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Disturbance of attention network functions in Chinese healthy older adults: an intra-individual perspective

Published online by Cambridge University Press:  28 September 2015

Hanna Lu ,
Ada W. T. Fung ,
Sandra S. M. Chan  and
Linda C. W. Lam
Hanna Lu
Affiliation:
Department of Psychiatry, the Chinese University of Hong Kong, G/F, Multi-Centre, Tai Po Hospital, Hong Kong, SARChina
Ada W. T. Fung
Affiliation:
Department of Psychiatry, the Chinese University of Hong Kong, G/F, Multi-Centre, Tai Po Hospital, Hong Kong, SARChina
Sandra S. M. Chan
Affiliation:
Department of Psychiatry, the Chinese University of Hong Kong, G/F, Multi-Centre, Tai Po Hospital, Hong Kong, SARChina
Linda C. W. Lam*
Affiliation:
Department of Psychiatry, the Chinese University of Hong Kong, G/F, Multi-Centre, Tai Po Hospital, Hong Kong, SARChina
*
Correspondence should be addressed to: Linda C. W. Lam, MD, Department of Psychiatry, The Chinese University of Hong Kong, G/F Multicenter, Tai Po Hospital, Tai Po, Hong Kong. Phone: +(852) 2607-6027; Fax: +(852) 2667-5464. Email: [email protected].
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Abstract

Background:

Intra-individual variability (IIV) and the change of attentional functions have been reported to be susceptible to both healthy ageing and pathological ageing. The current study aimed to evaluate the IIV of attention and the age-related effect on alerting, orienting, and executive control in cognitively healthy older adults.

Method:

We evaluated 145 Chinese older adults (age range of 65–80 years, mean age of 72.41 years) with a comprehensive neuropsychological battery and the Attention network test (ANT). A two-step strategy of analytical methods was used: Firstly, the IIV of older adults was evaluated by the intraindividual coefficient of variation of reaction time (ICV-RT). The correlation between ICV-RT and age was used to evaluate the necessity of subgrouping. Further, the comparisons of ANT performance among three age groups were performed with processing speed adjusted.

Results:

Person's correlation revealed significant positive correlations between age and IIV (r = 0.185, p = 0.032), age and executive control (r = 0.253, p = 0.003). Furthermore, one-way ANOVA comparisons among three age groups revealed a significant age-related disturbance on executive control (F = 4.55, p = 0.01), in which oldest group (group with age >75 years) showed less efficient executive control than young-old (group with age 65–70 years) (Conventional score, p = 0.012; Ratio score, p = 0.020).

Conclusion:

Advancing age has an effect on both IIV and executive attention in cognitively healthy older adults, suggesting that the disturbance of executive attention is a sensitive indicator to reflect healthy ageing. Its significance to predict further deterioration should be carefully evaluated with prospective studies.

Keywords

healthy ageingattention networkintra-individual variabilityexecutive control
Type
Research Article
Information
International Psychogeriatrics , Volume 28 , Issue 2 , February 2016 , pp. 291 - 301
Copyright
Copyright © International Psychogeriatric Association 2015 

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References

Alzheimer's Association (2014). 2014 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 10, e47–e92.Google Scholar
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, (DSM-5®). Washington, DC: American Psychiatric Association.Google Scholar
Beck, S. L., Schwartz, A. L., Towsley, G., Dudley, W. and Barsevick, A. (2004). Psychometric evaluation of the Pittsburgh sleep quality index in cancer patients. Journal of Pain and Symptom Management, 27, 140–148.Google Scholar
Bellgrove, M. A., Hester, R. and Garavan, H. (2004). The functional neuroanatomical correlates of response variability: evidence from a response inhibition task. Neuropsychologia, 42, 1910–1916.Google Scholar
Bielak, A. A M., Cherbuin, N., Bunce, D. and Anstey, K. J. (2014). Intra-individual variability is a fundamental phenomenon of aging: evidence from an 8-year longitudinal study across young, middle, and older adulthood. Developmental Psychology, 50, 143–151.Google Scholar
Blazer, D. (2013). Neurocognitive disorders in DSM-5. The American Journal of Psychiatry, 170, 585–587.CrossRefGoogle ScholarPubMed
Bunce, D., MacDonald, S. W. S. and Hultsch, D. F. (2004). Inconsistency in serial choice decision and motor reaction times dissociate in younger and older adults. Brain and Cognition, 56, 320–327.Google Scholar
Christensen, H. (2001). What cognitive changes can be expected with normal ageing? The Australian and New Zealand Journal of Psychiatry, 35, 768–775.Google Scholar
Chu, L. W., Chiu, K. C., Hui, S. L., Yu, G. K., Tsui, W. J. and Lee, P. W. (2000). The reliability and validity of the Alzheimer ‘ s disease assessment scale cognitive subscale (ADAS-Cog) among the elderly Chinese in Hong Kong. Annals of the Academy of Medicine, Singapore, 29, 474–485.Google Scholar
Dennis, T. A., Chen, C. C. and McCandliss, B. D. (2008). Threat-related attentional biases: an analysis of three attention systems. Depression and Anxiety, 25, E1–E10.Google Scholar
Dixon, R. A., Garrett, D. D., Lentz, T. L., MacDonald, S. W., Strauss, E. and Hultsch, D. F. (2007). Neurocognitive markers of cognitive impairment: exploring the roles of speed and inconsistency. Neuropsychology, 21, 381–399.Google Scholar
Drag, L. L. and Bieliauskas, L. A. (2010). Contemporary review 2009: cognitive aging. Journal of geriatric psychiatry and neurology, 23, 75–93.Google Scholar
Fan, J., McCandliss, B. D., Sommer, T., Raz, A. and Posner, M. I. (2002a). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14, 340–347.Google Scholar
Fernandez-Duque, D. and Black, S. E. (2006). Attentional networks in normal aging and Alzheimer's disease. Neuropsychology, 20, 133–143.CrossRefGoogle ScholarPubMed
Festa-Martino, E., Ott, B. R. and Heindel, W. C. (2004). Interactions between phasic alerting and spatial orienting: effects of normal aging and Alzheimer's disease. Neuropsychology, 18, 258–268.Google Scholar
Fisk, J. E. and Sharp, C. A. (2004). Age-related impairment in executive functioning: updating, inhibition, shifting, and access. Journal of Clinical and Experimental Neuropsychology, 26, 874–890.Google Scholar
Galvao-carmona, A. and Izquierdo, G. (2014). Disentangling the attention network test: behavioral, event related potentials and neural source analyses. Frontiers in Human Neuroscience, 8, 813.Google Scholar
Gamboz, N., Zamarian, S. and Cavallero, C. (2010). Age-related differences in the attention network test (ANT). Experimental Aging Research, 36, 287–305.Google Scholar
Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W., Lindenberger, U., McIntosh, A. R. and Grady, C. L. (2013). Moment-to-moment brain signal variability: a next frontier in human brain mapping?. Neuroscience & Biobehavioral Reviews, 37, 610–624.CrossRefGoogle ScholarPubMed
Grady, C. L. (2008). Cognitive neuroscience of aging. Annals of the New York Academy of Sciences, 1124, 127–144.Google Scholar
Greenwood, P. M. (2000). The frontal aging hypothesis evaluated. Journal of the International Neuropsychological Society: JINS, 6, 705–726.Google Scholar
Hazlett, E. A. et al. (2010). Effects of sex and normal aging on regional brain activation during verbal memory performance. Neurobiology of Aging, 31, 826–838.Google Scholar
Hedden, T. and Gabrieli, J. D. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nature Reviews. Neuroscience, 5, 87–96.Google Scholar
Hester, R. L., Kinsella, G. J. and Ong, B. (2004). Effect of age on forward and backward span tasks. Journal of the International Neuropsychological Society: JINS, 10, 475–481.Google Scholar
Hilborn, J. V, Strauss, E., Hultsch, D. F. and Hunter, M. A. (2009). Intraindividual variability across cognitive domains: investigation of dispersion levels and performance profiles in older adults. Journal of Clinical and Experimental Neuropsychology, 31, 412–424.Google Scholar
Jennings, J. M., Dagenbach, D., Engle, C. M. and Funke, L. J. (2007). Age-related changes and the attention network task: an examination of alerting, orienting, and executive function. Aging, Neuropsychology, and Cognition, 14, 353–369.Google Scholar
Kaasinen, V. et al. (2000). Age-related dopamine D2/D3 receptor loss in extrastriatal regions of the human brain. Neurobiology of Aging, 21, 683–688.CrossRefGoogle ScholarPubMed
Kennedy, Q., Taylor, J., Heraldez, D., Noda, A., Lazzeroni, L. C. and Yesavage, J. (2013). Intraindividual variability in basic reaction time predicts middle-aged and older pilots’ flight simulator performance. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 68, 487–494.Google Scholar
Lam, L. C., Tam, C. W., Chiu, H. F. and Lui, V. W. (2009). Depression and apathy affect functioning in community active subjects with questionable dementia and mild Alzheimer's disease. International Journal of Geriatric Psychiatry, 22, 431–437.Google Scholar
Lam, L. C. et al. (2008). Prevalence of very mild and mild dementia in community-dwelling older Chinese people in Hong Kong. International Psychogeriatrics, 20, 135–148.Google Scholar
Li, S. C., Lindenberger, U. and Sikström, S. (2001). Aging cognition: from neuromodulation to representation. Trends in Cognitive Sciences, 5, 479–486.Google Scholar
MacDonald, S. W., Hultsch, D. F. and 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., Li, S. C. and Bäckman, L. (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging, 24, 792–808.Google Scholar
Mahoney, J. R., Verghese, J., Goldin, Y., Lipton, R. and Holtzer, R. (2010). Alerting, orienting, and executive attention in older adults. Journal of the International Neuropsychological Society: JINS, 16, 877–889.Google Scholar
Martin, M. and Hofer, S. M. (2004). Intraindividual variability, change, and aging: conceptual and analytical issues. Gerontology, 50, 7–11.Google Scholar
Matsumoto, K. and Tanaka, K. (2004). The role of the medial prefrontal cortex in achieving goals. Current Opinion in Neurobiology, 14, 178–185.CrossRefGoogle ScholarPubMed
Mullane, J. C., Corkum, P. V., Klein, R. M., McLaughlin, E. N. and Lawrence, M. A. (2011). Alerting, orienting, and executive attention in children with ADHD. Journal of Attention Disorders, 15, 310–320.CrossRefGoogle ScholarPubMed
Nelson, E. A. and Dannefer, D. (1992). Aged heterogeneity: fact or fiction? The fate of diversity in gerontological research. Gerontology, 32, 17–23.Google Scholar
Petersen, S. E. and Posner, M. I. (2012b). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89.Google Scholar
Phillips, M., Rogers, P., Haworth, J., Bayer, A. and Tales, A. (2013). Intra-individual reaction time variability in mild cognitive impairment and Alzheimer's disease: gender, processing load and speed factors. PloS One, 8, e65712.Google Scholar
Posner, M. I. (2012). Imaging attention networks. NeuroImage, 61, 450–456.Google Scholar
Posner, M. I. (2014). Orienting of attention: then and now. Quarterly Journal of Experimental Psychology, 8, 1–12.Google Scholar
Posner, M. I. and Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.CrossRefGoogle ScholarPubMed
Rabbitt, P., Osman, P., Moore, B. and Stollery, B. (2001). There are stable individual differences in performance variability, both from moment to moment and from day to day. The Quarterly Journal of Experimental Psychology. A, Human Experimental Psychology, 54, 981–1003.Google Scholar
Raz, N., Briggs, S. D., Marks, W. and Acker, J. D. (1999). Age-related deficits in generation and manipulation of mental images II. The role of dorsolateral prefrontal cortex. Psychology and Aging, 14, 436.Google Scholar
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B. and Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23, 3295–3301.Google Scholar
Rueda, M. R. et al. (2004). Development of attentional networks in childhood. Neuropsychologia, 42, 1029–1040.Google Scholar
Salat, D. H. (2011). The declining infrastructure of the aging brain. Brain Connectivity, 1, 279–293.Google Scholar
Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.Google Scholar
Salthouse, T. A., Nesselroade, J. R. and Berish, D. E. (2006). Short-term variability in cognitive performance and the calibration of longitudinal change. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 61, 144–151.CrossRefGoogle ScholarPubMed
Schreiner, A. S., Hayakawa, H., Morimoto, T. and Kakuma, T. (2003). Screening for late life depression: cut-off scores for the geriatric depression scale and the cornell scale for depression in dementia among Japanese subjects. International Journal of Geriatric Psychiatry, 18, 498–505.Google Scholar
Shin, Y. S. et al. (2013). Increased intra-individual variability of cognitive processing in subjects at risk mental state and schizophrenia patients. PloS One, 8, e78354.Google Scholar
Stuss, D. T., Murphy, K. J., Binns, M. A. and Alexander, M. P. (2003). Staying on the job: the frontal lobes control individual performance variability. Brain, 126, 2363–2380.Google Scholar
Tractenberg, R. E. and Pietrzak, R. H. (2011). Intra-individual variability in Alzheimer's disease and cognitive aging: definitions, context, and effect sizes. PloS One, 6, e16973.Google Scholar
Van den Heuvel, M. P. and Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17, 683–696.Google Scholar
Verhaeghen, P. (2013). The Elements of Cognitive Aging: Meta-analyses of Age-related Differences in Processing Speed and their Consequences. Oxford: Oxford University Press.Google Scholar
Volkow, N. D. et al. (1998). Association between decline in brain dopamine activity with age and cognitive and motor impairment in healthy individuals. The American Journal of psychiatry, 155, 344–349.Google Scholar
Wang, Y.-F. et al. (2014). A new method for computing attention network scores and relationships between attention networks. PloS One, 9, e89733.CrossRefGoogle ScholarPubMed
Waszak, F., Li, S.-C. and Hommel, B. (2010). The development of attentional networks: cross-sectional findings from a life span sample. Developmental Psychology, 46, 337–349.Google Scholar
Weaver, B., Bédard, M. and McAuliffe, J. (2013). Evaluation of a 10-minute version of the attention network test. The Clinical Neuropsychologist, 27, 1281–1299.Google Scholar
Westlye, L. T., Grydeland, H., Walhovd, K. B. and Fjell, A. M. (2011). Associations between regional cortical thickness and attentional networks as measured by the attention network test. Cerebral Cortex, 21, 345–356.CrossRefGoogle ScholarPubMed
Wojtowicz, M. A, Ishigami, Y., Mazerolle, E. L. and Fisk, J. D. (2014). Stability of intraindividual variability as a marker of neurologic dysfunction in relapsing remitting multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 36, 1–9.Google Scholar
Yao, Z., Hu, B., Liang, C., Zhao, L. and Jackson, M. (2012). A longitudinal study of atrophy in amnestic mild cognitive impairment and normal aging revealed by cortical thickness. PloS One, 7, e48973.Google Scholar
Yin, X. et al. (2012). Anatomical substrates of the alerting, orienting and executive control components of attention: focus on the posterior parietal lobe. PloS One, 7, e50590.Google Scholar
Zhou, S., Fan, J., Lee, T. M., Wang, C. and Wang, K. (2011). Age-related differences in attentional networks of alerting and executive control in young, middle-aged, and older Chinese adults. Brain and Cognition, 75, 205–210.Google Scholar