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Difficulties in Planning Among Patients with Multiple Sclerosis: A Relative Consequence of Deficits in Information Processing Speed

Published online by Cambridge University Press:  21 February 2013

Emily M. Owens ,
Douglas R. Denney  and
Sharon G. Lynch
Emily M. Owens
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, KS 66045-7556. E-mail: denney@ku.edu
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Abstract

Previous studies show that MS patients take longer than healthy controls to plan their solutions to Tower of London (TOL) problems but yield conflicting results regarding the quality of their solutions. The present study evaluated performance under untimed or timed conditions to assess the possibility that differences in planning ability only occur when restrictions in solution times are imposed. MS patients (n = 39) and healthy controls (n = 43) completed a computerized version of the TOL under one of two conditions. In the untimed condition, participants were allowed as much time as needed on each problem. In the timed condition, limits were imposed on solution times and time remaining was displayed with each problem. Patients exhibited longer planning times than controls, and the disparity between groups increased with problem difficulty. Planning performance depended upon condition. In the untimed condition, patients and controls performed equally well. When solution times were restricted, however, patients solved fewer problems than controls. MS patients’ planning ability is intact when permitted sufficient time to formulate the required plan. Deficiencies in planning are only evident when time is restricted, and, therefore, are more accurately considered a relative consequence of disease-related problems in information processing speed. (JINS, 2013, 19, 1–8)

Keywords

Executive functionNeuropsychological testsTower of LondonCognitive impairmentInformation processing speedCognitive tempo
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 19 , Issue 5 , May 2013 , pp. 613 - 620
Copyright
Copyright © The International Neuropsychological Society 2013

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References

Arnett, P.A. (2004). Speed of presentation influences story recall in college students and persons with multiple sclerosis. Archives of Clinical Neuropsychology, 19, 507–523.CrossRefGoogle ScholarPubMed
Arnett, P.A., Higginson, C.I., Randolph, J.J. (2001). Depression in multiple sclerosis: Relationship to planning ability. Journal of the International Neuropsychological Society, 7, 665–674.CrossRefGoogle ScholarPubMed
Arnett, P.A., Rao, S.M., Grafman, J., Bernardin, L., Lucetta, T., Binder, J.R., Lobeck, L. (1997). Executive functions in multiple sclerosis: An analysis of temporal ordering, semantic encoding, and planning abilities. Neuropsychology, 11, 535–544.CrossRefGoogle ScholarPubMed
Berg, W.K., Byrd, D.L. (2002). The Tower of London spatial problem-solving task: Enhancing clinical and research implementation. Journal of Clinical and Experimental Neuropsychology, 24, 586–604.CrossRefGoogle 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
Brassington, J.C., Marsh, N.V. (1998). Neuropsychological aspects of multiple sclerosis. Neuropsychology Review, 8, 43–77.CrossRefGoogle ScholarPubMed
Chan, R.C., Shum, D., Toulopoulou, T., Chen, E.Y. (2008). Assessment of executive function: Review of instruments and identification of critical issues. Archives of Clinical Neuropsychology, 23, 201–216.CrossRefGoogle Scholar
De Sonneville, L.M.J., Boringa, J.B., Reuling, I.E.W., Lazeron, R.H.C., Adèr, H.J., Polman, C.H. (2002). Information processing characteristics in subtypes of multiple sclerosis. Neuropsychologia, 40, 1751–1765.CrossRefGoogle ScholarPubMed
Delis, D.C., Kaplan, E., Kramer, J.H. (2001). The Delis–Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.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, 550–562.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.CrossRefGoogle ScholarPubMed
Denney, D.R., Hughes, A.J., Owens, E.M., Lynch, S.G. (2012). Deficits in planning time but not performance in patients with multiple sclerosis. Archives of Clinical Neuropsychology, 27, 148–158.CrossRefGoogle 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
Denney, D.R., Lynch, S.G., Parmenter, B.A. (2008). A 3-year longitudinal study of cognitive impairment in patients with primary progressive multiple sclerosis: Speed matters. Journal of the Neurological Sciences, 267, 129–136.CrossRefGoogle ScholarPubMed
Denney, D.R., Lynch, S.G., Parmenter, B.A., Horne, N. (2004). Cognitive impairment in relapsing and primary progressive multiple sclerosis: Mostly a matter of speed. Journal of the International Neuropsychological Society, 10, 948–956.CrossRefGoogle ScholarPubMed
Denney, D.R., Sworowski, L.A., Lynch, S.G. (2005). Cognitive impairment in three subtypes of multiple sclerosis. Archives of Clinical Neuropsychology, 20, 967–981.CrossRefGoogle ScholarPubMed
Drew, M., Starkey, N.J., Isler, R.B. (2009). Examining the link between information processing speed and executive function in multiple sclerosis. Archives of Clinical Neuropsychology, 24, 47–58.CrossRefGoogle ScholarPubMed
Drew, M., Tippett, L.J., Starkey, N.J., Isler, R.B. (2008). Executive dysfunction and cognitive impairment in a large community-based sample with multiple sclerosis from New Zealand: A descriptive study. Archives of Clinical Neuropsychology, 23, 1–19.CrossRefGoogle Scholar
Fischer, J.S., Foley, F.W., Aikens, J.E., Ericson, G.D., Rao, S.M., Shindell, S. (1994). What do we really know about cognitive dysfunction, affective disorders, and stress in multiple sclerosis? A practitioner's guide. Journal of Neuro Rehabilitation, 8, 151–164.Google Scholar
Foong, J., Rozewicz, L., Davie, C.A., Thompson, A.J., Miller, D.H., Ron, M.A. (1999). Correlates of executive function in multiple sclerosis: The use of magnetic resonance spectroscopy as an index of focal pathology. Journal of Neuropsychiatry and Clinical Neuroscience, 11, 45–50.CrossRefGoogle ScholarPubMed
Foong, J., Rozewicz, L., Quaghebeur, G., Davie, C.A., Kartsounis, L.D., Thompson, A.J., Ron, M.A. (1997). Executive function in multiple sclerosis: The role of frontal lobe pathology. Brain, 120, 15–26.CrossRefGoogle ScholarPubMed
Grossman, M., Robinson, K.M., Onishi, K., Thompson, H., Cohen, J., D'Esposito, M. (1995). Sentence comprehension in multiple sclerosis. Acta Neurologica Scandinavica, 92, 324–331.CrossRefGoogle ScholarPubMed
Hohol, M., Orav, E., Weiner, H. (1995). Disease steps in multiple sclerosis: A simple approach to evaluate disease progression. Neurology, 45(2), 251–255.CrossRefGoogle ScholarPubMed
Hughes, A.J., Denney, D.R., Lynch, S.G. (2011). Reaction time and rapid serial processing measures of information processing speed in multiple sclerosis: Complexity, compounding, and augmentation. Journal of the International Neuropsychological Society, 17, 1113–1121.CrossRefGoogle ScholarPubMed
Krikorian, R., Bartok, J., Gay, N. (1994). Tower of London procedure: A standard method and developmental data. Journal of Clinical and Experimental Neuropsychology, 16, 840–850.CrossRefGoogle 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
Lazeron, R.H., Rombouts, S.A., Scheltens, P., Polman, C.H., Barkhof, F. (2004). An fMRI study of planning-related brain activity in patients with moderately advanced multiple sclerosis. Multiple Sclerosis, 10, 549–555.CrossRefGoogle ScholarPubMed
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.J., Kalmar, J.H., Moore, N.B., DeLuca, J. (2006). Processing speed interacts with working memory efficiency in multiple sclerosis. Archives of Clinical Neuropsychology, 21, 229–238.CrossRefGoogle ScholarPubMed
Litvan, I., Grafman, J., Vendrell, P., Martinez, J.M. (1988). Slowed information processing speed in multiple sclerosis. Archives of Neurology, 45, 281–285.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.CrossRefGoogle ScholarPubMed
McIntosh-Michaelis, S.A., Roberts, M.H., Wilkinson, S.M., Diamond, I.D., McLellan, D.L., Martin, J.P., Spackman, A.J. (1991). The prevalence of cognitive impairment in a community survey of multiple sclerosis. British Journal of Clinical Psychology, 30, 333–348.CrossRefGoogle Scholar
Polman, C.H., Reingold, S.C., Banwell, B., Clanet, M., Cohen, J.A., Filippi, M., Wolinsky, J.S. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald Criteria. Annals of Neurology, 69, 292–302.CrossRefGoogle Scholar
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
Rao, S.M. (1996). White matter disease and dementia. Brain and Cognition, 31, 250–268.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ryan, L., Clark, C.M., Klonoff, H., Li, D., Paty, D. (1996). Patterns of cognitive impairment in relapsing-remitting multiple sclerosis and their relationship to neuropathology on magnetic resonance images. Neuropsychology, 10, 176–193.CrossRefGoogle Scholar
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.CrossRefGoogle ScholarPubMed
Shallice, T. (1982). Specific impairments of planning. Philosophical Transactions of the Royal Society of London, Biology, 298, 199–209.Google ScholarPubMed
Zakzanis, K.K. (2000). Distinct neurocognitive profiles in multiple sclerosis subtypes. Archives of Clinical Neuropsychology, 15, 115–136.CrossRefGoogle ScholarPubMed