Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-14T11:14:12.758Z Has data issue: false hasContentIssue false
  • English
  • Français

Indices of Cognitive Dysfunction in Relapsing-Remitting Multiple Sclerosis: Intra-individual Variability, Processing Speed, and Attention Network Efficiency

Published online by Cambridge University Press:  21 February 2013

Magdalena Wojtowicz ,
Antonina Omisade  and
John D. Fisk
Magdalena Wojtowicz
Affiliation:
Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
Antonina Omisade
Affiliation:
Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada Psychology, QEII Health Sciences Centre, Halifax, Nova Scotia, Canada
John D. Fisk*
Affiliation:
Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada Psychology, QEII Health Sciences Centre, Halifax, Nova Scotia, Canada Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada Departmant of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
*
Correspondence and reprint requests to: John D. Fisk, Department of Psychology, 4066 Abbie J. Lane Building, 5909 Veteran's Memorial Lane, Halifax, NS, Canada, B3H 2E2. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Impairments in attention and information processing speed are common in multiple sclerosis (MS) and may contribute to impairments of other cognitive abilities. This study examined attentional efficiency, information processing speed and intra-individual variability in response speed using the Attention Network Test-Interactions (ANT-I) in mildy-affected patients with MS. Thirty-one patients with relapsing-remitting MS and 30 age, sex, and education-matched controls completed the ANT-I, as well as the Paced Auditory Serial Attention Test (PASAT), as a standard clinical measure of information processing efficiency. As expected, patients with MS were slower in reaction time performance on the ANT-I and had poorer performance on the PASAT compared to controls. Patients with MS also demonstrated poorer efficiency in their executive control of attention on the ANT-I, suggesting difficulties with top-down allocation of attention. In addition, the MS group demonstrated greater intra-individual variability in the responses to the ANT-I even when their slower overall response time and other factors such as practice were accounted for. Intra-individual variability was found to best predict group membership compared to PASAT scores and other ANT-I scores. These results suggest that intra-individual variability may provide sensitive, unique and important information regarding cognitive functioning in early MS. (JINS, 2013, 19, 1–8)

Keywords

Multiple sclerosisCognitionAttention network testAttentionInformation processing speedIntra-individual variability
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 19 , Issue 5 , May 2013 , pp. 551 - 558
Copyright
Copyright © The International Neuropsychological Society 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andreasen, K., Spliid, P.E., Andersen, H., Jakobsen, J. (2010). Fatigue and processing speed are related in multiple sclerosis. European Journal of Neurology, 17(2), 212–218. doi:10.1111/j.1468-1331.2009.02776.xCrossRefGoogle ScholarPubMed
Archibald, C.J., Fisk, J.D. (2000). Information processing efficiency in patients with multiple sclerosis. Journal of Clinical & Experimental Neuropsychology, 22, 686–701.CrossRefGoogle ScholarPubMed
Arnett, P.A., Strober, L.B. (2011). Cognitive and neurobehavioral features in multiple sclerosis. Expert Reviews of Neurotherapy, 411–424.CrossRefGoogle ScholarPubMed
Beck, A.T., Steer, R.A., Brown, G.K. (2000). BDI fast screen for medical patients: Manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Bellmann-Strobl, J., Wuerfel, J., Aktas, O., Dörr, J., Wernecke, K.D., Zipp, F., Paul, F. (2009). Poor PASAT performance correlates with MRI contrast enhancement in multiple sclerosis. Neurology, 73(20), 1624–1627. doi:10.1212/WNL.0b013e3181c1de4fCrossRefGoogle ScholarPubMed
Bobholz, J.A., Rao, S.M. (2003). Cognitive dysfunction in multiple sclerosis: A review of recent developments. Current Opinion in Neurology, 13, 283–288.CrossRefGoogle Scholar
Bodling, A.M., Denney, D.R., Lynch, S.G. (2012). Individual variability in speed of information processing: An index of cognitive impairment in multiple sclerosis. Neuropsychology, 26(3), 357–367. doi:10.1037/a0027972CrossRefGoogle ScholarPubMed
Boringa, J.B., Lazeron, R.H., Reuling, I.E., Ader, H.J., Pfennings, L.E., Lindeboom, J., Polman, C.L. (2001). The brief repeatable battery of neuropsychological tests: Normative values allow application in multiple sclerosis clinical practice. Multiple Sclerosis, 7(4), 263–267. doi:10.1177/135245850100700409CrossRefGoogle ScholarPubMed
Brochet, B., Deloire, M.S., Bonnet, M., Salort-Campana, E., Ouallet, J.C., Petry, K.G., Dousset, V. (2008). Should SDMT substitute for PASAT in MSFC? A 5-year longitudinal study. Multiple Sclerosis, 14(9), 1242–1249. doi:10.1177/1352458508094398CrossRefGoogle ScholarPubMed
Bruce, J.M., Bruce, A.S., Arnett, P.A. (2010). Response variability is associated with self-reported cognitive fatigue in multiple sclerosis. Neuropsychology, 24(1), 77–83. doi:10.1037/a0015046CrossRefGoogle ScholarPubMed
Burton, C.L., Strauss, E., Hultsch, D.F., Moll, A., Hunter, M.A. (2006). Intra-individual variability as a marker of neurological dysfunction: A comparison of Alzheimer's disease and Parkinson's disease. Journal of Clinical and Experimental Neuropsychology, 28(1), 67–83.CrossRefGoogle Scholar
Callejas, A., Lupiáñez, J., Funes, M.J., Tudela, P. (2005). Modulations among the alerting, orienting and executive control networks. Experimental Brain Research, 167(1), 27–37.CrossRefGoogle ScholarPubMed
Callejas, A., Lupiáñez, J., Tudela, P. (2004). The three attentional networks: On its independence and interactions. Brain and Cognition, 54, 225–227.CrossRefGoogle ScholarPubMed
Chiaravalloti, N.D., DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151. doi:10.1016/S1474-4422(08)70259-XCrossRefGoogle ScholarPubMed
Crivelli, L., Farez, M.F., González, C.D., Fiol, M., Amengual, A., Leiguarda, R., Correale, J. (2012). Alerting network dysfunction in early multiple sclerosis. Journal of the International Neuropsychological Society, 18(4), 757–763. doi:10.1017/S1355617712000410CrossRefGoogle ScholarPubMed
DeLuca, J., Chelune, G.J., Tulsky, D.S., Lengenfelder, J., Chiaravalloti, N.D. (2004). Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? Journal of Clinical Experimental Neuropsychology, 26(4), 550–562.CrossRefGoogle ScholarPubMed
Drake, S., Weinstock-Guttman, B., Morrow, S., Hojnacki, D., Munschauer, F.E., Benedict, R.H.B. (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:10.1177/1352458509354552CrossRefGoogle ScholarPubMed
Ericksen, B.A., Ericksen, C.W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16, 143–149.CrossRefGoogle Scholar
Fan, J., McCandliss, B.D., Fossella, J., Flombaum, J.I., Posner, M.I. (2005). The activation of attentional networks. Neuroimage, 26(2), 471–479. doi:10.1016/j.neuroimage.2005.02.004CrossRefGoogle ScholarPubMed
Fan, J., McCandliss, B.D., Sommer, T., Raz, A., Posner, M.I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14(3), 340–347. doi:10.1162/089892902317361886CrossRefGoogle ScholarPubMed
Fisher, J.S., Rudick, R.A., Cutter, G.R., Reingold, S.C. (1999). The Multiple Sclerosis Functional Composite measure (MSFC): An integrated approach to MS clinical outcome assessment. Multiple Sclerosis Journal, 5(4), 224–250.Google Scholar
Fisk, J.D., Archibald, C.J. (2001). Limitations of the paced auditory serial addition test (PASAT) as a measure of working memory in patients with multiple sclerosis. Journal of the International Neuropsychological Society, 7, 363–372.CrossRefGoogle ScholarPubMed
Geurts, J.J.G., Barkhof, F. (2008). Grey matter pathology in multiple sclerosis. Lancet Neurology, 7(9), 841–851. doi:10.1016/S1474-4422(08)70191-1CrossRefGoogle ScholarPubMed
Hayton, T., Furby, J., Smith, K.J., Altmann, D.R., Brenner, R., Chataway, J., Kapoor, R. (2012). Clinical and imaging correlates of the multiple sclerosis impact scale in secondary progressive multiple sclerosis. Journal of Neurology, 259(2), 237–245. doi:10.1007/s00415-011-6151-5CrossRefGoogle ScholarPubMed
Heaton, R.K. (1985). Neuropsychological findings in relapsing-remitting and chronic-progressive multiple sclerosis. Journal of Consulting & Clinical Psychology, 53, 103–110.CrossRefGoogle ScholarPubMed
Hultsch, D.F., MacDonald, S.W.S., Hunter, M.A., Levy-Bencheton, J., Strauss, E. (2000). Intra-individual variability in cognitive performance in older adults: Comparison of adults with mild dementia, adults with arthritis, and healthy adults. Neuropsychology, 14(4), 588–598.CrossRefGoogle Scholar
Ishigami, Y., Klein, R.M. (2009). Are individual differences in absentmindedness correlated with individual differences in attention? Journal of Individual Differences, 30(4), 220–237.CrossRefGoogle Scholar
Ishigami, Y., Klein, R.M. (2010). Repeated measurement of the components of attention using two versions of the Attention Network Test (ANT): Stability, isolability, robustness, and reliability. Journal of Neuroscience Methods, 190, 117–128.CrossRefGoogle ScholarPubMed
Jønsson, A., Andresen, J., Storr, L., Tscherning, T., Soelberg Sørensen, P., Ravnborg, M. (2006). Cognitive impairment in newly diagnosed multiple sclerosis patients: A 4-year follow-up study. Journal of the Neurological Sciences, 245, 77–85. doi:10.1016/j.jns.2005.09.016CrossRefGoogle ScholarPubMed
Koch-Henriksen, N., Sørensen, P.S. (2010). The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurology, 9(5), 520–532.CrossRefGoogle ScholarPubMed
Kurtzke, J.F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale. Neurology, 33(11), 1444–1452.CrossRefGoogle ScholarPubMed
Ludwin, S.K. (2006). The pathogenesis of multiple sclerosis: Relating human pathology to experimental studies. Journal of Neuropathology and Experimental Neurology, 65, 305–318. doi:1669111210.1097/01.jnen.0000225024.12074.80CrossRefGoogle ScholarPubMed
McCarthy, M., Beaumont, J.G., Thompson, R., Peacock, S. (2005). Modality-specific aspects of sustained and divided attentional performance in multiple sclerosis. Archives of Clinical Neuropsychology, 20, 705–718. doi:10.1016/j.acn.2005.04.007CrossRefGoogle ScholarPubMed
MacDonald, S.W.S., Li, S.C., Bäckman, L. (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging, 24(4), 792–808.CrossRefGoogle ScholarPubMed
MacDonald, S.W.S., Nyberg, L., Bäckman, L. (2006). Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences, 29(8), 474–480.CrossRefGoogle ScholarPubMed
McDonald, W.I., Compston, A., Edan, G., Goodkin, D., Hartung, H.P., Lublin, F.D., Wolinsky, J.S. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Annals of Neurology, 50, 121–127.CrossRefGoogle ScholarPubMed
Nebel, K., Wiese, H., Seyfarth, J., Gizewski, E.R., Stude, P., Diener, H.C., Limmroth, V. (2007). Activity of attention related structures in multiple sclerosis patients. Brain Research, 1151, 150–160. doi:10.1016/j.brainres.2007.03.007CrossRefGoogle ScholarPubMed
O'Connor, P., Devonshire, V., Canadian Network of MS Clinics (2008). The use of disease-modifying agents in multiple sclerosis--by the Canadian network of MS clinics. Canadian Journal of Neurological Science, 35, 127–132.CrossRefGoogle ScholarPubMed
Omisade, A., Fisk, J.D., Klein, R.M., Schmidt, M., Darvesh, S., Bhan, V. (2012). Information processing and magnetic resonance imaging indices of brain pathology in multiple sclerosis. International Journal of MS Care, 14(2), 84–91. doi:10.7224/1537-2073-14.2.84CrossRefGoogle ScholarPubMed
Paul, R.H., Beatty, W.W., Schneider, R., Blanco, C., Hames, K. (1998). Impairments of attention in individuals with Multiple Sclerosis. Multiple Sclerosis, 4, 433–439.CrossRefGoogle ScholarPubMed
Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25.CrossRefGoogle ScholarPubMed
Posner, M. I (1994). Attention: The mechanisms of consciousness. Proceeding of the National Academy of Sciences of the United States of America, 91, 7398–7403.CrossRefGoogle Scholar
Posner, M.I., Peterson, S.E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.CrossRefGoogle ScholarPubMed
Rao, S.M., Leo, G.J., Ellington, M.S., Nauertz, T., Bernardin, L., Unverzagt, F. (1991). Cognitive dysfunction in multiple sclerosis. II. Impact on employment and social functioning. Neurology, 41, 692–696.CrossRefGoogle ScholarPubMed
Rovaris, M., Filippi, M., Falautano, M., Minicucci, L., Rocca, M.A., Martinelli, V., Comi, G. (1998). Relation between MR abnormalities and patterns of cognitive impairment in multiple sclerosis. Neurology, 50, 1601–1608.CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol Digit Modalities Test (SDMT): Manual. Los Angeles, CA: Western Psychological Services.Google Scholar
Synder, P.J., Cappelleri, J.C. (2001). Information processing speed deficits may be better correlated with the extent of white matter sclerotic lesions in multiple sclerosis than previously suspected. Brain Cognition, 46, 279–284.CrossRefGoogle Scholar
Tombaugh, T. (2006). A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of Clinical Neuropsychology, 21, 53–76.CrossRefGoogle ScholarPubMed
Urbanek, C., Weinges-Evers, N., Bellmann-Strobl, J., Bock, M., Dorr, J., Hahn, E., Paul, F. (2010). Attention Network Test reveals alerting network dysfunction in multiple sclerosis. Multiple Sclerosis, 16(1), 93–99.CrossRefGoogle ScholarPubMed
Vercellino, M., Plano, F., Votta, B., Mutani, R., Giordana, M.T., Cavalla, P. (2005). Grey matter pathology in multiple sclerosis. Journal of Neuropathology and Experimental Neurology, 64(12), 1101–1107.CrossRefGoogle ScholarPubMed
Weinges-Evers, N., Brandt, A.U., Bock, M., Pfueller, C.F., Dörr, J., Bellmann-Strobl, J., Paul, F. (2010). Correlation of self-assessed fatigue and alertness in multiple sclerosis. Multiple Sclerosis Journal, 16(9), 1134–1140. doi:10.1177/1352458510374202CrossRefGoogle ScholarPubMed
Westerberg, H., Hirvikoski, T., Forssberg, H., Klingberg, T. (2004). Visuo-spatial working memory span: A sensitive measure of cognitive deficits in children with ADHD. Child Neuropsychology, 10(3), 155–161.CrossRefGoogle Scholar
Wojtowicz, M., Berrigan, L.I., Fisk, J.D. (2012). Intra-individual variability as a measure of information processing difficulties in multiple sclerosis. International Journal of MS Care, 14(2), 77–83. doi:10.7224/1537-2073-14.2.77CrossRefGoogle ScholarPubMed