Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-21T04:19:45.529Z Has data issue: false hasContentIssue false
  • English
  • Français

Diffusion Tensor Imaging Abnormalities in Cognitively Impaired Multiple Sclerosis Patients

Published online by Cambridge University Press:  02 December 2014

Nadine Akbar ,
Nancy J. Lobaugh ,
Paul O'Connor ,
Linda Moradzadeh ,
Christopher J. M. Scott  and
Anthony Feinstein
Nadine Akbar*
Affiliation:
Department of Psychiatry, Sunnybrook Health Sciences Centre University of Toronto
Nancy J. Lobaugh
Affiliation:
Department of Psychiatry, Sunnybrook Health Sciences Centre University of Toronto
Paul O'Connor
Affiliation:
University of Toronto St. Michael's Hospital, Toronto, Ontario, Canada
Linda Moradzadeh
Affiliation:
Department of Psychiatry, Sunnybrook Health Sciences Centre
Christopher J. M. Scott
Affiliation:
Department of Psychiatry, Sunnybrook Health Sciences Centre
Anthony Feinstein
Affiliation:
Department of Psychiatry, Sunnybrook Health Sciences Centre University of Toronto
*
Sunnybrook Health Sciences Centre, Department of Psychiatry, Room FG08, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Background:

Cognitive impairment can add to the burden of disease in patients with multiple sclerosis (MS). The aim of this study was to assess the relative importance of diffusion tensor imaging (DTI) indices derived from normal appearing white matter (NAWM) and grey matter (NAGM) in determining cognitive dysfunction in MS patients.

Methods:

Sixty two MS patients [51 female, mean age= 41 (sd=9.6) years, median expanded disability status scale (EDSS)=2.5] meeting modified McDonald criteria for MS underwent neuropsychological testing using the Neuropsychological Screening Battery for MS (NSBMS) and magnetic resonance imaging (MRI, 1.5T GE) that included DTI sequences. Total T1 hypointense and T2 hyperintense lesion volumes were obtained using semi-automated software. Lesion volumes were subtracted from whole-brain parenchyma to obtain measures of NAWM and NAGM. Fractional anisotropy (FA) of NAWM and mean diffusivity (MD) of NAGM were obtained.

Results:

Cognitive impairment was present in 11 patients (18%). These patients had higher EDSS scores, were less educated, and were more likely to have secondary progressive MS. They also had higher hypointense (p=0.001) and hyperintense (p=0.004) lesion volumes, greater NAWM atrophy (p=0.007), lower FA of total NAWM (p=0.003), and higher MD of total NAGM (p=0.015). Using a logistic regression analysis, and after controlling for demographic and disease-related differences between groups, FA of NAWM emerged as a significant predictor of cognitive impairment adding to the variance derived from lesion and atrophy data.

Conclusion:

This study underlies the important role of normal-appearing brain tissue in the pathogenesis of MS-related cognitive impairment.

Résumé:

RÉSUMÉ:Contexte:

L’atteinte cognitive peut augmenter le fardeau de la maladie chez les patients atteints de sclérose en plaques (SP). Le but de cette étude était d’évaluer l’importance relative d’indices à l’imagerie en tenseur de diffusion dérivés de la substance blanche et de la substance grise d’aspect normal (SBAN et SGAN) pour objectiver la dysfonction cognitive chez les patients atteints de SP.

Méthodes:

Soixante-deux patients atteints de SP (51 femmes ; àge moyen 41 ans -écart type 9,6 ; médiane EDSS 2,5) qui rencontraient les critères modifiés de McDonald pour la SP, ont subi une évaluation neuropsychologique au moyen de la Neuropsychological Screening Battery pour la SP et une imagerie par résonance magnétique (IRM, 1,5T GE) qui incluait des séquences DTI. Le volume total des lésions hypo intenses en T1 et hyper intenses en T2 a été évalué au moyen d’un logiciel semi-automatisé. Le volume des lésions était soustrait du parenchyme cérébral total pour obtenir les mesures de SBAN et de SGAN. L’anisotropie fractionnée (AF) de la SBAN et la diffusivité moyenne (DM) de la SGAN ont été évaluées.

Résultats:

Une atteinte cognitive était présente chez 11 patients (18%). Ces patients avaient des scores plus élevés à l’EDSS, étaient moins instruits et étaient plus susceptibles d’avoir une SP secondairement progressive. Ils avaient également un volume de lésions hypo intenses et hyper intenses plus élevé (p = 0,001 et p = 0,004 respectivement), plus d’atrophie de la SBAN (p = 0,007), une AF de la SBAN totale plus basse (p = 0,003) et une DM de la SGAN totale plus élevée (p = 0,015). L’analyse de régression logistique, après ajustement pour les différences démographiques et les différences liées à la maladie entre les groupes, a montré que l’AF de la SBAN est un facteur de prédiction significatif de l’atteinte cognitive, ce qui ajoute à la variance dérivée des données sur les lésions et sur l’atrophie.

Conclusion:

Cette étude souligne le role important du tissu cérébral d’apparence normale dans la pathogenèse de l’atteinte cognitive liée à la SP.

Type
Original Article
Information
Canadian Journal of Neurological Sciences , Volume 37 , Issue 5 , September 2010 , pp. 608 - 614
Copyright
Copyright © The Canadian Journal of Neurological 2010

References

REFERENCE

Rao, SM, Leo, GJ, Bernardin, L, et al. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology. 1991; 41(5):685–91.Google Scholar
Benit-LeÓn, J, Morales, JM, Rivera-Navarro, J., Health-related quality of life and its relationship to cognitive and emotional functioning in multiple sclerosis patients. Eur J Neurol. 2002;9(5):497–502.Google Scholar
Langdon, DW, Thompson, AJ. Multiple sclerosis: a preliminary study of selected variables affecting rehabilitation outcome. Mult Scler. 1999;5(2):94–100.Google Scholar
Rao, SM, Leo, GJ, Ellington, L, et al. Cognitive dysfunction in multiple sclerosis. II. Impact on employment and social functioning. Neurology. 1991;41(5):692–6.Google ScholarPubMed
Beatty, WW, Goodkin, DE, Hertsgaard, D, et al. Clinical and demographic predictors of cognitive performance in multiple sclerosis. Do diagnostic type, disease duration, and disability matter? Neurology. 1990;47(3):305–8.Google Scholar
Ron, MA, Callanan, MM, Warrington, EK. Cognitive abnormalities in multiple sclerosis: a psychometric and MRI study. Psychol Med.. 1991;21(1):59–68.CrossRefGoogle ScholarPubMed
Patti, F, Amato, MP, Trojano, M, et al. Cognitive impairment and its relation with disease measures in mildly disabled patients with relapsing–remitting multiple sclerosis: baseline results from the Cognitive Impairment in Multiple Sclerosis (COGIMUS) study. Mult Scler. 2009;15(7):779–88.CrossRefGoogle ScholarPubMed
Benedict, RH, Weinstock-Guttman, B, Fishman, I, et al. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol. 2004;61(2):226–30.CrossRefGoogle ScholarPubMed
Sanfilipo, MP, Benedict, RH, Weinstock-Guttman, B, et al. Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology. 2006;66(5):685–92.CrossRefGoogle ScholarPubMed
Rovaris, M, Iannucci, G, Falautano, M, et al. Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis: an exploratory study with diffusion tensor MR imaging. J Neurol Sci. 2002;195(2):103–9.CrossRefGoogle ScholarPubMed
Dineen, RA, Vilisaar, J, Hlinka, J, et al. Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain. 2009;132:239–49.CrossRefGoogle ScholarPubMed
Warlop, NP, Achten, E, Fieremans, E, et al. Transverse diffusivity of cerebral parenchyma predicts visual tracking performance in relapsing-remitting multiple sclerosis. Brain Cogn. 2009;71:(3):410–5.Google Scholar
Rovaris, M, Filippi, M, Minicucci, L, et al. Cortical/subcortical disease burden and cognitive impairment in patients with multiple sclerosis. Am J Neuroradiol. 2000;21:(2):402–8.Google Scholar
Filippi, M, Tortorella, C, Rovaris, M, et al. Changes in the normal appearing brain tissue and cognitive impairment in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2000;68:(2):157–61.Google Scholar
Cox, D, Pelletier, D, Genain, C, et al. The unique impact of changes in normal appearing brain tissue on cognitive dysfunction in secondary progressive multiple sclerosis patients. Mult Scler. 2004;10:(6):626–9.CrossRefGoogle ScholarPubMed
Polman, CH, Reingold, SC, Edan, G, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol. 2005;58:(6):840–6.Google Scholar
Benedict, RHB, Fishman, I, McClellan, MM, et al. Validity of the Beck Depression Inventory- Fast Screen in multiple sclerosis. Mult Scler. 2003;9(4):393–6.Google Scholar
Rao, SM., Neuropsychological screening battery for multiple sclerosis manual. Department of Neurology, Medical College of Wisconsin; 1991.Google Scholar
Buschke, H, Fuld, PA., Evaluating storage, retention, and retrieval in disordered memory and learning. Neurology. 1974;24:1019–25.Google Scholar
Rao, SM, Hammeke, TA, Memory disturbance in chronic progressive multiple sclerosis. Arch Neurol. 1984;41:625–31.Google Scholar
Benton, AL, Hamsher, KD. Multilingual aphasia examination. University of Iowa, Iowa City IA;1976.Google Scholar
Gronwall, DMA. Paced auditory serial-addition task: a measure of recovery from concussion. Percept Mot Skills. 1977;44:367–73.Google Scholar
Kovacevic, N, Lobaugh, NJ, Bronskill, MJ, et al. A robust method for extraction and automatic segmentation of brain images. Neuroimage. 2002;17:1087–1100.Google Scholar
Woods, RP, Grafton, ST, Watson, JDG, et al. Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr. 1998;22:153–65.Google Scholar
Amato, MP, Zipoli, V, Portaccio, E., Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci. 2006;245;(1–2):41–6.Google Scholar
Benedict, RH. Standards for sample composition and impairment classification in neuropsychological studies of multiple sclerosis. Mult Scler. 2009;15(7):777–8.CrossRefGoogle ScholarPubMed
Sormani, MP, Tintore, M, Rovaris, M, et al. Will Rogers phenomenon in multiple sclerosis. Ann Neurol. 2008;64(4):428–33.CrossRefGoogle Scholar
Feinstein, A, Kartsounis, LD Miller, DH, et al. Clinically isolated lesions of the type seen in multiple sclerosis: a cognitive, psychiatric, and MRI follow up study. J Neurol Neurosurg Psychiatry. 1992;55(10):869–76.Google Scholar
Blinkenberg, M, Rune, K, Jensen, CV, et al. Cortical cerebral metabolism correlates with MRI lesion load and cognitive dysfunction in MS. Neurology. 2000;54(3):558–64.CrossRefGoogle ScholarPubMed
Blinkenb, J, Rozewicz, L, Davie, CA, et al. Correlates of executive function in multiple sclerosis: the use of magnetic resonance spectroscopy as an index of focal pathology. J Neuropsychiatry Clin Neurosci. 1999;11(1):45–50.Google Scholar
Benedict, RH, Bruce, J, Dwyer, MG, et al. Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Mult Scler. 2007;13(6):722–30.Google Scholar
Rovaris, M, Riccitelli, G, Judica, E, et al. Cognitive impairment and structural brain damage in benign multiple sclerosis. Neurology. 2008;71:1521–6.CrossRefGoogle ScholarPubMed
Calabrese, M, Agosta, F, Rinaldi, F, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009;66(9):1144–50.Google Scholar
Amato, MP, Bartolozzi, ML, Zipoli, V, et al. Neocortical volume decrease in relapsing-remitting MS patients with mild cognitive impairment. Neurology. 2004;63(1):89–93.Google Scholar
Tekok-Kilic, A, Benedict, RH, Weinstock-Guttman, B, et al. Independent contributions of cortical gray matter atrophy and ventricle enlargement for predicting neuropsychological impairment in multiple sclerosis. Neuroimage. 2007;36(4):1294–300.CrossRefGoogle ScholarPubMed