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Information processing deficits in multiple sclerosis: A matter of complexity

Published online by Cambridge University Press:  20 March 2007

BRETT A. PARMENTER
Affiliation:
Division of Cognitive and Behavioral Neurosciences, Department of Neurology/The Jacobs Neurological Institute, University at Buffalo, State University of New York School of Medicine, and Biomedical Sciences, Buffalo, New York Dr. Brett Parmenter is now at the Department of Psychology, Washington State University, Pullman, WA 99164
JANET L. SHUCARD
Affiliation:
Division of Cognitive and Behavioral Neurosciences, Department of Neurology/The Jacobs Neurological Institute, University at Buffalo, State University of New York School of Medicine, and Biomedical Sciences, Buffalo, New York
DAVID W. SHUCARD
Affiliation:
Division of Cognitive and Behavioral Neurosciences, Department of Neurology/The Jacobs Neurological Institute, University at Buffalo, State University of New York School of Medicine, and Biomedical Sciences, Buffalo, New York

Abstract

This study examined the relationship between processing speed (PS) and working memory (WM), as measured by performance on an n-back task, in relapsing-remitting multiple sclerosis (RRMS) patients. Simple PS was defined as reaction time (RT) on the 0-back task and complex PS was defined as RT on both the 1-back and 2-back tasks. Participants were administered all three n-back tasks (0-, 1-, and 2-back). Total correct responses, total dyads, and RTs were recorded. As expected, RT for all participants slowed as WM load increased. MS patients had slower RTs than controls across all tasks, and the difference between groups for RT was greatest during the 2-back task. When RT for simple PS (0-back) was parsed from the 1- and 2-back tasks, MS patients still showed impaired complex PS compared to controls. MS patients also made significantly fewer total correct responses and had fewer dyads than controls only on the 2-back task. These findings suggest that both WM and PS deficits are present in RRMS, and that as WM demand increases (from 1- to 2-back) both PS and WM deficits become more prominent. (JINS, 2007, 13, 417–423.)

Type
Research Article
Copyright
© 2007 The International Neuropsychological Society

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References

REFERENCES

Amato, M.P., Bartolozzi, M.L., Zipoli, V., Portaccio, E., Mortilla, M., Guidi, L., Siracusa, G., Sorbi, S., Federico, A., & De Stefano, N. (2004). Neocortical volume decrease in relapsing-temitting MS patients with mild cognitive impairment. Neurology, 63, 8993.Google Scholar
Archibald, C.J. & Fisk, J.D. (2000). Information Processing Efficiency in Patients with Multiple Sclerosis. Journal of Clinical and Experimental Neuropsychology, 22, 686701.Google Scholar
Arnett, P.A., Higginson, C.I., Voss, W.D., Bender, W.I., Wurst, J.M., & Tippin, J.M. (1999). Depression in multiple sclerosis: Relationship to working memory capacity. Neuropsychology, 13, 546556.Google Scholar
Baddeley, A. (1992). Working memory. Science, 255, 556561.Google Scholar
Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4, 829839.Google Scholar
Baddeley, A. & Hitch, G.J. (1994). Developments in the concept of working memory. Neuropsychology, 8, 485493.Google Scholar
Baddeley, A.D. & Hitch, G.J. (1974). Working memory. In G. Bower (Ed.), The Psychology of Learning and Motivation (pp. 129148). San Diego, CA: Academic Press.
Bailes, S., Libman, E., Baltzan, M., Amsel, R., Schondorf, R., & Fichten, C.S. (2006). Brief and distinct empirical sleepiness and fatigue scales. Journal of Psychosomatic Research, 60, 605613.Google Scholar
Beatty, W.W., Goretti, B., Siracusa, G., Zipoli, V., Portaccio, E., & Amato, M.P. (2003). Changes in neuropsychological test performance over the workday in multiple sclerosis. The Clinical Neuropsychologist, 17, 551560.Google Scholar
Benedict, R.H.B., Fischer, J., Archibald, C.J., Arnett, P.A., Beatty, W.W., Bobholz, J., Chelune, G.J., Fisk, J.D., Langdon, D.W., Caruso, L., Foley, F., LaRocca, N.G., Vowels, L., Weinstein, A., DeLuca, J., Rao, S.M., & Munschauer, F. (2002). Minimal neuropsychological assessment of MS patients: A concensus approach. Clinical Neuropsychologist, 16, 381397.Google Scholar
Benedict, R.H.B., Weinstock-Guttman, B., Fishman, I., Sharma, J., Tjoa, C.W., & Bakshi, R. (2004). Prediction of neuropsychological impairment in multiple sclerosis. Archives of Neurology, 61, 226230.Google Scholar
Chiaravalloti, N.D., Christodoulou, C., Demaree, H.A., & DeLuca, J. (2003). Differentiating simple versus complex processing speed: Influence on new learning and memory performance. Journal of Clinical and Experimental Neuropsychology, 25, 489501.Google Scholar
Cohen, R.A. & Fisher, M. (1989). Amantadine treatment of fatigue associated with multiple sclerosis. Archives of Neurology, 46, 676680.Google Scholar
D'Esposito, M., Onishi, K., Thompson, H., Robinson, K., Armstrong, C., & Grossman, M. (1996). Working memory impairments in multiple sclerosis. Neuropsychology, 10, 5156.Google Scholar
Deloire, M.S.A., Salort, E., Bonnet, M., Arimone, Y., Boudineau, M., Amieva, H., Barroso, B., Ouallet, J.C., Pachai, C., Galliaud, E., Petry, K.G., Dousset, V., Fabrigoule, C., & Brochet, B. (2005). Cognitive impairment as marker of diffuse brain abnormalities in early relapsing remitting multiple sclerosis. Journal of Neurology, Neurosurgery, and Psychiatry, 76, 519526.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, 550562.Google Scholar
Diamond, B.J., DeLuca, J., Kim, H., & Kelley, S.M. (1997). The question of disproportionate impairments in visual and auditory information processing in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 19, 3442.Google Scholar
Ferguson, B., Matyszak, M.K., Esiri, M.M., & Perry, V.H. (1997). Axonal damage in acute multiple sclerosis lesions. Brain, 120, 393399.Google Scholar
Fisk, J.D. & Archibald, C.J. (2001). Limitations of the Paced Auditory Serial Addition Test as a measure of working memory in patients with multiple sclerosis. Journal of the International Neuropsychological Society, 7, 363372.Google Scholar
Geisler, M.W., Sliwinski, M., Coyle, P.K., Masur, D.M., Doscher, C., & Krupp, L.B. (1996). The effects of amantadine and pemoline on cognitive functioning in multiple sclerosis. Archives of Neurology, 53, 185188.Google Scholar
Grigsby, J., Ayarbe, S.D., Kravcisin, N., & Busenbark, D. (1994). Working memory impairment among persons with chronic progressive multiple sclerosis. Journal of Neurology, 241, 125131.Google Scholar
Heaton, R.K., Nelson, L.M., Thompson, D.S., Burks, J.S., & Franklin, G.M. (1985). Neuropsychological findings in relapsing-remitting and chronic-progressive multiple sclerosis. Journal of Consulting and Clinical Psychology, 53, 103110.Google Scholar
Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., & Dement, W.C. (1973). Quantification of sleepiness: A new approach. Psychophysiology, 10, 431436.Google Scholar
Krail, R. & Sanan, D.A. (1994). Processing speed as a mental capacity. Acta Psychologia, 86, 199225.Google Scholar
Krupp, L.B. & Elkins, L.E. (2000). Fatigue and declines in cognitive functioning in multiple sclerosis. Neurology, 55, 934939.Google Scholar
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, 229238.Google Scholar
Lengenfelder, J., Chiaravalloti, N.D., Ricker, J.H., & DeLuca, J. (2003). Deciphering components of impaired working memory in multiple sclerosis. Cognitive and Behavioral Neurology, 16, 2839.Google Scholar
Miller, A.E. (1996). Clinical features. In S.D. Cook (Ed.), Handbook of multiple sclerosis: Second edition, revised and expanded (pp. 201222). New York: Marcel Dekker, Inc.
Nebes, R.D., Butters, M.A., Mulsant, B.H., Pollock, B.G., Zmuda, M.D., Houck, P.R., & Reynolds, C.F. (2000). Decreased working memory and processing speed mediate cognitive impairment in geriatric depression. Psychological Medicine, 30, 679691.Google Scholar
Parmenter, B.A., Denney, D.R., & Lynch, S.G. (2003). The cognitive performance of patients with multiple sclerosis during periods of high and low fatigue. Multiple Sclerosis, 9, 111118.Google Scholar
Parmenter, B.A., Shucard, J.L., Benedict, R.H.B., & 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, 677687.Google Scholar
Perry, S. (1994). Living with multiple sclerosis. Hants, UK: Ashgate Publishing Limited.
Peyser, J.M., Edwards, K.R., Poser, C.M., & Filskov, S.B. (1980). Cognitive function in patients with multiple sclerosis. Archives of Neurology, 37, 577579.Google Scholar
Raine, C.S. & Cross, A.H. (1989). Axonal dystrophy as a consequence of long-term demyelination. Laboratory Investigation, 60, 714724.Google Scholar
Rao, S.M., Leo, G.J., Bernardin, L., & Unverzagt, F. (1991). Cognitive dysfunction in multiple sclerosis I: Frequency, patterns, and predictions. Neurology, 41, 685691.Google Scholar
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403428.Google Scholar
Shucard, J.L., Parrish, J., Shucard, D.W., McCabe, D.C., Benedict, R.H., & Ambrus, J. (2004). Working memory and processing speed deficits in systemic lupus erythematosus as measured by the Paced Auditory Serial Addition Test. Journal of the International Neuropsychological Society, 10, 3545.Google Scholar
Simon, J.H., Jacobs, L.D., Campion, M.K., Rudick, R.A., Cookfair, D.L., Herndon, R.M., Richert, J.R., Salazar, A.M., Fischer, J.S., Goodkin, D.E., Simonian, N., Lajaunie, M., Miller, D.E., Wende, K., Martens-Davidson, A., Kinkel, R.P., Munschauer, F., Brownscheidle, C.M., & The Multiple Sclerosis Collaborative Research Group. (1999). A longitudinal study of brain atrophy in relapsing multiple sclerosis. Neurology, 53, 139148.Google Scholar
Tiberio, M., Chard, D.T., Altmann, D.R., Davies, G., Griffin, C.M., Rashid, W., Sastre-Garriga, J., Thompson, A.J., & Miller, D.J. (2005). Gray and white matter volume changes in early RRMS: A 2-year longitudinal study. Neurology, 64, 10011007.Google Scholar
Trapp, B.D., Peterson, J., Ransohoff, R.M., Rudick, R., Mork, S., & Bo, L. (1998). Axonal transection in the lesions of multilple sclerosis. The New England Journal of Medicine, 338, 278285.Google Scholar
Valsasina, P., Benedetti, B., Rovaris, M., Sormani, M.P., Comi, G., & Filippi, M. (2005). Evidence for progressive gray matter loss in patients with relapsing-remitting MS. Neurology, 65, 11261128.Google Scholar
Zivadinov, R., Sepcic, J., Nasuelli, D., DeMasi, R., Monti Bragadin, L., Tommasi, M.A., Zambito-Marsala, S., Moretti, R., Bratina, A., Ukmar, M., Pozzi-Mucelli, R.S., Grop, A., Cazzato, G., & Zorzon, M. (2001). A longitudinal study of brain atrophy and cognitive disturbances in the early phase of relapsing-remitting multiple sclerosis. Journal of Neurology, Neurosurgery, and Psychiatry, 70, 773780.Google Scholar