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Relation between functional connectivity and disability in multiple sclerosis: a non-linear model

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Abstract

Objective

To characterize the relation between brain functional connectivity and disability in patients with multiple sclerosis; to investigate the existence of critical values of both disability and functional connectivity corresponding to exhaustion of functional adaptive mechanisms.

Methods

Hundred-and-nineteen patients with no-to-severe disability and 42 healthy subjects were studied via 3T resting state functional MRI. Out of 116 regions extracted from Automated Anatomical Labeling atlas, pairs of regions whose functional connectivity correlated with Expanded Disability Status Score were identified. In patients, mathematical modeling was applied to find the best models describing Expanded-Disability-Status-Score vs structural or functional measures. Functional vs structural models intersecting points were identified.

Results

Disability had direct linear relation with lesion load (r = 0.40, p < 5E−6), inverse of thalamic volume (r = 0.31 p < 1E−3) and functional connectivity in bi-frontal pairs of regions (r > 0.40, p < 0.04), while being non-linearly associated with functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions (F > 1.73, p < 0.02). Structural vs functional models intersecting points corresponded to Expanded Disability Status Score of 3.0. 85% of patients scoring more than 3.0 showed functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions below confidence intervals (z = [2.28–2.88] 95% CI) measured in healthy subjects.

Conclusions

Functional brain connectivity changes may represent mechanisms of adaptation to structural damage and inflammation and may be not always clinically beneficial. Functional connectivity decreases in comparison with structural measure at Expanded Disability Status Score greater than 3.0, which may be critical and indicate exhaustion of compensatory mechanisms.

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References

  1. Ciccarelli O, Barkhof F, Bodini B et al (2014) Pathogenesis of multiple sclerosis: insights from molecular and metabolic imaging. Lancet Neurol 13:807–822. https://doi.org/10.1016/S1474-4422(14)70101-2

    Article  CAS  PubMed  Google Scholar 

  2. 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. https://doi.org/10.1038/nn.3045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lee M, Reddy H, Johansen-Berg H et al (2000) The motor cortex shows adaptive functional changes to brain injury from multiple sclerosis. Ann Neurol 47:606–613

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  5. Tommasin S, Giannì C, De Giglio L et al (2017) Neuroimaging techniques to assess inflammation in Multiple Sclerosis. Neuroscience. https://doi.org/10.1016/j.neuroscience.2017.07.055 (published online first 29 July)

    Article  PubMed  Google Scholar 

  6. Rocca MA, Comi G, Filippi M (2017) The role of T1-weighted derived measures of neurodegeneration for assessing disability progression in multiple sclerosis. Front Neurol 8:433. https://doi.org/10.3389/fneur.2017.00433

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pantano P, Iannetti GD, Caramia F et al (2002) Cortical motor reorganization after a single clinical attack of multiple sclerosis. Brain J Neurol 125:1607–1615

    Article  Google Scholar 

  8. 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. https://doi.org/10.1177/1352458511435930

    Article  PubMed  Google Scholar 

  9. Tona F, Petsas N, Sbardella E et al (2014) Multiple sclerosis: altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology 271:814–821. https://doi.org/10.1148/radiol.14131688

    Article  PubMed  Google Scholar 

  10. Schoonheim MM, Geurts JJG, Barkhof F (2010) The limits of functional reorganization in multiple sclerosis. Neurology 74:1246–1247. https://doi.org/10.1212/WNL.0b013e3181db9957

    Article  PubMed  Google Scholar 

  11. Schoonheim MM, Meijer KA, Geurts JJG (2015) Network collapse and cognitive impairment in multiple sclerosis. Front Neurol 6:82. https://doi.org/10.3389/fneur.2015.00082

    Article  PubMed  PubMed Central  Google Scholar 

  12. Polman CH, Reingold SC, Banwell B et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302. https://doi.org/10.1002/ana.22366

    Article  PubMed  PubMed Central  Google Scholar 

  13. Battaglini M, Jenkinson M, De Stefano N (2012) Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062–2071. https://doi.org/10.1002/hbm.21344

    Article  PubMed  Google Scholar 

  14. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156

    Article  CAS  PubMed  Google Scholar 

  15. Smith SM, Zhang Y, Jenkinson M et al (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage 17:479–489

    Article  PubMed  Google Scholar 

  16. Patenaude B, Smith SM, Kennedy DN et al (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56:907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046

    Article  PubMed  Google Scholar 

  17. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57. https://doi.org/10.1109/42.906424

    Article  CAS  PubMed  Google Scholar 

  18. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155. https://doi.org/10.1002/hbm.10062

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jenkinson M, Bannister P, Brady M et al (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17:825–841

    Article  PubMed  Google Scholar 

  20. Pruim RHR, Mennes M, van Rooij D et al (2015) ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage 112:267–277. https://doi.org/10.1016/j.neuroimage.2015.02.064

    Article  PubMed  Google Scholar 

  21. Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289. https://doi.org/10.1006/nimg.2001.0978

    Article  CAS  PubMed  Google Scholar 

  22. Koziol LF, Budding D, Andreasen N et al (2014) Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum Lond Engl 13:151–177. https://doi.org/10.1007/s12311-013-0511-x

    Article  Google Scholar 

  23. Vasconcelos CCF, Aurenção JCK, Thuler LCS et al (2016) Prognostic factors associated with long-term disability and secondary progression in patients with Multiple Sclerosis. Mult Scler Relat Disord 8:27–34. https://doi.org/10.1016/j.msard.2016.03.011

    Article  PubMed  Google Scholar 

  24. 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. Neurorehabilit Neural Repair 27:284–295. https://doi.org/10.1177/1545968312465194

    Article  Google Scholar 

  25. 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. https://doi.org/10.1148/radiol.2016150710

    Article  PubMed  Google Scholar 

  26. Cohen EJ, Quarta E, Bravi R et al (2017) Neural plasticity and network remodeling: from concepts to pathology. Neuroscience 344:326–345. https://doi.org/10.1016/j.neuroscience.2016.12.048

    Article  CAS  PubMed  Google Scholar 

  27. Hawellek DJ, Hipp JF, Lewis CM et al (2011) Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci USA 108:19066–19071. https://doi.org/10.1073/pnas.1110024108

    Article  PubMed  PubMed Central  Google Scholar 

  28. Jaeger S, Paul F, Scheel M et al (2018) Multiple sclerosis-related fatigue: altered resting-state functional connectivity of the ventral striatum and dorsolateral prefrontal cortex. Mult Scler. https://doi.org/10.1177/1352458518758911

    Article  PubMed  Google Scholar 

  29. Uher T, Vaneckova M, Sobisek L et al (2017) Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis. Mult Scler 23:51–61. https://doi.org/10.1177/1352458516642314

    Article  PubMed  Google Scholar 

  30. Müller-Oehring EM, Schulte T, Raassi C et al (2007) Local-global interference is modulated by age, sex and anterior corpus callosum size. Brain Res 1142:189–205. https://doi.org/10.1016/j.brainres.2007.01.062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Araújo SES, Mendonça HR, Wheeler NA et al (2017) Inflammatory demyelination alters subcortical visual circuits. J Neuroinflamm 14:162. https://doi.org/10.1186/s12974-017-0936-0

    Article  CAS  Google Scholar 

  32. Freilich J, Manouchehrinia A, Trusheim M et al (2017) Characterization of annual disease progression of multiple sclerosis patients: a population-based study. Mult Scler. https://doi.org/10.1177/1352458517706252

    Article  PubMed  Google Scholar 

  33. Leray E, Yaouanq J, Le Page E et al (2010) Evidence for a two-stage disability progression in multiple sclerosis. Brain J Neurol 133:1900–1913. https://doi.org/10.1093/brain/awq076

    Article  Google Scholar 

  34. Kutzelnigg A, Lucchinetti CF, Stadelmann C et al (2005) Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128:2705–2712. https://doi.org/10.1093/brain/awh641

    Article  PubMed  Google Scholar 

  35. Conradsson D, Ytterberg C, von Koch L et al (2018) Changes in disability in people with multiple sclerosis: a 10-year prospective study. J Neurol 265:119–126. https://doi.org/10.1007/s00415-017-8676-8

    Article  PubMed  Google Scholar 

  36. Matthews PM, Honey GD, Bullmore ET (2006) Applications of fMRI in translational medicine and clinical practice. Nat Rev Neurosci 7:732–744. https://doi.org/10.1038/nrn1929

    Article  CAS  PubMed  Google Scholar 

  37. Stoessl AJ (2012) Neuroimaging in the early diagnosis of neurodegenerative disease. Transl Neurodegener 1:5. https://doi.org/10.1186/2047-9158-1-5

    Article  PubMed  PubMed Central  Google Scholar 

  38. Cohen JA, Reingold SC, Polman CH et al (2012) Disability outcome measures in multiple sclerosis clinical trials: current status and future prospects. Lancet Neurol 11:467–476. https://doi.org/10.1016/S1474-4422(12)70059-5

    Article  PubMed  Google Scholar 

  39. Feys P, Lamers I, Francis G et al (2017) The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler 23:711–720. https://doi.org/10.1177/1352458517690824

    Article  PubMed  PubMed Central  Google Scholar 

  40. Motl RW, Cohen JA, Benedict R et al (2017) Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis. Mult Scler 23:704–710. https://doi.org/10.1177/1352458517690823

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was partially supported by the Italian Foundation of multiple sclerosis (FISM), Grant number 2013/5/1. We thank Dr. Tommaso Gili for the useful suggestions in the analysis.

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Authors and Affiliations

  1. Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università, 30, 00185, Rome, Italy

    Silvia Tommasin, Laura De Giglio, Serena Ruggieri, Costanza Giannì, Carlo Pozzilli & Patrizia Pantano

  2. Sant’Andrea Hospital, MS Centre, Sapienza University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy

    Laura De Giglio & Carlo Pozzilli

  3. IRCCS Neuromed, Via Atinense, 18, 86077, Pozzilli, IS, Italy

    Nikolaos Petsas & Patrizia Pantano

Authors
  1. Silvia Tommasin
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  2. Laura De Giglio
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  5. Costanza Giannì
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Corresponding author

Correspondence to Patrizia Pantano.

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Conflicts of interest

During the conduct of the study ST reports grants from FISM; LDG received speaking onoraria from Genzyme and Novartis, travel grant from Biogen, Merk, Teva, consulting fee from Genzyme, Merk and Novartis; SR received fee as speaking honoraria from Teva, Merck Serono, Biogen, travel grant from Biogen, Merck Serono, fee as advisory board consultant from Merck Serono and Novartis; NP received speaker fees from Biogen Idec and mission support from Novartis; CG received founding for travel and speaker honoraria from Bracco; CP received consulting and lecture fees and research funding and travel grants from Almirall, Bayer, Biogen, Genzyme, Merck Serono, Novartis, Roche and Teva; PP received founding for travel from Novartis, Genzyme and Bracco and speaker honoraria from Biogen.

Ethical standards

The study protocol reported in this manuscript has been approved by the the ethical committee of Policlinico Umberto I, Sapienza, University of Rome, Italy, and has been performed in accordance with those ethical standards listed in the Declaration of Helsinki.

Informed consent

All subjects gave their informed consent prior inclusion in the study and details that might disclose their identity were omitted.

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Tommasin, S., De Giglio, L., Ruggieri, S. et al. Relation between functional connectivity and disability in multiple sclerosis: a non-linear model. J Neurol 265, 2881–2892 (2018). https://doi.org/10.1007/s00415-018-9075-5

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  • DOI: https://doi.org/10.1007/s00415-018-9075-5

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