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Identifying Perceptual, Motor, and Cognitive Components Contributing to Slowness of Information Processing in Multiple Sclerosis with and without Depressive Symptoms

Published online by Cambridge University Press:  19 June 2020

Genny Lubrini Open the ORCID record for Genny Lubrini [Opens in a new window] ,
José A. Periáñez ,
Mireya Fernández-Fournier ,
Antonio Tallón Barranco ,
Exuperio Díez-Tejedor ,
Ana Frank García  and
Marcos Ríos-Lago
Genny Lubrini*
Affiliation:
Universidad Complutense (Spain)
José A. Periáñez
Affiliation:
Universidad Complutense (Spain)
Mireya Fernández-Fournier
Affiliation:
Hospital Universitario La Paz (Spain)
Antonio Tallón Barranco
Affiliation:
Hospital Universitario La Paz (Spain)
Exuperio Díez-Tejedor
Affiliation:
Hospital Universitario La Paz (Spain)
Ana Frank García
Affiliation:
Hospital Universitario La Paz (Spain)
Marcos Ríos-Lago
Affiliation:
Universidad Nacional de Educación a Distancia (UNED) (Spain) Hospital Beata María Ana (Spain)
*
Correspondence concerning this article should be addressed to Genny Lubrini. Departamento de Psicología Experimental, Procesos Cognitivos y Logopedia de la Universidad Complutense. Madrid (Spain). E-mail: [email protected]
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Abstract

Increasing findings suggest that different components of the stimulus-response pathway (perceptual, motor or cognitive) may account for slowed performance in Multiple Sclerosis (MS). It has also been reported that depressive symptoms (DS) exacerbate slowness in MS. However, no prior studies have explored the independent and joint impact of MS and DS on each of these components in a comprehensive manner. The objective of this work was to identify perceptual, motor, and cognitive components contributing to slowness in MS patients with and without DS. The study includes 33 Relapsing-Remitting MS patients with DS, 33 without DS, and 26 healthy controls. Five information processing components were isolated by means of ANCOVA analyses applied to five Reaction Time tasks. Perceptual, motor, and visual search components were slowed down in MS, as revealed by ANCOVA comparisons between patients without DS, and controls. Moreover, the compounding effect of MS and DS exacerbated deficits in the motor component, and slowed down the decisional component, as revealed by ANCOVA comparisons between patients with and without DS. DS seem to exacerbate slowness caused by MS in specific processing components. Identifying the effects of having MS and of having both MS and DS may have relevant implications when targeting cognitive and mood interventions.

Keywords

depressioninformation processing speedmultiple sclerosisreaction timeslowness
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 23 , 2020 , e21
Copyright
© Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2020

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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. https://doi.org/10.1076/1380-3395(200010)22:5;1-9;FT686CrossRefGoogle ScholarPubMed
Arnett, P. A., Higginson, C. I., Voss, W. D., Wright, B., Bender, W. I., Wurst, J. M., & Tippin, J. M. (1999). Depressed mood in multiple sclerosis: Relationship to capacity-demanding memory and attentional functioning. Neuropsychology, 13, 434–446. http://doi.org/10.1037/0894-4105.13.3.434CrossRefGoogle ScholarPubMed
Bilbao, A. & Seisdedos-Cubero, N. (2004). Eficacia de una fórmula de estimación de la inteligencia premórbida en la población española [The efficacy of a formula for estimating premorbid intelligence in the Spanish population]. Revista de Neurología, 38, 431–434.Google Scholar
Caligiuri, M. P., & Ellwanger, J. (2000). Motor and cognitive aspects of motor retardation in depression. Journal of Affective Disorders, 57, 83–93. https://doi.org/10.1016/S0165-0327(99)00068-3CrossRefGoogle ScholarPubMed
Carter, L., Russell, P. N., & Helton, W. S. (2013). Target predictability, sustained attention, and response inhibition. Brain and Cognition, 82, 35–42. http://doi.org/10.1016/j.bandc.2013.02.002CrossRefGoogle ScholarPubMed
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, 489–501. https://doi.org/10.1076/jcen.25.4.489.13878CrossRefGoogle ScholarPubMed
Costa, S. L., Genova, H. M., DeLuca, J., & Chiaravalloti, N. D. (2016). Information processing speed in multiple sclerosis: Past, present, and future. Multiple Sclerosis Journal, 23, 772–789. https://doi.org/10.1177/1352458516645869CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1016/S0028-3932(02)00041-6CrossRefGoogle 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, 550–562. http://doi.org/10.1080/13803390490496641CrossRefGoogle ScholarPubMed
DeLuca, J., & Kalmar, J. H. (2007). Information processing speed in clinical populations. Psychology Press.Google Scholar
Feinstein, A., Magalhaes, S., Richard, J.-F., Audet, B., & Moore, C. (2014). The link between multiple sclerosis and depression. Nature Reviews Neurology, 10, 507–517. https://doi.org/10.1038/nrneurol.2014.139CrossRefGoogle ScholarPubMed
Goretti, B., Viterbo, R. G., Portaccio, E., Niccolai, C., Hakiki, B., Piscolla, E., Iaffaldano, P., Trojano, M., & Amato, M.P. (2014). Anxiety state affects information processing speed in patients with multiple sclerosis. Neurologinal Sciences, 35, 559–563. https://doi.org/10.1007/s10072-013-1544-0CrossRefGoogle ScholarPubMed
Hammar, Å. (2003). Automatic and effortful information processing in unipolar major depression. Scandinavian Journal of Psychology, 44, 409–413. https://doi.org/10.1046/j.1467-9450.2003.00361.xCrossRefGoogle ScholarPubMed
Hsieh, Y.-H., Chen, K.-J., Wang, C.-C., & Lai, C.-L. (2008). Cognitive and motor components of response speed in the stroop test in Parkinson's disease patients. The Kaohsiung Journal of Medical Sciences, 24, 197–203. http://doi.org/10.1016/S1607-551X(08)70117-7CrossRefGoogle ScholarPubMed
Jensen, A. R. (2006). Clocking the mind: Mental chronometry and individual differences. Elsevier.Google Scholar
Kennedy, J. E., Clement, P. F., & Curtiss, G. (2003). WAIS–III Processing Speed Index scores after TBI: The influence of working memory, psychomotor speed and perceptual processing. The Clinical Neuropsychologist, 17, 303–307. http://doi.org/10.1076/clin.17.3.303.18091CrossRefGoogle ScholarPubMed
Kujala, P., Portin, R., Revonsuo, A., & Ruutiainen, J. (1994). Automatic and controlled information processing in multiple sclerosis. Brain, 117, 1115–1126. https://doi.org/10.1093/brain/117.5.1115CrossRefGoogle ScholarPubMed
Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33, 1444–1452. https://doi.org/10.1212/WNL.33.11.1444CrossRefGoogle Scholar
Laatu, S., Revonsuo, A., Hämäläinen, P., Ojanen, V., & Ruutiainen, J. (2001). Visual object recognition in multiple sclerosis. Journal of the Neurological Sciences, 185, 77–88. https://doi.org/10.1016/S0022-510X(01)00461-0CrossRefGoogle ScholarPubMed
Leavitt, V. M., Wylie, G., Krch, D., Chiaravalloti, N., DeLuca, J., & Sumowski, J. F. (2014). Does slowed processing speed account for executive deficits in multiple sclerosis? Evidence from neuropsychological performance and structural neuroimaging. Rehabilitation Psychology, 59, 422–428. https://doi.org/10.1037/a0037517CrossRefGoogle ScholarPubMed
Lubrini, G., Periáñez, J. A., Ríos-Lago, M., & Frank, A. (2012). Velocidad de procesamiento en la esclerosis múltiple remitente recurrente: El papel de los síntomas depresivos [Processing speed in relapsing-remitting multiple sclerosis: The role played by the depressive symptoms]. Revista de Neurología, 55, 585–592. https://doi.org/10.33588/rn.5510.2012301CrossRefGoogle Scholar
Lubrini, G., Ríos Lago, M., Periañez, J. A., Tallón Barranco, A., De Dios, C., Fernández-Fourier, M., Díez Tejedor, E., & Frank García, A. (2016). The contribution of depressive symptoms to slowness of information processing in relapsing remitting multiple sclerosis. Multiple Sclerosis Journal, 22, 1607–1615. https://doi.org/10.1177/1352458516661047CrossRefGoogle ScholarPubMed
Macniven, J. A. B., 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. https://doi.org/10.1017/S1355617708080946CrossRefGoogle ScholarPubMed
Morrow, S. A., Rosehart, H., & Pantazopoulos, K. (2016). Anxiety and depressive symptoms are associated with worse performance on objective cognitive tests in MS. The Journal of Neuropsychiatry and Clinical Neurosciences, 28, 118–123. https://doi.org/10.1176/appi.neuropsych.15070167CrossRefGoogle ScholarPubMed
Neisser, U. (1964). Visual search. Scientific American, 210, 94–102. https://doi.org/10.1038/scientificamerican0664-94CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1017/S1355617707070580CrossRefGoogle ScholarPubMed
Pipingas, A., Harris, E., Tournier, E., King, R., Kras, M., & Stough, C. K. (2010). Assessing the efficacy of nutraceutical interventions on cognitive functioning in the elderly. Current Topics in Nutraceutical Research, 8, 79–88.Google Scholar
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. O., 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, 840–846. https://doi.org/10.1002/ana.20703CrossRefGoogle ScholarPubMed
Rao, S. M., Leo, G. J., Bernardin, L., & Unverzagt, F. (1991). Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology, 41, 685–691. https://doi.org/10.1212/WNL.41.5.685CrossRefGoogle 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. https://doi.org/10.1016/j.acn.2007.04.008CrossRefGoogle ScholarPubMed
Reitan, R. M., & Wolfson, D. (1996). Relationships between specific and general tests of cerebral functioning. The Clinical Neuropsychologist, 10, 37–42. https://doi.org/10.1080/13854049608406661CrossRefGoogle Scholar
Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). “Oops!”: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35, 747–758. https://doi.org/10.1016/S0028-3932(97)00015-8CrossRefGoogle ScholarPubMed
Sanz, J., & Vázquez, C. (1998). Fiabilidad, validez y datos normativos del Inventario para la Depresión de Beck [Reliability, validity, and normative data of the Beck Depression Inventory]. Psicothema, 10, 303–318.Google Scholar
Seli, P. (2016). The Attention-Lapse and Motor Decoupling accounts of SART performance are not mutually exclusive. Consciousness and Cognition, 41, 189–198. http://doi.org/10.1016/j.concog.2016.02.017CrossRefGoogle Scholar
Shum, D. H. K., Mcfarland, K., & Bain, J. D. (1994). Effects of closed-head injury on attentional processes: Generality of Sternberg's additive factor method. Journal of Clinical and Experimental Neuropsychology, 16, 547–555. https://doi.org/10.1080/01688639408402666CrossRefGoogle ScholarPubMed
Stoquart-Elsankari, S., Bottin, C., Roussel-Pieronne, M., & Godefroy, O. (2010). Motor and cognitive slowing in multiple sclerosis: An attentional deficit? Clinical Neurology and Neurosurgery, 112, 226–232. https://doi.org/10.1016/j.clineuro.2009.11.017CrossRefGoogle Scholar
Strauss, E. H., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. Oxford University Press.Google Scholar
Strober, L. B., & Arnett, P. A. (2010). Assessment of depression in multiple sclerosis: Development of a “Trunk And Branch” Model. The Clinical Neuropsychologist, 24, 1146–1166. https://doi.org/10.1080/13854046.2010.514863CrossRefGoogle Scholar
Teichner, W. H., & Krebs, M. J. (1974). Laws of visual choice reaction time. Psychological Review, 81, 75–98. http://doi.org/10.1037/h0035867CrossRefGoogle ScholarPubMed
Thomas, P., Goudemand, M., & Rousseaux, M. (1999). Attentional resources in major depression. European Archives of Psychiatry and Clinical Neuroscience, 249, 79–85. https://doi.org/10.1007/s004060050070CrossRefGoogle ScholarPubMed
Tombaugh, T. N., Berrigan, L. I., Walker, L. A. S., & 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. https://doi.org/10.1097/WNN.0b013e3181cc8bd4CrossRefGoogle ScholarPubMed
Treisman, A. (1988). Features and objects: The fourteenth Bartlett memorial lecture. The Quarterly Journal of Experimental Psychology Section A, 40, 201–237. https://doi.org/10.1080/02724988843000104CrossRefGoogle ScholarPubMed
Utz, K. S., Hankeln, T. M. A., Jung, L., Lämmer, A., Waschbisch, A., Lee, D.-H., Linker, R. A., & Schenk, T. (2013). Visual search as a tool for a quick and reliable assessment of cognitive functions in patients with multiple sclerosis. PLOS ONE, 8, Article e81531. https://doi.org/10.1371/journal.pone.0081531CrossRefGoogle ScholarPubMed
Whyte, J., Grieb-Neff, P., Gantz, C., & Polansky, M. (2006). Measuring sustained attention after traumatic brain injury: Differences in key findings from the sustained attention to response task (SART). Neuropsychologia, 44, 2007–2014. https://doi.org/10.1016/j.neuropsychologia.2006.02.012CrossRefGoogle Scholar