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Altered Effective Connectivity during a Processing Speed Task in Individuals with Multiple Sclerosis

Published online by Cambridge University Press:  18 February 2016

E. Dobryakova ,
S.L. Costa ,
G.R. Wylie ,
J. DeLuca  and
H.M. Genova
E. Dobryakova
Affiliation:
Kessler Foundation, Pleasant Valley Way, West Orange, New Jersey Rutgers, New Jersey Medical School, Newark, New Jersey
S.L. Costa
Affiliation:
Rutgers, New Jersey Medical School, Newark, New Jersey Kessler Foundation, Executive Drive, West Orange, New Jersey
G.R. Wylie
Affiliation:
Rutgers, New Jersey Medical School, Newark, New Jersey Kessler Foundation, Executive Drive, West Orange, New Jersey War Related Illness & Injury Study Center, Department of Veteran’s Affairs, East Orange, New Jersey
J. DeLuca
Affiliation:
Kessler Foundation, Pleasant Valley Way, West Orange, New Jersey Rutgers, New Jersey Medical School, Newark, New Jersey
H.M. Genova*
Affiliation:
Rutgers, New Jersey Medical School, Newark, New Jersey Kessler Foundation, Executive Drive, West Orange, New Jersey
*
Correspondence and reprint requests to: Helen M. Genova, Kessler Foundation Research Center, 300 Executive Drive, West Orange, NJ 07052. E-mail: hgenova@kesslerfoundation.org
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Abstract

Objectives: Processing speed impairment is the most prevalent cognitive deficit in individuals with multiple sclerosis (MS). However, the neural mechanisms associated with processing speed remain under debate. The current investigation provides a dynamic representation of the functioning of the brain network involved in processing speed by examining effective connectivity pattern during a processing speed task in healthy adults and in MS individuals with and without processing speed impairment. Methods: Group assignment (processing speed impaired vs. intact) was based on participants’ performance on the Symbol Digit Modalities test (Parmenter, Testa, Schretlen, Weinstock-Guttman, & Benedict, 2010). First, brain regions involved in the processing speed task were determined in healthy participants. Time series from these functional regions of interest of each group of participants were then subjected to the effective connectivity analysis (Independent Multiple-Sample Greedy Equivalence Search and Linear, Non-Gaussian Orientation, Fixed Structure algorithms) that showed causal influences of one region on another during task performance. Results: The connectivity pattern of the processing speed impaired group was significantly different from the connectivity pattern of the processing speed intact group and of the healthy control group. Differences in the strength of common connections were also observed. Conclusions: Effective connectivity results reveal that MS individuals with processing speed impairment not only have connections that differ from healthy participants and MS individuals without processing speed impairment, but also have increased strengths of connections. (JINS, 2016, 22, 216–224)

Keywords

Processing speedMultiple sclerosisAttentionFronto-parietal networkCognitive deficitsCausal connections
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 2: Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease , February 2016 , pp. 216 - 224
Copyright
Copyright © The International Neuropsychological Society 2016 

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References

Barker-Collo, S.L. (2006). Quality of life in multiple sclerosis: Does information-processing speed have an independent effect? Archives of Clinical Neuropsychology, 21(2), 167–174. http://doi.org/10.1016/j.acn.2005.08.008 CrossRefGoogle ScholarPubMed
Bester, M., Lazar, M., Petracca, M., Babb, J.S., Herbert, J., Grossman, R.I., & Inglese, M. (2013). Tract-specific white matter correlates of fatigue and cognitive impairment in benign multiple sclerosis. Journal of the Neurological Sciences, 330(1-2), 61–66. http://doi.org/10.1016/j.jns.2013.04.005 CrossRefGoogle ScholarPubMed
Bledowski, C., Kaiser, J., & Rahm, B. (2010). Basic operations in working memory: Contributions from functional imaging studies. Behavioural Brain Research, 214(2), 172–179. http://doi.org/10.1016/j.bbr.2010.05.041 CrossRefGoogle ScholarPubMed
Chiaravalloti, N.D., Stojanovic-Radic, J., & DeLuca, J. (2013). The role of speed versus working memory in predicting learning new information in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 35(2), 180–191. http://doi.org/10.1080/13803395.2012.760537 Google Scholar
Compston, A., & Coles, A. (2008). Multiple sclerosis. Lancet, 372(9648), 1502–1517. http://doi.org/10.1016/S0140-6736(08)61620-7 CrossRefGoogle ScholarPubMed
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.Google Scholar
Denney, D.R., & Lynch, S.G. (2009). The impact of multiple sclerosis on patients’ performance on the Stroop Test: Processing speed versus interference. Journal of the International Neuropsychological Society, 15(3), 451–458. http://doi.org/10.1017/S1355617709090730 Google Scholar
Deshpande, G., & Hu, X. (2012). Investigating effective brain connectivity from fMRI data: Past findings and current issues with reference to Granger causality analysis. Brain Connectivity, 2(5), 235–245. http://doi.org/10.1089/brain.2012.0091 CrossRefGoogle ScholarPubMed
Forn, C., Belloch, V., Bustamante, J.C., Garbin, G., Parcet-Ibars, M.A., Sanjuan, A., & Avila, C. (2009). A symbol digit modalities test version suitable for functional MRI studies. Neuroscience Letters, 456, 11–14.Google Scholar
Friston, K.J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2(1-2), 56–78. http://doi.org/10.1002/hbm.460020107 CrossRefGoogle Scholar
Genova, H.M., DeLuca, J., Chiaravalloti, N., & Wylie, G. (2013). The relationship between executive functioning, processing speed, and white matter integrity in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 35(6), 631–641. http://doi.org/10.1080/13803395.2013.806649 Google Scholar
Genova, H.M., Hillary, F.G., Wylie, G., Rypma, B., & Deluca, J. (2009). Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging. Journal of the International Neuropsychological Society, 15(3), 383–393. http://doi.org/10.1017/S1355617709090535 Google Scholar
Genova, H.M., Lengenfelder, J, Chiaravalloti, N.D, Moore, N.B. & DeLuca, J. (2012). Processing speed versus working memory: contributions to an information-processing task in multiple sclerosis. Appl. Neuropsychol. Adult, 19, 132–140.CrossRefGoogle Scholar
Kravits, D.J., Saleem, K.S., Baker, C.I., & Mishkin, M. (2012). A new neural framework for visuospatial processing. Nature Review Neuroscience, 12(4), 217–230. http://doi.org/10.1038/nrn3008.A Google Scholar
Larocque, J.J., Lewis-peacock, J.A., & Postle, B.R. (2014). Multiple neural states of representation in short-term memory? It’s a matter of attention. Frontiers in Human Neuroscience, 8(January), 1–14. http://doi.org/10.3389/fnhum.2014.00005 CrossRefGoogle ScholarPubMed
Leavitt, V.M., Wylie, G., Genova, H.M., Chiaravalloti, N., & DeLuca, J. (2012). Altered effective connectivity during performance of an information processing speed task in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 18(4), 409–417. http://doi.org/10.1177/1352458511423651 CrossRefGoogle ScholarPubMed
Lee, P., Li, P.-C., Liu, C.-H., & Hsieh, C.-L. (2011). Test-retest reliability of two attention tests in schizophrenia. Archives of Clinical Neuropsychology, 26(5), 405–411. http://doi.org/10.1093/arclin/acr038 CrossRefGoogle ScholarPubMed
Luckmann, H.C., Jacobs, H.I.L., Sack, A.T., & Lu, H.C. (2014). The cross-functional role of frontoparietal regions in cognition: Internal attention as the overarching mechanism. Progress in Neurobiology, 116, 66–86.Google Scholar
McDonald, W.I., Compston, A., Edan, G., Goodkin, D., Hartung, H.P., Lublin, F.D., & Wolinsky, J.S. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Annals of Neurology, 50, 121–127. http://doi.org/10.1002/ana.1032 CrossRefGoogle ScholarPubMed
Miller, G.A., & Chapman, J.P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110(1), 40–48. http://doi.org/10.1037/0021-843X.110.1.40 Google Scholar
Mumford, J.A., & Ramsey, J.D. (2014). Bayesian networks for fMRI: A primer. Neuroimage, 86, 573–582.Google Scholar
Papadopoulou, A., Müller-Lenke, N., Naegelin, Y., Kalt, G., Bendfeldt, K., Kuster, P., & Penner, I.-K. (2013). Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 19(10), 1290–1296. http://doi.org/10.1177/1352458513475490 Google Scholar
Parmenter, B.A., Testa, S.M., Schretlen, D.J., Weinstock-Guttman, B., & Benedict, R.H. (2010). The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). Journal of the International Neuropsychological Society, 16(1), 6–16. http://doi.org/10.1017/S1355617709990750 CrossRefGoogle ScholarPubMed
Poldrack, R.A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67–70. http://doi.org/10.1093/scan/nsm006 Google Scholar
Poldrack, R.A., Mumford, J.A., & Nichols, T.E. (2011). Handbook of functional MRI: Data analysis. New York, NY: Cambridge University Press.Google Scholar
Ramsey, J.D., Hanson, S.J., & Glymour, C. (2011). Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. Neuroimage, 58(3), 838–848. http://doi.org/10.1016/j.neuroimage.2011.06.068 Google Scholar
Ramsey, J.D., Hanson, S.J., Hanson, C., Halchenko, Y.O., Poldrack, R.A., & Glymour, C. (2010). Six problems for causal inference from fMRI. Neuroimage, 49(2), 1545–1558. http://doi.org/10.1016/j.neuroimage.2009.08.065 CrossRefGoogle ScholarPubMed
Ramsey, J.D., Sanchez-romero, R., & Glymour, C. (2014). Non-Gaussian methods and high-pass filters in the estimation of effective connections. Neuroimage, 84, 986–1006. http://doi.org/10.1016/j.neuroimage.2013.09.062 CrossRefGoogle ScholarPubMed
Sheridan, L.K., Fitzgerald, H.E., Adams, K.M., Nigg, J.T., Martel, M.M., Puttler, L.I., & Zucker, R.A. (2006). Normative Symbol Digit Modalities Test performance in a community-based sample. Archives of Clinical Neuropsychology, 21(1), 23–28. http://doi.org/10.1016/j.acn.2005.07.003 CrossRefGoogle Scholar
Shomstein, S. (2012). Cognitive functions of the posterior parietal cortex: Top-down and bottom-up attentional control. Frontiers in Integrative Neuroscience, 6(July), 38. http://doi.org/10.3389/fnint.2012.00038 CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol Digit Modalities Test (SDMT): Manual (revised). Los Angeles: Western Psychological Services.Google Scholar
Smith, S.M., Bandettini, P.A., Miller, K.L., Behrens, T.E.J., Friston, K.J., David, O., & Nichols, T.E. (2012). The danger of systematic bias in group-level FMRI-lag-based causality estimation. Neuroimage, 59(2), 1228–1229. http://doi.org/10.1016/j.neuroimage.2011.08.015 Google Scholar
Smith, S.M., Miller, K.L., Salimi-Khorshidi, G., Webster, M., Beckmann, C.F., Nichols, T.E., & Woolrich, M.W. (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875–891. http://doi.org/10.1016/j.neuroimage.2010.08.063 Google Scholar
Strober, L.B., Christodoulou, C., Benedict, R.H.B., Westervelt, H.J., Melville, P., Scherl, W.F., & Krupp, L.B. (2012). Unemployment in multiple sclerosis: The contribution of personality and disease. Multiple Sclerosis (Houndmills, Basingstoke, England), 18(5), 647–653. http://doi.org/10.1177/1352458511426735 Google Scholar
Van Schependom, J., D’hooghe, M.B., Cleynhens, K., D’hooge, M., Haelewyck, M.-C., De Keyser, J., & Nagels, G. (2015). Reduced information processing speed as primum movens for cognitive decline in MS. Multiple Sclerosis (Houndmills, Basingstoke, England), 21, 83–91. http://doi.org/10.1177/1352458514537012 Google Scholar