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Human Functional MRI

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Translational Methods for Multiple Sclerosis Research

Part of the book series: Neuromethods ((NM,volume 166))

Abstract

This chapter reviews the basic principles, main acquisition, and postprocessing techniques of functional magnetic resonance imaging (fMRI) applied to study multiple sclerosis (MS).

First, we describe the blood-oxygenation level dependent (BOLD ) effect and the principal analysis techniques used to process fMRI data, including those acquired during the performance of active tasks and those acquired at resting state.

Subsequently, we summarize how the different fMRI techniques have contributed to investigate MS pathophysiology, by demonstrating that functional reorganization occurs as a consequence of structural damage accumulation in MS patients and can contribute, at least in the earliest phases of the disease, to limit the clinical consequences of widespread structural abnormalities. We discuss also how the failure or exhaustion of central nervous system adaptive properties might be among the factors responsible for the accumulation of irreversible clinical disability and cognitive impairment.

The identification of MS-related adaptive and maladaptive reorganization is an attractive goal to understand the mechanisms of action of pharmacologic and rehabilitative treatments and to develop novel therapeutic strategies able to promote individual adaptive capacity.

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References

  1. Filippi M, Bruck W, Chard D et al (2019) Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol 18:198–210

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  3. Filippi M, Preziosa P, Rocca MA (2017) Microstructural MR imaging techniques in multiple sclerosis. Neuroimaging Clin N Am 27:313–333

    Article  PubMed  Google Scholar 

  4. Rocca MA, Preziosa P, Filippi M (2019) Application of advanced MRI techniques to monitor pharmacologic and rehabilitative treatment in multiple sclerosis: current status and future perspectives. Expert Rev Neurother 19:835–866

    Article  CAS  PubMed  Google Scholar 

  5. Enzinger C, Barkhof F, Ciccarelli O et al (2015) Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat Rev Neurol 11:676–686

    Article  PubMed  Google Scholar 

  6. Rocca MA, Amato MP, De Stefano N et al (2015) Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 14:302–317

    Article  PubMed  Google Scholar 

  7. Rocca MA, Battaglini M, Benedict RH et al (2017) Brain MRI atrophy quantification in MS: from methods to clinical application. Neurology 88:403–413

    Article  PubMed  PubMed Central  Google Scholar 

  8. Filippi M, Rocca MA (2004) Magnetization transfer magnetic resonance imaging in the assessment of neurological diseases. J Neuroimaging 14:303–313

    Article  PubMed  Google Scholar 

  9. Filippi M, Rocca MA, Comi G (2003) The use of quantitative magnetic-resonance-based techniques to monitor the evolution of multiple sclerosis. Lancet Neurol 2:337–346

    Article  CAS  PubMed  Google Scholar 

  10. Zatorre RJ, Fields RD, Johansen-Berg H (2012) Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci 15:528–536

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rocca MA, Filippi M (2006) Functional MRI to study brain plasticity in clinical neurology. Neurol Sci 27(Suppl 1):S24–S26

    Article  PubMed  Google Scholar 

  12. Rocca MA, Filippi M (2007) Functional MRI in multiple sclerosis. J Neuroimaging 17(Suppl 1):36S–41S

    Article  PubMed  Google Scholar 

  13. Ogawa S, Lee TM, Kay AR et al (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87:9868–9872

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ogawa S, Menon RS, Tank DW et al (1993) Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J 64:803–812

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Feinberg DA, Moeller S, Smith SM et al (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5:e15710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Buxton RB, Wong EC, Frank LR (1998) Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med 39:855–864

    Article  CAS  PubMed  Google Scholar 

  17. Friston KJ, Holmes AP, Poline JB et al (1995) Analysis of fMRI time-series revisited. NeuroImage 2:45–53

    Article  CAS  PubMed  Google Scholar 

  18. Flandin G, Friston KJ (2008) Statistical parametric mapping. Scholarpedia 3(4):6232

    Article  Google Scholar 

  19. Adler RJ (1981) The geometry of random fields. Wiley, New York

    Google Scholar 

  20. Worsley KJ, Marrett S, Neelin P et al (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4:58–73

    Article  CAS  PubMed  Google Scholar 

  21. Chumbley J, Worsley K, Flandin G et al (2010) Topological FDR for neuroimaging. NeuroImage 49:3057–3064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Friston KJ, Frith CD, Liddle PF et al (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13:5–14

    Article  CAS  PubMed  Google Scholar 

  23. Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1:13–36

    Article  PubMed  Google Scholar 

  24. Stephan KE, Friston KJ (2010) Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci 1:446–459

    Article  PubMed  PubMed Central  Google Scholar 

  25. Friston KJ, Buechel C, Fink GR et al (1997) Psychophysiological and modulatory interactions in neuroimaging. NeuroImage 6:218–229

    Article  CAS  PubMed  Google Scholar 

  26. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19:1273–1302

    Article  CAS  PubMed  Google Scholar 

  27. Au Duong MV, Boulanouar K, Audoin B et al (2005) Modulation of effective connectivity inside the working memory network in patients at the earliest stage of multiple sclerosis. NeuroImage 24:533–538

    Article  CAS  PubMed  Google Scholar 

  28. Ramsey JD, Hanson SJ, Glymour C (2011) Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage 58:838–848

    Article  PubMed  Google Scholar 

  29. Cordes D, Haughton VM, Arfanakis K et al (2001) Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. AJNR Am J Neuroradiol 22:1326–1333

    CAS  PubMed  Google Scholar 

  30. Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci U S A 107:4734–4739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Damoiseaux JS, Rombouts SA, Barkhof F et al (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 106:13040–13045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Raichle ME, MacLeod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711

    Article  CAS  PubMed  Google Scholar 

  35. Greicius MD, Srivastava G, Reiss AL et al (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 101:4637–4642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4:19

    PubMed  PubMed Central  Google Scholar 

  37. Lv H, Wang Z, Tong E et al (2018) Resting-state functional MRI: everything that nonexperts have always wanted to know. AJNR Am J Neuroradiol 39:1390–1399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tononi G, Sporns O, Edelman GM (1994) A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci U S A 91:5033–5037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lund TE, Madsen KH, Sidaros K et al (2006) Non-white noise in fMRI: does modelling have an impact? NeuroImage 29:54–66

    Article  PubMed  Google Scholar 

  40. Van Dijk KR, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59:431–438

    Article  PubMed  Google Scholar 

  41. Whitfield-Gabrieli S, Thermenos HW, Milanovic S et al (2009) Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 106:1279–1284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Glover GH, Li TQ, Ress D (2000) Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44:162–167

    Article  CAS  PubMed  Google Scholar 

  43. Griffanti L, Salimi-Khorshidi G, Beckmann CF et al (2014) ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95:232–247

    Article  PubMed  PubMed Central  Google Scholar 

  44. Behzadi Y, Restom K, Liau J et al (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37:90–101

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yang H, Long XY, Yang Y et al (2007) Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. NeuroImage 36:144–152

    Article  PubMed  Google Scholar 

  46. Zang Y, Jiang T, Lu Y et al (2004) Regional homogeneity approach to fMRI data analysis. NeuroImage 22:394–400

    Article  PubMed  Google Scholar 

  47. Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Natl Acad Sci U S A 107:9885–9890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Biswal B, Yetkin FZ, Haughton VM et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

    Article  CAS  PubMed  Google Scholar 

  49. McKeown MJ, Sejnowski TJ (1998) Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp 6:368–372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Calhoun VD, Adali T, Pearlson GD et al (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Beckmann CF, DeLuca M, Devlin JT et al (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond Ser B Biol Sci 360:1001–1013

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  53. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198

    Article  CAS  PubMed  Google Scholar 

  54. Tagliazucchi E, van Someren EJW (2017) The large-scale functional connectivity correlates of consciousness and arousal during the healthy and pathological human sleep cycle. NeuroImage 160:55–72

    Article  PubMed  Google Scholar 

  55. Hutchison RM, Womelsdorf T, Allen EA et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–378

    Article  PubMed  Google Scholar 

  56. Calhoun VD, Miller R, Pearlson G et al (2014) The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Allen EA, Damaraju E, Plis SM et al (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676

    Article  PubMed  Google Scholar 

  58. Sakoglu U, Pearlson GD, Kiehl KA et al (2010) A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA 23:351–366

    Article  PubMed  PubMed Central  Google Scholar 

  59. Liu X, Duyn JH (2013) Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci U S A 110:4392–4397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Reddy H, Narayanan S, Matthews PM et al (2000) Relating axonal injury to functional recovery in MS. Neurology 54:236–239

    Article  CAS  PubMed  Google Scholar 

  61. Mezzapesa DM, Rocca MA, Rodegher M et al (2008) Functional cortical changes of the sensorimotor network are associated with clinical recovery in multiple sclerosis. Hum Brain Mapp 29:562–573

    Article  PubMed  Google Scholar 

  62. Zaaraoui W, Rico A, Audoin B et al (2010) Unfolding the long-term pathophysiological processes following an acute inflammatory demyelinating lesion of multiple sclerosis. Magn Reson Imaging 28:477–486

    Article  CAS  PubMed  Google Scholar 

  63. Rocca MA, Colombo B, Falini A et al (2005) Cortical adaptation in patients with MS: a cross-sectional functional MRI study of disease phenotypes. Lancet Neurol 4:618–626

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  65. Filippi M, Rocca MA, Mezzapesa DM et al (2004) Simple and complex movement-associated functional MRI changes in patients at presentation with clinically isolated syndromes suggestive of multiple sclerosis. Hum Brain Mapp 21:108–117

    Article  PubMed  Google Scholar 

  66. Rocca MA, Mezzapesa DM, Falini A et al (2003) Evidence for axonal pathology and adaptive cortical reorganization in patients at presentation with clinically isolated syndromes suggestive of multiple sclerosis. NeuroImage 18:847–855

    Article  PubMed  Google Scholar 

  67. Pantano P, Mainero C, Lenzi D et al (2005) A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain 128:2146–2153

    Article  PubMed  Google Scholar 

  68. Rocca MA, Absinta M, Ghezzi A et al (2009) Is a preserved functional reserve a mechanism limiting clinical impairment in pediatric MS patients? Hum Brain Mapp 30:2844–2851

    Article  PubMed  Google Scholar 

  69. Rocca MA, Ceccarelli A, Rodegher M et al (2010) Preserved brain adaptive properties in patients with benign multiple sclerosis. Neurology 74:142–149

    Article  CAS  PubMed  Google Scholar 

  70. Rocca MA, Gavazzi C, Mezzapesa DM et al (2003) A functional magnetic resonance imaging study of patients with secondary progressive multiple sclerosis. NeuroImage 19:1770–1777

    Article  PubMed  Google Scholar 

  71. Filippi M, Rocca MA, Falini A et al (2002) Correlations between structural CNS damage and functional MRI changes in primary progressive MS. NeuroImage 15:537–546

    Article  CAS  PubMed  Google Scholar 

  72. Rocca MA, Matthews PM, Caputo D et al (2002) Evidence for widespread movement-associated functional MRI changes in patients with PPMS. Neurology 58:866–872

    Article  CAS  PubMed  Google Scholar 

  73. Rocca MA, Falini A, Colombo B et al (2002) Adaptive functional changes in the cerebral cortex of patients with nondisabling multiple sclerosis correlate with the extent of brain structural damage. Ann Neurol 51:330–339

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  75. Au Duong MV, Audoin B, Boulanouar K 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 25:1245–1253

    Article  PubMed  Google Scholar 

  76. Audoin B, Ibarrola D, Ranjeva JP et al (2003) Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS. Hum Brain Mapp 20:51–58

    Article  PubMed  PubMed Central  Google Scholar 

  77. Audoin B, Au Duong MV, Ranjeva JP et al (2005) Magnetic resonance study of the influence of tissue damage and cortical reorganization on PASAT performance at the earliest stage of multiple sclerosis. Hum Brain Mapp 24:216–228

    Article  PubMed  Google Scholar 

  78. Hillary FG, Chiaravalloti ND, Ricker JH et al (2003) An investigation of working memory rehearsal in multiple sclerosis using fMRI. J Clin Exp Neuropsychol 25:965–978

    Article  CAS  PubMed  Google Scholar 

  79. Parry AM, Scott RB, Palace J et al (2003) Potentially adaptive functional changes in cognitive processing for patients with multiple sclerosis and their acute modulation by rivastigmine. Brain 126:2750–2760

    Article  PubMed  Google Scholar 

  80. Penner IK, Rausch M, Kappos L et al (2003) Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. J Neurol 250:461–472

    Article  PubMed  Google Scholar 

  81. Mainero C, Caramia F, Pozzilli C et al (2004) fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. NeuroImage 21:858–867

    Article  PubMed  Google Scholar 

  82. Sweet LH, Rao SM, Primeau M et al (2004) Functional magnetic resonance imaging of working memory among multiple sclerosis patients. J Neuroimaging 14:150–157

    Article  PubMed  Google Scholar 

  83. Sweet LH, Rao SM, Primeau M et al (2006) Functional magnetic resonance imaging response to increased verbal working memory demands among patients with multiple sclerosis. Hum Brain Mapp 27:28–36

    Article  PubMed  Google Scholar 

  84. Wishart HA, Saykin AJ, McDonald BC et al (2004) Brain activation patterns associated with working memory in relapsing-remitting MS. Neurology 62:234–238

    Article  CAS  PubMed  Google Scholar 

  85. Chiaravalloti N, Hillary F, Ricker J et al (2005) Cerebral activation patterns during working memory performance in multiple sclerosis using FMRI. J Clin Exp Neuropsychol 27:33–54

    Article  PubMed  Google Scholar 

  86. Cader S, Cifelli A, Abu-Omar Y et al (2006) Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain 129:527–537

    Article  PubMed  Google Scholar 

  87. Li Y, Chiaravalloti ND, Hillary FG et al (2004) Differential cerebellar activation on functional magnetic resonance imaging during working memory performance in persons with multiple sclerosis. Arch Phys Med Rehabil 85:635–639

    Article  PubMed  Google Scholar 

  88. Rocca MA, Valsasina P, Ceccarelli A et al (2009) Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Mapp 30:276–290

    Article  PubMed  Google Scholar 

  89. Lazeron RHC, Rombouts SARB, Scheltens P et al (2004) An fMRI study of planning-related brain activity in patients with moderately advanced multiple sclerosis. Mult Scler 10:549–555

    Article  PubMed  Google Scholar 

  90. Forn C, Rocca MA, Valsasina P et al (2012) Functional magnetic resonance imaging correlates of cognitive performance in patients with a clinically isolated syndrome suggestive of multiple sclerosis at presentation: an activation and connectivity study. Mult Scler 18:153–163

    Article  CAS  PubMed  Google Scholar 

  91. Rocca MA, Valsasina P, Hulst HE et al (2014) Functional correlates of cognitive dysfunction in multiple sclerosis: a multicenter fMRI study. Hum Brain Mapp 35:5799–5814

    Article  PubMed  PubMed Central  Google Scholar 

  92. Audoin B, Reuter F, Duong MV et al (2008) Efficiency of cognitive control recruitment in the very early stage of multiple sclerosis: a one-year fMRI follow-up study. Mult Scler 14:786–792

    Article  CAS  PubMed  Google Scholar 

  93. Loitfelder M, Fazekas F, Koschutnig K et al (2014) Brain activity changes in cognitive networks in relapsing-remitting multiple sclerosis—insights from a longitudinal FMRI study. PLoS One 9:e93715

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Rocca MA, Riccitelli G, Rodegher M et al (2010) Functional MR imaging correlates of neuropsychological impairment in primary-progressive multiple sclerosis. AJNR Am J Neuroradiol 31:1240–1246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Roosendaal SD, Schoonheim MM, Hulst HE et al (2010) Resting state networks change in clinically isolated syndrome. Brain 133:1612–1621

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  97. Dogonowski AM, Siebner HR, Soelberg Sorensen P et al (2013) Resting-state connectivity of pre-motor cortex reflects disability in multiple sclerosis. Acta Neurol Scand 128:328–335

    PubMed  Google Scholar 

  98. Rocca MA, Absinta M, Amato MP et al (2014) Posterior brain damage and cognitive impairment in pediatric multiple sclerosis. Neurology 82:1314–1321

    Article  PubMed  Google Scholar 

  99. Rocca MA, Valsasina P, Leavitt VM et al (2018) Functional network connectivity abnormalities in multiple sclerosis: correlations with disability and cognitive impairment. Mult Scler 24:459–471

    Article  PubMed  Google Scholar 

  100. Rocca MA, Valsasina P, Martinelli V et al (2012) Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology 79:1449–1457

    Article  PubMed  Google Scholar 

  101. Basile B, Castelli M, Monteleone F et al (2014) Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis. Mult Scler 20:1050–1057

    Article  CAS  PubMed  Google Scholar 

  102. Janssen AL, Boster A, Patterson BA et al (2013) Resting-state functional connectivity in multiple sclerosis: an examination of group differences and individual differences. Neuropsychologia 51:2918–2929

    Article  PubMed  Google Scholar 

  103. Dogonowski AM, Siebner HR, Sorensen PS et al (2013) Expanded functional coupling of subcortical nuclei with the motor resting-state network in multiple sclerosis. Mult Scler 19:559–566

    Article  PubMed  Google Scholar 

  104. Meijer KA, Eijlers AJC, Geurts JJG et al (2018) Staging of cortical and deep grey matter functional connectivity changes in multiple sclerosis. J Neurol Neurosurg Psychiatry 89:205–210

    Article  PubMed  Google Scholar 

  105. Cui F, Zhou L, Wang Z et al (2017) Altered functional connectivity of striatal subregions in patients with multiple sclerosis. Front Neurol 8:129

    Article  PubMed  PubMed Central  Google Scholar 

  106. Prosperini L, Fanelli F, Petsas N et al (2014) Multiple sclerosis: changes in microarchitecture of white matter tracts after training with a video game balance board. Radiology 273:529–538

    Article  PubMed  Google Scholar 

  107. De Giglio L, Tona F, De Luca F et al (2016) Multiple sclerosis: changes in thalamic resting-state functional connectivity induced by a home-based cognitive rehabilitation program. Radiology 280:202–211

    Article  PubMed  Google Scholar 

  108. Liu Y, Duan Y, Huang J et al (2015) Multimodal quantitative MR imaging of the thalamus in multiple sclerosis and neuromyelitis optica. Radiology 277:784–792

    Article  PubMed  Google Scholar 

  109. d’Ambrosio A, Hidalgo de la Cruz M, Valsasina P et al (2017) Structural connectivity-defined thalamic subregions have different functional connectivity abnormalities in multiple sclerosis patients: implications for clinical correlations. Hum Brain Mapp 38:6005–6018

    Article  PubMed  PubMed Central  Google Scholar 

  110. Cirillo S, Rocca MA, Ghezzi A et al (2016) Abnormal cerebellar functional MRI connectivity in patients with paediatric multiple sclerosis. Mult Scler 22:292–301

    Article  CAS  PubMed  Google Scholar 

  111. Sbardella E, Upadhyay N, Tona F et al (2017) Dentate nucleus connectivity in adult patients with multiple sclerosis: functional changes at rest and correlation with clinical features. Mult Scler 23:546–555

    Article  PubMed  Google Scholar 

  112. Zhou F, Zhuang Y, Gong H 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:e101198

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Basile B, Castelli M, Monteleone F et al (2013) Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis. Mult Scler J 20:1050–1057

    Article  Google Scholar 

  114. Bonavita S, Gallo A, Sacco R et al (2011) Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Mult Scler 17:411–422

    Article  PubMed  Google Scholar 

  115. Rocca MA, Valsasina P, Absinta M et al (2010) Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 74:1252–1259

    Article  CAS  PubMed  Google Scholar 

  116. Huang MH, Zhou FQ, Wu L et al (2018) Synchronization within, and interactions between, the default mode and dorsal attention networks in relapsing-remitting multiple sclerosis. Neuropsychiatr Dis Treat 14:1241–1252

    Article  PubMed  PubMed Central  Google Scholar 

  117. Giorgio A, Zhang J, Stromillo ML et al (2017) Pronounced structural and functional damage in early adult pediatric-onset multiple sclerosis with no or minimal clinical disability. Front Neurol 8:608

    Article  PubMed  PubMed Central  Google Scholar 

  118. Shu N, Duan Y, Xia M et al (2016) Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Sci Rep 6:29383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Schoonheim MM, Hulst HE, Landi D et al (2012) Gender-related differences in functional connectivity in multiple sclerosis. Mult Scler 18:164–173

    Article  PubMed  Google Scholar 

  120. Rocca MA, Valsasina P, Meani A et al (2016) Impaired functional integration in multiple sclerosis: a graph theory study. Brain Struct Funct 221:115–131

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  122. Abidin AZ, Chockanathan U, AM DS et al (2017) Using large-scale granger causality to study changes in brain network properties in the clinically isolated syndrome (CIS) stage of multiple sclerosis. Proc SPIE Int Soc Opt Eng 10137:101371B

    PubMed  PubMed Central  Google Scholar 

  123. Gamboa OL, Tagliazucchi E, von Wegner F 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 

  124. Eijlers AJ, Meijer KA, Wassenaar TM et al (2017) Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 88:952–960

    Article  PubMed  Google Scholar 

  125. Lin SJ, Vavasour I, Kosaka B et al (2018) Education, and the balance between dynamic and stationary functional connectivity jointly support executive functions in relapsing-remitting multiple sclerosis. Hum Brain Mapp 39(12):5039–5049

    Article  PubMed  PubMed Central  Google Scholar 

  126. Bosma RL, Kim JA, Cheng JC et al (2018) Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain. Pain 159(11):2267–2276

    Article  PubMed  Google Scholar 

  127. Zhou F, Zhuang Y, Gong H et al (2016) Resting state brain entropy alterations in relapsing remitting multiple sclerosis. PLoS One 11:e0146080

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Leonardi N, Richiardi J, Gschwind M et al (2013) Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage 83:937–950

    Article  PubMed  Google Scholar 

  129. d’Ambrosio A, Valsasina P, Gallo A et al (2020) Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis. Mult Scler 26(4):476–488

    Article  PubMed  Google Scholar 

  130. Rocca MA, Hidalgo de La Cruz M, Valsasina P et al (2020) Two-year dynamic functional network connectivity in clinically isolated syndrome. Mult Scler 26(6):645–658

    Article  CAS  PubMed  Google Scholar 

  131. Huang M, Zhou F, Wu L et al (2019) White matter lesion loads associated with dynamic functional connectivity within attention network in patients with relapsing-remitting multiple sclerosis. J Clin Neurosci 65:59–65

    Article  PubMed  Google Scholar 

  132. van Geest Q, Douw L, van ’t Klooster S et al (2018) Information processing speed in multiple sclerosis: relevance of default mode network dynamics. Neuroimage Clin 19:507–515

    Article  PubMed  PubMed Central  Google Scholar 

  133. van Geest Q, Hulst HE, Meijer KA et al (2018) The importance of hippocampal dynamic connectivity in explaining memory function in multiple sclerosis. Brain Behav 8:e00954

    Article  PubMed  PubMed Central  Google Scholar 

  134. Rocca MA, Agosta F, Colombo B et al (2007) fMRI changes in relapsing-remitting multiple sclerosis patients complaining of fatigue after IFNbeta-1a injection. Hum Brain Mapp 28:373–382

    Article  PubMed  Google Scholar 

  135. Tomassini V, d’Ambrosio A, Petsas N et al (2016) The effect of inflammation and its reduction on brain plasticity in multiple sclerosis: MRI evidence. Hum Brain Mapp 37:2431–2445

    Article  PubMed  PubMed Central  Google Scholar 

  136. Romero K, Pavisian B, Staines WR et al (2015) Multiple sclerosis, cannabis, and cognition: a structural MRI study. Neuroimage Clin 8:140–147

    Article  PubMed  PubMed Central  Google Scholar 

  137. Pavisian B, MacIntosh BJ, Szilagyi G et al (2014) Effects of cannabis on cognition in patients with MS: a psychometric and MRI study. Neurology 82:1879–1887

    Article  PubMed  PubMed Central  Google Scholar 

  138. Pavisian B, Staines WR, Feinstein A (2015) Cannabis-induced alterations in brain activation during a test of information processing speed in patients with MS. Mult Scler J Exp Transl Clin 1:2055217315588223

    PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  140. Huolman S, Hamalainen P, Vorobyev V et al (2011) The effects of rivastigmine on processing speed and brain activation in patients with multiple sclerosis and subjective cognitive fatigue. Mult Scler 17:1351–1361

    Article  CAS  PubMed  Google Scholar 

  141. Hulst HE, Goldschmidt T, Nitsche MA et al (2017) rTMS affects working memory performance, brain activation and functional connectivity in patients with multiple sclerosis. J Neurol Neurosurg Psychiatry 88:386–394

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  143. Khavari R, Elias SN, Pande R et al (2019) Higher neural correlates in patients with multiple sclerosis and neurogenic overactive bladder following treatment with Intradetrusor injection of OnabotulinumtoxinA. J Urol 201:135–140

    Article  PubMed  PubMed Central  Google Scholar 

  144. Prosperini L, Di Filippo M (2019) Beyond clinical changes: rehabilitation-induced neuroplasticity in MS. Mult Scler 25:1348–1362

    Article  PubMed  Google Scholar 

  145. Cordani C, Valsasina P, Preziosa P et al (2019) Action observation training promotes motor improvement and modulates functional network dynamic connectivity in multiple sclerosis. Mult Scler:1352458519887332

    Google Scholar 

  146. Rocca MA, Meani A, Fumagalli S et al (2019) Functional and structural plasticity following action observation training in multiple sclerosis. Mult Scler 25:1472–1487

    Article  PubMed  Google Scholar 

  147. Sandroff BM, Wylie GR, Sutton BP et al (2018) Treadmill walking exercise training and brain function in multiple sclerosis: preliminary evidence setting the stage for a network-based approach to rehabilitation. Mult Scler J Exp Transl Clin 4:2055217318760641

    PubMed  PubMed Central  Google Scholar 

  148. Cerasa A, Gioia MC, Valentino P et al (2013) Computer-assisted cognitive rehabilitation of attention deficits for multiple sclerosis: a randomized trial with fMRI correlates. Neurorehabil Neural Repair 27:284–295

    Article  PubMed  Google Scholar 

  149. Sastre-Garriga J, Alonso J, Renom M et al (2011) A functional magnetic resonance proof of concept pilot trial of cognitive rehabilitation in multiple sclerosis. Mult Scler 17:457–467

    Article  CAS  PubMed  Google Scholar 

  150. Chiaravalloti ND, Wylie G, Leavitt V et al (2012) Increased cerebral activation after behavioral treatment for memory deficits in MS. J Neurol 259:1337–1346

    Article  PubMed  Google Scholar 

  151. Leavitt VM, Wylie GR, Girgis PA et al (2014) Increased functional connectivity within memory networks following memory rehabilitation in multiple sclerosis. Brain Imaging Behav 8:394–402

    Article  PubMed  Google Scholar 

  152. Hubacher M, DeLuca J, Weber P et al (2015) Cognitive rehabilitation of working memory in juvenile multiple sclerosis-effects on cognitive functioning, functional MRI and network related connectivity. Restor Neurol Neurosci 33:713–725

    PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  154. Parisi L, Rocca MA, Valsasina P et al (2014) Cognitive rehabilitation correlates with the functional connectivity of the anterior cingulate cortex in patients with multiple sclerosis. Brain Imaging Behav 8:387–393

    Article  PubMed  Google Scholar 

  155. Parisi L, Rocca MA, Mattioli F et al (2014) Changes of brain resting state functional connectivity predict the persistence of cognitive rehabilitation effects in patients with multiple sclerosis. Mult Scler 20:686–694

    Article  PubMed  Google Scholar 

  156. De Giglio L, Upadhyay N, De Luca F et al (2016) Corpus callosum microstructural changes associated with Kawashima Nintendo brain training in patients with multiple sclerosis. J Neurol Sci 370:211–213

    Article  PubMed  Google Scholar 

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Conflict of Interest Statement

Paolo Preziosa received speakers’ honoraria from Biogen Idec, Novartis, and ExceMED.

Paola Valsasina received speakers’ honoraria from Biogen Idec, Novartis, and ExceMED.

Massimo Filippi is Editor-in-Chief of the Journal of Neurology and Associate Editor of Human Brain Mapping; received compensation for consulting services and/or speaking activities from Almiral, Alexion, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck-Serono, Novartis, Roche, Sanofi, Takeda, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA).

Maria A. Rocca received speaker honoraria from Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva, and receives research support from the MS Society of Canada and Fondazione Italiana Sclerosi Multipla.

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Authors and Affiliations

  1. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Paola Valsasina

  2. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Paolo Preziosa & Maria A. Rocca

  3. Vita-Salute San Raffaele University, Milan, Italy

    Massimo Filippi & Maria A. Rocca

  4. Neuroimaging Research Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Paolo Preziosa, Massimo Filippi & Maria A. Rocca

  5. Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

Authors
  1. Paolo Preziosa
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  2. Paola Valsasina
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  3. Massimo Filippi
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  4. Maria A. Rocca
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Corresponding author

Correspondence to Maria A. Rocca .

Editor information

Editors and Affiliations

  1. Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany

    Sergiu Groppa

  2. Department of Neurology, University of Düsseldorf, Düsseldorf, Germany

    Sven G. Meuth

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Preziosa, P., Valsasina, P., Filippi, M., Rocca, M.A. (2021). Human Functional MRI. In: Groppa, S., G. Meuth, S. (eds) Translational Methods for Multiple Sclerosis Research. Neuromethods, vol 166. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1213-2_15

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  • DOI: https://doi.org/10.1007/978-1-0716-1213-2_15

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  • Publisher Name: Humana, New York, NY

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