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Utility of Intraindividual Reaction Time Variability to Predict White Matter Hyperintensities: A Potential Assessment Tool for Clinical Contexts?

Published online by Cambridge University Press:  08 August 2013

David Bunce ,
Allison A. M. Bielak ,
Nicolas Cherbuin ,
Philip J. Batterham ,
Wei Wen ,
Perminder Sachdev  and
Kaarin J. Anstey
David Bunce*
Affiliation:
Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia
Allison A. M. Bielak
Affiliation:
Department of Human Development and Family Studies, Colorado State University, Fort Collins, Colorado
Nicolas Cherbuin
Affiliation:
Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia
Philip J. Batterham
Affiliation:
Centre for Mental Health Research, The Australian National University, Canberra, Australia
Wei Wen
Affiliation:
Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia Centre for Health Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
Perminder Sachdev
Affiliation:
Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia Centre for Health Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
Kaarin J. Anstey
Affiliation:
Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia
*
Correspondence and reprint requests to: David Bunce, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9JT, UK. E-mail: [email protected]
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Abstract

Intraindividual variability (IIV) refers to reaction time (RT) variation across the trials of a given cognitive task. Little research has contrasted different measures of IIV or assessed how many RT trials are required to provide a robust measure of the construct. We, therefore, investigated three measures of IIV (raw SD, coefficient of variation, and intraindividual SD statistically removing time-on-task effects) in relation to frontal white matter hyperintensities (obtained through structural MRI) in 415 cognitively normal community-dwelling adults aged 44 to 48 years. Results indicated the three IIV measures did not differ greatly in predictions of white matter hyperintensities, although it is possible that time-on-task effects were influential. As few as 20 trials taking approximately 52 s to administer provided a reliable prediction of frontal white matter hyperintensities. We conclude that future work should evaluate the comparative utility of different IIV measures in relation to persons exhibiting clear neuropathology. (JINS, 2013, 19, 1–6)

Keywords

AgeWithin-person variabilityWhite matter hyperintensitiesFrontal cortex lesionsNeuropsychological assessmentReaction timeCognition
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 19 , Issue 9 , October 2013 , pp. 971 - 976
Copyright
Copyright © The International Neuropsychological Society 2013 

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References

Anstey, K.J., Christensen, H., Butterworth, P., Easteal, S., Mackinnon, A., Jacomb, T., Jorm, A.F. (2012). Cohort profile: The PATH Through Life Project. International Journal of Epidemiology, 41, 951–960.CrossRefGoogle ScholarPubMed
Bellgrove, M.A., Hester, R., Garavan, H. (2004). The functional neuroanatomical correlates of response variability: Evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916.CrossRefGoogle ScholarPubMed
Bielak, A.A.M., Cherbuin, N., Bunce, D., Anstey, K.J. (2013). Intraindividual variability is a fundamental phenomenon of aging: Evidence from an 8-year longitudinal study across young, middle, and older adulthood. Developmental Psychology, [Epub ahead of print].Google ScholarPubMed
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
Bunce, D., Anstey, K.J., Cherbuin, N., Burns, R., Christensen, H., Wen, W., Sachdev, P.S. (2010). Cognitive deficits are associated with frontal and temporal lobe white matter lesions in middle-aged adults living in the community. PLoS One, 5(10), e13567, doi:13510.11371/journal.pone.0013567CrossRefGoogle ScholarPubMed
Bunce, D., Handley, R., Gaines, S.O. Jr. (2008). Depression, anxiety, and within-person variability in adults aged 18 to 85 years. Psychology and Aging, 23(4), 848–858.CrossRefGoogle Scholar
Bunce, D., MacDonald, S.W.S., Hultsch, D.F. (2004). Inconsistency in serial choice decision and motor reaction times dissociate in younger and older adults. Brain and Cognition, 56(3), 320–327.CrossRefGoogle ScholarPubMed
Bunce, D., Warr, P.B., Cochrane, T. (1993). Blocks in choice responding as a function of age and physical fitness. Psychology and Aging, 8(1), 26–33.CrossRefGoogle ScholarPubMed
Cherbuin, N., Sachdev, P., Anstey, K.J. (2010). Neuropsychological predictors of transition from healthy cognitive aging to mild cognitive impairment: The PATH Through Life Study. American Journal of Geriatric Psychiatry, 18(8), 723–733.CrossRefGoogle ScholarPubMed
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. doi:10.1136/bmj.c3666CrossRefGoogle ScholarPubMed
de Frias, C.M., Dixon, R.A., Fisher, N., Camicioli, R. (2007). Intraindividual variability in neurocognitive speed: A comparison of Parkinson's disease and normal older adults. Neuropsychologia, 45(11), 2499–2507.CrossRefGoogle Scholar
Dixon, R.A., Garrett, D.D., Lentz, T.L., MacDonald, S.W.S., Strauss, E., Hultsch, D.F. (2007). Neurocognitive markers of cognitive impairment: Exploring the roles of speed and inconsistency. Neuropsychology, 21(3), 381–399. doi:10.1037/0894-4105.21.3.381CrossRefGoogle ScholarPubMed
Dykiert, D., Der, G., Starr, J.M., Deary, I.J. (2012a). Age differences in intra-individual variability in simple and choice reaction time: Systematic review and meta-analysis. PLoS One, 7(10), e45759. doi:10.1371/journal.pone.0045759CrossRefGoogle ScholarPubMed
Dykiert, D., Der, G., Starr, J.M., Deary, I.J. (2012b). Sex differences in reaction time mean and intraindividual variability across the life span. [Research Support, Non-U.S. Gov't]. Developmental Psychology, 48(5), 1262–1276. doi:10.1037/a0027550CrossRefGoogle ScholarPubMed
Gouw, A.A., Seewann, A., van der Flier, W.M., Barkhof, F., Rozemuller, A.M., Scheltens, P., Geurts, J.J. (2011). Heterogeneity of small vessel disease: A systematic review of MRI and histopathology correlations. Journal of Neurology, Neurosurgery, and Psychiatry, 82(2), 126–135. doi:10.1136/jnnp.2009.204685CrossRefGoogle ScholarPubMed
Hultsch, D.F., MacDonald, S.W., Dixon, R.A. (2002). Variability in reaction time performance of younger and older adults. Journal of Gerontology B Psychological Sciences, 57(2), P101–P115.CrossRefGoogle ScholarPubMed
Hultsch, D.F., MacDonald, S.W., Hunter, M.A., Levy-Bencheton, J., Strauss, E. (2000). Intraindividual variability in cognitive performance in older adults: Comparison of adults with mild dementia, adults with arthritis, and healthy adults. Neuropsychology, 14(4), 588–598.CrossRefGoogle ScholarPubMed
Hultsch, D.F., Strauss, E., Hunter, M.A., MacDonald, S.W.S. (2008). Intraindividual variability, cognition and aging. In: F.I.M. Craik, and T.A. Salthouse (Eds.), The handbook of aging and cognition (3rd ed., pp. 491–556). New York: Psychology Press.Google Scholar
Jokinen, H., Gouw, A.A., Madureira, S., Ylikoski, R., van Straaten, E.C., van der Flier, W.M., Erkinjuntti, T. (2011). Incident lacunes influence cognitive decline: The LADIS study. Neurology, 76(22), 1872–1878. doi:10.1212/WNL.0b013e31821d752fCrossRefGoogle ScholarPubMed
Lovden, M., Li, S.C., Shing, Y.L., Lindenberger, U. (2007). Within-person trial-to-trial variability precedes and predicts cognitive decline in old and very old age: Longitudinal data from the Berlin Aging Study. Neuropsychologia, 45(12), 2827–2838.CrossRefGoogle ScholarPubMed
MacDonald, S.W., Nyberg, L., Backman, L. (2006). Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity. Trends in Neuroscience, 29(8), 474–480.CrossRefGoogle ScholarPubMed
Stuss, D.T., Murphy, K.J., Binns, M.A., Alexander, M.P. (2003). Staying on the job: The frontal lobes control individual performance variability. Brain, 126(Pt 11), 2363–2380. doi:10.1093/brain/awg237CrossRefGoogle ScholarPubMed
Stuss, D.T., Pogue, J., Buckle, L., Bonder, J. (1994). Characterization of stability of performance in patients with traumatic brain injury: Variability and consistency on reaction time tests. Neuropsychology, 8, 316–324.CrossRefGoogle Scholar
Tales, A., Leonards, U., Bompas, A., Snowden, R.J., Philips, M., Porter, G., Bayer, A. (2012) … Intra-individual reaction time variability in amnestic mild cognitive impairment: A precursor to dementia? Journal of Alzheimer's Disease, 32, 457–466.CrossRefGoogle ScholarPubMed
van der Flier, W.M., van Straaten, E.C., Barkhof, F., Verdelho, A., Madureira, S., Pantoni, L., Scheltens, P. (2005). Small vessel disease and general cognitive function in nondisabled elderly: The LADIS study. Stroke, 36(10), 2116–2120. doi:10.1161/01.STR.0000179092.59909.42CrossRefGoogle ScholarPubMed
Wen, W., Sachdev, P.S., Li, J.J., Chen, X., Anstey, K.J. (2009). White matter hyperintensities in the forties: Their prevalence and topography in an epidemiological sample aged 44-48. Human Brain Mapping, 30(4), 1155–1167.CrossRefGoogle Scholar
West, R., Murphy, K.J., Armilio, M.L., Craik, F.I., Stuss, D.T. (2002). Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain and Cognition, 49(3), 402–419.CrossRefGoogle ScholarPubMed