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Brain Imaging and Behavior

, Volume 12, Issue 1, pp 20–28 | Cite as

Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume

  • Alvino Bisecco
  • Svetlana Stamenova
  • Giuseppina Caiazzo
  • Alessandro d’Ambrosio
  • Rosaria Sacco
  • Renato Docimo
  • Sabrina Esposito
  • Mario Cirillo
  • Fabrizio Esposito
  • Simona Bonavita
  • Gioacchino Tedeschi
  • Antonio Gallo
Original Research

Abstract

Cognitive impairment (CI), mainly involving attention and processing speed (A-PS), is a common and disabling symptom in multiple sclerosis (MS). Symbol Digit Modalities Test (SDMT) is one of the more sensitive and reliable tests to assess A-PS deficits in MS. Structural MRI correlates of A-PS in MS still need to be clarified. This study aimed to investigate, in a large group of MS patients, the relationship between regional gray matter (GM) atrophy and SDMT performance. 125 relapsing remitting MS patients and 52 healthy controls (HC) underwent a 3 T–MRI protocol including high-resolution 3D–T1 imaging. All subjects underwent a neurological evaluation and SDMT. A Voxel Based Morphometry analysis was performed to assess: 1) correlations between regional GM volume and SDMT performance in MS patients; 2) regional differences in GM volume between MS patients and HC. Thalamic, putamen and cerebellar volumes were also calculated using FIRST tool from the FMRIB Software Library. A linear regression analysis was performed to assess the contribution of each one of these structures to A-PS performance. A significant negative correlation was found between regional GM volume and SDMT score at the level of the thalamus, cerebellum, putamen, and occipital cortex in MS patients. Thalamus, cerebellum and putamen also showed significant GM atrophy in MS patients compared to HC. Thalamic atrophy is also an independent and additional contributor to A-PS deficits in MS patients. These findings support the role of thalamus as the most relevant GM structure subtending A-PS performance in MS, as measured by SDMT.

Keywords

Multiple sclerosis Attention Processing speed Gray matter Magnetic resonance imaging Thalamus 

Notes

Acknowledgements

The authors thank all subjects, especially the patients with MS for the time and effort devoted to this study; Antonella Paccone, MRI Center “SUN-FISM,” University of Campania “Luigi Vanvitelli” and Institute of Diagnosis and Care “Hermitage-Capodimonte,” Naples, Italy for technical support; Mattia Siciliano, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania “Luigi Vanvitelli”, Italy for contributing to statistical analysis.

Compliance with ethical standards

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest

Alvino Bisecco, Svetlana Stamenova, Giuseppina Caiazzo, Alessandro d’Ambrosio, Rosaria Sacco, Renato Docimo, Sabrina Esposito, Mario Cirillo and Fabrizio Esposito have no disclosures. Simona Bonavita received speakers’ honoraria from Biogen Idec, Novartis, and Merck-Serono. Gioacchino Tedeschi has received compensation for consulting services and/or speaking activities from Bayer Schering Pharma, Biogen Idec, Merck Serono, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck Serono, and Fondazione Italiana Sclerosi Multipla. Antonio Gallo received honoraria for speaking and travel grants from Biogen, Sanofi-Aventis, Merck Serono, Genzyme, Teva, Bayer-Schering and Novartis.

Ethical approval

This study was approved by the by the local Ethic Committee. 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.

References

  1. Amato, M. P., Portaccio, E., Goretti, B., Zipoli, V., Ricchiuti, L., De Caro, M. F., et al. (2006). The Rao's brief repeatable battery and Stroop test: normative values with age, education and gender corrections in an Italian population. Multiple Sclerosis, 12(6), 787–793.CrossRefPubMedGoogle Scholar
  2. Amato, M. P., Portaccio, E., Goretti, B., Zipoli, V., Iudice, A., Della Pina, D., et al. (2010). Relevance of cognitive deterioration in early relapsing-remitting MS: a 3-year follow-up study. Multiple Sclerosis, 16(12), 1474–1482. doi: 10.1177/1352458510380089.CrossRefPubMedGoogle Scholar
  3. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113. doi: 10.1016/j.neuroimage.2007.07.007.CrossRefPubMedGoogle Scholar
  4. Batista, S., Zivadinov, R., Hoogs, M., Bergsland, N., Heininen-Brown, M., Dwyer, M. G., et al. (2012). Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. Journal of Neurology, 259(1), 139–146. doi: 10.1007/s00415-011-6147-1.CrossRefPubMedGoogle Scholar
  5. Battaglini, M., Jenkinson, M., & De Stefano, N. (2012). Evaluating and reducing the impact of white matter lesions on brain volume measurements. Human Brain Mapping, 33(9), 2062–2071. doi: 10.1002/hbm.21344.CrossRefPubMedGoogle Scholar
  6. Benedict, R. H., & Zivadinov, R. (2011). Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nature Reviews. Neurology, 7(6), 332–342. doi: 10.1038/nrneurol.2011.61.CrossRefPubMedGoogle Scholar
  7. Benedict, R. H., Duquin, J. A., Jurgensen, S., Rudick, R. A., Feitcher, J., Munschauer, F. E., et al. (2008). Repeated assessment of neuropsychological deficits in multiple sclerosis using the symbol digit modalities test and the MS neuropsychological screening questionnaire. Multiple Sclerosis, 14(7), 940–946. doi: 10.1177/1352458508090923.CrossRefPubMedGoogle Scholar
  8. Benedict, R. H., Hulst, H. E., Bergsland, N., Schoonheim, M. M., Dwyer, M. G., Weinstock-Guttman, B., et al. (2013). Clinical significance of atrophy and white matter mean diffusivity within the thalamus of multiple sclerosis patients. Multiple Sclerosis, 19(11), 1478–1484. doi: 10.1177/1352458513478675.CrossRefPubMedGoogle Scholar
  9. Bergsland, N., Zivadinov, R., Dwyer, M. G., Weinstock-Guttman, B., & Benedict, R. H. (2016). Localized atrophy of the thalamus and slowed cognitive processing speed in MS patients. Multiple Sclerosis, 22(10), 1327–1336. doi: 10.1177/1352458515616204.CrossRefPubMedGoogle Scholar
  10. Bisecco, A., Rocca, M. A., Pagani, E., Mancini, L., Enzinger, C., Gallo, A., et al. (2015). Connectivity-based parcellation of the thalamus in multiple sclerosis and its implications for cognitive impairment: a multicenter study. Human Brain Mapping. doi: 10.1002/hbm.22809.PubMedGoogle Scholar
  11. Bonnet, M. C., Allard, M., Dilharreguy, B., Deloire, M., Petry, K. G., & Brochet, B. (2010). Cognitive compensation failure in multiple sclerosis. Neurology, 75(14), 1241–1248. doi: 10.1212/WNL.0b013e3181f612e3.CrossRefPubMedGoogle Scholar
  12. Ceccarelli, A., Jackson, J. S., Tauhid, S., Arora, A., Gorky, J., Dell'Oglio, E., et al. (2012). The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. AJNR. American Journal of Neuroradiology, 33(8), 1579–1585. doi: 10.3174/ajnr.A3083.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Cerasa, A., Valentino, P., Chiriaco, C., Pirritano, D., Nistico, R., Gioia, C. M., et al. (2013). MR imaging and cognitive correlates of relapsing-remitting multiple sclerosis patients with cerebellar symptoms. Journal of Neurology, 260(5), 1358–1366. doi: 10.1007/s00415-012-6805-y.CrossRefPubMedGoogle Scholar
  14. Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151. doi: 10.1016/S1474-4422(08)70259-X.CrossRefPubMedGoogle 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. doi: 10.1006/nimg.1998.0395.CrossRefPubMedGoogle Scholar
  16. Damasceno, A., Damasceno, B. P., & Cendes, F. (2014). The clinical impact of cerebellar grey matter pathology in multiple sclerosis. PloS One, 9(5), e96193. doi: 10.1371/journal.pone.0096193.CrossRefPubMedPubMedCentralGoogle Scholar
  17. DeLuca, G. C., Yates, R. L., Beale, H., & Morrow, S. A. (2015). Cognitive impairment in multiple sclerosis: clinical, radiologic and pathologic insights. Brain Pathology, 25(1), 79–98. doi: 10.1111/bpa.12220.CrossRefPubMedGoogle Scholar
  18. Drake, A. S., Weinstock-Guttman, B., Morrow, S. A., Hojnacki, D., Munschauer, F. E., & Benedict, R. H. (2010). Psychometrics and normative data for the multiple sclerosis functional composite: replacing the PASAT with the symbol digit modalities test. Multiple Sclerosis, 16(2), 228–237. doi: 10.1177/1352458509354552.CrossRefPubMedGoogle Scholar
  19. Genova, H. M., Hillary, F. G., Wylie, G., Rypma, B., & Deluca, J. (2009). Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging. Journal of the International Neuropsychological Society, 15(3), 383–393. doi: 10.1017/S1355617709090535.CrossRefPubMedGoogle Scholar
  20. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36. doi: 10.1006/nimg.2001.0786.CrossRefPubMedGoogle Scholar
  21. Houtchens, M. K., Benedict, R. H., Killiany, R., Sharma, J., Jaisani, Z., Singh, B., et al. (2007). Thalamic atrophy and cognition in multiple sclerosis. Neurology, 69(12), 1213–1223. doi: 10.1212/01.wnl.0000276992.17011.b5.CrossRefPubMedGoogle Scholar
  22. Julian, L. J. (2011). Cognitive functioning in multiple sclerosis. Neurologic Clinics, 29(2), 507–525. doi: 10.1016/j.ncl.2010.12.003.CrossRefPubMedGoogle Scholar
  23. Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology, 33(11), 1444–1452.CrossRefPubMedGoogle Scholar
  24. Lansley, J., Mataix-Cols, D., Grau, M., Radua, J., & Sastre-Garriga, J. (2013). Localized grey matter atrophy in multiple sclerosis: a meta-analysis of voxel-based morphometry studies and associations with functional disability. Neuroscience and Biobehavioral Reviews, 37(5), 819–830. doi: 10.1016/j.neubiorev.2013.03.006.CrossRefPubMedGoogle Scholar
  25. Lopez-Gongora, M., Querol, L., & Escartin, A. (2015). A one-year follow-up study of the symbol digit modalities test (SDMT) and the paced auditory serial addition test (PASAT) in relapsing-remitting multiple sclerosis: an appraisal of comparative longitudinal sensitivity. BMC Neurology, 15, 40. doi: 10.1186/s12883-015-0296-2.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Middleton, F. A., & Strick, P. L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research. Brain Research Reviews, 31(2–3), 236–250.CrossRefPubMedGoogle Scholar
  27. Minagar, A., Barnett, M. H., Benedict, R. H., Pelletier, D., Pirko, I., Sahraian, M. A., et al. (2013). The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology, 80(2), 210–219. doi: 10.1212/WNL.0b013e31827b910b.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Morrow, S. A., Drake, A., Zivadinov, R., Munschauer, F., Weinstock-Guttman, B., & Benedict, R. H. (2010). Predicting loss of employment over three years in multiple sclerosis: clinically meaningful cognitive decline. The Clinical Neuropsychologist, 24(7), 1131–1145. doi: 10.1080/13854046.2010.511272.CrossRefPubMedGoogle Scholar
  29. Nocentini, U., Bozzali, M., Spano, B., Cercignani, M., Serra, L., Basile, B., et al. (2014). Exploration of the relationships between regional grey matter atrophy and cognition in multiple sclerosis. Brain Imaging and Behavior, 8(3), 378–386. doi: 10.1007/s11682-012-9170-7.CrossRefPubMedGoogle Scholar
  30. Parmenter, B. A., Weinstock-Guttman, B., Garg, N., Munschauer, F., & Benedict, R. H. (2007). Screening for cognitive impairment in multiple sclerosis using the symbol digit modalities test. Multiple Sclerosis, 13(1), 52–57.CrossRefPubMedGoogle Scholar
  31. Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922. doi: 10.1016/j.neuroimage.2011.02.046.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 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. Annals of Neurology, 69(2), 292–302. doi: 10.1002/ana.22366.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Prinster, A., Quarantelli, M., Orefice, G., Lanzillo, R., Brunetti, A., Mollica, C., et al. (2006). Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. NeuroImage, 29(3), 859–867. doi: 10.1016/j.neuroimage.2005.08.034.CrossRefPubMedGoogle Scholar
  34. Rao, S. M., Leo, G. J., Bernardin, L., & Unverzagt, F. (1991a). Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology, 41(5), 685–691.CrossRefPubMedGoogle Scholar
  35. Rao, S. M., Leo, G. J., Ellington, L., Nauertz, T., Bernardin, L., & Unverzagt, F. (1991b). Cognitive dysfunction in multiple sclerosis. II. Impact on employment and social functioning. Neurology, 41(5), 692–696.CrossRefPubMedGoogle Scholar
  36. Rao, S. M., Martin, A. L., Huelin, R., Wissinger, E., Khankhel, Z., Kim, E., et al. (2014). Correlations between MRI and information processing speed in MS: a meta-analysis. Multiple Sclerosis International, 2014, 975803. doi: 10.1155/2014/975803.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Rocca, M. A., Valsasina, P., Meani, A., Falini, A., Comi, G., & Filippi, M. (2016). Impaired functional integration in multiple sclerosis: a graph theory study. Brain Structure & Function, 221(1), 115–131. doi: 10.1007/s00429-014-0896-4.CrossRefGoogle Scholar
  38. Sarica, A., Cerasa, A., & Quattrone, A. (2015). The neurocognitive profile of the cerebellum in multiple sclerosis. International Journal of Molecular Sciences, 16(6), 12185–12198. doi: 10.3390/ijms160612185.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Sastre-Garriga, J., Arevalo, M. J., Renom, M., Alonso, J., Gonzalez, I., Galan, I., et al. (2009). Brain volumetry counterparts of cognitive impairment in patients with multiple sclerosis. Journal of the Neurological Sciences, 282(1–2), 120–124. doi: 10.1016/j.jns.2008.12.019.CrossRefPubMedGoogle Scholar
  40. Sastre-Garriga, J., Alonso, J., Renom, M., Arevalo, M. J., Gonzalez, I., Galan, I., et al. (2011). A functional magnetic resonance proof of concept pilot trial of cognitive rehabilitation in multiple sclerosis. Multiple Sclerosis, 17(4), 457–467. doi: 10.1177/1352458510389219.CrossRefPubMedGoogle Scholar
  41. Schoonheim, M. M., Popescu, V., Rueda Lopes, F. C., Wiebenga, O. T., Vrenken, H., Douw, L., et al. (2012). Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology, 79(17), 1754–1761. doi: 10.1212/WNL.0b013e3182703f46.CrossRefPubMedGoogle Scholar
  42. Smith, A. (1982). Symbol digits modalities test: Manual. Los Angeles: Western Psychological Services.Google Scholar
  43. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. doi: 10.1002/hbm.10062.CrossRefPubMedGoogle Scholar
  44. Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P. M., Federico, A., et al. (2002). Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1), 479–489.CrossRefPubMedGoogle Scholar
  45. Strober, L. B., Christodoulou, C., Benedict, R. H., Westervelt, H. J., Melville, P., Scherl, W. F., et al. (2012). Unemployment in multiple sclerosis: the contribution of personality and disease. Multiple Sclerosis, 18(5), 647–653. doi: 10.1177/1352458511426735.CrossRefPubMedGoogle Scholar
  46. Tedesco, A. M., Chiricozzi, F. R., Clausi, S., Lupo, M., Molinari, M., & Leggio, M. G. (2011). The cerebellar cognitive profile. Brain, 134(Pt 12), 3672–3686. doi: 10.1093/brain/awr266.CrossRefPubMedGoogle Scholar
  47. Weier, K., Penner, I. K., Magon, S., Amann, M., Naegelin, Y., Andelova, M., et al. (2014). Cerebellar abnormalities contribute to disability including cognitive impairment in multiple sclerosis. PloS One, 9(1), e86916. doi: 10.1371/journal.pone.0086916.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Zipoli, V., Goretti, B., Hakiki, B., Siracusa, G., Sorbi, S., Portaccio, E., et al. (2010). Cognitive impairment predicts conversion to multiple sclerosis in clinically isolated syndromes. Multiple Sclerosis, 16(1), 62–67. doi: 10.1177/1352458509350311.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Alvino Bisecco
    • 1
    • 2
  • Svetlana Stamenova
    • 1
    • 3
  • Giuseppina Caiazzo
    • 2
  • Alessandro d’Ambrosio
    • 1
  • Rosaria Sacco
    • 1
    • 2
  • Renato Docimo
    • 1
  • Sabrina Esposito
    • 1
  • Mario Cirillo
    • 2
    • 4
  • Fabrizio Esposito
    • 2
    • 5
  • Simona Bonavita
    • 1
    • 2
  • Gioacchino Tedeschi
    • 1
    • 2
  • Antonio Gallo
    • 1
    • 2
  1. 1.I Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
  2. 2.MRI Center “SUN-FISM”University of Campania “Luigi Vanvitelli” and Institute of Diagnosis and Care “Hermitage-Capodimonte”NaplesItaly
  3. 3.Multiprofile Hospital For Active Treatment in Neurology and Psychiatry “St. Naum“, Medical FacultyMedical UniversitySofiaBulgaria
  4. 4.Neuroradiology Service, Department of RadiologyUniversity of Campania “Luigi Vanvitelli”NaplesItaly
  5. 5.Department of Medicine and SurgeryUniversity of SalernoSalernoItaly

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