google Scholar 

  119. Boutière, C. et al. Improvement of spasticity following intermittent theta burst stimulation in multiple sclerosis is associated with modulation of resting-state functional connectivity of the primary motor cortices. Mult. Scler. 23, 855–863 (2017).

    PubMed  Google Scholar 

  120. van den Heuvel, M. P., Bullmore, E. T. & Sporns, O. Comparative connectomics. Trends Cogn. Sci. 20, 345–361 (2016).

    PubMed  Google Scholar 

  121. Sha, Z. et al. Meta-connectomic analysis reveals commonly disrupted functional architectures in network modules and connectors across brain disorders. Cereb. Cortex 28, 4179–4194 (2018).

    PubMed  Google Scholar 

  122. Covey, J. T. et al. Improved cognitive performance and event-related potential changes following working memory training in patients with multiple sclerosis. Mult. Scler. J. Exp. Transl. Clin. 4, 2055217317747626 (2018).

    PubMed  PubMed Central  Google Scholar 

  123. D’Angelo, E. & Gandini Wheeler-Kingshott, C. Modelling the brain: elementary components to explain ensemble functions. Riv. Nuovo Cimento 40, 297–333 (2017).

    Google Scholar 

  124. Kiljan, S. et al. Structural network topology relates to tissue properties in multiple sclerosis. J. Neurol. 266, 212–222 (2019).

    CAS  PubMed  Google Scholar 

  125. Chard, D. T. & Miller, D. H. What lies beneath grey matter atrophy in multiple sclerosis? Brain 139, 7–10 (2016).

    PubMed  Google Scholar 

  126. Warren, J. D. et al. Molecular nexopathies: a new paradigm of neurodegenerative disease. Trends Neurosci. 36, 561–569 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Benedict, R. H. B., Amato, M. P., John DeLuca, J. & Geurts, J. J. G. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol. 19, 860–871 (2020).

    PubMed  Google Scholar 

  128. Wegner, C. et al. Relating functional changes during hand movement to clinical parameters in patients with multiple sclerosis in a multi-centre fMRI study. Eur. J. Neurol. 15, 113–122 (2008).

    CAS  PubMed  Google Scholar 

  129. Manson, S. C. et al. Impairment of movement-associated brain deactivation in multiple sclerosis: further evidence for a functional pathology of interhemispheric neuronal inhibition. Exp. Brain Res. 187, 25–31 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Mancini, L. et al. Short-term adaptation to a simple motor task: a physiological process preserved in multiple sclerosis. Neuroimage 45, 500–511 (2009).

    CAS  PubMed  Google Scholar 

  131. Colorado, R. A., Shukla, K., Zhou, Y., Wolinsky, J. S. & Narayana, P. A. Multi-task functional MRI in multiple sclerosis patients without clinical disability. Neuroimage 59, 573–581 (2012).

    PubMed  Google Scholar 

  132. Rocca, M. A. et al. Abnormal adaptation over time of motor network recruitment in multiple sclerosis patients with fatigue. Mult. Scler. 22, 1144–1153 (2016).

    PubMed  Google Scholar 

  133. Rocca, M. A. et al. Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology 79, 1449–1457 (2012).

    PubMed  Google Scholar 

  134. Rocca, M. A. et al. Hippocampal-DMN disconnectivity in MS is related to WM lesions and depression. Hum. Brain Mapp. 36, 5051–5063 (2015).

    PubMed  PubMed Central  Google Scholar 

  135. Rocca, M. A. et al. Impaired functional integration in multiple sclerosis: a graph theory study. Brain Struct. Funct. 221, 115–131 (2016).

    PubMed  Google Scholar 

  136. Eijlers, A. J. et al. Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 88, 952–960 (2017).

    PubMed  Google Scholar 

  137. Meijer, K. A. et al. Increased connectivity of hub networks and cognitive impairment in multiple sclerosis. Neurology 88, 2107–2114 (2017).

    PubMed  Google Scholar 

  138. Tommasin, S. et al. Relation between functional connectivity and disability in multiple sclerosis: a non-linear model. J. Neurol. 265, 2881–2892 (2018).

    PubMed  Google Scholar 

  139. Thirion, B. et al. Analysis of a large fMRI cohort: statistical and methodological issues for group analyses. Neuroimage 35, 105–120 (2007).

    PubMed  Google Scholar 

  140. Chen, X., Lu, B. & Yan, C.-G. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum. Brain Mapp. 39, 300–318 (2018).

    PubMed  Google Scholar 

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Acknowledgements

This work arose out of a Magnetic Resonance Imaging in MS (MAGNIMS) workshop on brain connectivity and networks in multiple sclerosis. The workshop was supported by the Multiple Sclerosis Society UK and Novartis, but they had no involvement in the workshop programme or in the writing of this manuscript.

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D. T. C., A. A. S. A., B. A., T. C., C. E., H. E. H., M.A.R., J. S.-G., B. T., C. T. and A. M. W. researched data for the article. All authors made a substantial contribution to discussion of content, wrote the article, and reviewed and/or edited the manuscript before submission.

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Correspondence to Declan T. Chard.

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Over the last 3 years, D. C. has received honoraria (paid to his employer) from Excemed for faculty-led education work. He is a consultant for Biogen and Hoffmann-La Roche. He has received research funding from the International Progressive MS Alliance, the MS Society UK, and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. A. B. reports travel grants from Biogen France SAS, Genzyme, Novartis Pharma SAS, Teva Santé SAS. C. E. has received funding for travel and speaker honoraria from Biogen, Bayer Schering, Celgene, Genzyme, Merck, Novartis, Roche, and Teva Pharmaceutical Industries Ltd/Sanofi-Aventis, received research support from Biogen, Merck, and Teva Pharmaceutical Industries Ltd/Sanofi-Aventis, and serves on scientific advisory boards for Bayer, Biogen, Genzyme, Merck, Novartis, Roche, and Teva Pharmaceutical Industries Ltd/Sanofi-Aventis. H. E. H. has received compensation for consulting services or speaker honoraria from Biogen Idec, Celgene, Merck Serono, and Sanofi Genzyme and serves on the editorial board of the Multiple Sclerosis Journal. M. A. R. has received speaker’s honoraria from Bayer, Biogen Idec, Calgene, Genzyme, Merck Serono, Novartis, Roche and Teva, and receives research support from the Italian Ministry of Health and Fondazione Italiana Sclerosi Multipla. A. R. serves as Editorial Board member of Neuroradiology and American Journal of Neuroradiology, on scientific advisory boards for Bayer, Biogen, Novartis, OLEA Medical, Sanofi Genzyme, SyntheticMR and Roche, and has received speaker honoraria from Bayer, Biogen, Merck Serono, Novartis, Roche, Sanofi Genzyme and Teva Pharmaceutical Industries Ltd. J. S.-G. reports grants and personal fees from Genzyme received over the last 36 months and personal fees from Almirall, Bial, Biogen, Celgene, Merck, Novartis, Roche and Teva; he is Director of Revista de Neurologia, for which he does not receive any compensation, and serves as member of the Editorial Board of Multiple Sclerosis Journal, for which he receives compensation. M. M. S. serves on the Editorial Board of Frontiers in Neurology and has received compensation for consulting services or speaker honoraria from Biogen, ExceMed and Genzyme. B. T. received funding from the ZonMW Memorabel grant programme #73305056. C. T. has received a postdoctoral research ECTRIMS fellowship (2015); she has also received honoraria and support for travelling from Bayer, Biogen, Ismar Healthcare, Merck Serono, Novartis, Roche, Sanofi and Teva Pharmaceuticals and provides consultancy services to Roche. C. G. W.-K. reports receiving research funding from the International Spinal Research Trust, the Craig H. Neilsen Foundation (the INSPIRED study), the MS Society (#77), Wings for Life (the INSPIRED study, #169111) and Horizon 2020 (CDS-QUAMRI, #634541). A. M. W. receives funding from the European Prevention of Alzheimer’s Dementia consortium, the Amyloid Imaging to Prevent Alzheimer’s Disease initiative (Innovative Medicines Initiative grants 115736 and 115962) and the European Progression of Neurological Disease Initiative (Horizon 2020 grant 666992). O. C. serves as a consultant for Merck, Novartis, and Roche; she receives an honorarium from the American Academy of Neurology as Associate Editor of Neurology. F. B. serves as an Editorial Board member of Brain, European Radiology, Neurology, Multiple Sclerosis Journal and Radiology; he has accepted consulting fees from Apitope Ltd, Bayer-Schering Pharma, Biogen-IDEC, GeNeuro, Sanofi Genzyme, IXICO Ltd, Jansen Research, Merck Serono, Novartis, Roche, and TEVA and speaker fees from Biogen-IDEC and IXICO. He has received grants from the Amyloid Imaging to Prevent Alzheimer’s Disease Initiative (Innovative Medicines Initiative), the European Progression of Neurological Disease Initiative (H2020), UK MS Society, Dutch MS Society, NIHR University College London Hospital Biomedical Research Centre, the European Committee for Treatment and Research in Multiple Sclerosis and the Magnetic Resonance Imaging in MS network. The other authors declare no competing interests.

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Glossary

Connectomics

The comprehensive study of neural networks with the ultimate aim of generating maps that represent all connections in the CNS.

Hub

A network node that has a high number of direct connections with other nodes.

Nexopathies

The interactions of pathogenic proteins with neural networks that are vulnerable to their effects, resulting in characteristic patterns and spread of abnormalities across the CNS.

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Chard, D.T., Alahmadi, A.A.S., Audoin, B. et al. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol 17, 173–184 (2021). https://doi.org/10.1038/s41582-020-00439-8

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