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Reaction Time and Rapid Serial Processing Measures of Information Processing Speed in Multiple Sclerosis: Complexity, Compounding, and Augmentation

Published online by Cambridge University Press:  28 September 2011

Abbey J. Hughes ,
Douglas R. Denney  and
Sharon G. Lynch
Abbey J. Hughes
Affiliation:
Department of Psychology, University of Kansas, Lawrence, Kansas
Douglas R. Denney*
Affiliation:
Department of Psychology, University of Kansas, Lawrence, Kansas
Sharon G. Lynch
Affiliation:
Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas
*
Correspondence and reprint requests to: Douglas R. Denney, Department of Psychology, 1415 Jayhawk Blvd., Lawrence, Kansas 66045-7556. E-mail: denney@ku.edu
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Abstract

Information processing speed is frequently cited as the primary cognitive domain impacted by multiple sclerosis (MS) and is usually evaluated with reaction time (RT) or rapid serial processing (RSP) measures. The present study compared the efficacy of RT and RSP measures to distinguish between patients with MS (N = 42) and healthy controls (N = 40). The RT measure was patterned after the Computerized Tests of Information Processing and included measures of simple, choice, and semantic RT. The RSP measures consisted of the Symbol Digit Modalities Test (SDMT) and the Stroop Test. Substantial differences in information processing speed between patients and controls were found on all tests, with slightly larger effect sizes for RSP measures than RT measures and for the SDMT than the Stroop Test. Binary logistic regression analyses showed RSP measures performed better than RT measures at distinguishing patients from controls, and likewise, the SDMT score performed better than the scores derived from the Stroop Test. Results are discussed in the context of three effects associated with common measures of processing speed: complexity, compounding, and augmentation. (JINS, 2011, 17, 1113–1121)

Keywords

Neuropsychological testsAutoimmune demyelinating diseasesCognitionCognitive aspectsResponse timeStroop Test

Information

Type
Regular Articles
Information
Journal of the International Neuropsychological Society , Volume 17 , Issue 6 , November 2011 , pp. 1113 - 1121
Copyright
Copyright © The International Neuropsychological Society 2011

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References

Archibald, C.J., Fisk, J.D. (2000). Information processing efficiency in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 22, 686–701.CrossRefGoogle ScholarPubMed
Arnett, P.A., Smith, M.M., Barwick, F.H., Benedict, R.H., Ahlstrom, B.P. (2008). Oralmotor slowing in multiple sclerosis: Relationship to neuropsychological tasks requiring an oral response. Journal of the International Neuropsychological Society, 14, 454–462.Google Scholar
Bergendal, G., Fredrikson, S., Almkvist, O. (2007). Selective decline in information processing in subgroups of multiple sclerosis: An 8-year longitudinal study. European Neurology, 57, 193–202.CrossRefGoogle ScholarPubMed
Bodling, A.M., Denney, D.R., Lynch, S.G. (2008). Rapid serial processing in patients with multiple sclerosis: The contribution of peripheral deficits. Journal of the International Neuropsychological Society, 14, 646–650.CrossRefGoogle ScholarPubMed
Bodling, A.M., Denney, D.R., Lynch, S.G. (2009). Cognitive aging in patients with multiple sclerosis: A cross-sectional analysis of speeded processing. Archives of Clinical Neuropsychology, 24, 761–767.CrossRefGoogle ScholarPubMed
Bodling, A.M., Denney, D.R., Lynch, S.G. (2010). Variability in speed of information processing: A new measure of cognitive impairment in multiple sclerosis. European Committee for Treatment and Research in Multiple Sclerosis, Gothenburg, Sweden. (manuscript currently under review)Google Scholar
Brochet, B., Deloire, M.S., Bonnet, M., Salort-Campana, E., Quallet, J.C., Petry, K.G., Dousset, V. (2008). Should SDMT substitute for PASAT in MSFC? A 5-year longitudinal study. Multiple Sclerosis, 14, 1242–1249.CrossRefGoogle ScholarPubMed
Bruce, J.M., Bruce, A.S., Arnett, P.A. (2007). Mild visual acuity disturbances are associated with performance on tests of complex attention in MS. Journal of the International Neuropsychological Society, 13, 544–548.CrossRefGoogle ScholarPubMed
de Frias, C.M., Dixon, R.A., Fisher, N., Camicioli, R. (2007). Intraindividual variability in neurocognitive speed: A comparison of Parkinson's disease and normal older adults. Neuropsychologia, 45, 2499–2507.Google Scholar
de Sonneville, L.M., Boringa, J.B., Reuling, I.E., Lazeron, R.H., Adèr, H.J., Polman, C.H. (2002). Information processing characteristics in subtypes of multiple sclerosis. Neuropsychologia, 40, 1751–1765.Google Scholar
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 and Experimental Neuropsychology, 26(4), 550–562.Google Scholar
DeLuca, J., Johnson, S.K., Natelson, B.H. (1993). Information processing efficiency in chronic fatigue syndrome and multiple sclerosis. Archives of Neurology, 50, 301–304.CrossRefGoogle ScholarPubMed
Demaree, H.A., DeLuca, J., Gaudino, E.A., Diamond, B.J. (1999). Speed of processing as the key deficit in multiple sclerosis: Implications for rehabilitation. Journal of Neurology, Neurosurgery, and Psychiatry, 67, 661–663.CrossRefGoogle ScholarPubMed
Denney, D.R., Gallagher, K.A., Lynch, S.G. (2011). Deficits in processing speed in patients with multiple sclerosis: Evidence from explicitly-timed and covertly-timed measures. Archives of Clinical Neuropsychology, 26, 110–119.Google Scholar
Denney, D.R., Lynch, S.G. (2009). The impact of multiple sclerosis on patients’ performance on the Stroop Test: Processing speed vs. interference. Journal of the International Neuropsychological Society, 15, 451–458.CrossRefGoogle Scholar
Drake, A.S., Weinstock-Guttman, B., Morrow, S.A., Hojnacki, D., Munshauer, 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, 228–237.Google Scholar
Drew, M.A., Starkey, N.J., Isler, R.B. (2009). Examining the link between information processing speed and executive functioning in multiple sclerosis. Archives of Clinical Neuropsychology, 24, 47–58.CrossRefGoogle ScholarPubMed
Elsass, P., Zeeberg, I. (1983). Reaction time deficit in multiple sclerosis. Acta Neurologica Scandinavica, 68, 257–261.CrossRefGoogle ScholarPubMed
Forn, C., Belenguer, A., Parcet-Ibars, M.A., Avila, C. (2008). Information-processing speed is the primary deficit underlying the poor performance of multiple sclerosis patients in the Paced Auditory Serial Addition Test (PASAT). Journal of Clinical and Experimental Neuropsychology, 30(7), 789–796.CrossRefGoogle ScholarPubMed
Golden, C.J. (1978). The Stroop Color and Word Test. Wood Dale, IL: Stoelting Company.Google Scholar
Hinton-Bayre, A., Geffen, G. (2005). Comparability, reliability, and practice effects on alternate forms of the Digit Symbol Substitution and Symbol Digit Modalities Tests. Psychological Assessment, 17(2), 237–241.Google Scholar
Holdwick, D.J., Wingenfeld, S.A. (1999). The subjective experience of PASAT testing: Does the PASAT induce negative mood? Archives of Clinical Neuropsychology, 14, 273–284.CrossRefGoogle ScholarPubMed
Hultsch, D.F., MacDonald, S.W., Hunter, M.A., Levy-Bencheton, J., Strauss, E. (2000). Intraindividual 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 ScholarPubMed
Kail, R. (1997). The neural noise hypothesis: Evidence from processing speed in adults with multiple sclerosis. Aging, Neuropsychology, and Cognition, 4, 157–165.Google Scholar
Kail, R. (1998). Speed of information processing in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 20, 98–106.Google Scholar
Krupp, L.B., LaRocca, N.G., Muir-Nash, J., Steinberg, A.D. (1989). The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46(10), 1121–1123.CrossRefGoogle ScholarPubMed
Kujala, P., Portin, R., Revonsuo, A., Ruutiainen, J. (1994). Automatic and controlled information processing in multiple sclerosis. Brain, 117, 1115–1126.Google Scholar
Kurtzke, M.D. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33, 1444.CrossRefGoogle ScholarPubMed
Laatu, A., Revonsuo, A., Hamalainen, P., Ojanen, V., Ruutiainen, J. (2001). Visual object recognition in multiple sclerosis. Journal of the Neurological Sciences, 185, 77–88.Google Scholar
Leavitt, V.M., Lengenfelder, J., Moore, N.B., Chiaravalloti, N.D., DeLuca, J. (2011). The relative contributions of processing speed and cognitive load to working memory accuracy in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 33, 580–586.CrossRefGoogle ScholarPubMed
Lengenfelder, J., Bryant, D., Diamond, B., Kalmar, J., Moor, N., DeLuca, J. (2006). Processing speed interacts with working memory efficiency in multiple sclerosis. Archives of Clinical Neuropsychology, 21, 229–238.CrossRefGoogle ScholarPubMed
Lynch, S.G., Dickerson, K.J., Denney, D.R. (2010). Evaluating speed in multiple sclerosis: A comparison of two rapid serial processing measures. Clinical Neuropsychologist, 24, 963–976.CrossRefGoogle ScholarPubMed
Macniven, J.A., Davis, C., Ho, M.Y., Bradshaw, C.M., Szabadi, E., Constantinescu, C.S. (2008). Stroop performance in multiple sclerosis: Information processing, selective attention, or executive functioning? Journal of the International Neuropsychological Society, 14, 805–814.Google Scholar
McCaffrey, R.J., Cousins, J.P., Westervelt, H.J., Martynowicz, M., Remick, S.C., Szebenyi, S., Haase, R.F. (1995). Practice effects with the NIMH AID abbreviated neuropsychological battery. Archives of Clinical Neuropsychology, 10, 241–250.Google Scholar
Nelson, H.E. (1982). National Adult Reading Test (NART): Test manual. Windsor, UK: NFER Nelson.Google Scholar
Parmenter, B.A., Shucard, J.L., Benedict, R.H., Shucard, D.W. (2006). Working memory deficits in multiple sclerosis: Comparison between the n-back task and the Paced Auditory Serial Addition Test. Journal of the International Neuropsychological Society, 12, 677–687.Google Scholar
Parmenter, B.A., Shucard, J.L., Shucard, D.W. (2007). Information processing deficits in multiple sclerosis: A matter of complexity. Journal of the International Neuropsychological Society, 13, 417–423.CrossRefGoogle ScholarPubMed
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, 52–57.Google Scholar
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.Google Scholar
Poser, C.M., Paty, D.W., Scheinberg, L., McDonald, W.I., Davis, F.A., Ebers, G.C., Tourtellotte, W.W. (1983). New diagnostic criteria for multiple sclerosis: Guidelines for research protocols. Annals of Neurology, 13, 227–231.Google Scholar
Pujol, J., Vendrell, P., Deus, J., Junque, C., Bello, J., Marti-Vilalta, J.L., Capdevila, A. (2001). The effect of medial frontal and posterior parietal demyelinating lesions on Stroop interference. Neuroimage, 13, 68–75.CrossRefGoogle ScholarPubMed
Radloff, L.S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.CrossRefGoogle Scholar
Reicker, L.I., Tombaugh, T.N., Walker, L., Freedman, M.S. (2007). Reaction time: An alternative method for assessing the effects of multiple sclerosis on information processing speed. Archives of Clinical Neuropsychology, 22, 655–664.Google Scholar
Salo, R., Henik, A., Robertson, L.C. (2001). Interpreting Stroop interference: An analysis of differences between task versions. Neuropsychology, 15(4), 462–471.Google Scholar
Schulz, D., Kopp, B., Kunkel, A., Faiss, J.H. (2006). Cognition in the early stage of multiple sclerosis. Journal of Neurology, 253, 1002–1010.Google Scholar
Smith, A. (1982). Symbol Digit Modalities Test-Revised. Los Angeles: Western Psychological Services.Google Scholar
Smith, M.M., Arnett, P.A. (2007). Dysarthria predicts poorer performance on cognitive tasks required speeded oral response in an MS population. Journal of Clinical and Experimental Neuropsychology, 29, 804–812.Google Scholar
Tombaugh, T.N. (2006). A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of Clinical Neuropsychology, 21, 53–76.Google Scholar
Tombaugh, T.N., Berrigan, L.I., Walker, L.A., Freedman, M.S. (2010). The Computerized Test of Information Processing (CTIP) offers an alternative to the PASAT for assessing cognitive processing speed in individuals with multiple sclerosis. Cognitive and Behavioral Neurology, 23, 192–198.Google Scholar
Tombaugh, T.N., Rees, L. (2008). Computerized Test of Information Processing (CTIP). Toronto, Ontario, Canada: Multi-Health Systems.Google Scholar
Zakzanis, K.K. (2001). Statistics to tell the truth, the whole truth, and nothing but the truth: Formulae, illustrative numerical examples, and heuristic interpretation of effect size analyses for neuropsychological researchers. Archives of Clinical Neuropsychology, 16, 653–667.Google Scholar