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The Neuroimaging of Brain Diseases pp 171–213Cite as

Structural and Functional Neuroimaging in Multiple Sclerosis: From Atrophy, Lesions to Global Network Disruption

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Part of the book series: Contemporary Clinical Neuroscience ((CCNE))

Abstract

Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease that affects the central nervous system. There is a clinico-radiological paradox in MS: A discrepancy between clinical symptoms and the amount of focal brain lesions. In this chapter we explore how new sophisticated neuroimaging approaches could help elucidate the clinico-radiological paradox, as they quantify structural and functional pathology beyond focal MRI-visible white matter lesions. The observed triad of structural MS pathology (focal lesions, diffuse changes and brain atrophy) throughout the grey and white matter seems to induce highly complex functional network changes that are currently understudied. The current debate on beneficial and maladaptive functional changes remains ongoing. The high variability in all forms of structural and functional pathology in MS highlights the need for a more holistic, network-based approach to study the disease. Hopefully, such future studies could then provide the much-needed missing links essential to unravelling the clinico-radiological paradox.

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Abbreviations

BOLD:

Blood-oxygenation level dependent

CIS:

Clinically isolated syndrome

DTI:

Diffusion tensor imaging

EAE:

Experimental allergic encephalomyelitis

EE:

Gelectroencephalogram

fMRI:

Functional magnetic resonance

MAGNIMS:

Magnetic resonance imaging in multiple sclerosis

MEG:

Magnetoencephalography

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

MST:

Minimum spanning tree

MTI:

Magnetisation transfer imaging

NMSS:

National MS society

PASAT:

Paced auditory serial addition test

PP:

Primary progressive

RR:

Relapsing remitting

SP:

Secondary progressive

References

  1. Kamm CP, Uitdehaag BM, Polman CH (2014) Multiple sclerosis: current knowledge and future outlook. Eur Neurol 72(3–4):132–141

    Article  CAS  PubMed  Google Scholar 

  2. Alonso A, Hernán MA (2008) Temporal trends in the incidence of multiple sclerosis a systematic review. Neurology 71(2):129–135

    Article  PubMed  PubMed Central  Google Scholar 

  3. Stys PK, Zamponi GW, van Minnen J, Geurts JJ (2012) Will the real multiple sclerosis please stand up? Nat Rev Neurosci 13(7):507–514

    Article  CAS  PubMed  Google Scholar 

  4. Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ et al (2014) Defining the clinical course of multiple sclerosis the 2013 revisions. Neurology 83(3):278–286

    Article  PubMed  PubMed Central  Google Scholar 

  5. La Mantia L, Vacchi L, Di Pietrantonj C, Ebers G, Rovaris M, Fredrikson S et al (2012) Interferon beta for secondary progressive multiple sclerosis. Cochrane Database Syst Rev 1:CD005181

    PubMed  Google Scholar 

  6. Miller DH, Leary SM (2007) Primary-progressive multiple sclerosis. Lancet Neurol 6(10):903–912

    Article  PubMed  Google Scholar 

  7. Filippini G, Del Giovane C, Vacchi L, D'Amico R, Di Pietrantonj C, Beecher D et al (2013) Immunomodulators and immunosuppressants for multiple sclerosis: a network meta-analysis. Cochrane Database Syst Rev 6:CD008933

    Google Scholar 

  8. Tramacere I, Del Giovane C, Salanti G, D'Amico R, Filippini G. Immunomodulators and immunosuppressants for relapsing-remitting multiple sclerosis: a network meta-analysis. Cochrane Database Syst Rev 2015;9:CD011381.

    Google Scholar 

  9. Rolak LA. The history of MS; the basic facts. http://www.nationalmssociety.org. National Multiple Sclerosis Society; 2016

  10. Ormerod IE, Miller DH, McDonald WI, du Boulay EP, Rudge P, Kendall BE et al (1987) The role of NMR imaging in the assessment of multiple sclerosis and isolated neurological lesions. A quantitative study. Brain 110(Pt 6):1579–1616

    Article  PubMed  Google Scholar 

  11. Miller DH, Grossman RI, Reingold SC, McFarland HF (1998) The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 121(Pt 1):3–24

    Article  PubMed  Google Scholar 

  12. Neema M, Stankiewicz J, Arora A, Dandamudi VS, Batt CE, Guss ZD et al (2007) T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. J Neuroimaging 17(Suppl 1):16S–21S

    Article  PubMed  Google Scholar 

  13. Seewann A, Vrenken H, van der Valk P, Blezer EL, Knol DL, Castelijns JA et al (2009) Diffusely abnormal white matter in chronic multiple sclerosis: imaging and histopathologic analysis. Arch Neurol 66(5):601–609

    Article  PubMed  Google Scholar 

  14. Grossman RI, Gonzalez-Scarano F, Atlas SW, Galetta S, Silberberg DH (1986) Multiple sclerosis: gadolinium enhancement in MR imaging. Radiology 161(3):721–725

    Article  CAS  PubMed  Google Scholar 

  15. van Walderveen MA, Kamphorst W, Scheltens P, van Waesberghe JH, Ravid R, Valk J et al (1998) Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 50(5):1282–1288

    Article  PubMed  Google Scholar 

  16. Grossman RI, Braffman BH, Brorson JR, Goldberg HI, Silberberg DH, Gonzalez-Scarano F (1988) Multiple sclerosis: serial study of gadolinium-enhanced MR imaging. Radiology 169(1):117–122

    Article  CAS  PubMed  Google Scholar 

  17. van Waesberghe JH, van Walderveen MA, Castelijns JA, Scheltens P, GJ L a N, Polman CH et al (1998) Patterns of lesion development in multiple sclerosis: longitudinal observations with T1-weighted spin-echo and magnetization transfer MR. AJNR Am J Neuroradiol 19(4):675–683

    PubMed  PubMed Central  Google Scholar 

  18. van den Elskamp IJ, Boden B, Dattola V, Knol DL, Filippi M, Kappos L et al (2010) Cerebral atrophy as outcome measure in short-term phase 2 clinical trials in multiple sclerosis. Neuroradiology 52(10):875–881

    Article  PubMed  PubMed Central  Google Scholar 

  19. Filippi M, Horsfield MA, Tofts PS, Barkhof F, Thompson AJ, Miller DH (1995) Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis. Brain 118(Pt 6):1601–1612

    Article  PubMed  Google Scholar 

  20. Miller DH, Barkhof F, Nauta JJ (1993) Gadolinium enhancement increases the sensitivity of MRI in detecting disease activity in multiple sclerosis. Brain 116(Pt 5):1077–1094

    Article  PubMed  Google Scholar 

  21. Barkhof F, Filippi M, Miller DH, Scheltens P, Campi A, Polman CH et al (1997) Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 120(Pt 11):2059–2069

    Article  PubMed  Google Scholar 

  22. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302

    Article  PubMed  PubMed Central  Google Scholar 

  23. Filippi M, Rocca MA, Ciccarelli O, De Stefano N, Evangelou N, Kappos L et al (2016) MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 15(3):292–303

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gonzalez-Scarano F, Grossman RI, Galetta S, Atlas SW, Silberberg DH (1987) Multiple sclerosis disease activity correlates with gadolinium-enhanced magnetic resonance imaging. Ann Neurol 21(3):300–306

    Article  CAS  PubMed  Google Scholar 

  25. Kappos L, Moeri D, Radue EW, Schoetzau A, Schweikert K, Barkhof F et al (1999) Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. Gadolinium MRI Meta-analysis Group. Lancet 353(9157):964–969

    Article  CAS  PubMed  Google Scholar 

  26. Truyen L, van Waesberghe JH, van Walderveen MA, van Oosten BW, Polman CH, Hommes OR et al (1996) Accumulation of hypointense lesions (“black holes”) on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology 47(6):1469–1476

    Article  CAS  PubMed  Google Scholar 

  27. Rovaris M, Agosta F, Sormani MP, Inglese M, Martinelli V, Comi G et al (2003) Conventional and magnetization transfer MRI predictors of clinical multiple sclerosis evolution: a medium-term follow-up study. Brain 126(Pt 10):2323–2332

    Article  PubMed  Google Scholar 

  28. Barkhof F (1999) MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS). Mult Scler 5(4):283–286

    Article  CAS  PubMed  Google Scholar 

  29. Thompson AJ, Kermode AG, MacManus DG, Kendall BE, Kingsley DP, Moseley IF et al (1990) Patterns of disease activity in multiple sclerosis: clinical and magnetic resonance imaging study. BMJ 300(6725):631–634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Montalban X (2005) Primary progressive multiple sclerosis. Curr Opin Neurol 18(3):261–266

    Article  PubMed  Google Scholar 

  31. Nijeholt GJ, van Walderveen MA, Castelijns JA, van Waesberghe JH, Polman C, Scheltens P et al (1998) Brain and spinal cord abnormalities in multiple sclerosis. Correlation between MRI parameters, clinical subtypes and symptoms. Brain 121(Pt 4):687–697

    Article  PubMed  Google Scholar 

  32. Khaleeli Z, Ciccarelli O, Manfredonia F, Barkhof F, Brochet B, Cercignani M et al (2008) Predicting progression in primary progressive multiple sclerosis: a 10-year multicenter study. Ann Neurol 63(6):790–793

    Article  PubMed  Google Scholar 

  33. Camp SJ, Stevenson VL, Thompson AJ, Miller DH, Borras C, Auriacombe S et al (1999) Cognitive function in primary progressive and transitional progressive multiple sclerosis: a controlled study with MRI correlates. Brain 122(Pt 7):1341–1348

    Article  PubMed  Google Scholar 

  34. Benedict RH, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R (2004) Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 61(2):226–230

    Article  PubMed  Google Scholar 

  35. Barkhof F (2002) The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 15(3):239–245

    Article  PubMed  Google Scholar 

  36. Vellinga MM, Geurts JJ, Rostrup E, Uitdehaag BM, Polman CH, Barkhof F et al (2009) Clinical correlations of brain lesion distribution in multiple sclerosis. J Magn Reson Imaging 29(4):768–773

    Article  CAS  PubMed  Google Scholar 

  37. Hackmack K, Weygandt M, Wuerfel J, Pfueller CF, Bellmann-Strobl J, Paul F et al (2012) Can we overcome the ‘clinico-radiological paradox’ in multiple sclerosis? J Neurol 259(10):2151–2160

    Article  PubMed  Google Scholar 

  38. Kincses ZT, Ropele S, Jenkinson M, Khalil M, Petrovic K, Loitfelder M et al (2011) Lesion probability mapping to explain clinical deficits and cognitive performance in multiple sclerosis. Mult Scler 17(6):681–689

    Article  CAS  PubMed  Google Scholar 

  39. Bodini B, Battaglini M, De Stefano N, Khaleeli Z, Barkhof F, Chard D et al (2011) T2 lesion location really matters: a 10 year follow-up study in primary progressive multiple sclerosis. J Neurol Neurosurg Psychiatry 82(1):72–77

    Article  CAS  PubMed  Google Scholar 

  40. Dalton CM, Bodini B, Samson RS, Battaglini M, Fisniku LK, Thompson AJ et al (2012) Brain lesion location and clinical status 20 years after a diagnosis of clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler 18(3):322–328

    Article  CAS  PubMed  Google Scholar 

  41. Peterson JW, Bo L, Mork S, Chang A, Trapp BD (2001) Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol 50(3):389–400

    Article  CAS  PubMed  Google Scholar 

  42. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H, Bergmann M et al (2005) Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128(Pt 11):2705–2712

    Article  PubMed  Google Scholar 

  43. Kidd D, Barkhof F, McConnell R, Algra PR, Allen IV, Revesz T (1999) Cortical lesions in multiple sclerosis. Brain 122(Pt 1):17–26

    Article  PubMed  Google Scholar 

  44. Filippi M, Preziosa P, Rocca MA (2014) Magnetic resonance outcome measures in multiple sclerosis trials: time to rethink? Curr Opin Neurol 27(3):290–299

    Article  PubMed  Google Scholar 

  45. Bo L, Geurts JJ, Mork SJ, van der Valk P (2006) Grey matter pathology in multiple sclerosis. Acta Neurol Scand Suppl 183:48–50

    Article  CAS  PubMed  Google Scholar 

  46. Calabrese M, Rocca MA, Atzori M, Mattisi I, Bernardi V, Favaretto A et al (2009) Cortical lesions in primary progressive multiple sclerosis: a 2-year longitudinal MR study. Neurology 72(15):1330–1336

    Article  CAS  PubMed  Google Scholar 

  47. Pirko I, Lucchinetti CF, Sriram S, Bakshi R (2007) Gray matter involvement in multiple sclerosis. Neurology 68(9):634–642

    Article  PubMed  Google Scholar 

  48. Fisniku LK, Chard DT, Jackson JS, Anderson VM, Altmann DR, Miszkiel KA et al (2008) Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol 64(3):247–254

    Article  PubMed  Google Scholar 

  49. Calabrese M, Agosta F, Rinaldi F, Mattisi I, Grossi P, Favaretto A et al (2009) Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66(9):1144–1150

    Article  PubMed  Google Scholar 

  50. Calabrese M, Rinaldi F, Grossi P, Gallo P (2011) Cortical pathology and cognitive impairment in multiple sclerosis. Expert Rev Neurother 11(3):425–432

    Article  PubMed  Google Scholar 

  51. Rinaldi F, Calabrese M, Grossi P, Puthenparampil M, Perini P, Gallo P (2010) Cortical lesions and cognitive impairment in multiple sclerosis. Neurol Sci 31(Suppl 2):S235–S237

    Article  CAS  PubMed  Google Scholar 

  52. Rocca MA, Amato MP, De Stefano N, Enzinger C, Geurts JJ, Penner IK et al (2015) Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 14(3):302–317

    Article  PubMed  Google Scholar 

  53. Roosendaal SD, Moraal B, Vrenken H, Castelijns JA, Pouwels PJ, Barkhof F et al (2008) In vivo MR imaging of hippocampal lesions in multiple sclerosis. J Magn Reson Imaging 27(4):726–731

    Article  PubMed  Google Scholar 

  54. Calabrese M, Grossi P, Favaretto A, Romualdi C, Atzori M, Rinaldi F et al (2012) Cortical pathology in multiple sclerosis patients with epilepsy: a 3 year longitudinal study. J Neurol Neurosurg Psychiatry 83(1):49–54

    Article  CAS  PubMed  Google Scholar 

  55. Uribe-San-Martin R, Ciampi-Diaz E, Suarez-Hernandez F, Vasquez-Torres M, Godoy-Fernandez J, Carcamo-Rodriguez C (2014) Prevalence of epilepsy in a cohort of patients with multiple sclerosis. Seizure 23(1):81–83

    Article  PubMed  Google Scholar 

  56. Calabrese M, Magliozzi R, Ciccarelli O, Geurts JJ, Reynolds R, Martin R (2015) Exploring the origins of grey matter damage in multiple sclerosis. Nat Rev Neurosci 16(3):147–158

    Article  CAS  PubMed  Google Scholar 

  57. van de Pavert SH, Muhlert N, Sethi V, Wheeler-Kingshott CA, Ridgway GR, Geurts JJ et al (2016) DIR-visible grey matter lesions and atrophy in multiple sclerosis: partners in crime? J Neurol Neurosurg Psychiatry 87(5):461–467

    Article  PubMed  Google Scholar 

  58. Bakshi R, Ariyaratana S, Benedict RH, Jacobs L (2001) Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions. Arch Neurol 58(5):742–748

    Article  CAS  PubMed  Google Scholar 

  59. Geurts JJ, Bo L, Pouwels PJ, Castelijns JA, Polman CH, Barkhof F (2005) Cortical lesions in multiple sclerosis: combined postmortem MR imaging and histopathology. AJNR Am J Neuroradiol 26(3):572–577

    PubMed  PubMed Central  Google Scholar 

  60. Geurts JJ, Blezer EL, Vrenken H, van der Toorn A, Castelijns JA, Polman CH et al (2008) Does high-field MR imaging improve cortical lesion detection in multiple sclerosis? J Neurol 255(2):183–191

    Article  PubMed  Google Scholar 

  61. Geurts JJ, Pouwels PJ, Uitdehaag BM, Polman CH, Barkhof F, Castelijns JA (2005) Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 236(1):254–260

    Article  PubMed  Google Scholar 

  62. Seewann A, Vrenken H, Kooi EJ, van der Valk P, Knol DL, Polman CH et al (2011) Imaging the tip of the iceberg: visualization of cortical lesions in multiple sclerosis. Mult Scler 17(10):1202–1210

    Article  PubMed  Google Scholar 

  63. Seewann A, Kooi EJ, Roosendaal SD, Pouwels PJ, Wattjes MP, van der Valk P et al (2012) Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology 78(5):302–308

    Article  CAS  PubMed  Google Scholar 

  64. Geurts JJ, Roosendaal SD, Calabrese M, Ciccarelli O, Agosta F, Chard DT et al (2011) Consensus recommendations for MS cortical lesion scoring using double inversion recovery MRI. Neurology 76(5):418–424

    Article  CAS  PubMed  Google Scholar 

  65. de Graaf WL, Kilsdonk ID, Lopez-Soriano A, Zwanenburg JJ, Visser F, Polman CH et al (2013) Clinical application of multi-contrast 7-T MR imaging in multiple sclerosis: increased lesion detection compared to 3 T confined to grey matter. Eur Radiol 23(2):528–540

    Article  PubMed  Google Scholar 

  66. Kilsdonk ID, de Graaf WL, Soriano AL, Zwanenburg JJ, Visser F, Kuijer JP et al (2013) Multicontrast MR imaging at 7T in multiple sclerosis: highest lesion detection in cortical gray matter with 3D-FLAIR. AJNR Am J Neuroradiol 34(4):791–796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kilsdonk ID, Jonkman LE, Klaver R, van Veluw SJ, Zwanenburg JJ, Kuijer JP et al (2016) Increased cortical grey matter lesion detection in multiple sclerosis with 7 T MRI: a post-mortem verification study. Brain 8

    Google Scholar 

  68. McDonald WI (1974) Pathophysiology in multiple sclerosis. Brain 97(1):179–196

    Article  CAS  PubMed  Google Scholar 

  69. Simon JH, Holtas SL, Schiffer RB, Rudick RA, Herndon RM, Kido DK et al (1986) Corpus callosum and subcallosal-periventricular lesions in multiple sclerosis: detection with MR. Radiology 160(2):363–367

    Article  CAS  PubMed  Google Scholar 

  70. Losseff NA, Wang L, Lai HM, Yoo DS, Gawne-Cain ML, McDonald WI et al (1996) Progressive cerebral atrophy in multiple sclerosis. A serial MRI study. Brain 119(Pt 6):2009–2019

    Article  PubMed  Google Scholar 

  71. Losseff NA, Webb SL, O'Riordan JI, Page R, Wang L, Barker GJ et al (1996) Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. Brain 119(Pt 3):701–708

    Article  PubMed  Google Scholar 

  72. Lukas C, Knol DL, Sombekke MH, Bellenberg B, Hahn HK, Popescu V et al (2015) Cervical spinal cord volume loss is related to clinical disability progression in multiple sclerosis. J Neurol Neurosurg Psychiatry 86(4):410–418

    Article  PubMed  Google Scholar 

  73. Lukas C, Sombekke MH, Bellenberg B, Hahn HK, Popescu V, Bendfeldt K et al (2013) Relevance of spinal cord abnormalities to clinical disability in multiple sclerosis: MR imaging findings in a large cohort of patients. Radiology 269(2):542–552

    Article  PubMed  Google Scholar 

  74. Huber SJ, Paulson GW, Shuttleworth EC, Chakeres D, Clapp LE, Pakalnis A et al (1987) Magnetic resonance imaging correlates of dementia in multiple sclerosis. Arch Neurol 44(7):732–736

    Article  CAS  PubMed  Google Scholar 

  75. Pozzilli C, Bastianello S, Padovani A, Passafiume D, Millefiorini E, Bozzao L et al (1991) Anterior corpus callosum atrophy and verbal fluency in multiple sclerosis. Cortex 27(3):441–445

    Article  CAS  PubMed  Google Scholar 

  76. Pozzilli C, Fieschi C, Perani D, Paulesu E, Comi G, Bastianello S et al (1992) Relationship between corpus callosum atrophy and cerebral metabolic asymmetries in multiple sclerosis. J Neurol Sci 112(1–2):51–57

    Article  CAS  PubMed  Google Scholar 

  77. Saindane AM, Ge Y, Udupa JK, Babb JS, Mannon LJ, Grossman RI (2000) The effect of gadolinium-enhancing lesions on whole brain atrophy in relapsing-remitting MS. Neurology 55(1):61–65

    Article  CAS  PubMed  Google Scholar 

  78. Pelletier J, Habib M, Lyon-Caen O, Salamon G, Poncet M, Khalil R (1993) Functional and magnetic resonance imaging correlates of callosal involvement in multiple sclerosis. Arch Neurol 50(10):1077–1082

    Article  CAS  PubMed  Google Scholar 

  79. Eikelenboom MJ, Petzold A, Lazeron RH, Silber E, Sharief M, Thompson EJ et al (2003) Multiple sclerosis: Neurofilament light chain antibodies are correlated to cerebral atrophy. Neurology 60(2):219–223

    Article  CAS  PubMed  Google Scholar 

  80. Popescu V, Klaver R, Voorn P, Galis-de Graaf Y, Knol DL, Twisk JW et al (2015) What drives MRI-measured cortical atrophy in multiple sclerosis? Mult Scler 21(10):1280–1290

    Article  CAS  PubMed  Google Scholar 

  81. Klaver R, Popescu V, Voorn P, Galis-de Graaf Y, van der Valk P, de Vries HE et al (2015) Neuronal and axonal loss in normal-appearing gray matter and subpial lesions in multiple sclerosis. J Neuropathol Exp Neurol 74(5):453–458

    Article  CAS  PubMed  Google Scholar 

  82. Derakhshan M, Caramanos Z, Giacomini PS, Narayanan S, Maranzano J, Francis SJ et al (2010) Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis. NeuroImage 52(4):1261–1267

    Article  PubMed  Google Scholar 

  83. Popescu V, Schoonheim MM, Versteeg A, Chaturvedi N, Jonker M, Xavier de Menezes R et al (2016) Grey matter atrophy in multiple sclerosis: clinical interpretation depends on choice of analysis method. PLoS One 11(1):e0143942

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Simon JH, Jacobs LD, Campion MK, Rudick RA, Cookfair DL, Herndon RM et al (1999) A longitudinal study of brain atrophy in relapsing multiple sclerosis. The multiple sclerosis collaborative research group (MSCRG). Neurology 53(1):139–148

    Article  CAS  PubMed  Google Scholar 

  85. Barkhof FJ, Elton M, Lindeboom J, Tas MW, Schmidt WF, Hommes OR et al (1998) Functional correlates of callosal atrophy in relapsing-remitting multiple sclerosis patients. A preliminary MRI study. J Neurol 245(3):153–158

    Article  CAS  PubMed  Google Scholar 

  86. Filippi M, Mastronardo G, Rocca MA, Pereira C, Comi G (1998) Quantitative volumetric analysis of brain magnetic resonance imaging from patients with multiple sclerosis. J Neurol Sci 158(2):148–153

    Article  CAS  PubMed  Google Scholar 

  87. Rovaris M, Bozzali M, Rodegher M, Tortorella C, Comi G, Filippi M (1999) Brain MRI correlates of magnetization transfer imaging metrics in patients with multiple sclerosis. J Neurol Sci 166(1):58–63

    Article  CAS  PubMed  Google Scholar 

  88. Molyneux PD, Kappos L, Polman C, Pozzilli C, Barkhof F, Filippi M et al (2000) The effect of interferon beta-1b treatment on MRI measures of cerebral atrophy in secondary progressive multiple sclerosis. European study group on interferon beta-1b in secondary progressive multiple sclerosis. Brain 123(Pt 11):2256–2263

    Article  PubMed  Google Scholar 

  89. Ge Y, Grossman RI, Udupa JK, Wei L, Mannon LJ, Polansky M et al (2000) Brain atrophy in relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis: longitudinal quantitative analysis. Radiology 214(3):665–670

    Article  CAS  PubMed  Google Scholar 

  90. Miller DH, Barkhof F, Frank JA, Parker GJ, Thompson AJ (2002) Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 125(Pt 8):1676–1695

    Article  PubMed  Google Scholar 

  91. Sastre-Garriga J, Ingle GT, Chard DT, Cercignani M, Ramio-Torrenta L, Miller DH et al (2005) Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study. Brain 128(Pt 6):1454–1460

    Article  PubMed  Google Scholar 

  92. Popescu V, Agosta F, Hulst HE, Sluimer IC, Knol DL, Sormani MP et al (2013) Brain atrophy and lesion load predict long term disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 84(10):1082–1091

    Article  PubMed  Google Scholar 

  93. Chard DT, Brex PA, Ciccarelli O, Griffin CM, Parker GJ, Dalton C et al (2003) The longitudinal relation between brain lesion load and atrophy in multiple sclerosis: a 14 year follow up study. J Neurol Neurosurg Psychiatry 74(11):1551–1554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. van Munster CE, Jonkman LE, Weinstein HC, Uitdehaag BM, Geurts JJ (2015) Gray matter damage in multiple sclerosis: impact on clinical symptoms. Neuroscience 303:446–461

    Article  PubMed  CAS  Google Scholar 

  95. Grassiot B, Desgranges B, Eustache F, Defer G (2009) Quantification and clinical relevance of brain atrophy in multiple sclerosis: a review. J Neurol 256(9):1397–1412

    Article  PubMed  Google Scholar 

  96. Ge Y, Grossman RI, Udupa JK, Babb JS, Nyul LG, Kolson DL (2001) Brain atrophy in relapsing-remitting multiple sclerosis: fractional volumetric analysis of gray matter and white matter. Radiology 220(3):606–610

    Article  CAS  PubMed  Google Scholar 

  97. Chard DT, Griffin CM, Parker GJ, Kapoor R, Thompson AJ, Miller DH (2002) Brain atrophy in clinically early relapsing-remitting multiple sclerosis. Brain 125(Pt 2):327–337

    Article  CAS  PubMed  Google Scholar 

  98. Hardmeier M, Wagenpfeil S, Freitag P, Fisher E, Rudick RA, Kooijmans-Coutinho M et al (2003) Atrophy is detectable within a 3-month period in untreated patients with active relapsing remitting multiple sclerosis. Arch Neurol 60(12):1736–1739

    Article  PubMed  Google Scholar 

  99. Jacobsen C, Hagemeier J, Myhr KM, Nyland H, Lode K, Bergsland N et al (2014) Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry 85(10):1109–1115

    Article  PubMed  Google Scholar 

  100. Lin X, Blumhardt LD, Constantinescu CS (2003) The relationship of brain and cervical cord volume to disability in clinical subtypes of multiple sclerosis: a three-dimensional MRI study. Acta Neurol Scand 108(6):401–406

    Article  CAS  PubMed  Google Scholar 

  101. Fisher E, Lee JC, Nakamura K, Rudick RA (2008) Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol 64(3):255–265

    Article  PubMed  Google Scholar 

  102. Popescu V, Battaglini M, Hoogstrate WS, Verfaillie SC, Sluimer IC, van Schijndel RA et al (2012) Optimizing parameter choice for FSL-brain extraction tool (BET) on 3D T1 images in multiple sclerosis. NeuroImage 61(4):1484–1494

    Article  CAS  PubMed  Google Scholar 

  103. Vrenken H, Jenkinson M, Horsfield MA, Battaglini M, van Schijndel RA, Rostrup E et al (2013) Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis. J Neurol 260(10):2458–2471

    Article  CAS  PubMed  Google Scholar 

  104. Ceccarelli A, Jackson JS, Tauhid S, Arora A, Gorky J, Dell'Oglio E et al (2012) The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. AJNR Am J Neuroradiol 33(8):1579–1585

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Popescu V, Ran NC, Barkhof F, Chard DT, Wheeler-Kingshott CA, Vrenken H (2014) Accurate GM atrophy quantification in MS using lesion-filling with co-registered 2D lesion masks. Neuroimage Clin 4:366–373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA (2010) Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32(1):223–228

    Article  PubMed  Google Scholar 

  107. Gabilondo I, Martinez-Lapiscina EH, Martinez-Heras E, Fraga-Pumar E, Llufriu S, Ortiz S et al (2014) Trans-synaptic axonal degeneration in the visual pathway in multiple sclerosis. Ann Neurol 75(1):98–107

    Article  CAS  PubMed  Google Scholar 

  108. Bendfeldt K, Blumhagen JO, Egger H, Loetscher P, Denier N, Kuster P et al (2010) Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis. Hum Brain Mapp 31(10):1542–1555

    Article  PubMed  PubMed Central  Google Scholar 

  109. Steenwijk MD, Daams M, Pouwels PJ, Balk LJ, Tewarie PK, Killestein J et al (2014) What explains gray matter atrophy in long-standing multiple sclerosis? Radiology 272(3):832–842

    Article  PubMed  Google Scholar 

  110. Bodini B, Khaleeli Z, Cercignani M, Miller DH, Thompson AJ, Ciccarelli O (2009) 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 30(9):2852–2861

    Article  PubMed  PubMed Central  Google Scholar 

  111. Tillema JM, Hulst HE, Rocca MA, Vrenken H, Steenwijk MD, Damjanovic D et al (2016) Regional cortical thinning in multiple sclerosis and its relation with cognitive impairment: a multicenter study. Mult Scler 22(7):901–909

    Article  CAS  PubMed  Google Scholar 

  112. Achiron A, Chapman J, Tal S, Bercovich E, Gil H, Achiron A (2013) Superior temporal gyrus thickness correlates with cognitive performance in multiple sclerosis. Brain Struct Funct 218(4):943–950

    Article  CAS  PubMed  Google Scholar 

  113. Charil A, Dagher A, Lerch JP, Zijdenbos AP, Worsley KJ, Evans AC (2007) Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability. NeuroImage 34(2):509–517

    Article  PubMed  Google Scholar 

  114. Geisseler O, Pflugshaupt T, Bezzola L, Reuter K, Weller D, Schuknecht B et al (2016) Cortical thinning in the anterior cingulate cortex predicts multiple sclerosis patients’ fluency performance in a lateralised manner. Neuroimage Clin 10:89–95

    Article  PubMed  Google Scholar 

  115. Nygaard GO, Walhovd KB, Sowa P, Chepkoech JL, Bjornerud A, Due-Tonnessen P et al (2015) Cortical thickness and surface area relate to specific symptoms in early relapsing-remitting multiple sclerosis. Mult Scler 21(4):402–414

    Article  PubMed  Google Scholar 

  116. Sailer M, Fischl B, Salat D, Tempelmann C, Schonfeld MA, Busa E et al (2003) Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126(Pt 8):1734–1744

    Article  PubMed  Google Scholar 

  117. Steenwijk MD, Daams M, Pouwels PJ, JB L, Tewarie PK, Geurts JJ et al (2015) Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long-standing multiple sclerosis. Hum Brain Mapp 36(5):1796–1807

    Article  PubMed  PubMed Central  Google Scholar 

  118. Bergsland N, Lagana MM, Tavazzi E, Caffini M, Tortorella P, Baglio F et al (2015) Corticospinal tract integrity is related to primary motor cortex thinning in relapsing-remitting multiple sclerosis. Mult Scler 21(14):1771–1780

    Article  CAS  PubMed  Google Scholar 

  119. Parisi L, Rocca MA, Mattioli F, Riccitelli GC, Capra R, Stampatori C et al (2014) Patterns of regional gray matter and white matter atrophy in cortical multiple sclerosis. J Neurol 261(9):1715–1725

    Article  PubMed  Google Scholar 

  120. Steenwijk MD, Geurts JJ, Daams M, Tijms BM, Wink AM, Balk LJ et al (2016) Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 139(Pt 1):115–126

    Article  PubMed  Google Scholar 

  121. Prinster A, Quarantelli M, Orefice G, Lanzillo R, Brunetti A, Mollica C et al (2006) Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. NeuroImage 29(3):859–867

    Article  CAS  PubMed  Google Scholar 

  122. Audoin B, Zaaraoui W, Reuter F, Rico A, Malikova I, Confort-Gouny S et al (2010) Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis. J Neurol Neurosurg Psychiatry 81(6):690–695

    Article  PubMed  Google Scholar 

  123. Cappellani R, Bergsland N, Weinstock-Guttman B, Kennedy C, Carl E, Ramasamy DP et al (2014) Subcortical deep gray matter pathology in patients with multiple sclerosis is associated with white matter lesion burden and atrophy but not with cortical atrophy: a diffusion tensor MRI study. AJNR Am J Neuroradiol 35(5):912–919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Houtchens MK, Benedict RH, Killiany R, Sharma J, Jaisani Z, Singh B et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurology 69(12):1213–1223

    Article  CAS  PubMed  Google Scholar 

  125. Benedict RH, Ramasamy D, Munschauer F, Weinstock-Guttman B, Zivadinov R (2009) Memory impairment in multiple sclerosis: correlation with deep grey matter and mesial temporal atrophy. J Neurol Neurosurg Psychiatry 80(2):201–206

    Article  CAS  PubMed  Google Scholar 

  126. Riccitelli G, Rocca MA, Pagani E, Rodegher ME, Rossi P, Falini A et al (2011) Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp 32(10):1535–1543

    Article  PubMed  Google Scholar 

  127. Schoonheim MM, Popescu V, Rueda Lopes FC, Wiebenga OT, Vrenken H, Douw L et al (2012) Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology 79(17):1754–1761

    Article  PubMed  Google Scholar 

  128. Batista S, Zivadinov R, Hoogs M, Bergsland N, Heininen-Brown M, Dwyer MG et al (2012) Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol 259(1):139–146

    Article  PubMed  Google Scholar 

  129. Benedict RH, Hulst HE, Bergsland N, Schoonheim MM, Dwyer MG, Weinstock-Guttman B et al (2013) Clinical significance of atrophy and white matter mean diffusivity within the thalamus of multiple sclerosis patients. Mult Scler 19(11):1478–1484

    Article  PubMed  Google Scholar 

  130. Schoonheim MM, Hulst HE, Brandt RB, Strik M, Wink AM, Uitdehaag BM et al (2015) Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 84(8):776–783

    Article  PubMed  Google Scholar 

  131. Benedict RH, Zivadinov R (2011) Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol 7(6):332–342

    Article  PubMed  Google Scholar 

  132. Minagar A, Barnett MH, Benedict RH, Pelletier D, Pirko I, Sahraian MA et al (2013) The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology 80(2):210–219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Wylezinska M, Cifelli A, Jezzard P, Palace J, Alecci M, Matthews PM (2003) Thalamic neurodegeneration in relapsing-remitting multiple sclerosis. Neurology 60(12):1949–1954

    Article  CAS  PubMed  Google Scholar 

  134. Mesaros S, Rocca MA, Absinta M, Ghezzi A, Milani N, Moiola L et al (2008) Evidence of thalamic gray matter loss in pediatric multiple sclerosis. Neurology 70(13 Pt 2):1107–1112

    Article  CAS  PubMed  Google Scholar 

  135. Zivadinov R, Havrdova E, Bergsland N, Tyblova M, Hagemeier J, Seidl Z et al (2013) Thalamic atrophy is associated with development of clinically definite multiple sclerosis. Radiology 268(3):831–841

    Article  PubMed  Google Scholar 

  136. Shiee N, Bazin PL, Zackowski KM, Farrell SK, Harrison DM, Newsome SD et al (2012) Revisiting brain atrophy and its relationship to disability in multiple sclerosis. PLoS One 7(5):e37049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Filippi M, Rocca MA, Pagani E, De Stefano N, Jeffery D, Kappos L et al (2014) Placebo-controlled trial of oral laquinimod in multiple sclerosis: MRI evidence of an effect on brain tissue damage. J Neurol Neurosurg Psychiatry 85(8):851–858

    Article  PubMed  Google Scholar 

  138. Vrenken H, Geurts JJ (2007) Gray and normal-appearing white matter in multiple sclerosis: an MRI perspective. Expert Rev Neurother 7(3):271–279

    Article  PubMed  Google Scholar 

  139. Evangelou N, Esiri MM, Smith S, Palace J, Matthews PM (2000) Quantitative pathological evidence for axonal loss in normal appearing white matter in multiple sclerosis. Ann Neurol 47(3):391–395

    Article  CAS  PubMed  Google Scholar 

  140. Werring DJ, Clark CA, Barker GJ, Thompson AJ, Miller DH (1999) Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 52(8):1626–1632

    Article  CAS  PubMed  Google Scholar 

  141. Oreja-Guevara C, Rovaris M, Iannucci G, Valsasina P, Caputo D, Cavarretta R et al (2005) Progressive gray matter damage in patients with relapsing-remitting multiple sclerosis: a longitudinal diffusion tensor magnetic resonance imaging study. Arch Neurol 62(4):578–584

    Article  PubMed  Google Scholar 

  142. Rovaris M, Judica E, Gallo A, Benedetti B, Sormani MP, Caputo D et al (2006) Grey matter damage predicts the evolution of primary progressive multiple sclerosis at 5 years. Brain 129(Pt 10):2628–2634

    Article  CAS  PubMed  Google Scholar 

  143. Vrenken H, Pouwels PJ, Geurts JJ, Knol DL, Polman CH, Barkhof F et al (2006) Altered diffusion tensor in multiple sclerosis normal-appearing brain tissue: cortical diffusion changes seem related to clinical deterioration. J Magn Reson Imaging 23(5):628–636

    Article  PubMed  Google Scholar 

  144. van Walderveen MA, van Schijndel RA, Pouwels PJ, Polman CH, Barkhof F (2003) Multislice T1 relaxation time measurements in the brain using IR-EPI: reproducibility, normal values, and histogram analysis in patients with multiple sclerosis. J Magn Reson Imaging 18(6):656–664

    Article  PubMed  Google Scholar 

  145. Vrenken H, Geurts JJ, Knol DL, van Dijk LN, Dattola V, Jasperse B et al (2006) Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology 240(3):811–820

    Article  PubMed  Google Scholar 

  146. Steenwijk MD, Vrenken H, Jonkman LE, Daams M, Geurts JJ, Barkhof F et al (2015) High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis. Mult Scler 12

    Google Scholar 

  147. Matthews PM, Arnold DL (2001) Magnetic resonance imaging of multiple sclerosis: new insights linking pathology to clinical evolution. Curr Opin Neurol 14(3):279–287

    Article  CAS  PubMed  Google Scholar 

  148. De Stefano N, Narayanan S, Francis GS, Arnaoutelis R, Tartaglia MC, Antel JP et al (2001) Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. Arch Neurol 58(1):65–70

    Article  PubMed  Google Scholar 

  149. De Stefano N, Iannucci G, Sormani MP, Guidi L, Bartolozzi ML, Comi G et al (2002) MR correlates of cerebral atrophy in patients with multiple sclerosis. J Neurol 249(8):1072–1077

    Article  PubMed  Google Scholar 

  150. Inglese M, Li BS, Rusinek H, Babb JS, Grossman RI, Gonen O (2003) Diffusely elevated cerebral choline and creatine in relapsing-remitting multiple sclerosis. Magn Reson Med 50(1):190–195

    Article  CAS  PubMed  Google Scholar 

  151. Rovaris M, Bozzali M, Santuccio G, Ghezzi A, Caputo D, Montanari E et al (2001) In vivo assessment of the brain and cervical cord pathology of patients with primary progressive multiple sclerosis. Brain 124(Pt 12):2540–2549

    Article  CAS  PubMed  Google Scholar 

  152. Traboulsee A, Dehmeshki J, Peters KR, Griffin CM, Brex PA, Silver N et al (2003) Disability in multiple sclerosis is related to normal appearing brain tissue MTR histogram abnormalities. Mult Scler 9(6):566–573

    Article  CAS  PubMed  Google Scholar 

  153. Deloire MS, Salort E, Bonnet M, Arimone Y, Boudineau M, Amieva H et al (2005) Cognitive impairment as marker of diffuse brain abnormalities in early relapsing remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 76(4):519–526

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Khaleeli Z, Sastre-Garriga J, Ciccarelli O, Miller DH, Thompson AJ (2007) Magnetisation transfer ratio in the normal appearing white matter predicts progression of disability over 1 year in early primary progressive multiple sclerosis. J Neurol Neurosurg Psychiatry 78(10):1076–1082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Vrenken H, Pouwels PJ, Ropele S, Knol DL, Geurts JJ, Polman CH et al (2007) Magnetization transfer ratio measurement in multiple sclerosis normal-appearing brain tissue: limited differences with controls but relationships with clinical and MR measures of disease. Mult Scler 13(6):708–716

    Article  CAS  PubMed  Google Scholar 

  156. Vrenken H, Geurts JJ, Knol DL, Polman CH, Castelijns JA, Pouwels PJ et al (2006) Normal-appearing white matter changes vary with distance to lesions in multiple sclerosis. AJNR Am J Neuroradiol 27(9):2005–2011

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Enzinger C, Barkhof F, Ciccarelli O, Filippi M, Kappos L, Rocca MA et al (2015) Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat Rev Neurol 11(12):676–686

    Article  PubMed  Google Scholar 

  158. Kacar K, Rocca MA, Copetti M, Sala S, Mesaros S, Stosic Opincal T et al (2011) Overcoming the clinical-MR imaging paradox of multiple sclerosis: MR imaging data assessed with a random forest approach. AJNR Am J Neuroradiol 32(11):2098–2102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Fabiano AJ, Sharma J, Weinstock-Guttman B, Munschauer FE, 3rd, Benedict RH, Zivadinov R, et al. Thalamic involvement in multiple sclerosis: a diffusion-weighted magnetic resonance imaging study. J Neuroimaging 2003;13(4):307–314.

    Google Scholar 

  160. Deppe M, Kramer J, Tenberge JG, Marinell J, Schwindt W, Deppe K et al (2016) Early silent microstructural degeneration and atrophy of the thalamocortical network in multiple sclerosis. Hum Brain Mapp 37(5):1866–1879

    Article  PubMed  PubMed Central  Google Scholar 

  161. Mesaros S, Rocca MA, Pagani E, Sormani MP, Petrolini M, Comi G et al (2011) Thalamic damage predicts the evolution of primary-progressive multiple sclerosis at 5 years. AJNR Am J Neuroradiol 32(6):1016–1020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Hannoun S, Durand-Dubief F, Confavreux C, Ibarrola D, Streichenberger N, Cotton F et al (2012) Diffusion tensor-MRI evidence for extra-axonal neuronal degeneration in caudate and thalamic nuclei of patients with multiple sclerosis. AJNR Am J Neuroradiol 33(7):1363–1368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Schoonheim MM, Vigeveno RM, Rueda Lopes FC, Pouwels PJ, Polman CH, Barkhof F et al (2014) Sex-specific extent and severity of white matter damage in multiple sclerosis: implications for cognitive decline. Hum Brain Mapp 35(5):2348–2358

    Article  PubMed  Google Scholar 

  164. Hulst HE, Steenwijk MD, Versteeg A, Pouwels PJ, Vrenken H, Uitdehaag BM et al (2013) Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions. Neurology 80(11):1025–1032

    Article  CAS  PubMed  Google Scholar 

  165. Filippi M, Rocca MA, De Stefano N, Enzinger C, Fisher E, Horsfield MA et al (2011) Magnetic resonance techniques in multiple sclerosis: the present and the future. Arch Neurol 68(12):1514–1520

    Article  PubMed  Google Scholar 

  166. Roosendaal SD, Geurts JJ, Vrenken H, Hulst HE, Cover KS, Castelijns JA et al (2009) Regional DTI differences in multiple sclerosis patients. NeuroImage 44(4):1397–1403

    Article  CAS  PubMed  Google Scholar 

  167. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC et al (2009) Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19(3):524–536

    Article  PubMed  Google Scholar 

  168. van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31(44):15775–15786

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  169. Ciccarelli O, Catani M, Johansen-Berg H, Clark C, Thompson A (2008) Diffusion-based tractography in neurological disorders: concepts, applications, and future developments. Lancet Neurol 7(8):715–727

    Article  PubMed  Google Scholar 

  170. Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14(5):322–336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Bright MG, Murphy K (2015) Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure. NeuroImage 114:158–169

    Article  PubMed  Google Scholar 

  172. Bright MG, Murphy K (2013) Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data. NeuroImage 64:526–537

    Article  PubMed  Google Scholar 

  173. Nair A, Frederick TJ, Miller SD (2008) Astrocytes in multiple sclerosis: a product of their environment. Cell Mol Life Sci 65(17):2702–2720

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Brosnan CF, Raine CS (2013) The astrocyte in multiple sclerosis revisited. Glia 61(4):453–465

    Article  PubMed  Google Scholar 

  175. Correale J, Farez MF (2015) The role of astrocytes in multiple sclerosis progression. Front Neurol 6

    Google Scholar 

  176. Rossi DJ (2006) Another BOLD role for astrocytes: coupling blood flow to neural activity. Nat Neurosci 9(2):159–161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Haydon PG, Carmignoto G (2006) Astrocyte control of synaptic transmission and neurovascular coupling. Physiol Rev 86(3):1009–1031

    Article  CAS  PubMed  Google Scholar 

  178. Petzold GC, Murthy VN (2011) Role of astrocytes in neurovascular coupling. Neuron 71(5):782–797

    Article  CAS  PubMed  Google Scholar 

  179. Bennett M, Farnell L, Gibson W (2008) Origins of the BOLD changes due to synaptic activity at astrocytes abutting arteriolar smooth muscle. J Theor Biol 252(1):123–130

    Article  CAS  PubMed  Google Scholar 

  180. Aquino KM, Robinson PA, Schira MM, Breakspear M (2014) Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function. NeuroImage 94:203–215

    Article  CAS  PubMed  Google Scholar 

  181. Drysdale P, Huber J, Robinson P, Aquino K (2010) Spatiotemporal BOLD dynamics from a poroelastic hemodynamic model. J Theor Biol 265(4):524–534

    Article  CAS  PubMed  Google Scholar 

  182. Staffen W, Mair A, Zauner H, Unterrainer J, Niederhofer H, Kutzelnigg A et al (2002) Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task. Brain 125(6):1275–1282

    Article  CAS  PubMed  Google Scholar 

  183. Audoin B, Ibarrola D, Ranjeva JP, Confort-Gouny S, Malikova I, Ali-Chérif A et al (2003) Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of multiple sclerosis. Hum Brain Mapp 20(2):51–58

    Article  PubMed  PubMed Central  Google Scholar 

  184. Mainero C, Caramia F, Pozzilli C, Pisani A, Pestalozza I, Borriello G et al (2004) fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. NeuroImage 21(3):858–867

    Article  PubMed  Google Scholar 

  185. Rocca MA, Valsasina P, Hulst HE, Abdel-Aziz K, Enzinger C, Gallo A et al (2014) Functional correlates of cognitive dysfunction in multiple sclerosis: a multicenter fMRI study. Hum Brain Mapp 35(12):5799–5814

    Article  PubMed  PubMed Central  Google Scholar 

  186. Hulst HE, Schoonheim MM, Roosendaal SD, Popescu V, Schweren LJ, van der Werf YD et al (2012) Functional adaptive changes within the hippocampal memory system of patients with multiple sclerosis. Hum Brain Mapp 33(10):2268–2280

    Article  PubMed  Google Scholar 

  187. Sumowski JF, Wylie GR, DeLuca J, Chiaravalloti N (2010) Intellectual enrichment is linked to cerebral efficiency in multiple sclerosis: functional magnetic resonance imaging evidence for cognitive reserve. Brain 133(2):362–374

    Article  PubMed  Google Scholar 

  188. Schoonheim MM, Meijer KA, Geurts JJ (2015) Network collapse and cognitive impairment in multiple sclerosis. Front Neurol 6

    Google Scholar 

  189. Schoonheim MM, Geurts JJ, Barkhof F (2010) The limits of functional reorganization in multiple sclerosis. Neurology 74(16):1246–1247

    Article  PubMed  Google Scholar 

  190. Roosendaal SD, Schoonheim MM, Hulst HE, Sanz-Arigita EJ, Smith SM, Geurts JJ et al (2010) Resting state networks change in clinically isolated syndrome. Brain 133(6):1612–1621

    Article  PubMed  Google Scholar 

  191. Rocca MA, Valsasina P, Absinta M, Riccitelli G, Rodegher ME, Misci P et al (2010) Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 74(16):1252–1259

    Article  CAS  PubMed  Google Scholar 

  192. Cader S, Cifelli A, Abu-Omar Y, Palace J, Matthews PM (2006) Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain 129(2):527–537

    Article  PubMed  Google Scholar 

  193. Bonnet M, Allard M, Dilharreguy B, Deloire M, Petry K, Brochet B (2010) Cognitive compensation failure in multiple sclerosis. Neurology 75(14):1241–1248

    Article  CAS  PubMed  Google Scholar 

  194. Audoin B, Boulanouar K, Ibarrola D, Malikova I, Confort-Gouny S, Celsis P et al (2005) Altered functional connectivity related to white matter changes inside the working memory network at the very early stage of MS. J Cereb Blood Flow Metab 25(10):1245–1253

    Article  PubMed  Google Scholar 

  195. Ranjeva J-P, Audoin B, Duong MVA, Confort-Gouny S, Malikova I, Viout P et al (2006) Structural and functional surrogates of cognitive impairment at the very early stage of multiple sclerosis. J Neurol Sci 245(1):161–167

    Article  PubMed  Google Scholar 

  196. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM et al (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103(37):13848–13853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Fornito A, Zalesky A, Breakspear M (2015) The connectomics of brain disorders. Nat Rev Neurosci 16(3):159–172

    Article  CAS  PubMed  Google Scholar 

  198. Rocca MA, Valsasina P, Martinelli V, Misci P, Falini A, Comi G et al (2012) Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology 79(14):1449–1457

    Article  PubMed  Google Scholar 

  199. Cruz-Gómez ÁJ, Ventura-Campos N, Belenguer A, Ávila C, Forn C (2014) The link between resting-state functional connectivity and cognition in MS patients. Mult Scler J 20(3):338–348

    Article  Google Scholar 

  200. Louapre C, Perlbarg V, García-Lorenzo D, Urbanski M, Benali H, Assouad R et al (2014) Brain networks disconnection in early multiple sclerosis cognitive deficits: an anatomofunctional study. Hum Brain Mapp 35(9):4706–4717

    Article  PubMed  PubMed Central  Google Scholar 

  201. Bonavita S, Gallo A, Sacco R, Della Corte M, Bisecco A, Docimo R et al (2011) Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Mult Scler J 17(4):411–422

    Article  Google Scholar 

  202. Leavitt VM, Paxton J, Sumowski JF (2014) Default network connectivity is linked to memory status in multiple sclerosis. J Int Neuropsychol Soc 20(09):937–944

    Article  PubMed  Google Scholar 

  203. Zhou F, Zhuang Y, Gong H, Wang B, Wang X, Chen Q et al (2014) Altered inter-subregion connectivity of the default mode network in relapsing remitting multiple sclerosis: a functional and structural connectivity study. PLoS One 9(7):e101198

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  204. de Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L et al (2010) Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci 107(13):6040–6045

    Article  PubMed  PubMed Central  Google Scholar 

  205. de Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL et al (2012) A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74(4):753–764

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  206. Wojtowicz M, Mazerolle EL, Bhan V, Fisk JD (2014) Altered functional connectivity and performance variability in relapsing–remitting multiple sclerosis. Mult Scler J 1352458514524997

    Google Scholar 

  207. Faivre A, Rico A, Zaaraoui W, Crespy L, Reuter F, Wybrecht D et al (2012) Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler J 18(9):1251–1258

    Article  Google Scholar 

  208. Rocca MA, Absinta M, Amato MP, Moiola L, Ghezzi A, Veggiotti P et al (2014) Posterior brain damage and cognitive impairment in pediatric multiple sclerosis. Neurology 82(15):1314–1321

    Article  PubMed  Google Scholar 

  209. Swaab D (1991) Brain aging and Alzheimer’s disease, “wear and tear” versus “use it or lose it”. Neurobiol Aging 12(4):317–324

    Article  CAS  PubMed  Google Scholar 

  210. de Haan W, Mott K, van Straaten EC, Scheltens P, Stam CJ (2012) Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease. PLoS Comput Biol 8(8):e1002582

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  211. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65(2):413

    Article  Google Scholar 

  212. Supek S, Aine CJ (2014) Magnetoencephalography: from signals to dynamic cortical networks. Springer, Berlin/Heidelberg

    Google Scholar 

  213. Murakami S, Okada Y (2006) Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J Physiol 575(3):925–936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  214. da Silva FL (2013) EEG and MEG: relevance to neuroscience. Neuron 80(5):1112–1128

    Article  CAS  Google Scholar 

  215. Hillebrand A, Barnes GR (2005) Beamformer analysis of MEG data. Int Rev Neurobiol 68:149–171

    Article  PubMed  Google Scholar 

  216. Hillebrand A, Singh KD, Holliday IE, Furlong PL, Barnes GR (2005) A new approach to neuroimaging with magnetoencephalography. Hum Brain Mapp 25(2):199–211

    Article  PubMed  PubMed Central  Google Scholar 

  217. O’Neill GC, Barratt EL, Hunt BA, Tewarie PK, Brookes MJ (2015) Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Phys Med Biol 60(21):R271

    Article  PubMed  Google Scholar 

  218. Schoffelen JM, Gross J (2009) Source connectivity analysis with MEG and EEG. Hum Brain Mapp 30(6):1857–1865

    Article  PubMed  PubMed Central  Google Scholar 

  219. Hillebrand A, Barnes GR, Bosboom JL, Berendse HW, Stam CJ (2012) Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution. NeuroImage 59(4):3909–3921

    Article  PubMed  Google Scholar 

  220. Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. Signal Processing Magazine, IEEE 18(6):14–30

    Article  Google Scholar 

  221. Van Veen BD, Buckley KM (1988) Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag 5(2):4–24

    Article  Google Scholar 

  222. Robinson S, Vrba J (1999) Functional neuroimaging by synthetic aperture magnetometry (SAM). In: Recent advances in biomagnetism. Tohoku University Press, Sendai, pp 302–305

    Google Scholar 

  223. Pereda E, Quiroga RQ, Bhattacharya J (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 77(1):1–37

    Article  PubMed  Google Scholar 

  224. Kida T, Tanaka E, Kakigi R (2015) Multi-dimensional dynamics of human electromagnetic brain activity. Front Hum Neurosci 9:713

    PubMed  Google Scholar 

  225. Licker MD, Geller E (2002) Dictionary of physics, XI ed. McGraw-Hill, Clarinda

    Google Scholar 

  226. Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28(11):1178–1193

    Article  PubMed  PubMed Central  Google Scholar 

  227. Brookes MJ, Woolrich MW, Barnes GR (2012) Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. NeuroImage 63(2):910–920

    Article  CAS  PubMed  Google Scholar 

  228. Stam C, Van Dijk B (2002) Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Physica D 163(3):236–251

    Article  Google Scholar 

  229. Karhu J, Hari R, Mäkelä JP, Huttunen J, Knuutila J (1992) Cortical somatosensory magnetic responses in multiple sclerosis. Electroencephalogr Clin Neurophysiol 83(3):192–200

    Article  CAS  PubMed  Google Scholar 

  230. Kassubek J, Sörös P, Kober H, Stippich C, Vieth JB (1999) Focal slow and beta brain activity in patients with multiple sclerosis revealed by magnetoencephalography. Brain Topogr 11(3):193–200

    Article  CAS  PubMed  Google Scholar 

  231. Leocani L, Locatelli T, Martinelli V, Rovaris M, Falautano M, Filippi M et al (2000) Electroencephalographic coherence analysis in multiple sclerosis: correlation with clinical, neuropsychological, and MRI findings. J Neurol Neurosurg Psychiatry 69(2):192–198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  232. Cover KS, Vrenken H, Geurts JJ, van Oosten BW, Jelles B, Polman CH et al (2006) Multiple sclerosis patients show a highly significant decrease in alpha band interhemispheric synchronization measured using MEG. NeuroImage 29(3):783–788

    Article  PubMed  Google Scholar 

  233. Van der Meer M, Tewarie P, Schoonheim M, Douw L, Barkhof F, Polman C et al (2013) Cognition in MS correlates with resting-state oscillatory brain activity: an explorative MEG source-space study. Neuroimage Clin 2:727–734

    Article  PubMed  PubMed Central  Google Scholar 

  234. Valdés-Hernández PA, Ojeda-González A, Martínez-Montes E, Lage-Castellanos A, Virués-Alba T, Valdés-Urrutia L et al (2010) White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm. NeuroImage 49(3):2328–2339

    Article  PubMed  Google Scholar 

  235. Hindriks R, Woolrich M, Luckhoo H, Joensson M, Mohseni H, Kringelbach M et al (2015) Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex. NeuroImage 106:328–339

    Article  CAS  PubMed  Google Scholar 

  236. Tecchio F, Zito G, Zappasodi F, Dell’Acqua ML, Landi D, Nardo D et al (2008) Intra-cortical connectivity in multiple sclerosis: a neurophysiological approach. Brain 131(7):1783–1792

    Article  PubMed  Google Scholar 

  237. Schoonheim MM, Geurts JJ, Landi D, Douw L, van der Meer ML, Vrenken H et al (2013) Functional connectivity changes in multiple sclerosis patients: a graph analytical study of MEG resting state data. Hum Brain Mapp 34(1):52–61

    Article  PubMed  Google Scholar 

  238. Tewarie P, Schoonheim MM, Stam CJ, van der Meer ML, van Dijk BW, Barkhof F et al (2013) Cognitive and clinical dysfunction, altered MEG resting-state networks and thalamic atrophy in multiple sclerosis. PLoS One 8(7):e69318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  239. Tewarie P, Schoonheim MM, Schouten DI, Polman CH, Balk LJ, Uitdehaag BM et al (2015) Functional brain networks: linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG study. Hum Brain Mapp 36(2):603–618

    Article  PubMed  Google Scholar 

  240. Tewarie P, Steenwijk MD, Tijms BM, Daams M, Balk LJ, Stam CJ et al (2014) Disruption of structural and functional networks in long-standing multiple sclerosis. Hum Brain Mapp 35(12):5946–5961

    Article  PubMed  PubMed Central  Google Scholar 

  241. Stam CJ (2014) Modern network science of neurological disorders. Nat Rev Neurosci 15(10):683–695

    Article  CAS  PubMed  Google Scholar 

  242. Cv S, Van Straaten E (2012) The organization of physiological brain networks. Clin Neurophysiol 123(6):1067–1087

    Article  Google Scholar 

  243. Van Wijk BC, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5(10):e13701

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  244. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  245. Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13(5):336–349

    Article  CAS  PubMed  Google Scholar 

  246. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    Article  CAS  PubMed  Google Scholar 

  247. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  PubMed  Google Scholar 

  248. Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P et al (2013) Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging 34(8):2023–2036

    Article  PubMed  Google Scholar 

  249. Diessen E, Diederen SJ, Braun KP, Jansen FE, Stam CJ (2013) Functional and structural brain networks in epilepsy: what have we learned? Epilepsia 54(11):1855–1865

    Article  PubMed  Google Scholar 

  250. Van Diessen E, Numan T, van Dellen E, van der Kooi A, Boersma M, Hofman D et al (2015) Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol 126(8):1468–1481

    Article  PubMed  Google Scholar 

  251. Tewarie P, Van Dellen E, Hillebrand A, Stam C (2015) The minimum spanning tree: an unbiased method for brain network analysis. NeuroImage 104:177–188

    Article  CAS  PubMed  Google Scholar 

  252. Van Mieghem P, Magdalena SM. Phase transition in the link weight structure of networks. Phys Rev E 2005;72(5):056138

    Google Scholar 

  253. Meier J, Tewarie P, Van Mieghem P (2015) The Union of shortest path trees of functional brain networks. Brain Connect

    Google Scholar 

  254. Schoonheim MM, Hulst HE, Landi D, Ciccarelli O, Roosendaal SD, Sanz-Arigita EJ et al (2012) Gender-related differences in functional connectivity in multiple sclerosis. Mult Scler J 18(2):164–173

    Article  Google Scholar 

  255. Rocca MA, Valsasina P, Meani A, Falini A, Comi G, Filippi M (2016) Impaired functional integration in multiple sclerosis: a graph theory study. Brain Struct Funct 221(1):115–131

    Article  PubMed  Google Scholar 

  256. Gamboa OL, Tagliazucchi E, von Wegner F, Jurcoane A, Wahl M, Laufs H et al (2014) Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks. NeuroImage 94:385–395

    Article  CAS  PubMed  Google Scholar 

  257. Hardmeier M, Schoonheim MM, Geurts JJ, Hillebrand A, Polman CH, Barkhof F et al (2012) Cognitive dysfunction in early multiple sclerosis: altered centrality derived from resting-state functional connectivity using magneto-encephalography. PLoS One 7(7):e42087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  258. Schoonheim M, Geurts J, Wiebenga O, De Munck J, Polman C, Stam C et al (2014) Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis. Mult Scler J 20(8):1058–1065

    Article  CAS  Google Scholar 

  259. Tewarie P, Bright M, Hillebrand A, Robson S, Gascoyne L, Morris P et al (2016) Predicting haemodynamic networks using electrophysiology: the role of non-linear and cross-frequency interactions. NeuroImage 130:273–292

    Article  CAS  PubMed  Google Scholar 

  260. Tewarie P, Hillebrand A, Schoonheim M, Van Dijk B, Geurts J, Barkhof F et al (2014) Functional brain network analysis using minimum spanning trees in multiple sclerosis: an MEG source-space study. NeuroImage 88:308–318

    Article  CAS  PubMed  Google Scholar 

  261. Sbardella E, Tona F, Petsas N, Upadhyay N, Piattella M, Filippini N et al (2015) Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing–remitting multiple sclerosis. Mult Scler J 21(13):1681–1692

    Article  CAS  Google Scholar 

  262. Meier J, Tewarie P, Hillebrand A, Douw L, van Dijk B, Stufflebeam S et al (2016) A mapping between structural and functional brain networks. Brain Connect 6(4):298–311

    Article  PubMed  PubMed Central  Google Scholar 

  263. Robinson P (2012) Interrelating anatomical, effective, and functional brain connectivity using propagators and neural field theory. Phys Rev E 85(1):011912

    Article  CAS  Google Scholar 

  264. Deco G, Tononi G, Boly M, Kringelbach ML (2015) Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 16(7):430–439

    Article  CAS  PubMed  Google Scholar 

  265. Hindriks R, Adhikari M, Murayama Y, Ganzetti M, Mantini D, Logothetis N et al Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 2015

    Google Scholar 

  266. Braun U, Schäfer A, Walter H, Erk S, Romanczuk-Seiferth N, Haddad L et al (2015) Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci 112(37):11678–11683

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  267. Baker AP, Brookes MJ, Rezek IA, Smith SM, Behrens T, Smith PJP et al (2014) Fast transient networks in spontaneous human brain activity. elife e01867:3

    Google Scholar 

  268. Brookes MJ, O'Neill GC, Hall EL, Woolrich MW, Baker A, Corner SP et al (2014) Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity. NeuroImage 91:282–299

    Article  PubMed  Google Scholar 

  269. O'Neill GC, Bauer M, Woolrich MW, Morris PG, Barnes GR, Brookes MJ (2015) Dynamic recruitment of resting state sub-networks. NeuroImage 115:85–95

    Article  PubMed  Google Scholar 

  270. Roebroeck A, Formisano E, Goebel R (2005) Mapping directed influence over the brain using granger causality and fMRI. NeuroImage 25(1):230–242

    Article  PubMed  Google Scholar 

  271. Hillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EWS, Douw L et al (2016) Direction of information flow in large-scale resting-state networks is frequency dependent. Proc Natl Acad Sci

    Google Scholar 

  272. Lobier M, Siebenhühner F, Palva S, Palva JM (2014) Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. NeuroImage 85:853–872

    Article  PubMed  Google Scholar 

  273. Brookes MJ, Tewarie PK, Hunt BA, Robson SE, Gascoyne LE, Liddle EB et al (2016) A multi-layer network approach to MEG Connectivity Analysis. NeuroImage 132:425–438

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands

    Prejaas Tewarie & Arjan Hillebrand

  2. Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands

    Menno Schoonheim

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  1. Prejaas Tewarie
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  1. Service de NeuroImagerie CHNO des 15/20, Paris, France

    Christophe Habas

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Tewarie, P., Schoonheim, M., Hillebrand, A. (2018). Structural and Functional Neuroimaging in Multiple Sclerosis: From Atrophy, Lesions to Global Network Disruption. In: Habas, C. (eds) The Neuroimaging of Brain Diseases. Contemporary Clinical Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-78926-2_8

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