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Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging

Published online by Cambridge University Press:  01 May 2009

HELEN M. GENOVA*
Affiliation:
Graduate School of Biomedical Sciences, University of Medicine and Dentistry of New Jersey, Newark, New Jersey Department of Physical Medicine and Rehabilitation, University of Medicine and Dentistry of New Jersey, Newark, New Jersey Kessler Foundation Research Center, West Orange, New Jersey
FRANK G. HILLARY
Affiliation:
Kessler Foundation Research Center, West Orange, New Jersey
GLENN WYLIE
Affiliation:
Department of Physical Medicine and Rehabilitation, University of Medicine and Dentistry of New Jersey, Newark, New Jersey
BART RYPMA
Affiliation:
School of Behavioral and Brain Sciences, University of Texas—Dallas, Dallas, Texas Department of Psychiatry University of Texas Southwestern Medical Center, Dallas, Texas
JOHN DELUCA
Affiliation:
Graduate School of Biomedical Sciences, University of Medicine and Dentistry of New Jersey, Newark, New Jersey Department of Physical Medicine and Rehabilitation, University of Medicine and Dentistry of New Jersey, Newark, New Jersey Kessler Foundation Research Center, West Orange, New Jersey
*
*Correspondence and reprint requests to: Helen M. Genova, Neuropsychology and Neuroscience Laboratory, Kessler Foundation Research Center, 300 Executive Drive, Suite 010, West Orange, New Jersey 07052. E-mail: hgenova@kmrrec.org

Abstract

Although it is known that processing speed deficits are one of the primary cognitive impairments in multiple sclerosis (MS), the underlying neural mechanisms responsible for impaired processing speed remain undetermined. Using BOLD functional magnetic resonance imaging, the current study compared the brain activity of 16 individuals with MS to 17 healthy controls (HCs) during performance of a processing speed task, a modified version of the Symbol Digit Modalities Task. Although there were no differences in performance accuracy, the MS group was significantly slower than HCs. Although both groups showed similar activation involving the precentral gyrus and occipital cortex, the MS showed significantly less cerebral activity than HCs in bilateral frontal and parietal regions, similar to what has been reported in aging samples during speeded tasks. In the HC group, processing speed was mediated by frontal and parietal regions, as well as the cerebellum and thalamus. In the MS group, processing speed was mediated by insula, thalamus and anterior cingulate. It therefore appears that neural networks involved in processing speed differ between MS and HCs, and our findings are similar to what has been reported in aging, where damage to both white and gray matter is linked to processing speed impairments (JINS, 2009, 15, 383–393).

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2009

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References

REFERENCES

Allison, J.D., Meador, K.J., Loring, D.W., Figueroa, R.E., & Wright, J.C. (2000). Functional MRI cerebral activation and deactivation during finger movement. Neurology, 54(1), 135142.CrossRefGoogle ScholarPubMed
Archibald, C.J. & Fisk, J.D. (2000). Information processing efficiency in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 22, 686701.CrossRefGoogle ScholarPubMed
Arnett, P.A. (2004). Speed of presentation influences story recall in college students and persons with multiple sclerosis. Archives of Clinical Neuropsychology, 19, 507523.CrossRefGoogle ScholarPubMed
Arnett, P.A. (2005). Longitudinal consistency of the relationship between depression symptoms and cognitive functioning in multiple sclerosis. CNS Spectrums, 10, 372382.CrossRefGoogle ScholarPubMed
Arnett, P.A., Higginson, C.I., Voss, W.D., Randolph, J.J., & Grandey, A.A. (2002). Relationship between coping, cognitive dysfunction and depression in multiple sclerosis. The Clinical Neuropsychologist, 16, 341355.CrossRefGoogle ScholarPubMed
Audoin, B., Ibarrola, D., Au Duong, M.V., Pelletier, J., Confort-Gouny, S., Malikova, I., Ali-Cherif, A., Cozzone, P.J., & Ranjeva, J.P. (2005). Functional MRI study of PASAT in normal subjects. Magma, 18, 96102.CrossRefGoogle ScholarPubMed
Barker-Collo, S.L. (2006). Quality of life in multiple sclerosis: Does information-processing speed have an independent effect? Archives of Clinical Neuropsychology, 21, 167174.CrossRefGoogle ScholarPubMed
Benedict, R.H., Bruce, J., Dwyer, M.G., Weinstock-Guttman, B., Tjoa, C., Tavazzi, E., Munschauer, F.E., & Zivadinov, R. (2007). Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Multiple Sclerosis, 13(6), 722730.CrossRefGoogle ScholarPubMed
Benedict, R.H., Carone, D.A., & Bakshi, R. (2004). Correlating brain atrophy with cognitive dysfunction, mood disturbances, and personality disorder in multiple sclerosis. Journal of Neuroimaging, 14(3 Suppl 1), 36S45S.CrossRefGoogle ScholarPubMed
Benedict, R.H., Cookfair, D., Gavett, R., Gunther, M., Munschauer, F., Garg, N., & Weinstock-Guttman, B. (2006). Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). Journal of the International Neuropsychological Society, 12(4), 549558.CrossRefGoogle Scholar
Brandt, J. & Benedict, R.H.B. (2001). Hopkins Verbal Learning Test—Revised Professional Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Brittain, J.L., La Marche, J.L., Reeder, K.P., Roth, D.L., & Boll, T.S. (1991). Effects of age and IQ on paced auditory serial additional task (PASAT) performance. The Clinical Neuropsychologist, 5, 163175.CrossRefGoogle Scholar
Canli, T., Sivers, H., Thomason, M.E., Whitfield-Gabrieli, S., Gabrieli, J.D., & Gotlib, I.H. (2004). Brain activation to emotional words in depressed vs healthy subjects. Neuroreport, 15, 25852588.CrossRefGoogle ScholarPubMed
Caplan, B. (1985). Stimulus effects in unilateral neglect? Cortex, 21, 6980.CrossRefGoogle ScholarPubMed
Chiaravalloti, N., Hillary, F., Ricker, J., Christodoulou, C., Kalnin, A., Liu, W.C., Steffener, J., & DeLuca, J. (2005). Cerebral activation patterns during working memory performance in multiple sclerosis using FMRI. Journal of Clinical and Experimental Neuropsychology, 27, 3354.CrossRefGoogle ScholarPubMed
Christodoulou, C., Krupp, L.B., Liang, Z., Huang, W., Melville, P., Roque, C., Scherl, W.F., Morgan, T., MacAllister, W.S., Li, L., Tudorica, L.A., Li, X., Roche, P., & Peyster, R. (2003). Cognitive performance and MR markers of cerebral injury in cognitively impaired MS patients. Neurology, 60(11), 17931798.CrossRefGoogle Scholar
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162173.CrossRefGoogle ScholarPubMed
Deloire, M.S., Bonnet, M.C., Salort, E., Arimone, Y., Boudineau, M., Petry, K.G., & Brochet, B. (2006). How to detect cognitive dysfunction at early stages of multiple sclerosis? Multiple Sclerosis, 12(4), 445452.CrossRefGoogle ScholarPubMed
DeLuca, J., Barbieri-Berger, S., & Johnson, S.K. (1994). The nature of memory impairments in multiple sclerosis: Acquisition versus retrieval. Journal of Clinical and Experimental Neuropsychology, 16, 183189.CrossRefGoogle 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 and Experimental Neuropsychology, 26, 550562.CrossRefGoogle ScholarPubMed
Demaree, H.A., DeLuca, J., Gaudino, E.A., & Diamond, B.J. (1999). Speed of information processing as a key deficit in multiple sclerosis: Implications for rehabilitation. Journal of Neurology, Neurosurgery, and Psychiatry, 67, 661663.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, 948956.CrossRefGoogle ScholarPubMed
Gaudino, E.A., Chiaravalloti, N.D., DeLuca, J., & Diamond, B.J. (2001). A comparison of memory performance in relapsing-remitting, primary progressive and secondary progressive, multiple sclerosis. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 14, 3244.Google ScholarPubMed
Greicius, M.D. & Menon, V. (2004). Default-mode activity during a passive sensory task: Uncoupled from deactivation but impacting activation. Journal of Cognitive Neuroscience, 16(9), 14841492.CrossRefGoogle ScholarPubMed
Harvey, P.O., Fossati, P., Pochon, J.B., Levy, R., Lebastard, G., Lehericy, S., Allilaire, J.F., & Dubois, B. (2005). Cognitive control and brain resources in major depression: An fMRI study using the n-back task. NeuroImage, 26, 860869.CrossRefGoogle ScholarPubMed
Henry, J.D. & Beatty, W.W. (2006). Verbal fluency deficits in multiple sclerosis. Neuropsychologia, 44, 11661174.CrossRefGoogle ScholarPubMed
Hester, R., Fassbender, C., & Garavan, H. (2004). Individual differences in error processing: A review and reanalysis of three event-related fMRI studies using the GO/NOGO task. Cerebral Cortex, 14, 986994.CrossRefGoogle ScholarPubMed
Hillary, F.G., Chiaravalloti, N.D., Ricker, J.H., Steffener, J., Bly, B.M., Lange, G., Liu, W.C., Kalnin, A.J., & DeLuca, J. (2003). An investigation of working memory rehearsal in multiple sclerosis using fMRI. Journal of Clinical and Experimental Neuropsychology, 25, 965978.CrossRefGoogle ScholarPubMed
Kim, H.J., Park, H.K., Park, J.R., Choi, M.H., Lee, H.W., & Chung, S.C. (2008). Effects of aging on visuospatial performance and cerebral activation and lateralization: An FMRI study. The International Journal of Neuroscience, 118(6), 781791.CrossRefGoogle Scholar
Landro, N.I., Celius, E.G., & Sletvold, H. (2004). Depressive symptoms account for deficient information processing speed but not for impaired working memory in early phase multiple sclerosis (MS). Journal of the Neurological Sciences, 217, 211216.CrossRefGoogle Scholar
Lazeron, R.H., Rombouts, S.A., de Sonneville, L., Barkhof, F., & Scheltens, P. (2003). A paced visual serial addition test for fMRI. Journal of the Neurological Sciences, 213, 2934.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, 229238.CrossRefGoogle ScholarPubMed
Litvan, I., Grafman, J., Vendrell, P., & Martinez, J.M. (1988). Slowed information processing in multiple sclerosis. Archives of Neurology, 45, 281285.CrossRefGoogle ScholarPubMed
Madden, D.J., Whiting, W.L., Huettel, S.A., White, L.E., MacFall, J.R., & Provenzale, J.M. (2004a). Diffusion tensor imaging of adult age differences in cerebral white matter: Relation to response time. NeuroImage, 21(3), 11741181.CrossRefGoogle ScholarPubMed
Madden, D.J., Whiting, W.L., Provenzale, J.M., & Huettel, S.A. (2004b). Age-related changes in neural activity during visual target detection measured by fMRI. Cerebral Cortex, 14(2), 143155.CrossRefGoogle ScholarPubMed
Mainero, C., Caramia, F., Pozzilli, C., Pisani, A., Pestalozza, I., Borriello, G., Bozzao, L., & Pantano, P. (2004). fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. NeuroImage, 21, 858867.CrossRefGoogle ScholarPubMed
Morgen, K., Sammer, G., Courtney, S.M., Wolters, T., Melchior, H., Blecker, C.R., Oschmann, P., Kaps, M., & Vaitl, D. (2007). Distinct mechanisms of altered brain activation in patients with multiple sclerosis. NeuroImage, 37(3), 937946.CrossRefGoogle ScholarPubMed
Nocentini, U., Pasqualetti, P., Bonavita, S., Buccafusca, M., De Caro, M.F., Farina, D., Girlanda, P., Le Pira, F., Lugaresi, A., Quattrone, A., Reggio, A., Salemi, G., Savettieri, G., Tedeschi, G., Trojano, M., Valentino, P., & Caltagirone, C. (2006). Cognitive dysfunction in patients with relapsing-remitting multiple sclerosis. Multiple Sclerosis, 12, 7787.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(1), 5257.CrossRefGoogle ScholarPubMed
Penner, I.K., Rausch, M., Kappos, L., Opwis, K., & Radü, E.W. (2003). Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. Journal of Neurology, 250(4), 461472.CrossRefGoogle ScholarPubMed
Polman, C.H., Reingold, S.C., Edan, G., Filippi, M., Hartung, H.P., Kappos, L., Lublin, F.D., Metz, L.M., McFarland, H.F., O’Connor, P.W., Sandberg-Wollheim, M., Thompson, A.J., Weinshenker, B.G., & Wolinsky, J.S. (2005). Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Annals of Neurology, 58(6), 840846.CrossRefGoogle ScholarPubMed
Rabbitt, P., Scott, M., Lunn, M., Thacker, N., Lowe, C., Pendleton, N., Horan, M., & Jackson, A. (2007). White matter lesions account for all age-related declines in speed but not in intelligence. Neuropsychology, 21(3), 363370.CrossRefGoogle ScholarPubMed
Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., & Shulman, G.L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676682.CrossRefGoogle ScholarPubMed
Reitan, R. (1958). Validity of TMT as an indication of organic brain damage. Perceptual and Motor Skills, 8, 271276.CrossRefGoogle Scholar
Robb, R.A. (2001). The biomedical imaging resource at Mayo Clinic. IEEE Transactions on Medical Imaging, 20, 854867.CrossRefGoogle ScholarPubMed
Rose, E.J., Simonotto, E., & Ebmeier, K.P. (2006). Limbic over-activity in depression during preserved performance on the n-back task. NeuroImage, 29, 203215.CrossRefGoogle ScholarPubMed
Rypma, B., Berger, J.S., Genova, H.M., Rebbechi, D., & D’Esposito, M. (2005). Dissociating age-related changes in cognitive strategy and neural efficiency using event-related fMRI. Cortex, 41, 582594.CrossRefGoogle ScholarPubMed
Rypma, B., Berger, J.S., Prabhakaran, V., Bly, B.M., Kimberg, D.Y., Biswal, B.B., & D’Esposito, M. (2006). Neural correlates of cognitive efficiency. NeuroImage, 33, 969979.CrossRefGoogle ScholarPubMed
Rypma, B., Eldreth, D.A., & Rebbechi, D. (2007). Age-related differences in activation-performance relations in delayed-response tasks: A multiple component analysis. Cortex, 43(1), 6576.CrossRefGoogle ScholarPubMed
Rypma, B., Prabhakaran, V., Desmond, J.E., Glover, G.H., & Gabrieli, J.D. (1999). Load-dependent roles of frontal brain regions in the maintenance of working memory. NeuroImage, 9(2), 216226.CrossRefGoogle ScholarPubMed
Sanfilipo, M.P., Benedict, R.H., Weinstock-Guttman, B., & Bakshi, R. (2006). Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology, 66(5), 685692.CrossRefGoogle ScholarPubMed
Sepulcre, J., Vanotti, S., Hernandez, R., Sandoval, G., Caceres, F., Garcea, O., & Villoslada, P. (2006). Cognitive impairment in patients with multiple sclerosis using the Brief Repeatable Battery-Neuropsychology test. Multiple Sclerosis, 12, 187195.CrossRefGoogle ScholarPubMed
Shmuel, A., Augath, M., Oeltermann, A., & Logothetis, N.K. (2006). Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nature Neuroscience, 9(4), 569577.CrossRefGoogle ScholarPubMed
Shum, D.H.K., McFarland, K.A., & Bain, J.D. (1990). Construct validity of eight tests of attention: Comparison of normal and closed head injured samples. The Clinical Neuropsychologist, 4, 151162.CrossRefGoogle Scholar
Smith, A. (1982). Symbol Digits Modalities Test. Los Angeles, CA: Western Psychological Services.Google Scholar
Staffen, W., Mair, A., Zauner, H., Unterrainer, J., Niederhofer, H., Kutzelnigg, A., Ritter, S., Golaszewski, S., Iglseder, B., & Ladurner, G. (2002). Cognitive function and fMRI in patients with multiple sclerosis: Evidence for compensatory cortical activation during an attention task. Brain, 125, 12751282.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). Wechsler Adult Intelligence Scale—Third Addition. San Antonio, TX: Psychological Corporation.Google Scholar
Wilkinson, G.S. (1993). Wide Range Achievement Test-3, Administration Manual. Wilmington, DE: Wide Range.Google Scholar
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