Journal of Neurology

, Volume 259, Issue 1, pp 139–146 | Cite as

Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis

  • Sonia Batista
  • Robert Zivadinov
  • Marietta Hoogs
  • Niels Bergsland
  • Mari Heininen-Brown
  • Michael G. Dwyer
  • Bianca Weinstock-Guttman
  • Ralph H. B. Benedict
Original Communication

Abstract

Information-processing speed (IPS) slowing is a primary cognitive deficit in multiple sclerosis (MS). Basal ganglia, thalamus and neocortex are thought to have a key role for efficient information-processing, yet the specific relative contribution of these structures for MS-related IPS impairment is poorly understood. To determine if basal ganglia and thalamus atrophy independently contribute to visual and auditory IPS impairment in MS, after controlling for the influence of neocortical volume, we enrolled 86 consecutive MS patients and 25 normal controls undergoing 3T brain MRI and neuropsychological testing. Using Sienax and FIRST software, neocortical and deep gray matter (DGM) volumes were calculated. Neuropsychological testing contributed measures of auditory and visual IPS using the Paced Auditory Serial Addition Test (PASAT) and the Symbol Digit Modalities Test (SDMT), respectively. MS patients exhibited significantly slower IPS relative to controls and showed reduction in neocortex, caudate, putamen, globus pallidus, thalamus and nucleus accumbens volume. SDMT and PASAT were significantly correlated with all DGM regions. These effects were mitigated by controlling for the effects of neocortical volume, but all DGM volumes remained significantly correlated with SDMT, putamen (r = 0.409, p < 0.001) and thalamus (r = 0.362, p < 0.001) having the strongest effects, whereas for PASAT, the correlation was significant for putamen (r = 0.313, p < 0.01) but not for thalamus. We confirm the significant role of thalamus atrophy in MS-related IPS slowing and find that putamen atrophy is also a significant contributor to this disorder. These DGM structures have independent, significant roles, after controlling for the influence of neocortex atrophy.

Keywords

Multiple sclerosis Cognitive dysfunction Magnetic resonance imaging Basal ganglia Thalamus Neocortex 

References

  1. 1.
    Denney DR et al (2004) Cognitive impairment in relapsing and primary progressive multiple sclerosis: mostly a matter of speed. J Int Neuropsychol Soc 10(7):948–956PubMedCrossRefGoogle Scholar
  2. 2.
    Rao SM, St Aubin-Faubert P, Leo GJ (1989) Information processing speed in patients with multiple sclerosis. J Clin Exp Neuropsychol 11(4):471–477PubMedCrossRefGoogle Scholar
  3. 3.
    DeLuca J et al (2004) Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? J Clin Exp Neuropsychol 26(4):550–562PubMedCrossRefGoogle Scholar
  4. 4.
    Forn C et al (2008) Information-processing speed is the primary deficit underlying the poor performance of multiple sclerosis patients in the Paced Auditory Serial Addition Test (PASAT). J Clin Exp Neuropsychol 30(7):789–796PubMedCrossRefGoogle Scholar
  5. 5.
    Barker-Collo SL (2006) Quality of life in multiple sclerosis: does information-processing speed have an independent effect? Arch Clin Neuropsychol 21(2):167–174PubMedCrossRefGoogle Scholar
  6. 6.
    Archibald CJ, Fisk JD (2000) Information processing efficiency in patients with multiple sclerosis. J Clin Exp Neuropsychol 22(5):686–701PubMedCrossRefGoogle Scholar
  7. 7.
    Sanfilipo MP et al (2006) Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology 66(5):685–692PubMedCrossRefGoogle Scholar
  8. 8.
    Dineen RA et al (2009) Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain 132(Pt 1):239–249PubMedGoogle Scholar
  9. 9.
    Benedict RHB et al (2006) Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Arch Neurol 63(9):1301–1306PubMedCrossRefGoogle Scholar
  10. 10.
    Amato MP et al (2007) Association of neocortical volume changes with cognitive deterioration in relapsing-remitting multiple sclerosis. Arch Neurol 64(8):1157–1161PubMedCrossRefGoogle Scholar
  11. 11.
    Leyden J, Kleinig T (2008) The role of the basal ganglia in data processing. Med Hypotheses 71(1):61–64PubMedCrossRefGoogle Scholar
  12. 12.
    Houtchens MK et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurol 69(12):1213–1223CrossRefGoogle Scholar
  13. 13.
    Benedict RHB et al (2009) Memory impairment in multiple sclerosis: correlation with deep grey matter and mesial temporal atrophy. J Neurol Neurosurg Psychiatry 80(2):201–206PubMedCrossRefGoogle Scholar
  14. 14.
    Polman CH et al (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 58(6):840–846PubMedCrossRefGoogle Scholar
  15. 15.
    Zivadinov R et al (2001) Effects of IV methylprednisolone on brain atrophy in relapsing-remitting MS. Neurol 57(7):1239–1247Google Scholar
  16. 16.
    Zivadinov R et al (2007) Preservation of gray matter volume in multiple sclerosis patients with the Met allele of the rs6265 (Val66Met) SNP of brain-derived neurotrophic factor. Hum Mol Genet 16(22):2659–2668PubMedCrossRefGoogle Scholar
  17. 17.
    Patenaude B et al (2008) Improved surface models for FIRST. In: human brain mapping conferenceGoogle Scholar
  18. 18.
    Benedict RHB et al (2002) Minimal neuropsychological assessment of ms patients: a consensus approach. Clin Neuropsychol 16:381–397PubMedCrossRefGoogle Scholar
  19. 19.
    Benedict RHB et al (2006) Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc 12:549–558PubMedCrossRefGoogle Scholar
  20. 20.
    Rao SM (1991) A manual for the brief, repeatable battery of neuropsychological tests in multiple sclerosis. National Multiple Sclerosis Society, New YorkGoogle Scholar
  21. 21.
    Gronwall DM (1977) Paced auditory serial-addition task: a measure of recovery from concussion. Percept Mot Skills 44(2):367–373PubMedCrossRefGoogle Scholar
  22. 22.
    Smith A (1982) Symbol digit modalities test: manual. Western Psychological Services, Los AngelesGoogle Scholar
  23. 23.
    Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7(12):1139–1151PubMedCrossRefGoogle Scholar
  24. 24.
    Delis DC et al (2000) California verbal learning test manual: second edition, adult version. Psychological Corporation, San AntonioGoogle Scholar
  25. 25.
    Benedict RHB (1997) Brief visuospatial memory test-revised: professional manual. Psychological Assessment Resources, OdessaGoogle Scholar
  26. 26.
    Benton AL et al (1994) Contributions to neuropsychological assessment. Oxford University Press, New YorkGoogle Scholar
  27. 27.
    Delis DC, Kaplan E, Krammer JH (2001) Delis–Kaplan executive function system. Psychological Corporation, San AntonioGoogle Scholar
  28. 28.
    Beck AT, Steer RA, Brown GK (2000) BDI-fast screen for medical patients: manual. Psychological Corporation, San AntonioGoogle Scholar
  29. 29.
    Krupp LB et al (1989) The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 46(10):1121–1123PubMedCrossRefGoogle Scholar
  30. 30.
    Cifelli A et al (2002) Thalamic neurodegeneration in multiple sclerosis. Ann Neurol 52(5):650–653PubMedCrossRefGoogle Scholar
  31. 31.
    Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9:357–381PubMedCrossRefGoogle Scholar
  32. 32.
    Berman RA, Wurtz RH (2008) Exploring the pulvinar path to visual cortex. Prog Brain Res 171:467–473PubMedCrossRefGoogle Scholar
  33. 33.
    Casanova C et al (2001) Higher-order motion processing in the pulvinar. Prog Brain Res 134:71–82PubMedCrossRefGoogle Scholar
  34. 34.
    Koziol L, Budding DE (eds) (2010) Subcortical structures and cognition: implications for neuropsychological assessment. Springer, BerlinGoogle Scholar
  35. 35.
    Feifel M et al (2004) Inhibitory deficits in ocular motor behavior in adults with attention-defficit/hyperactivity disorder. Biol Psychiatry 56:333–339PubMedCrossRefGoogle Scholar
  36. 36.
    Grossman M et al (2002) Information processing speed and sentence comprehension in Parkinson’s disease. Neuropsychology 16(2):174–181PubMedCrossRefGoogle Scholar
  37. 37.
    Vieregge P et al (1994) Auditory selective attention is impaired in Parkinson’s disease–event-related evidence from EEG potentials. Brain Res Cogn Brain Res 2(2):117–129PubMedCrossRefGoogle Scholar
  38. 38.
    Saft C et al (2008) fMRI reveals altered auditory processing in manifest and premanifest Huntington’s disease. Neuropsychology 46(5):1279–1289CrossRefGoogle Scholar
  39. 39.
    Opitz B, Schroger E, von Cramon DY (2005) Sensory and cognitive mechanisms for preattentive change detection in auditory cortex. Eur J Neurosci 21(2):531–535PubMedCrossRefGoogle Scholar
  40. 40.
    Pirko I et al (2007) Gray matter involvement in multiple sclerosis. Neurology 68(9):634–642PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Sonia Batista
    • 1
  • Robert Zivadinov
    • 2
    • 3
  • Marietta Hoogs
    • 2
  • Niels Bergsland
    • 3
  • Mari Heininen-Brown
    • 3
  • Michael G. Dwyer
    • 3
  • Bianca Weinstock-Guttman
    • 2
  • Ralph H. B. Benedict
    • 2
  1. 1.Department of NeurologyCoimbra University HospitalsCoimbraPortugal
  2. 2.Department of Neurology, Jacobs Neurological Institute, Buffalo General HospitalUniversity at BuffaloBuffaloUSA
  3. 3.Buffalo Neuroimaging Analysis Center, Jacobs Neurological InstituteState University of New York at BuffaloBuffaloUSA

Personalised recommendations