3rd Faculty of Medicine Charles University Prague Czechia

Department of Applied Neuroscience and Neuroimaging National Institute of Mental Health Klecany Czechia

Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czechia

Department of Computing and Control Engineering University of Chemistry and Technology Prague Czechia

Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Prague Czechia

Radiodiagnostic and Interventional Radiology Department Institute for Clinical and Experimental Medicine Prague Czechia

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Andersson J. L., Hutton C., Ashburner J., Turner R., Friston K. (2001). Modeling geometric deformations in EPI time series. Neuroimage 13, 903–919. 10.1006/nimg.2001.0746 PubMed DOI

Ashburner J., Friston K. (1997). Multimodal image coregistration and partitioning—a unified framework. Neuroimage 6, 209–217. 10.1006/nimg.1997.0290 PubMed DOI

Bisecco A., Nardo F. D., Docimo R., Caiazzo G., d'Ambrosio A., Bonavita S., et al. . (2018). Fatigue in multiple sclerosis: the contribution of resting-state functional connectivity reorganization. Mult. Scler. J. 24, 1696–1705. 10.1177/1352458517730932 PubMed DOI

Bosma R. L., Kim J. A., Cheng J. C., Rogachov A., Hemington K. S., Osborne N. R., et al. . (2018). Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain. Pain 159, 2267–2276. 10.1097/j.pain.0000000000001332 PubMed DOI

Chong C. D., Schwedt T. J., Hougaard A. (2019). Brain functional connectivity in headache disorders: a narrative review of MRI investigations. J. Cereb. Blood Flow Metab. 39, 650–669. 10.1177/0271678X17740794 PubMed DOI PMC

d'Ambrosio A., Valsasina P., Gallo A., De Stefano N., Pareto D., Barkhof F., et al. . (2020). Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis. Mul. Scler. J. 26, 476–488. 10.1177/1352458519837707 PubMed DOI

De Giglio L., Tommasin S., Petsas N., Pantano P. (2018). The role of fMRI in the assessment of neuroplasticity in MS: a systematic review. Neural Plast. 2018:5181649. 10.1155/2019/5181649 PubMed DOI PMC

Du Y., Fu Z., Calhoun V. D. (2018). Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front. Neurosci. 12:525. 10.3389/fnins.2018.00525 PubMed DOI PMC

Enzinger C., Pinter D., Rocca M. A., De Luca J., Sastre-Garriga J., Audoin B., et al. . (2016). Longitudinal fMRI studies: exploring brain plasticity and repair in MS. Mult. Scler. J. 22, 269–278. 10.1177/1352458515619781 PubMed DOI

Fling B. W., Martini D. N., Zeeboer E., Hildebrand A., Cameron M. (2019). Neuroplasticity of the sensorimotor neural network associated with walking aid training in people with multiple sclerosis. Mult. Scler. Relat. Disord. 31, 1–4. 10.1016/j.msard.2019.03.004 PubMed DOI PMC

Friston K. J., Frith C. D., Liddle P. F., Frackowiak R. S. J. (1993). Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14. 10.1038/jcbfm.1993.4 PubMed DOI

Hallquist M. N., Hwang K., Luna B. (2013). The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208–225. 10.1016/j.neuroimage.2013.05.116 PubMed DOI PMC

Hartman D., Hlinka J., Paluš M., Mantini D., Corbetta M. (2011). The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks. Chaos 21:013119. 10.1063/1.3553181 PubMed DOI PMC

Henson R., Buechel C., Josephs O., Friston K. (1999). The slice-timing problem in event-related fMRI. Neuroimage 9:125. PubMed

Hlinka J., Paluš M., Vejmelka M., Mantini D., Corbetta M. (2011). Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage 54, 2218–2225. 10.1016/j.neuroimage.2010.08.042 PubMed DOI PMC

Kister I., Bacon T. E., Chamot E., Salter A. R., Cutter G. R., Kalina J. T., et al. . (2013). Natural history of multiple sclerosis symptoms. Int. J. MS Care 15, 146–156. 10.7224/1537-2073.2012-053 PubMed DOI PMC

Lee M. H., Smyser C. D., Shimony J. S. (2013). Resting-state fMRI: a review of methods and clinical applications. Am. J. Neuroradiol. 34, 1866–1872. 10.3174/ajnr.A3263 PubMed DOI PMC

Pascual-Leone A., Amedi A., Fregni F., Merabet L. B. (2005). The plastic human brain cortex. Annu. Rev. Neurosci. 28, 377–401. 10.1146/annurev.neuro.27.070203.144216 PubMed DOI

Péran P., Nemmi F., Dutilleul C., Finamore L., Caravasso C. F., Troisi E., et al. . (2020). Neuroplasticity and brain reorganization associated with positive outcomes of multidisciplinary rehabilitation in progressive multiple sclerosis: a fMRI study. Mult. Scler. Relat. Disord. 42:102127. 10.1016/j.msard.2020.102127 PubMed DOI

Polman C. H., Reingold S. C., Banwell B., Clanet M., Cohen J. A., Filippi M., et al. . (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302. 10.1002/ana.22366 PubMed DOI PMC

Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., Petersen S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154. 10.1016/j.neuroimage.2011.10.018 PubMed DOI PMC

Power J. D., Mitra A., Laumann T. O., Snyder A. Z., Schlaggar B. L., Petersen S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341. 10.1016/j.neuroimage.2013.08.048 PubMed DOI PMC

Prochazkova M., Tintera J., Spanhelova S., Prokopiusova T., Rydlo J., Pavlikova M., et al. . (2020). Brain activity changes following neuroproprioceptive “facilitation, inhibition” physiotherapy in multiple sclerosis: a parallel group randomized comparison of two approaches. Eur. J. Phys. Rehabil. Med. 10.23736/S1973-9087.20.06336-4. [Epub ahead of print]. PubMed DOI

Prosperini L., Di Filippo M. (2019). Beyond clinical changes: rehabilitation-induced neuroplasticity in MS. Mult. Scler. J. 25, 1348–1362. 10.1177/1352458519846096 PubMed DOI

Prosperini L., Piattella M. C., Giannì C., Pantano P. (2015). Functional and structural brain plasticity enhanced by motor and cognitive rehabilitation in multiple sclerosis. Neural Plast. 2015:481574. 10.1155/2015/481574 PubMed DOI PMC

Rasova K., Krasensky J., Havrdova E., Obenberger J., Seidel Z., Dolezal O., et al. . (2005). Is it possible to actively and purposely make use of plasticity and adaptability in the neurorehabilitation treatment of multiple sclerosis patients? A pilot project. Clin. Rehabil. 19, 170–181. 10.1191/0269215505cr831oa PubMed DOI

Rasova K., Prochazkova M., Tintera J., Ibrahim I., Zimova D., Stetkarova I. (2015). Motor programme activating therapy influences adaptive brain functions in multiple sclerosis: clinical and MRI study. Int. J. Rehabil. Res. 38, 49–54. 10.1097/MRR.0000000000000090 PubMed DOI

Richiardi J., Gschwind M., Simioni S., Annoni J. M., Greco B., Hagmann P., et al. . (2012). Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity. Neuroimage 62, 2021–2033. 10.1016/j.neuroimage.2012.05.078 PubMed DOI

Rocca M. A., Meani A., Fumagalli S., Pagani E., Gatti R., Martinelli-Boneschi F., et al. . (2019). Functional and structural plasticity following action observation training in multiple sclerosis. Mult. Scler. J. 25, 1472–1487. 10.1177/1352458518792771 PubMed DOI

Smyser C. D., Inder T. E., Shimony J. S., Hill J. E., Degnan A. J., Snyder A. Z., et al. . (2010). Longitudinal analysis of neural network development in preterm infants. Cereb. Cortex 20, 2852–2862. 10.1093/cercor/bhq035 PubMed DOI PMC

Tavazzi E., Bergsland N., Cattaneo D., Gervasoni E., Laganà M. M., Dipasquale O., et al. . (2018). Effects of motor rehabilitation on mobility and brain plasticity in multiple sclerosis: a structural and functional MRI study. J. Neurol. 265, 1393–1401. 10.1007/s00415-018-8859-y PubMed DOI

Tomassini V., Johansen-Berg H., Jbabdi S., Wise R. G., Pozzilli C., Palace J., et al. . (2012). Relating brain damage to brain plasticity in patients with multiple sclerosis. Neurorehabil. Neural Repair 26, 581–593. 10.1177/1545968311433208 PubMed DOI PMC

Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., 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. 10.1006/nimg.2001.0978 PubMed DOI

Wallin M. T., Culpepper W. J., Nichols E., Bhutta Z. A., Gebrehiwot T. T., Hay S. I., et al. . (2019). Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 18, 269–285. 10.1016/S1474-4422(18)30443-5 PubMed DOI PMC

Identifying Biomarkers for Remyelination and Recovery in Multiple Sclerosis: A Measure of Progress

Virtual reality-based neuroproprioceptive physiotherapy in multiple sclerosis: a protocol for a double-arm randomised assessor-blinded controlled trial on upper extremity function, postural function and quality of life, with molecular and functional MRI assessment

Global functional connectivity reorganization reflects cognitive processing speed deficits and fatigue in multiple sclerosis