ABSTRACT
Susceptibility to scams is a significant issue among older adults, even among those with intact cognition. Age-related changes in brain macrostructure may be associated with susceptibility to scams; however, this has yet to be explored. Based on previous work implicating frontal and temporal lobe functioning as important in decision making, we tested the hypothesis that susceptibility to scams is associated with smaller grey matter volume in frontal and temporal lobe regions in a large community-dwelling cohort of non-demented older adults. Participants (N = 327, mean age = 81.55, mean education = 15.30, 78.9 % female) completed a self-report measure used to assess susceptibility to scams and an MRI brain scan. Results indicated an inverse association between overall grey matter and susceptibility to scams in models adjusted for age, education, and sex; and in models further adjusted for cognitive function. No significant associations were observed for white matter, cerebrospinal fluid, or total brain volume. Models adjusted for age, education, and sex revealed seven clusters showing smaller grey matter in the right parahippocampal/hippocampal/fusiform, left middle temporal, left orbitofrontal, right ventromedial prefrontal, right middle temporal, right precuneus, and right dorsolateral prefrontal regions. In models further adjusted for cognitive function, results revealed three significant clusters showing smaller grey matter in the right parahippocampal/hippocampal/fusiform, right hippocampal, and right middle temporal regions. Lower grey matter concentration in specific brain regions may be associated with susceptibility to scams, even after adjusting for cognitive ability. Future research is needed to determine whether grey matter reductions in these regions may be a biomarker for susceptibility to scams in old age.
Similar content being viewed by others
References
AARP (1999). AARP poll: Nearly one in five Americans report they’ve been victimized by fraud. Washington, DC:Author.
Agarwal, S., Driscoll, J. C., Gabaix, X., & Laibson, D. (2009). The age of reason: financial decisions over the lifecycle with implications for regulation. Brookings Papers on Economic Activity, 2, 51–117.
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38, 95–113.
Authority, F. I. R. (2013). Financial industry regulatory authority risk meter Retrieved from http://apps.finra.org/meters/1/riskmeter.aspx.
Barber, B. M., & Odean, T. (2001). Boys will be boys: gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116, 261–292.
Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making, and the orbitofrontal cortex. Cerebral Cortex, 10, 295–307.
Bennett, D. A., Schneider, J. A., Arvanitakis, Z., Kelly, J. F., Aggarwal, N. T., Shah, R. C., et al. (2006). Neuropathology of older person without cognitive impairment from two community-based studies. Neurology, 27, 1837–1844.
Bennett, D. A., Schneider, J. A., Buchman, A. S., Barnes, L. L., Boyle, P. A., & Wilson, R. S. (2012). Overview and findings from the rush memory and aging project. Current Alzheimer Research, 9(6), 646–663.
Boyle, P. A., Buchman, A. S., Barnes, L. L., & Bennett, D. A. (2010). Effect of a purpose in life on risk of incident Alzheimer’s disease and mild cognitive impairment in community-dwelling older persons. Archives of General Psychiatry, 67, 304–310.
Boyle, P. A., Yu, L., Wilson, R. S., Gamble, K., Buchman, A. S., & Bennett, D. A. (2012). Poor decision making is a consequence of cognitive decline among older persons without Alzheimer’s disease or mild cognitive impairment. PloS One, 7, e43647.
Buchman, A. S., Boyle, P. A., Yu, L., Shah, R. C., Wilson, R. S., & Bennett, D. A. (2012). Total daily physical activity and the risk of cognitive decline in older adults. Neurology, 78, 1323–1329.
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.
Chen, Y., & Sun, Y. (2011). Age differences in financial decision-making: using simple heuristics. Educational Gerontology, 29, 627–635.
Cole, S., Paulson, A., & Shastry, G. K. (2014). Smart money? The effect of education on financial outcomes. The Review of Financial Studies, 27, 2022–2051.
Coutlee, C. G., & Huettel, S. A. (2012). The functional neuroanatomy of decision making: prefrontal control of thought and action. Brain Research, 1428, 3–12.
Curiati, P. K., Tamashiro, J. H., Squarzoni, P., Duran, F. L. S., Santos, L. C., Wajngarten, M., Leite, C. C., Vallada, H., Menezes, P. R., Scazufca, M., Busatto, G. F., & Alves, T. C. T. F. (2009). Brain structural variability due to aging and gender in cognitive healthy elders: results from the Sao Paulo ageing and health study. American Journal of Neuroradiology, 30, 1850–1856.
Delazer, M., Zamarian, L., Bonatti, E., Kuchukhidze, G., Koppelstater, F., Bodner, T., et al. (2010). Decision making under ambiguity and under risk in mesial temporal lobe epilepsy. Neuropsychologia, 48, 194–200.
Dessin, C. L. (2000). Financial abuse of the elderly. Idaho Law Review, 36(2), 203–226.
Draganski, B., Gaser, C., Kempermann, G., Kuhn, H. G., Winkler, J., Buchel, C., & May, A. (2006). Temporal and spatial dynamics of brain structure changes during extensive learning. Journal of Neuroscience, 26, 6314–6317.
Fellows, L. K., & Farah, M. J. (2007). The role of the ventromedial prefrontal cortex in decision making: judgment under uncertainty or judgment per se? Cerebral Cortex, 17, 2668–2674.
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14, 21–36.
Guitart-Masip, M., Barnes, G. R., Horner, A., Bauer, M., Dolan, R. J., & Duzel, E. (2013). Synchronization of medial temporal lobe and prefrontal rhythms in human decision making. The Journal of Neuroscience, 33, 442–451.
Jackson, S. L., & Hafemeister, T. L. (2011). Financial abuse of elderly people vs. other forms of elder abuse: Assessing their dynamics, risk factors, and society’s response. Final Report Presented to the National Institute of Justice.
James, B. D., Boyle, P. S., Buchman, A. S., Barnes, L. L., & Bennett, D. A. (2011). Life space and risk of Alzheimer’s disease, mild cognitive impairment, and cognitive decline in old age. The American Journal of Geriatric Psychiatry, 19, 961–969.
James, B. D., Boyle, P. A., & Bennett, D. A. (2014). Correlates of susceptibility to scams in older adults without dementia. Journal of Elder Abuse & Neglect, 26, 107–122.
Kennedy, K. M., Erickson, K. I., Rodrigue, K. M., Voss, M. W., Colcombe, S. J., Kramer, A. F., et al. (2009). Age-related differences in regional grey matter volumes: a comparison of optimized voxel-based morphometry to manual volumetry. Neurobiology of Aging, 30, 1657–1676.
Kennerley, S. W., Walton, M. E., Behrens, T. E. J., Buckley, M. J., & Rushworth, M. F. S. (2006). Optimal decision making and the anterior cingulate cortex. Nature Neuroscience, 9, 940–947.
Krawczyk, D. C. (2002). Contributions of the prefrontal cortex to the neural basis of decision making. Neuroscience and Biobehavioral Reviews, 26, 631–664.
Laibson, D. (2011). Age of reason. In Closing keynote presentation at the 23rd annual Morningstar investment conference. Chicago: IL.
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Standlan, E. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS/ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology, 34, 939–944.
Peters, J., & Buchel, C. (2010). Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron, 66, 138–148.
Peters, J., & Buchel, C. (2012). The neural mechanisms of inter-temporal decision-making: understanding variability. Trends in Cognitive Sciences, 15, 207–239.
U.S. Senate. (2005). Old Scams-New Victims: Breaking the Cycle of Victimization (109th Congress, First Session, Serial No. 109–113). Washington, DC: U.S. Government Printing Office. http://www.gpo.gov/fdsys/pkg/CHRG-109shrg25878/html/CHRG-109shrg25878.htm
Taki, Y., Kinomura, S., Sato, K., Goto, R., Kawashima, R., & Fukuda, H. (2011). A longitudinal study of grey matter volume decline with age and modifying factors. Neurobiology of Aging, 32, 907–915.
Templeton, V. H., & Kirkman, D. N. (2007). Fraud, vulnerability, and aging. Alzheimer’s Care Today, 8(3), 265–277.
Tymula, A., Rosenberg Belmaker, L. A., Ruderman, L., Glimcher, P. W., & Levy, I. (2013). Like cognitive function, decision making across the life span showed profound age-related changes. PNAS, 110(42), 17143–17148.
Volz, K. G., Schubotz, R. I., & Yves von Cramon, D. (2006). Decision-making and the frontal lobes. Current Opinion in Neurology, 19, 401–406.
Wilson, R. S., Barnes, L. L., & Bennett, D. A. (2003). Assessment of lifetime participation in cognitively stimulating activities. Journal of Clinical and Experimental Psychopathology, 25, 634–642.
Wilson, R. S., Boyle, P. A., Buchman, A. S., Yu, L., Arnold, S. E., & Bennett, D. A. (2011). Harm avoidance and risk of Alzheimer’s disease. Psychosomatic Medicine, 73, 690–696.
Worsley, K. (2011). Random field theory. statistical parametric mapping: the analysis of functional brain images: The Analysis of Functional Brain Images.
Yu, L., Boyle, P. A., Wilson, R. S., Segawa, E., Leurgans, S., De Jager, P. L., & Bennett, D. A. (2012). A random change point model for cognitive decline in Alzheimer’s disease and mild cognitive impairment. Neuroepidemiology, 39, 73–83.
Acknowledgments
This research was supported by National Institute on Aging grants R01AG017917, R01AG033678, K23AG040625, the American Federation for Aging Research, and the Illinois Department of Public Health. The authors gratefully thank the Rush Memory and Aging Project staff and participants.
Disclosure statement
S. Duke Han, Patricia A. Boyle, Lei Yu, Konstantinos Arfanakis, Bryan D. James, Debra Fleischman, and David A. Bennett declare no conflicts of interests.
Ethical statement
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Supplementary Table 1
(DOCX 40 kb)
Supplementary Table 2
(DOCX 29 kb)
Supplementary Figure 1
(DOCX 103 kb)
Supplementary Figure 2
(DOCX 108 kb)
Supplementary Figure 3
(DOCX 97 kb)
Supplementary Figure 4
(DOCX 97 kb)
Supplementary Figure 5
(DOCX 106 kb)
Supplementary Figure 6
(DOCX 94 kb)
Supplementary Figure 7
(DOCX 95 kb)
Supplementary Figure 8
(DOCX 95 kb)
Supplementary Table 3
(DOCX 34 kb)
Supplementary Table 4
(DOCX 26 kb)
Supplementary Figure 9
(DOCX 106 kb)
Supplementary Figure 10
(DOCX 95 kb)
Rights and permissions
About this article
Cite this article
Duke Han, S., Boyle, P.A., Yu, L. et al. Grey matter correlates of susceptibility to scams in community-dwelling older adults. Brain Imaging and Behavior 10, 524–532 (2016). https://doi.org/10.1007/s11682-015-9422-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-015-9422-4