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Enhanced Recruitment During Executive Control Processing in Cognitively Preserved Patients With Pediatric-Onset MS

Published online by Cambridge University Press:  28 February 2019

Emily Barlow-Krelina*
Affiliation:
Department of Psychology, York University, Toronto, Canada
Gary R. Turner
Affiliation:
Department of Psychology, York University, Toronto, Canada
Nadine Akbar
Affiliation:
School of Rehabilitation Therapy, Queens University, Kingston, Canada
Brenda Banwell
Affiliation:
Department of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
Magdalena Lysenko
Affiliation:
Department of Psychology, York University, Toronto, Canada
E. Ann Yeh
Affiliation:
Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
Sridar Narayanan
Affiliation:
McConnell Brain Imaging Centre, McGill University, Montreal, Canada
D. Louis Collins
Affiliation:
McConnell Brain Imaging Centre, McGill University, Montreal, Canada
Bérengère Aubert-Broche
Affiliation:
McConnell Brain Imaging Centre, McGill University, Montreal, Canada
Christine Till
Affiliation:
Department of Psychology, York University, Toronto, Canada Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
*
Correspondence and reprint requests to: Emily Barlow-Krelina, 130 BSB, 4700 Keele Street, Toronto, Ontario, M3J 1P3. E-mail: embarlow@yorku.ca

Abstract

Objectives: Youth and young adults with pediatric-onset multiple sclerosis (MS) are vulnerable to executive dysfunction; however, some patients do not demonstrate functional deficits despite showing abnormalities on structural magnetic resonance imaging (MRI). Cognitively intact adults with MS have shown enhanced activation patterns relative to healthy controls on working memory tasks. We aim to evaluate whether cognitively preserved pediatric-onset MS patients engage compensatory recruitment strategies to facilitate age-normative performance on a task of working memory. Methods: Twenty cognitively preserved patients (mean age=18.7±2.7 years; 15 female) and 20 age- and sex-matched controls (mean age=18.5±2.9 years; 15 female) underwent neuropsychological testing and 3.0 Tesla MRI, including structural and functional acquisitions. Patterns of activation during the Alphaspan task, a working memory paradigm with two levels of executive control demand, were examined via whole-brain and region of interest (ROI) analyses. Results: Across all participants, lower accuracy and greater activation of regions implicated in working memory were observed during the high demand condition. MS patients demonstrated 0.21 s longer response time than controls. ROI analyses revealed enhanced activation for pediatric-onset MS patients relative to controls in the right middle frontal, left paracingulate, right supramarginal, and left superior parietal gyri during the low executive demand condition, over and above differences in response time. MS patients also demonstrated heightened activation in the right supramarginal gyrus in the high executive demand condition. Conclusions: Our findings suggest that pediatric-onset MS patients may engage compensatory recruitment strategies during working memory processing. (JINS, 2019, 25, 432–442)

Type
Regular Research
Copyright
Copyright © The International Neuropsychological Society, 2019. 

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References

REFERENCES

Akbar, N., Banwell, B., Sled, J.G., Binns, M.A., Doesburg, S.M., Rypma, B., … Till, C. (2016). Brain activation patterns and cognitive processing speed in patients with pediatric-onset multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 38(4), 393403.CrossRefGoogle ScholarPubMed
Amann, M., Dössegger, L.S., Penner, I.K., Hirsch, J.G., Raselli, C., Calabrese, P., … Gass, A. (2011). Altered functional adaptation to attention and working memory tasks with increasing complexity in relapsing‐remitting multiple sclerosis patients. Human Brain Mapping, 32(10), 17041719.CrossRefGoogle ScholarPubMed
Amato, M.P., Goretti, B., Ghezzi, A., Hakiki, B., Niccolai, C., Lori, S., … Cilia, S. (2014). Neuropsychological features in childhood and juvenile multiple sclerosis: Five-year follow-up. Neurology, 83(16), 14321438.CrossRefGoogle ScholarPubMed
Audoin, B., Ibarrola, D., Ranjeva, J.P., Confort‐Gouny, S., Malikova, I., Ali‐Chérif, A., … Cozzone, P. (2003). Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of multiple sclerosis. Human Brain Mapping, 20(2), 5158.CrossRefGoogle Scholar
Baldo, J.V., & Dronkers, N.F. (2006). The role of inferior parietal and inferior frontal cortex in working memory. Neuropsychology, 20(5), 529.CrossRefGoogle ScholarPubMed
Banwell, B.L., & Anderson, P.E. (2005). The cognitive burden of multiple sclerosis in children. Neurology, 64(5), 891894.CrossRefGoogle ScholarPubMed
Bigi, S., & Banwell, B. (2012). Pediatric multiple sclerosis. Journal of Child Neurology, 27(11), 13781383.CrossRefGoogle ScholarPubMed
Casey, B.J., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Science, 9(3), 104110.CrossRefGoogle ScholarPubMed
Cavanna, A.E., & Trimble, M.R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129(3), 564583.CrossRefGoogle ScholarPubMed
Collins, D.L., Neelin, P., Peters, T.M., & Evans, A.C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192205.CrossRefGoogle ScholarPubMed
Collins, D.L., Holmes, C.J., Peters, T.M., & Evans, A.C. (1995). Automatic 3‐D model‐based neuroanatomical segmentation. Human brain mapping, 3(3), 190208.CrossRefGoogle Scholar
Craik, F. I. M. (1986). A functional account of differences in memory. In F. Klix & H. Hagendorf (Eds.), Human memory and cognitive capacities (pp. 409421). North Holland: Elsevier Science Publishers.Google Scholar
Curtis, C.E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 415423.CrossRefGoogle ScholarPubMed
Dennis, M. (2000). Developmental plasticity in children: The role of biological risk, development, time and reserve. Journal of Communication Disorders, 33(4), 321332.CrossRefGoogle ScholarPubMed
Dennis, M., Spiegler, B.J., Simic, N., Sinopoli, K.J., Wilkinson, A., Yeates, K.O., … Fletcher, J.M. (2014). Functional plasticity in childhood brain disorders: When, what, how, and whom to assess. Neuropsychology Review, 24(4), 389408.CrossRefGoogle ScholarPubMed
Deschamps, I., Baum, S.R., & Gracco, V.L. (2014). On the role of the supramarginal gyrus in phonological processing and verbal working memory: Evidence from rTMS studies. Neuropsychologia, 53, 3946.CrossRefGoogle ScholarPubMed
Eskildsen, S.F., Coupe, P., Fonov, V., Manjon, J.V., Leung, K.K., Guizard, N., … the Alzheimer’s Disease Neuroimaging Initiative. (2012). BEaST: Brain extraction based on nonlocal segmentation technique. Neuroimage, 59(3), 23622373.CrossRefGoogle ScholarPubMed
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., & the Brain Development Cooperative Group. (2011). Unbiased average age-appropriate atlases for pediatric studies. Neuroimage, 54(1), 313327.CrossRefGoogle ScholarPubMed
Forn, C., Barros-Loscertales, A., Escudero, J., Benlloch, V., Campos, S., Parcet, M.A., & Avila, C. (2006). Cortical reorganization during PASAT task in MS patients with preserved working memory functions. Neuroimage, 31(2), 686691.CrossRefGoogle ScholarPubMed
Forn, C., Barros-Loscertales, A., Escudero, J., Benlloch, V., Campos, S., Parcet, M.A., & Avila, C. (2007). Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory N-back task. Human Brain Mapping, 28(5), 424430.CrossRefGoogle ScholarPubMed
Ghassemi, R., Nayahanan, S., Banwell, B., Sled, J.G., Shroff, M., & Arnold, D.L. (2014). Quantitative determination of regional lesion volume and distribution in children and adults with relapsing-remitting multiple sclerosis. PLoS One, 9(2), e85741.CrossRefGoogle ScholarPubMed
Greve, D.N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary based registration. Neuroimage, 48(1), 6372.CrossRefGoogle ScholarPubMed
Ingraham, L.J., & Aiken, C.B. (1996). An empirical approach to determining criteria for abnormality in test batteries with multiple measures. Neuropsychology, 10(1), 120.CrossRefGoogle Scholar
Jenkinson, M. (2003). Fast, automated, N‐dimensional phase‐unwrapping algorithm. Magnetic Resonance in Medicine, 49(1), 193197.CrossRefGoogle ScholarPubMed
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825841.CrossRefGoogle ScholarPubMed
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., & Smith, S.M. (2012 ). Fsl. Neuroimage, 62(2), 782790.CrossRefGoogle ScholarPubMed
Jenkinson, M., & Smith, S. (2001). A global optimization method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143156.CrossRefGoogle Scholar
MacAllister, W.S., Christodoulou, C., Milazzo, M., & Krupp, L.B. (2007). Longitudinal neuropsychological assessment in pediatric multiple sclerosis. Developmental Neuropsychology, 32(2), 625644.CrossRefGoogle ScholarPubMed
Mainero, C., Caramia, F., Pozzilli, C., Pisani, A., Pestalozza, I., Borriello, G., … Pantano, P. (2004). fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. Neuroimage, 21(3), 858867.CrossRefGoogle ScholarPubMed
Mathiowetz, V., Volland, G., Kashman, N., & Weber, K. (1992). Nine Hole Peg Test. New York, NY: Oxford University Press.Google Scholar
Morgen, K., Sammer, G., Courtney, S.M., Wolters, T., Melchior, H., Blecker, C.R., … Vaitl, D. (2007). Distinct mechanisms of altered brain activation in patients with multiple sclerosis. Neuroimage, 37(3), 937946.CrossRefGoogle ScholarPubMed
Owen, A.M., McMillan, K.M., Laird, A.R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 4659.CrossRefGoogle ScholarPubMed
Polman, C.H., Reingold, S.C., Banwell, B., Clanet, M., Cohen, J.A., Filippi, M., … Lublin, F.D. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald Criteria. Annals of Neurology, 69(2), 292302.CrossRefGoogle ScholarPubMed
Prakash, R.S., Snook, E.M., Erickson, K.I., Colcombe, S.J., Voss, M.W., Motl, R.W., & Kramer, A.F. (2007). Cardiorespiratory fitness: A predictor of cortical plasticity in multiple sclerosis. Neuroimage, 34(3), 12381244.CrossRefGoogle ScholarPubMed
Reitan, R.M. (1992). Trail Making Test: Manual for Administration and Scoring. Tucson, AZ: Reitan Neuropsychology Laboratory.Google Scholar
Reuter-Lorenz, P.A., & Cappell, K.A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science. 17(3), 177182.CrossRefGoogle Scholar
Rocca, M.A., De Meo, E., Amato, M.P., Copetti, M., Moiola, L., Ghezzi, A., … Pera, M.C. (2015). Cognitive impairment in paediatric multiple sclerosis patients is not related to cortical lesions. Multiple Sclerosis Journal, 21(7), 956957.CrossRefGoogle Scholar
Schmidt, M. (1996). Rey Auditory Verbal Learning Test: A handbook. Los Angeles, CA: Western Psychological Services.Google Scholar
Schoonheim, M.M., Geurts, J.J., & Barkhof, F. (2010). The limits of functional reorganization in multiple sclerosis. Neurology, 74(16), 12461247.CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol Digit Modalities Test: Manual. Los Angeles, CA: Western Psychological Services.Google Scholar
Smith, E.E., & Jonides, J. (1999). Storage and executive processes in the frontal lobes. Science, 283(5408), 16571661.CrossRefGoogle ScholarPubMed
Smith, S.M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143155.CrossRefGoogle ScholarPubMed
Smith, S.M., De Stefano, N., Jenkinson, M., & Matthews, P.M. (2001). Normalized accurate measurement of longitudinal brain change. Journal of Computer Assisted Tomography, 25(3), 466475.CrossRefGoogle ScholarPubMed
Smith, S.M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P.M., Federico, A., & De Stefano, N. (2002). Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage, 17(1), 479489.CrossRefGoogle ScholarPubMed
Staffen, W., Mair, A., Zauner, H., Unterrainer, J., Niederhofer, H., Kutzelnigg, A., … Ladurner, G. (2002). Cognitive function and fMRI in patients with multiple sclerosis: Evidence for compensatory cortical activation during an attention task. Brain, 125(6), 12751282.CrossRefGoogle ScholarPubMed
Sweet, L.H., Rao, S.M., Primeau, M., Durgerian, S., & Cohen, R.A. (2006). Functional magnetic resonance imaging response to increased verbal working memory demands among patients with multiple sclerosis. Human Brain Mapping, 27(1), 2836.CrossRefGoogle ScholarPubMed
Till, C., Deotto, A., Tipu, V., Sled, J.G., Bethune, A., Narayanan, S., … Banwell, B.L. (2011). White matter integrity and math performance in pediatric multiple sclerosis: A diffusion tensor imaging study. Neuroreport, 22(18), 10051009.CrossRefGoogle ScholarPubMed
Till, C., Ho, C., Dudani, A., Garcia-Lorenzo, D., Collins, D.L., & Banwell, B.L. (2012). Magnetic resonance imaging predictors of executive functioning in patients with pediatric-onset multiple sclerosis. Archives of Clinical Neuropsychology, 27(5), 495509.CrossRefGoogle ScholarPubMed
Tsukiura, T., Fujii, T., Takahashi, T., Xiao, R., Inase, M., Iijima, T., … Okuda, J. (2001). Neuroanatomical discrimination between manipulating and maintaining processes involved in verbal working memory: A functional MRI study. Cognitive Brain Research, 11(1), 1321.CrossRefGoogle ScholarPubMed
Turner, G.R., & Levine, B. (2008). Augmented neural activity during executive control processing following diffuse axonal injury. Neurology, 71(11), 812818.CrossRefGoogle ScholarPubMed
Turner, G.R., & Spreng, R.N. (2012). Executive functions and neurocognitive aging: Dissociable patterns of brain activity. Neurobiology of Aging, 33(4), 826e1.CrossRefGoogle ScholarPubMed
Uddin, L.Q., Kelly, A.M., Biswal, B.B., Castellanos, F.X., & Milham, M.P. (2009). Functional connectivity of default mode network components: Correlation, anticorrelation, and causality. Human Brain Mapping, 30(2), 625637.CrossRefGoogle ScholarPubMed
Van Strien, J.W. (2002). The Dutch Handedness Questionnaire. Rotterdam: Faculty of Social Sciences (FSW), Department of Psychology, Erasmus University Rotterdam.Google Scholar
Wager, T.D., & Smith, E.E. (2003). Neuroimaging studies of working memory. Cognitive, Affective, & Behavioral Neuroscience, 3(4), 255274.CrossRefGoogle ScholarPubMed
Waubant, E., & Chabas, D. (2009). Pediatric multiple sclerosis. Current Treatment Options in Neurology, 11(3), 203210.CrossRefGoogle ScholarPubMed
Weschler, D. (1999) Wechsler Abbreviated Scale of Intelligence (WASI). San Antonio, TX: The Psychological Corporation.Google Scholar
Woo, C.W., Krishnan, A., & Wager, T.D. (2014). Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. Neuroimage, 91, 412419.CrossRefGoogle ScholarPubMed
Woodcock, R.W., McGrew, K.S., & Mather, N. (2001). Woodcock-Johnson III Tests of Cognitive Abilities. Itasca, IL: Riverside Publishing.Google Scholar
Yeh, E.A., Chitnis, T., Krupp, L., Ness, J., Chabas, D., Kuntz, N., & Waubant, E. (2009). Pediatric multiple sclerosis. Nature Reviews Neurology, 5(11), 621.CrossRefGoogle ScholarPubMed
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging, 20(1), 4557.CrossRefGoogle ScholarPubMed