medigraphic.com
ISSN 2225-4676 (Electronic)

2020, Number 2

<< Back Next >>

Rev Cubana Neurol Neurocir 2020; 10 (2)

Visual memory change pattern and its predictive factors in healthy aging

García ML, Balmaseda SR, Cruz LT, Lucas MA, Álvarez GMÁ

Language: Spanish
References: 32
Page: 1-14
PDF size: 284.95 Kb.


ABSTRACT

Objective: To identify the pattern of visual memory change in healthy aging and its possible predictive factors.
Methods: A cross-sectional, correlational, causal study was carried out September 2018 to February 2019. Two hundred twenty nine (229) healthy persons older than 60 were included, through intentional sampling. They are part of the population assisted at Héroes del Moncada polyclinic in Havana (Cuba) and at El Parque and Concepción Arenal de Rivas-Vaciamadrid centers for the elderly in Madrid (Spain). The participants were assessed with the Figural Memory test of the VINCI.1 neuropsychological battery. Sex, school level and age were analyzed as independent variables and memory efficiency as the dependent one. The comparison of means was carried out, using a t distribution, and a multivariate analysis with the general linear model.
Results: A hundred sixty four (164) women and 65 men were studied, with a mean age of 70.5 years, there was a standard deviation = 6.9 and age ranged between 65 and 90 years. The Cuban and Spanish samples did not differ in memory efficiency (F= 2.28; p= 0.13), therefore they were treated as a single homogeneous group for subsequent analyzes. The efficiency of recall showed a gradual decline. Age (r= -. 35; p<.001) and level of education (F= 7.02; p< 0.001) were the predictor variables.
Conclusions: There is a gradual decline in visual memory in healthy aging, and it is influenced by schooling and age. It is necessary to broaden the normative criteria common to sex and age in neuropsychological memory tests, and include the variable of educational level.


REFERENCES

  1. Nyberg L, Pudas S. Successful memory aging. Annu Rev Phsychol 2019;70:219-43.

  2. Hirsiger S, Koppelmans V, Mérillat S, Liem F, Erdeniz B, Seidler RD, et al. Structural and functional connectivity in healthy aging: associations for cognition and motor behavior. Hum Brain Mapp. 2016;37:855-67.

  3. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643-62.

  4. Grant DA, Berg EA. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem. J Exp Psychol. 1948;38(4):404-11.

  5. Álvarez MA. Datos blandos para ciencias duras. Madrid: EOS; 2016.

  6. Álvarez MA. Neuropsicología de la endocrinología. Madrid: EOS; 2017.

  7. Burgess PW, Alderman N, Forbes C, Costello A, Coates L, Dawson DR, et al. The case for the development and use of ‘ecologically valid’ measures of executive function in experimental and clinical neuropsychology. J Int Neuropsychol Soc. 2006;12(2):194-209.

  8. Wasserman JD. Nonverbal Neuropsychological Assessment. En: McCallum R, editor. Handbook of Nonverbal Assessment. 2 ed. Cham: Springer; 2017. p. 287-310.

  9. Foster PS, Wakefield C, Pryjmak S, Roosa KM, Branch KK, Drago V, et al. Spreading activation in nonverbal memory networks. Brain Informatics. 2017;4:187-99.

  10. Sporns O, Betzel RF. Modular brain networks. Annu Rev Psychol. 2016;67:613-40.

  11. Cherry KE, Hawley KS, Jackson EM, Volaufova J, Su LJ, Jazwinski M. Pictorial superiority effects in oldest-old people. Memory. 2008;16:728-41.

  12. Baddeley AD. The multi-component model of working memory: explorations in experimental cognitive psychology. Neuroscience. 2006;139:5-21.

  13. Zokaei N, Burnett Heyes S, Gorgoraptis N, Budhdeo S, Husain M. Working memory recall precision is a more sensitive index than span. J Neuropsychol. 2015;9(2):319-29.

  14. Manzanero A, Álvarez MA. La memoria humana. Madrid: Pirámide; 2015.

  15. Ardila A. The impact of culture on neuropsychological test performance. En: Uzzell BP, Ponton M, Ardila A, editores. International handbook of cross-cultural neuropsychology. Mahwah, NJ: Lawrence Erlbaum Associates; 2007. p. 23-44.

  16. Wen TY, Burgess GH, Green RJ. The effects of acculturation on neuropsychological test performance: A systematic literature review. Clin Neuropsychol. 2020 (en prensa). Disponible en: https://doi.org/10.1080/13854046.2020.1714740.

  17. Hernández Sampieri R, Fernández C, Baptista P. Metodología de la investigación. 4 ed. México: McGraw Hill; 2006.

  18. Cruz T, García L, Álvarez MA, Manzanero AL. Calidad del sueño y déficit de memoria en el envejecimiento sano. Neurología. 2019 (en prensa). Disponible en: https://doi.org/10.1016/j.nrl.2018.10.001.

  19. Morales C, Manzanero AL, Wong A, Gómez-Gutiérrez M, Iglesias AM, Barón S, et al. Stability of autobiographical memory in young people with intellectual disabilities. Anuario de Psicología Jurídica. 2017;27(1):79-84.

  20. Mardia KV, Kent JT, Bibby JM. Multivariate Analysis. Londres: Academic Press; 1979.

  21. Paige LE, Ksander JC, Johndro HA, Gutchess AH. Cross-Cultural Differences in the Neural Correlates of Specific and General Recognition. Cortex. 2017 June;91:250-61.

  22. Chiao JY, Li SC, Turner R, Lee-Tauler SY, Pringle BA. Cultural neuroscience and global mental health: addressing grand challenges. Cult Brain. 2017 April;5(1):4-13.

  23. Ghanavati E, Nejati V, Salehinejad MA. Transcranial direct current stimulation over the posterior parietal cortex (PPC) enhances figural fluency: implications for creative cognition. J Cogn Enhanc. 2018;2(1):88-96.

  24. Jamadar S, Assaf M, Jagannathan K, Anderson K, Pearlson GD. Figural memory performance and fMRI activity across the adult lifespan. Neurobiol Aging. 2013;34(1):110-27.

  25. Dager AD, Jamadar S, Stevens MC, Rosen R, Jiantonio-Kelly RE, Sisante JF, et al. fMRI response during figural memory task performance in college drinkers. Psychopharmacology (Berl). 2014;231(1):167-79.

  26. Treisman A. Features and objects in visual processing. Scientific American. 1986;255:114-25.

  27. Giovachini G, Giovannini E, Borsò E, Lazzeri P, Riondato M, Leoncini R, et al. The brain cognitive reserve hypothesis: A review with emphasis on the contribution of nuclear medicine neuroimaging techniques. J Cell Physiol. 2019:1-8.

  28. Clouston SAP, Smith DM, Mukherjee S, Zhang Y, Hou W, Link BG, et al. Education and cognitive decline: An integrative analysis of global longitudinal studies of cognitive aging. J Gerontol. Series B, Psychological sciences and social sciences. 2019 May 6 (en prensa). Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/31059564.

  29. Schubert CR, Fischer ME, Pinto AA, Chen Y, Klein BEK, Klein R, et al. Brain Aging in Midlife: The Beaver Dam Offspring Study. JAGS. 2019;67:1610-6.

  30. Wiegand I, Wolfea JM. Age doesn’t matter much: hybrid visual and memory search is preserved in older adults. Aging Neuropsychol Cogn. 2020;27(2):220-5.

  31. Beason-Held L, Hohman TJ, Venkatraman V, An Y, Resnick SM. Brain network changes and memory decline in aging. Brain Imaging and Behavior. 2017;11:859-73.

  32. Xu X, Yuan H, Lei X. Activation and Connectivity within the Default Mode Network Contribute Independently to Future-Oriented Thought. Sci Rep. 2016;6:21001.




2020     |     www.medigraphic.com