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White Matter and Cognitive Decline in Aging: A Focus on Processing Speed and Variability

Published online by Cambridge University Press:  17 February 2014

Jonna Nilsson ,
Alan J. Thomas ,
John T. O'Brien  and
Peter Gallagher
Jonna Nilsson
Affiliation:
Institute of Ageing and Health, Newcastle University, United Kingdom
Alan J. Thomas
Affiliation:
Institute of Ageing and Health, Newcastle University, United Kingdom
John T. O'Brien
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
Peter Gallagher*
Affiliation:
Institute of Neuroscience, Newcastle University, United Kingdom
*
Correspondence and reprint requests to: Peter Gallagher, Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Framlington Place, Newcastle upon Tyne, NE2 4HH UK. E-mail: [email protected]
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Abstract

White matter (WM) change plays an important role in age-related cognitive decline. In this review, we consider methodological advances with particular relevance to the role of WM in age-related changes in processing speed. In this context, intra-individual variability in processing speed performance has emerged as a sensitive proxy of cognitive and neurological decline while neuroimaging techniques used to assess WM change have become increasingly more sensitive. Together with a carefully designed task protocol, we emphasize that the combined implementation of intra-individual variability and neuroimaging techniques hold promise for specifying the WM-processing speed relationship with implications for normative and clinical samples. (JINS, 2014, 20, 1–6)

Keywords

Reaction timeInformation processingIntra-individual variabilityDiffusion tensor imagingDementiaDepression
Type
Short Review
Information
Journal of the International Neuropsychological Society , Volume 20 , Issue 3 , March 2014 , pp. 262 - 267
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Albinet, C.T., Boucard, G., Bouquet, C.A., Audiffren, M. (2012). Processing speed and executive functions in cognitive aging: How to disentangle their mutual relationship? Brain and Cognition, 79, 1–11.Google Scholar
Assaf, Y., Pasternak, O. (2008). Diffusion tensor imaging (DTI)-based white matter mapping in brain research: A review. Journal of Molecular Neuroscience, 34, 51–61.CrossRefGoogle ScholarPubMed
Balota, D.A., Yap, M.J. (2011). Moving beyond the mean in studies of mental chronometry: The power of response time distributional analyses. Current Directions in Psychological Science, 20, 160–166.Google Scholar
Bartzokis, G. (2004). Age-related myelin breakdown: A developmental model of cognitive decline and Alzheimer's disease. Neurobiology of Aging, 25, 5–18.Google Scholar
Bennett, I.J., Madden, D.J., Vaidya, C.J., Howard, D.V., Howard, J.H. (2010). Age-related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Human Brain Mapping, 31, 378–390.Google Scholar
Bielak, A.A.M., Hultsch, D.F., Strauss, E., MacDonald, S.W.S., Hunter, M.A. (2010). Intraindividual variability in reaction time predicts cognitive outcomes 5 years later. Neuropsychology, 24, 731–741.CrossRefGoogle ScholarPubMed
Brickman, A.M., Siedlecki, K.L., Muraskin, J., Manly, J.J., Luchsinger, J.A., Yeung, L.K., Stern, Y. (2011). White matter hyperintensities and cognition: Testing the reserve hypothesis. Neurobiology of Aging, 32, 1588–1598.CrossRefGoogle ScholarPubMed
Bunce, D., Anstey, K.J., Christensen, H., Dear, K., Wen, W., Sachdev, P. (2007). White matter hyperintensities and within-person variability in community-dwelling adults aged 60–64 years. Neuropsychologia, 45, 2009–2015.Google Scholar
Bunce, D., Bielak, A.A., Cherbuin, N., Batterham, P.J., Wen, W., Sachdev, P., Anstey, K.J. (2013). Utility of intraindividual reaction time variability to predict white matter hyperintensities: A potential assessment tool for clinical contexts? Journal of the International Neuropsychological Society, 19, 1–6.Google Scholar
Burgmans, S., Gronenschild, E.H., Fandakova, Y., Shing, Y.L., van Boxtel, M.P., Vuurman, E.F., Raz, N. (2011). Age differences in speed of processing are partially mediated by differences in axonal integrity. Neuroimage, 55, 1287–1297.Google Scholar
Cepeda, N.J., Blackwell, K.A., Munakata, Y. (2013). Speed isn't everything: Complex processing speed measures mask individual differences and developmental changes in executive control. [Research Support, N.I.H., Extramural]. Developmental Science, 16, 269–286.CrossRefGoogle ScholarPubMed
Charlton, R.A., Landau, S., Schiavone, F., Barrick, T.R., Clark, C.A., Markus, H.S., Morris, R.G. (2008). A structural equation modeling investigation of age-related variance in executive function and DTI measured white matter damage. Neurobiology of Aging, 29, 1547–1555.Google Scholar
Craik, F.I.M., Salthouse, A.S. (2008). The handbook of ageing and cognition (Vol. 2). Great Britain: Psychology Press.Google Scholar
Davis, S.W., Dennis, N.A., Buchler, N.G., White, L.E., Madden, D.J., Cabeza, R. (2009). Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage, 46, 530–541.CrossRefGoogle Scholar
de Groot, J.C., de Leeuw, F.E., Oudkerk, M., van Gijn, J., Hofman, A., Jolles, J., Breteler, M.M.B. (2000). Cerebral white matter lesions and cognitive function: The Rotterdam Scan Study. Annals of Neurology, 47, 145–151.Google Scholar
Debette, S., Markus, H.S. (2010). The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. British Medical Journal, 341, c3666.Google Scholar
Dixon, R.A., Lentz, T.L., Garrett, D.D., MacDonald, S.W.S., Strauss, E., Hultsch, D.F. (2007). Neurocognitive markers of cognitive impairment: Exploring the roles of speed and inconsistency. Neuropsychology, 21, 381–399.Google Scholar
Duchek, J.M., Balota, D.A., Tse, C.S., Holtzman, D.M., Fagan, A.M., Goate, A.M. (2009). The utility of intraindividual variability in selective attention tasks as an early marker for Alzheimer's disease. Neuropsychology, 23, 746–758.CrossRefGoogle ScholarPubMed
Dykiert, D., Der, G., Starr, J.M., Deary, I.J. (2012). Age differences in intra-individual variability in simple and choice reaction time: Systematic review and meta-analysis. Plos One, 7, e45759.Google Scholar
Firbank, M.J., Teodorczuk, A., van der Flier, W.M., Gouw, A.A., Wallin, A., Erkinjuntti, T., Grp, L. (2012). Relationship between progression of brain white matter changes and late-life depression: 3-year results from the LADIS study. British Journal of Psychiatry, 201, 40–45.CrossRefGoogle ScholarPubMed
Fjell, A.M., Westlye, L.T., Amlien, I.K., Walhovd, K.B. (2011). Reduced white matter integrity is related to cognitive instability. Journal of Neuroscience, 31, 18060–18072.Google Scholar
Gogtay, N., Giedd, J.N., Lusk, L., Hayashi, K.M., Greenstein, D., Vaituzis, A.C., Thompson, P.M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101, 8174–8179.CrossRefGoogle ScholarPubMed
Gunning-Dixon, F.M., Brickman, A.M., Cheng, J.C., Alexopoulos, G.S. (2009). Aging of cerebral white matter: A review of MRI findings. International Journal of Geriatric Psychiatry, 24, 109–117.Google Scholar
Gunning-Dixon, F.M., Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology, 14, 224–232.Google Scholar
Hedden, T., Gabrieli, J.D.E. (2004). Insights into the ageing mind: A view from cognitive neuroscience. Nature Reviews Neuroscience, 5, 87–96.Google Scholar
Hultsch, D.F., Macdonald, S.W.S. (2004). Intraindividual variability in performance as a theoretical window onto cognitive aging. New York: Oxford University Press.CrossRefGoogle Scholar
Hultsch, D.F., Macdonald, S.W.S., Dixon, R. (2002). Variability in reaction time performance of younger and older adults. Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 57, P101–P115.Google Scholar
Inzitari, D., Pracucci, G., Poggesi, A., Carlucci, G., Barkhof, F., Chabriat, H., … LADIS Study Group (2009). Changes in white matter as determinant of global functional decline in older independent outpatients: Three year follow-up of LADIS (leukoaraiosis and disability) study cohort. British Medical Journal, 339, b2477.Google Scholar
Jackson, J.D., Balota, D.A., Duchek, J.M., Head, D. (2012). White matter integrity and reaction time intraindividual variability in healthy aging and early-stage Alzheimer disease. Neuropsychologia, 50, 357–366.Google Scholar
Jacobs, H.I.L., Leritz, E.C., Williams, V.J., Van Boxtel, M.P.J., van der Elst, W., Jolles, J., Salat, D.H. (2013). Association between white matter microstructure, executive functions, and processing speed in older adults: The impact of vascular health. Human Brain Mapping, 34, 77–95.Google Scholar
Jernigan, T.L., Archibald, S.L., Fennema-Notestine, C., Gamst, A.C., Stout, J.C., Bonner, J., Hesselink, J.R. (2001). Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of Aging, 22, 581–594.CrossRefGoogle ScholarPubMed
Joy, S., Fein, D., Kaplan, E., Freedman, M. (2000). Speed and memory in WAIS-R-NI Digit Symbol performance among healthy older adults. Journal of the International Neuropsychological Society, 6, 770–780.CrossRefGoogle ScholarPubMed
Joy, S., Kaplan, E., Fein, D. (2004). Speed and memory in the eWAIS-III digit symbol – Coding subtest across the adult lifespan. Archives of Clinical Neuropsychology, 19, 759–767.Google Scholar
Kaiser, S., Roth, A., Rentrop, M., Friederich, H.C., Bender, S., Weisbrod, M. (2008). Intra-individual reaction time variability in schizophrenia, depression and borderline personality disorder. Brain and Cognition, 66, 73–82.Google Scholar
Kerchner, G.A., Racine, C.A., Hale, S., Wilheim, R., Laluz, V., Miller, B.L., Kramer, J.H. (2012). Cognitive processing speed in older adults: Relationship with white matter integrity. Plos One, 7, e50425.Google Scholar
Li, S.C., Lindenberger, U., Sikstrom, S. (2001). Aging cognition: From neuromodulation to representation. Trends in Cognitive Sciences, 5, 479–486.Google Scholar
Lu, P.H., Lee, G.J., Tishler, T.A., Meghpara, M., Thompson, P.M., Bartzokis, G. (2013). Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy elderly men. Brain and Cognition, 81, 131–138.CrossRefGoogle ScholarPubMed
MacDonald, S.W.S., Hultsch, D.F., Dixon, R.A. (2003). Performance variability is related to change in cognition: Evidence from the victoria longitudinal study. Psychology and Aging, 18, 510–523.Google Scholar
MacDonald, S.W.S., Hultsch, D.F., Dixon, R.A. (2008). Predicting impending death: Inconsistency in speed is a selective and early marker. Psychology and Aging, 23, 595–607.Google Scholar
MacDonald, S.W.S., Li, S.C., Backman, L. (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging, 24, 792–808.CrossRefGoogle ScholarPubMed
Madden, D.J., Bennett, I.J., Burzynska, A., Potter, G.G., Chen, N.K., Song, A.W. (2012). Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochimica Et Biophysica Acta, 1822, 386–400.CrossRefGoogle ScholarPubMed
Madden, D.J., Bennett, I.J., Song, A.W. (2009). Cerebral white matter integrity and cognitive aging: Contributions from diffusion tensor imaging. Neuropsychology Review, 19, 415–435.Google Scholar
Madden, D.J., Spaniol, J., Costello, M.C., Bucur, B., White, L.E., Cabeza, R., Huettel, S.A. (2009). Cerebral white matter integrity mediates adult age differences in cognitive performance. Journal of Cognitive Neuroscience, 21, 289–302.Google Scholar
Madden, D.J., Whiting, W.L., Huettel, S.A., White, L.E., MacFall, J.R., Provenzale, J.M. (2004). Diffusion tensor imaging of adult age differences in cerebral white matter: Relation to response time. Neuroimage, 21, 1174–1181.CrossRefGoogle ScholarPubMed
Maillard, P., Carmichael, O., Fletcher, E., Reed, B., Mungas, D., DeCarli, C. (2012). Coevolution of white matter hyperintensities and cognition in the elderly. Neurology, 79, 442–448.Google Scholar
Matzke, D., Wagenmakers, E.J. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16, 798–817.Google Scholar
Nilsson, L.G., Sternang, O., Ronnlund, M., Nyberg, L. (2009). Challenging the notion of an early-onset of cognitive decline. Neurobiology of Aging, 30, 521–524.Google Scholar
O'Sullivan, M., Jones, D.K., Summers, P.E., Morris, R.G., Williams, S.C.R., Markus, H.S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology, 57, 632–638.CrossRefGoogle ScholarPubMed
Penke, L., Deary, I.J. (2010). Some guidelines for structural equation modelling in cognitive neuroscience: The case of Charlton et al.'s study on white matter integrity and cognitive ageing. Neurobiology of Aging, 31, 1656–1660.Google Scholar
Peters, A. (2002). The effects of normal aging on myelin and nerve fibers: A review. Journal of Neurocytology, 31, 581–593.Google Scholar
Rabbitt, P., Scott, M., Lunn, M., Thacker, N., Lowe, C., Pendleton, N., Jackson, A. (2007). White matter lesions account for all age-related declines in speed but not in intelligence. [Research Support, Non-U.S. Gov't]. Neuropsychology, 21, 363–370.Google Scholar
Ratcliff, R., Van Zandt, T., McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261–300.CrossRefGoogle ScholarPubMed
Raz, N., Lindenberger, U., Rodrigue, K.M., Kennedy, K.M., Head, D., Williamson, A., Acker, J.D. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15, 1676–1689.Google Scholar
Raz, N., Rodrigue, K.M., Kennedy, K.M., Acker, J.D. (2007). Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology, 21, 149–157.Google Scholar
Sachdev, P.S., Wen, W., Christensen, H., Jorm, A.F. (2005). White matter hyperintensities are related to physical disability and poor motor function. Journal of Neurology, Neurosurgery, and Psychiatry, 76, 362–367.Google Scholar
Salami, A., Eriksson, J., Nilsson, L.G., Nyberg, L. (2012). Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. Biochimica Et Biophysica Acta, 1822, 408–415.CrossRefGoogle Scholar
Salthouse, T.A. (1993). Speed mediation of adult age-differences in cognition. Developmental Psychology, 29, 722–738.Google Scholar
Salthouse, T.A. (1996a). General and specific speed mediation of adult age differences in memory. Journals of Gerontology Series B: Psychological Sciences, 51, P30–P42.Google Scholar
Salthouse, T.A. (1996b). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.Google Scholar
Salthouse, T.A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30, 507–514.Google Scholar
Salthouse, T.A. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16, 754–760.Google Scholar
Salthouse, T.A. (2011). Neuroanatomical substrates of age-related cognitive decline. Psychological Bulletin, 137, 753–784.Google Scholar
Sasson, E., Doniger, G.M., Pasternak, O., Tarrasch, R., Assaf, Y. (2013). White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Frontiers in Neuroscience, 7, 32.Google Scholar
Schmiedek, F., Oberauer, K., Wilhelm, O., Suss, H.M., Wittmann, W.W. (2007). Individual differences in components their relations to working of reaction time distributions and memory and intelligence. Journal of Experimental Psychology-General, 136, 414–429.Google Scholar
Song, S.K., Sun, S.W., Ramsbottom, M.J., Chang, C., Russell, J., Cross, A.H. (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage, 17, 1429–1436.Google Scholar
Spieler, D.H., Balota, D.A., Faust, M.E. (1996). Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer's type. Journal of Experimental Psychology-Human Perception and Performance, 22, 461–479.Google Scholar
Strauss, E., Bielak, A.A.M., Bunce, D., Hunter, M.A., Hultsch, D.F. (2007). Within-person variability in response speed as an indicator of cognitive impairment in older adults. Aging Neuropsychology and Cognition, 14, 608–630.Google Scholar
Strauss, E., MacDonald, S.W.S., Hunter, M., Moll, A., Hultsch, D.F. (2002). Intraindividual variability in cognitive performance in three groups of older adults: Cross-domain links to physical status and self-perceived affect and beliefs. Journal of the International Neuropsychological Society, 8, 893–906.Google Scholar
Tamnes, C.K., Fjell, A.M., Westlye, L.T., Ostby, Y., Walhovd, K.B. (2012). Becoming consistent: Developmental reductions in intraindividual variability in reaction time are related to white matter integrity. Journal of Neuroscience, 32, 972–982.Google Scholar
Tse, C.S., Balota, D.A., Yap, M.J., Duchek, J.M., McCabe, D.P. (2010). Effects of healthy aging and early stage dementia of the Alzheimer's type on components of response time distributions in three attention tasks. Neuropsychology, 24, 300–315.Google Scholar
Verhaeghen, P., Salthouse, T.A. (1997). Meta-analyses of age-cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models. Psychological Bulletin, 122, 231–249.Google Scholar
Walhovd, K.B., Fjell, A.M. (2007). White matter volume predicts reaction time instability. Neuropsychologia, 45, 2277–2284.Google Scholar
West, R., Murphy, K.J., Armilio, M.L., Craik, F.I.M., Stuss, D.T. (2002). Lapses of intention and performance variability reveal age-related increases in sfluctuations of executive control. Brain and Cognition, 49, 402–419.Google Scholar
Williams, B.R., Strauss, E.H., Hultsch, D.F., Hunter, M.A. (2007). Reaction time inconsistency in a spatial Stroop task: Age-related differences through childhood and adulthood. Aging Neuropsychology and Cognition, 14, 417–439.Google Scholar
Ylikoski, A., Erkinjuntti, T., Raininko, R., Sarna, S., Sulkava, R., Tilvis, R. (1995). White-matter hyperintensities on mri in the neurologically nondiseased elderly – Analysis of cohorts of consecutive subjects aged 55 to 85 years living at home. Stroke, 26, 1171–1177.CrossRefGoogle ScholarPubMed