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Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

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Abstract

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

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References

  • Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., et al. (2005). Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biological Psychiatry, 57(10), 1079–1088.

    Article  PubMed  Google Scholar 

  • Audoin, B., Ibarrola, D., Malikova, I., Soulier, E., Confort-Gouny, S., Duong, M. V. A., et al. (2007). Onset and underpinnings of white matter atrophy at the very early stage of multiple sclerosis–a two-year longitudinal MRI/MRSI study of corpus callosum. Multiple Sclerosis (Houndmills, Basingstoke, England), 13(1), 41–51.

    Article  CAS  Google Scholar 

  • Barkhof, F. (2002). The clinico-radiological paradox in multiple sclerosis revisited. Current Opinion in Neurology, 15(3), 239–245.

    Article  PubMed  Google Scholar 

  • Basile, B., Castelli, M., Monteleone, F., Nocentini, U., Caltagirone, C., Centonze, D., et al. (2013). Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 20(8), 1050–1057.

    Article  Google Scholar 

  • Bendfeldt, K., Klöppel, S., Nichols, T. E., Smieskova, R., Kuster, P., Traud, S., et al. (2012). Multivariate pattern classification of gray matter pathology in multiple sclerosis. NeuroImage, 60(1), 400–408.

    Article  PubMed  Google Scholar 

  • Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. In Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR ‘10), 3121-3124. Washington, DC: IEEE Computer Society, doi:10.1109/ICPR.2010.764

  • Calabrese, M., Atzori, M., Bernardi, V., Morra, A., Romualdi, C., Rinaldi, L., et al. (2007). Cortical atrophy is relevant in multiple sclerosis at clinical onset. Journal of Neurology, 254(9), 1212–1220.

    Article  PubMed  Google Scholar 

  • Calabrese, M., Rinaldi, F., Grossi, P., Mattisi, I., Bernardi, V., Favaretto, A., et al. (2010). Basal ganglia and frontal/parietal cortical atrophy is associated with fatigue in relapsing-remitting multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 16(10), 1220–1228.

    Article  Google Scholar 

  • Chang, Y.-W., & Lin, C.-J. (2008). Feature ranking using linear svm. Journal of Machine Learning Research: Workshop and Conference Proceedings, 3, 53–64.

  • Chang, C., & Lin, C. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1–39.

    Article  Google Scholar 

  • Charil, A., Dagher, A., Lerch, J. P., Zijdenbos, A. P., Worsley, K. J., & Evans, A. C. (2007). Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability. NeuroImage, 34(2), 509–517.

    Article  PubMed  Google Scholar 

  • Cifelli, A., Arridge, M., Jezzard, P., Esiri, M. M., Palace, J., & Matthews, P. M. (2002). Thalamic neurodegeneration in multiple sclerosis. Annals of Neurology, 52(5), 650–653.

    Article  PubMed  Google Scholar 

  • Cover, K. S., Vrenken, H., Geurts, J. J. G., Van Oosten, B. W., Jelles, B., Polman, C. H., 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 

  • Crespy, L., Zaaraoui, W., Lemaire, M., Rico, A., Faivre, A., Reuter, F., et al. (2011). Prevalence of grey matter pathology in early multiple sclerosis assessed by magnetization transfer ratio imaging. PloS One, 6(9), 2–7.

    Article  Google Scholar 

  • Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.

    CAS  PubMed  Google Scholar 

  • de Kwaasteniet, B., Ruhe, E., Caan, M., Rive, M., Olabarriaga, S., Groefsema, M., et al. (2013). Relation between structural and functional connectivity in major depressive disorder. Biological Psychiatry, 74(1), 40–47.

    Article  PubMed  Google Scholar 

  • Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.

    Article  PubMed  Google Scholar 

  • Dogonowski, A. M., Siebner, H. R., Soelberg Sørensen, P., Paulson, O. B., Dyrby, T. B., Blinkenberg, M., & Madsen, K. H. (2013). Resting-state connectivity of pre-motor cortex reflects disability in multiple sclerosis. Acta Neurologica Scandinavica, 128(5), 328–335.

    PubMed  Google Scholar 

  • Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, D., Church, J. A., et al. (2010). Prediction of Individua brain maturity using fMRI. Science, 329(5997), 1358–1361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Douaud, G., Behrens, T. E., Poupon, C., Cointepas, Y., Jbabdi, S., Gaura, V., et al. (2009). In vivo evidence for the selective subcortical degeneration in Huntington’s disease. NeuroImage, 46(4), 958–966.

    Article  PubMed  Google Scholar 

  • Evangelou, N., Konz, D., Esiri, M. M., Smith, S., Palace, J., & Matthews, P. M. (2000). Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis. Brain, 123(9), 1845–1849.

    Article  PubMed  Google Scholar 

  • 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. Multiple Sclerosis Journal, 18(9), 1251–1258.

    Article  PubMed  Google Scholar 

  • Feis, D. L., Brodersen, K. H., von Cramon, D. Y., Luders, E., & Tittgemeyer, M. (2013). Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data. NeuroImage, 70, 250–257.

    Article  PubMed  Google Scholar 

  • Ferreira, L. K., & Busatto, G. F. (2013). Resting-state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37(3), 384–400.

    Article  PubMed  Google Scholar 

  • Filippi, M., & Agosta, F. (2010). Imaging biomarkers in multiple sclerosis. Journal of Magnetic Resonance Imaging, 31(4), 770–788.

    Article  CAS  PubMed  Google Scholar 

  • Filippi, M., Rovaris, M., Inglese, M., Barkhof, F., De Stefano, N., Smith, S., et al. (2004). Interferon beta-1a for brain tissue loss in patients at presentation with syndromes suggestive of multiple sclerosis: a randomised, double-blind, placebo-controlled trial. The Lancet, 364(9444), 1489–1496.

    Article  CAS  Google Scholar 

  • Filippi, M., Preziosa, P., & Rocca, M. A. (2014). Magnetic resonance outcome measures in multiple sclerosis trials: time to rethink? Current Opinion in Neurology, 27(3), 290–299.

    Article  PubMed  Google Scholar 

  • Filippi, M., Valsasina, P., Vacchi, L., Leavitt, V., Comi, G., Falini, A., & Rocca, M. (2015). Consistent decreased functional connectivity among the main cortical and subcortical functional networks in MS: relationship with disability and cognitive impairment. Neurology, 84(14), Supplement P6.133.

  • Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97(20), 11050–11055.

    Article  CAS  Google Scholar 

  • Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. NeuroImage, 9(2), 195–207.

    Article  CAS  PubMed  Google Scholar 

  • Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., et al. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355.

    Article  CAS  PubMed  Google Scholar 

  • Fix, J. D. (2008). Basal Ganglia and the Striatal Motor System. Neuroanatomy (Board Review Series) (4th ed.), Baltimore: Wulters Kluwer & Lippincott Wiliams & Wilkins, 274–281.

  • Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19.

    PubMed  PubMed Central  Google Scholar 

  • Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8(9), 700–711.

    CAS  PubMed  Google Scholar 

  • Francis, S. J. (2004). Automatic lesion identification in MRI of multiple sclerosis patients. Montreal: McGill University.

    Google Scholar 

  • Gallo, A., Esposito, F., Sacco, R., & Rosa, N. (2012). Visual resting-state network in relapsing- remitting MS with and without previous optic neuritis. Neurology, 79, 1458–1465.

    Article  PubMed  Google Scholar 

  • Gean-Marton, A. D., Vezina, L. G., Marton, K. I., Stimac, G. K., Peyster, R. G., Taveras, J. M., & Davis, K. R. (1991). Abnormal corpus callosum: a sensitive and specific indicator of multiple sclerosis. Radiology, 180(1), 215–221.

    Article  CAS  PubMed  Google Scholar 

  • Geurts, J. J., & Barkhof, F. (2008). Grey matter pathology in multiple sclerosis. The Lancet Neurology, 7(9), 841–851.

    Article  PubMed  Google Scholar 

  • Geurts, J. J., Calabrese, M., Fisher, E., & Rudick, R. A. (2012). Measurement and clinical effect of grey matter pathology in multiple sclerosis. The Lancet Neurology, 11(12), 1082–1092.

    Article  PubMed  Google Scholar 

  • Giorgio, A., Battaglini, M., Smith, S. M., & De Stefano, N. (2008). Brain atrophy assessment in multiple sclerosis: importance and limitations. Neuroimaging Clinics of North America, 18(4), 675–686.

    Article  PubMed  Google Scholar 

  • Gould, I. C., Shepherd, A. M., Laurens, K. R., Cairns, M. J., Carr, V. J., & Green, M. J. (2014). Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach. NeuroImage: Clinical, 6, 229–236.

    Article  Google Scholar 

  • Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hayton, T., Furby, J., Smith, K. J., Altmann, D. R., Brenner, R., Chataway, J., et al. (2009). Grey matter magnetization transfer ratio independently correlates with neurological deficit in secondary progressive multiple sclerosis. Journal of Neurology, 256, 427–435.

    Article  CAS  PubMed  Google Scholar 

  • Honey, C. J., & Sporns, O. (2008). Dynamical consequences of lesions in cortical networks. Human Brain Mapping, 29(7), 802–809.

    Article  PubMed  Google Scholar 

  • Honey, C. J., Honey, C. J., Kotter, R., Kotter, R., Breakspear, M., Breakspear, M., et al. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS, 104(24), 10240–10245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Iwabuchi, S. J., & Kirk, I. J. (2014). Association between structural and functional connectivity in the verb generation network. Brain Connectivity, 4(3), 221–229.

    Article  PubMed  Google Scholar 

  • Janssen, A. L., Boster, A., Patterson, B. A., Abduljalil, A., & Prakash, R. S. (2013). Resting-state functional connectivity in multiple sclerosis: an examination of group differences and individual differences. Neuropsychologia, 51(13), 2918–2929.

    Article  PubMed  Google Scholar 

  • Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17, 825–841.

    Article  PubMed  Google Scholar 

  • Johnson, D. E. (1998). Applied multivariate methods for data analysts. Pacific Grove: Duxbury Press.

    Google Scholar 

  • Kalkers, N. F., Polman, C. H., & Uitdehaag, B. M. J. (2001). Measuring clinical disability: the MS functional composite. Int. MSJ, 8(3), 79–87.

    Google Scholar 

  • Karagkouni, A., Alevizos, M., & Theoharides, T. C. (2013). Effect of stress on brain inflammation and multiple sclerosis. Autoimmunity Reviews, 12(10), 947–953.

    Article  CAS  PubMed  Google Scholar 

  • Kister, I., Bacon, T. E., Chamot, E., Salter, A. R., Cutter, G. R., Kalina, J. T., & Herbert, J. (2013). Natural history of multiple sclerosis symptoms. International Journal of MS Care, 15(3), 146–158.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12(5), 535–540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • LaConte, S., Strother, S., Cherkassky, V., Anderson, J., & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26(2), 317–329.

    Article  PubMed  Google Scholar 

  • Llufriu, S., Blanco, Y., Martinez-Heras, E., Casanova-Molla, J., Gabilondo, I., Sepulveda, M., et al. (2012). Influence of corpus callosum damage on cognition and physical disability in multiple sclerosis: a multimodal study. PloS One, 7(5), 1–7.

    Article  Google Scholar 

  • Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., & Brovelli, A. (2012). Multivoxel pattern analysis for fMRI data: A review. Computational and Mathematical Methods in Medicine, 2012, Article ID 961257.

  • Makris N, Kennedy DN, Meyer J, Worth A, Caviness VS, Jr., Seidman L, Goldstein J, Goodman J, Hoge E, Macpherson C, Tourville J, Klaveness S, Hodge SM, Melrose R, Rauch S, Kim H, Harris G, Boehland A, Glode B, Koch J, Segal E, Sonricker A, Dieterich M, Papadimitriou G, Normandin JJ, Cullen N, Boriel D, Sanders H (2004). Segmentation manual. Center for Morphometric Analysis (CMA), Massachusetts General Hospital (MGH), http://www.cma.mgh.harvard.edu/manuals/segmentation/.

    Google Scholar 

  • Mallucci, G., Peruzzotti-Jametti, L., Bernstock, J. D., & Pluchino, S. (2015). The role of immune cells, glia and neurons in white and gray matter pathology in multiple sclerosis. Progress in Neurobiology, 127, 1–22.

    Article  PubMed  Google Scholar 

  • Marzelli, M. J., Hoeft, F., Hong, D. S., & Reiss, A. L. (2011). Neuroanatomical spatial patterns in turner syndrome. NeuroImage, 55(2), 439–447.

    Article  PubMed  Google Scholar 

  • McDonald, I., & Compston, A. (2006). The symptoms and signs of multiple sclerosis. In A. Compston, G. Ebers, & H. Lassmann (Eds.), McAlpine’s Multiple Sclerosis (4th ed., pp. 287–346). London: Churchill Livingstone.

    Chapter  Google Scholar 

  • McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.

    Article  CAS  PubMed  Google Scholar 

  • Minagar, A., Sheremata, W. A., & Weiner, W. J. (2002). Transient movement disorders and multiple sclerosis. Parkinsonism and Related Disorders, 9(2), 111–113.

    Article  PubMed  Google Scholar 

  • Mink, J. W. (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381–425.

    Article  CAS  PubMed  Google Scholar 

  • Mitchell, A. S., Sherman, S. M., Sommer, M. A., Mair, R. G., Vertes, R. P., & Chudasama, Y. (2014). Advances in understanding mechanisms of thalamic relays in cognition and behavior. The Journal of Neuroscience, 34(46), 15340–15346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage, 28(4), 980–995.

    Article  PubMed  Google Scholar 

  • Mourão-Miranda, J., Reinders, A., Rocha-Rego, V., Lappin, J., Rondina, J., Morgan, C., et al. (2012). Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychological Medicine, 42, 1037–1047.

    Article  PubMed  Google Scholar 

  • Müller, K.-R., Krauledat, M., Dornhege, G., Curio, G., & Blankertz, B. (2004). Machine learning techniques for brain-computer interfaces. Biomedizinische Technik, 49, 11–22.

    Article  Google Scholar 

  • Nantes, J. C., Zhong, J., Holmes, S. A., Whatley, B., Narayanan, S., Lapierre, Y., & Koski, L. M. (2015). Intracortical inhibition abnormality during the remission phase of multiple sclerosis is related to upper limb dexterity and lesions. Clinical Neurophysiology. doi:10.1016/j.clinph.2015.08.011.

    PubMed  Google Scholar 

  • Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.

    Article  PubMed  Google Scholar 

  • Nygaard, G. O., Walhovd, K. B., Sowa, P., Chepkoech, J. L., Bjørnerud, A., Due-Tønnessen, P., et al. (2015). Cortical thickness and surface area relate to specific symptoms in early relapsing–remitting multiple sclerosis. Multiple Sclerosis Journal, 21(4), 402–414.

    Article  PubMed  Google Scholar 

  • Oxford Grice, K., Vogel, K. A., Le, V., Mitchell, A., Muniz, S., & Vollmer, M. A. (2003). Adult norms for a commercially available nine hole peg test for finger dexterity. American Journal of Occupational Therapy, 57(5), 570–573.

    Article  PubMed  Google Scholar 

  • Ozturk, A., Smith, S. A., Gordon-Lipkin, E. M., Harrison, D. M., Shiee, N., Pham, D. L., et al. (2010). MRI of the corpus callosum in multiple sclerosis: association with disability. Multiple Sclerosis (Houndmills, Basingstoke, England), 16(2), 166–177.

    Article  CAS  Google Scholar 

  • Pagani, E., Rocca, M. A., Gallo, A., Rovaris, M., Martinelli, V., Comi, G., & Filippi, M. (2005). Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. American Journal of Neuroradiology, 26(2), 341–346.

  • Parent, A., & Hazrati, L. N. (1995). Functional anatomy of the basal ganglia. I. The cortico-basal ganglia-thalamo-cortical loop. Brain Research Reviews, 20(1), 91–127.

    CAS  PubMed  Google Scholar 

  • Pariyadath, V., Stein, E. A., & Ross, T. J. (2014). Machine learning classification of resting state functional connectivity predicts smoking status. Frontiers in Human Neuroscience, 8, 1–10.

    Article  Google Scholar 

  • Ponten, S. C., Daffertshofer, A., Hillebrand, A., & Stam, C. J. (2010). The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model. NeuroImage, 52(3), 985–994.

    Article  CAS  PubMed  Google Scholar 

  • Rehme, A. K., Volz, L. J., Feis, D.-L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S. B., et al. (2015). Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cerebral Cortex, 25(9), 3046–3056.

    Article  CAS  PubMed  Google Scholar 

  • 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(3), 2021–2033.

    Article  PubMed  Google Scholar 

  • Rocca, M. A., Valsasina, P., Martinelli, V., Misci, P., Falini, A., Comi, G., & Filippi, M. (2012). Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology, 79(14), 1449–1457.

    Article  PubMed  Google Scholar 

  • Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517.

    Article  CAS  PubMed  Google Scholar 

  • Schmierer, K., Niehaus, L., Röricht, S., & Meyer, B. U. (2000). Conduction deficits of callosal fibres in early multiple sclerosis. Journal of Neurology, Neurosurgery, and Psychiatry, 68(5), 633–638.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schmierer, K., Irlbacher, K., Grosse, P., Röricht, S., & Meyer, B. U. (2002). Correlates of disability in multiple sclerosis detected by transcranial magnetic stimulation. Neurology, 59(8), 1218–1224.

    Article  CAS  PubMed  Google Scholar 

  • Seiss, E., & Praamstra, P. (2004). The basal ganglia and inhibitory mechanisms in response selection: evidence from subliminal priming of motor responses in Parkinson’s disease. Brain, 127(2), 330–339.

    Article  PubMed  Google Scholar 

  • Siegle, G. J., Thompson, W., Carter, C. S., Steinhauer, S. R., & Thase, M. E. (2007). Increased amygdala and decreased dorsolateral prefrontal BOLD responses in unipolar depression: related and independent features. Biological Psychiatry, 61(2), 198–209.

    Article  PubMed  Google Scholar 

  • Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P. M., Federico, A., & De Stefano, N. (2002). Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1), 479–489.

    Article  PubMed  Google Scholar 

  • Sripada, R. K., King, A. P., Garfinkel, S. N., Wang, X., Sripada, C. S., Welsh, R. C., & Liberzon, I. (2012). Altered resting-state amygdala functional connectivity in men with posttraumatic stress disorder. Journal of Psychiatry and Neuroscience, 37(4), 241–249.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stevens, J. S., Jovanovic, T., Fani, N., Ely, T. D., Glover, E. M., Bradley, B., & Ressler, K. J. (2013). Disrupted amygdala-prefrontal functional connectivity in civilian women with posttraumatic stress disorder. Journal of Psychiatric Research, 47(10), 1469–1478.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vapnik, V. N. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer-Verlag.

    Book  Google Scholar 

  • Vercellino, M., Masera, S., Lorenzatti, M., Condello, C., Merola, A., Mattioda, A., et al. (2009). Demyelination, inflammation, and neurodegeneration in multiple sclerosis deep gray matter. Journal of Neuropathology and Experimental Neurology, 68(5), 489–502.

    Article  PubMed  Google Scholar 

  • Wang, X., & Tian, J. (2012). Gene selection for cancer classification using support vector machines. Computational and Mathematical Methods in Medicine, 2012, 586246.

    PubMed  PubMed Central  Google Scholar 

  • Wang, F., Kalmar, J. H., He, Y., Jackowski, M., Chepenik, L. G., Edmiston, E. E., et al. (2009). Functional and structural connectivity between the Perigenual anterior cingulate and amygdala in bipolar disorder. Biological Psychiatry, 66(5), 516–521.

    Article  PubMed  PubMed Central  Google Scholar 

  • Warren, S., Greenhill, S., & Warren, K. G. (1982). Emotional stress and the development of multiple sclerosis: case-control evidence of a relationship. Journal of Chronic Disease, 35(11), 821–831.

    Article  CAS  Google Scholar 

  • Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: a network visualization tool for human brain connectomics. PloS One, 8(7), e68910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yaldizli, Ö., Glassl, S., Sturm, D., Papadopoulou, A., Gass, A., Tettenborn, B., & Putzki, N. (2011). Fatigue and progression of corpus callosum atrophy in multiple sclerosis. Journal of Neurology, 258(12), 2199–2205.

    Article  PubMed  Google Scholar 

  • Yozbatiran, N., Baskurt, F., Baskurt, Z., Ozakbas, S., & Idiman, E. (2006). Motor assessment of upper extremity function and its relation with fatigue, cognitive function and quality of life in multiple sclerosis patients. Journal of the Neurological Sciences, 246(1–2), 117–122.

    Article  PubMed  Google Scholar 

  • Zar, J. H. (2010). Biostatistical analysis. New Jersey USA: Prentice Hall.

    Google Scholar 

  • Zeng, L.-L., Shen, H., Liu, L., & Hu, D. (2014). Unsupervised classification of major depression using functional connectivity MRI. Human Brain Mapping, 35(4), 1630–1641.

    Article  PubMed  Google Scholar 

  • Zito, G., Luders, E., Tomasevic, L., Lupoi, D., Toga, A. W., Thompson, P. M., et al. (2014). Inter-hemispheric functional connectivity changes with corpus callosum morphology in multiple sclerosis. Neuroscience, 266, 47–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zivadinov, R., Reder, A. T., Filippi, M., Minagar, A., Stüve, O., Lassmann, H., et al. (2008). Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology, 71(2), 136–144.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Dr. Douglas Arnold, Dr. David Araujo, Serge Gallant, Dr. Elena Lebedeva, Afiqua Yusef, Ben Whatley, Rebecca Sussex, Haz-Edine Assemlal, Dr. Kunio Nakamura and Stanley Hum for their contributions to data collection and processing.

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Authors and Affiliations

  1. Research Institute of the McGill University Health Centre, Montreal, QC, Canada

    Jidan Zhong & Lisa Koski

  2. Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada

    Jidan Zhong, Julia C. Nantes, Scott A. Holmes & Lisa Koski

  3. Toronto Western Hospital, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada

    Jidan Zhong

  4. Institute of Medical Science, University of Toronto, Toronto, ON, Canada

    David Qixiang Chen & Mojgan Hodaie

  5. Division of Brain, Imaging and Behaviour-Systems, Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada

    David Qixiang Chen & Mojgan Hodaie

  6. Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada

    Julia C. Nantes & Scott A. Holmes

  7. Division of Neurosurgery, Toronto Western Hospital & University of Toronto, Toronto, ON, Canada

    Mojgan Hodaie

  8. Department of Psychology, McGill University, Montreal, QC, Canada

    Lisa Koski

Authors
  1. Jidan Zhong
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  2. David Qixiang Chen
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  3. Julia C. Nantes
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  4. Scott A. Holmes
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  5. Mojgan Hodaie
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  6. Lisa Koski
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Corresponding author

Correspondence to Jidan Zhong.

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Funding

This study was funded by the Canadian Institutes of Health Research (grant number: MOP119428), and by the Research Institute of the McGill University Health Centre.

Conflict of interest

Author Jidan Zhong, Author David Qixiang Chen, Author Julia C. Nantes, Author Scott A. Holmes, Author Mojgan Hodaie and Author Lisa Koski declare no conflicts of interest.

Ethical approval

This study was approved by the Research Ethics Board of the Montreal Neurological Institute and Hospital. All procedures performed in this study involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Zhong, J., Chen, D.Q., Nantes, J.C. et al. Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches. Brain Imaging and Behavior 11, 754–768 (2017). https://doi.org/10.1007/s11682-016-9551-4

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  • DOI: https://doi.org/10.1007/s11682-016-9551-4

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