Advertisement

Brain Imaging and Behavior

, Volume 11, Issue 3, pp 754–768 | Cite as

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

  • Jidan ZhongEmail author
  • David Qixiang Chen
  • Julia C. Nantes
  • Scott A. Holmes
  • Mojgan Hodaie
  • Lisa Koski
Original Research

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.

Keywords

Cortical thickness Deep grey matter volume Functional connectivity Motor disability Multiple sclerosis Multivariate analysis Support vector machine 

Notes

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.

Compliance with ethical standards

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.

Supplementary material

11682_2016_9551_MOESM1_ESM.docx (259 kb)
Supplementary Fig S1 (DOCX 259 kb)
11682_2016_9551_MOESM2_ESM.docx (19 kb)
Supplementary Table S1 (DOCX 18 kb)
11682_2016_9551_MOESM3_ESM.docx (28 kb)
Supplementary Table S2 (DOCX 28 kb)
11682_2016_9551_MOESM4_ESM.docx (19 kb)
Supplementary Table S3 (DOCX 19 kb)

References

  1. 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.CrossRefPubMedGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. Barkhof, F. (2002). The clinico-radiological paradox in multiple sclerosis revisited. Current Opinion in Neurology, 15(3), 239–245.CrossRefPubMedGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefPubMedGoogle Scholar
  6. 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 (ICPR10), 3121-3124. Washington, DC: IEEE Computer Society, doi: 10.1109/ICPR.2010.764
  7. 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.CrossRefPubMedGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Chang, Y.-W., & Lin, C.-J. (2008). Feature ranking using linear svm. Journal of Machine Learning Research: Workshop and Conference Proceedings, 3, 53–64.Google Scholar
  10. Chang, C., & Lin, C. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1–39.CrossRefGoogle Scholar
  11. 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.CrossRefPubMedGoogle Scholar
  12. 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.CrossRefPubMedGoogle Scholar
  13. 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.CrossRefPubMedGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.PubMedGoogle Scholar
  16. 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.CrossRefPubMedGoogle Scholar
  17. 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.CrossRefPubMedGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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.CrossRefPubMedGoogle Scholar
  21. 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.CrossRefPubMedGoogle Scholar
  22. 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.CrossRefPubMedGoogle Scholar
  23. 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.CrossRefPubMedGoogle Scholar
  24. Ferreira, L. K., & Busatto, G. F. (2013). Resting-state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37(3), 384–400.CrossRefPubMedGoogle Scholar
  25. Filippi, M., & Agosta, F. (2010). Imaging biomarkers in multiple sclerosis. Journal of Magnetic Resonance Imaging, 31(4), 770–788.CrossRefPubMedGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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.CrossRefPubMedGoogle Scholar
  28. 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.Google Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefPubMedGoogle Scholar
  31. 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.CrossRefPubMedGoogle Scholar
  32. 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.Google Scholar
  33. Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19.PubMedPubMedCentralGoogle Scholar
  34. 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.PubMedGoogle Scholar
  35. Francis, S. J. (2004). Automatic lesion identification in MRI of multiple sclerosis patients. Montreal: McGill University.Google Scholar
  36. 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.CrossRefPubMedGoogle Scholar
  37. 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.CrossRefPubMedGoogle Scholar
  38. Geurts, J. J., & Barkhof, F. (2008). Grey matter pathology in multiple sclerosis. The Lancet Neurology, 7(9), 841–851.CrossRefPubMedGoogle Scholar
  39. 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.CrossRefPubMedGoogle Scholar
  40. 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.CrossRefPubMedGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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.CrossRefPubMedGoogle Scholar
  44. Honey, C. J., & Sporns, O. (2008). Dynamical consequences of lesions in cortical networks. Human Brain Mapping, 29(7), 802–809.CrossRefPubMedGoogle Scholar
  45. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Iwabuchi, S. J., & Kirk, I. J. (2014). Association between structural and functional connectivity in the verb generation network. Brain Connectivity, 4(3), 221–229.CrossRefPubMedGoogle Scholar
  48. 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.CrossRefPubMedGoogle Scholar
  49. 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.CrossRefPubMedGoogle Scholar
  50. Johnson, D. E. (1998). Applied multivariate methods for data analysts. Pacific Grove: Duxbury Press.Google Scholar
  51. 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
  52. Karagkouni, A., Alevizos, M., & Theoharides, T. C. (2013). Effect of stress on brain inflammation and multiple sclerosis. Autoimmunity Reviews, 12(10), 947–953.CrossRefPubMedGoogle Scholar
  53. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 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.CrossRefPubMedGoogle Scholar
  56. 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.CrossRefGoogle Scholar
  57. 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.Google Scholar
  58. 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
  59. 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.CrossRefPubMedGoogle Scholar
  60. Marzelli, M. J., Hoeft, F., Hong, D. S., & Reiss, A. L. (2011). Neuroanatomical spatial patterns in turner syndrome. NeuroImage, 55(2), 439–447.CrossRefPubMedGoogle Scholar
  61. 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.CrossRefGoogle Scholar
  62. McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.CrossRefPubMedGoogle Scholar
  63. Minagar, A., Sheremata, W. A., & Weiner, W. J. (2002). Transient movement disorders and multiple sclerosis. Parkinsonism and Related Disorders, 9(2), 111–113.CrossRefPubMedGoogle Scholar
  64. Mink, J. W. (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381–425.CrossRefPubMedGoogle Scholar
  65. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 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.CrossRefPubMedGoogle Scholar
  67. 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.CrossRefPubMedGoogle Scholar
  68. Müller, K.-R., Krauledat, M., Dornhege, G., Curio, G., & Blankertz, B. (2004). Machine learning techniques for brain-computer interfaces. Biomedizinische Technik, 49, 11–22.CrossRefGoogle Scholar
  69. 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.PubMedGoogle Scholar
  70. 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.CrossRefPubMedGoogle Scholar
  71. 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.CrossRefPubMedGoogle Scholar
  72. 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.CrossRefPubMedGoogle Scholar
  73. 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.CrossRefGoogle Scholar
  74. 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.Google Scholar
  75. 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.PubMedGoogle Scholar
  76. 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.CrossRefGoogle Scholar
  77. 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.CrossRefPubMedGoogle Scholar
  78. 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.CrossRefPubMedGoogle Scholar
  79. 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.CrossRefPubMedGoogle Scholar
  80. 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.CrossRefPubMedGoogle Scholar
  81. Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517.CrossRefPubMedGoogle Scholar
  82. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 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.CrossRefPubMedGoogle Scholar
  84. 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.CrossRefPubMedGoogle Scholar
  85. 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.CrossRefPubMedGoogle Scholar
  86. 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.CrossRefPubMedGoogle Scholar
  87. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  88. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  89. Vapnik, V. N. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer-Verlag.CrossRefGoogle Scholar
  90. 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.CrossRefPubMedGoogle Scholar
  91. Wang, X., & Tian, J. (2012). Gene selection for cancer classification using support vector machines. Computational and Mathematical Methods in Medicine, 2012, 586246.PubMedPubMedCentralGoogle Scholar
  92. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 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.CrossRefGoogle Scholar
  94. Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: a network visualization tool for human brain connectomics. PloS One, 8(7), e68910.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 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.CrossRefPubMedGoogle Scholar
  96. 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.CrossRefPubMedGoogle Scholar
  97. Zar, J. H. (2010). Biostatistical analysis. New Jersey USA: Prentice Hall.Google Scholar
  98. 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.CrossRefPubMedGoogle Scholar
  99. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  100. 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.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jidan Zhong
    • 1
    • 2
    • 3
    Email author
  • David Qixiang Chen
    • 4
    • 5
  • Julia C. Nantes
    • 2
    • 6
  • Scott A. Holmes
    • 2
    • 6
  • Mojgan Hodaie
    • 4
    • 5
    • 7
  • Lisa Koski
    • 1
    • 2
    • 8
  1. 1.Research Institute of the McGill University Health CentreMontrealCanada
  2. 2.Department of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
  3. 3.Toronto Western HospitalTorontoCanada
  4. 4.Institute of Medical ScienceUniversity of TorontoTorontoCanada
  5. 5.Division of Brain, Imaging and Behaviour-Systems, Neuroscience, Krembil Research InstituteUniversity Health NetworkTorontoCanada
  6. 6.Integrated Program in NeuroscienceMcGill UniversityMontrealCanada
  7. 7.Division of NeurosurgeryToronto Western Hospital & University of TorontoTorontoCanada
  8. 8.Department of PsychologyMcGill UniversityMontrealCanada

Personalised recommendations