Advertisement

Brain Structure and Function

, Volume 225, Issue 1, pp 375–386 | Cite as

Functional and structural correlates of working memory performance and stability in healthy older adults

  • Lídia Vaqué-Alcázar
  • Roser Sala-LlonchEmail author
  • Kilian Abellaneda-Pérez
  • Nina Coll-Padrós
  • Cinta Valls-Pedret
  • Núria Bargalló
  • Emilio Ros
  • David Bartrés-FazEmail author
Original Article

Abstract

Despite the well-described deleterious effects of aging on cognition, some individuals are able to show stability. Here, we aimed to describe the functional and structural brain characteristics of older individuals, particularly focusing on those with stable working memory (WM) performance, as measured with a verbal N-back task across a 2-year follow-up interval. Forty-seven subjects were categorized as stables or decliners based on their WM change. Stables were further subdivided into high performers (SHP) and low performers (SLP), based on their baseline scores. At both time points, magnetic resonance imaging (MRI) data were acquired, including task-based functional MRI (fMRI) and structural T1-MRI. Although there was no significant interaction between overall stables and decliners as regards fMRI patterns, decliners exhibited over-activation in the right superior parietal lobule at follow-up as compared to baseline, while SHP showed reduced the activity in this region. Further, at follow-up, decliners exhibited more activity than SHP but in left temporo-parietal cortex and posterior cingulate (i.e., non-task-related areas). Also, at the cross-sectional level, SLP showed lower activity than SHP at both time points and less activity than decliners at follow-up. Concerning brain structure, a generalized significant cortical thinning over time was identified for the whole sample. Notwithstanding, the decliners evidenced a greater rate of atrophy comprising the posterior middle and inferior temporal gyrus as compared to the stable group. Overall, fMRI data suggest unsuccessful compensation in the case of decliners, shown as increases in functional recruitment during the task in the context of a loss in WM performance and brain atrophy. On the other hand, among older individuals with WM cognitive stability, differences in baseline performance might determine dissimilar fMRI trajectories. In this vein, the findings in the SHP subgroup support the brain maintenance hypothesis, suggesting that stable and high WM performance in aging is sustained by functional efficiency and maintained brain structure rather than compensatory changes.

Keywords

Aging Brain stability Cortical thickness (CTh) Functional magnetic resonance imaging (fMRI) Working memory 

Abbreviations

BL

Baseline

FU

Follow-up

SHP

Stables high performers

SLP

Stables low performers

WM

Working memory

WMf

Working memory factor

Notes

Acknowledgements

We are indebted to the Magnetic Resonance Imaging Core Facility of the IDIBAPS for the technical help.

Funding

This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) through grants to D.B.-F. [Grant number PSI2015-64227-R] and L.V.-A. [Grant number BES-2016-077620]. Partially funded by EU Horizon 2020 project ‘Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”)’, [Grant agreement number: 732592. Call: Societal challenges: Health, demographic change and well-being]; and by the Walnuts and Healthy Aging (WAHA) study (https://www.clinicaltrials.gov), [Grant number NCT01634841] funded by the California Walnut Commission, Sacramento, California, USA.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

429_2019_2009_MOESM1_ESM.pdf (495 kb)
Supplementary file1 (PDF 495 kb)

References

  1. Archer JA, Lee A, Qiu A, Chen SHA (2018) Working memory, age and education: a lifespan fMRI study. PLoS ONE 13:1–19.  https://doi.org/10.1371/journal.pone.0194878 CrossRefGoogle Scholar
  2. Bartrés-Faz D, González-Escamilla G, Vaqué-Alcázar L et al (2019) Characterizing the molecular architecture of cortical regions associated with high educational attainment in older individuals. J Neurosci 39:4566–4575.  https://doi.org/10.1523/jneurosci.2370-18.2019 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Burianová H, Marstaller L, Choupan J et al (2015) The relation of structural integrity and task-related functional connectivity in the aging brain. Neurobiol Aging 36:2830–2837.  https://doi.org/10.1016/j.neurobiolaging.2015.07.006 CrossRefPubMedGoogle Scholar
  4. Burzynska AZ, Garrett DD, Preuschhof C et al (2013) A scaffold for efficiency in the human brain. J Neurosci 33:17150–17159.  https://doi.org/10.1523/jneurosci.1426-13.2013 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Cabeza R, Anderson ND, Locantore JK, McIntosh AR (2002) Aging gracefully: compensatory brain activity in high-performing older adults. Neuroimage 17:1394–1402CrossRefGoogle Scholar
  6. Cabeza R, Dennis NA (2013) Principles of Frontal Lobe Function. Princ Front Lobe Funct.  https://doi.org/10.1093/acprof:oso/9780195134971.001.0001 CrossRefGoogle Scholar
  7. Cabeza R, Albert M, Belleville S et al (2018) Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat Rev Neurosci.  https://doi.org/10.1038/s41583-018-0068-2 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Carp J, Park J, Polk TA, Park DC (2011) Age differences in neural distinctiveness revealed by multi-voxel pattern analysis. Neuroimage 56:736–743.  https://doi.org/10.1016/j.neuroimage.2010.04.267 CrossRefPubMedGoogle Scholar
  9. Cook AH, Sridhar J, Ohm D et al (2017) Rates of cortical atrophy in adults 80 years and older with superior vs average episodic memory. JAMA 317:1373–1375.  https://doi.org/10.1001/jama.2017.0627 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Dekhtyar M, Papp KV, Buckley R et al (2017) Neuroimaging markers associated with maintenance of optimal memory performance in late-life. Neuropsychologia 100:164–170.  https://doi.org/10.1016/j.neuropsychologia.2017.04.037 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Dickerson B, Goncharova I, Sullivana M et al (2001) MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging 22:747–754.  https://doi.org/10.1016/S0197-4580(01)00271-8 CrossRefPubMedGoogle Scholar
  12. Eklund A, Nichols TE, Knutsson H (2016) Erratum: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. PNAS 113:7900–7905.  https://doi.org/10.1073/pnas.1612033113 CrossRefPubMedGoogle Scholar
  13. Eriksson J, Vogel EK, Lansner A et al (2015) Neurocognitive architecture of working memory. Neuron 88:33–46.  https://doi.org/10.1016/j.neuron.2015.09.020 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Eyler LT, Sherzai A, Kaup AR, Jeste DV (2011) A review of functional brain imaging correlates of successful cognitive aging. Biol Psychiatry 70:115–122.  https://doi.org/10.1016/j.biopsych.2010.12.032 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Fjell AM, McEvoy L, Holland D et al (2014a) What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog Neurobiol 117:20–40.  https://doi.org/10.1016/j.pneurobio.2014.02.004 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Fjell AM, Westlye LT, Grydeland H et al (2014b) Accelerating cortical thinning: unique to dementia or universal in aging? Cereb Cortex 24:919–934.  https://doi.org/10.1093/cercor/bhs379 CrossRefPubMedGoogle Scholar
  17. Gefen T, Peterson M, Papastefan ST et al (2015) Morphometric and histologic substrates of cingulate integrity in elders with exceptional memory capacity. J Neurosci 35:1781–1791.  https://doi.org/10.1523/JNEUROSCI.2998-14.2015 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Grady CL (2012) Trends in neurocognitive aging. Nat Rev Neurosci 123:106–116.  https://doi.org/10.1038/nrn3256 CrossRefGoogle Scholar
  19. Grady CL, Springer MV, Hongwanishkul D et al (2006) Age-related changes in brain activity across the adult lifespan. J Cogn Neurosci 18:227–241.  https://doi.org/10.1162/jocn.2006.18.2.227 CrossRefPubMedGoogle Scholar
  20. Habib R, Nyberg L, Nilsson LG (2007) Cognitive and non-cognitive factors contributing to the longitudinal identification of successful older adults in the Betula study. Aging, Neuropsychol Cogn 14:257–273.  https://doi.org/10.1080/13825580600582412 CrossRefGoogle Scholar
  21. Harrison TM, Weintraub S, Mesulam M-M, Rogalski E (2012) Superior memory and higher cortical volumes in unusually successful cognitive aging. J Int Neuropsychol Soc 18:1081–1085.  https://doi.org/10.1016/j.dcn.2011.01.002.The CrossRefPubMedPubMedCentralGoogle Scholar
  22. Jamadar S, Assaf M, Jagannathan K et al (2013) Figural memory performance and functional magnetic resonance imaging activity across the adult lifespan. Neurobiol Aging 34:110–127.  https://doi.org/10.1016/j.neurobiolaging.2012.07.013 CrossRefPubMedGoogle Scholar
  23. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841.  https://doi.org/10.1016/S1053-8119(02)91132-8 CrossRefPubMedGoogle Scholar
  24. Jenkinson M, Beckmann CF, Behrens TEJ et al (2012) Fsl. Neuroimage 62:782–790.  https://doi.org/10.1016/j.neuroimage.2011.09.015 CrossRefPubMedGoogle Scholar
  25. Josefsson M, De Luna X, Pudas S et al (2012) Genetic and lifestyle predictors of 15-year longitudinal change in episodic memory. J Am Geriatr Soc 60:2308–2312.  https://doi.org/10.1111/jgs.12000 CrossRefPubMedGoogle Scholar
  26. Lee JS, Kim S, Yoo H et al (2018) Trajectories of physiological brain aging and related factors in people aged from 20 to over-80. J Alzheimer’s Dis 65:1237–1246.  https://doi.org/10.3233/JAD-170537 CrossRefGoogle Scholar
  27. Li HJ, Hou XH, Liu HH et al (2015) Putting age-related task activation into large-scale brain networks: a meta-analysis of 114 fMRI studies on healthy aging. Neurosci Biobehav Rev 57:156–174.  https://doi.org/10.1016/j.neubiorev.2015.08.013 CrossRefPubMedGoogle Scholar
  28. Logan JM, Sanders AL, Snyder AZ et al (2002) Under-recruitment and nonselective recruitment. Neuron 33:827–840.  https://doi.org/10.1016/S0896-6273(02)00612-8 CrossRefPubMedGoogle Scholar
  29. Miller SL, Celone K, DePeau K et al (2008) Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proc Natl Acad Sci 105:2181–2186.  https://doi.org/10.1073/pnas.0706818105 CrossRefPubMedGoogle Scholar
  30. Mitchell AJ (2009) A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. J Psychiatr Res 43:411–431.  https://doi.org/10.1016/j.jpsychires.2008.04.014 CrossRefGoogle Scholar
  31. Morcom AM, Henson RNA (2018) Increased prefrontal activity with aging reflects nonspecific neural responses rather than compensation. J Neurosci 38:1701–1717.  https://doi.org/10.1523/JNEUROSCI.1701-17.2018 CrossRefGoogle Scholar
  32. Nagel IE, Preuschhof C, Li S et al (2008) Load modulation response and connectivity predicts working memory performance in younger and older adults. J Cogn Neurosci 23:2030–2045. https://doi.org/10.1162/jocn.2010.21560 CrossRefGoogle Scholar
  33. Nagel IE, Preuschhof C, Li S-C et al (2009) Performance level modulates adult age differences in brain activation during spatial working memory. Proc Natl Acad Sci 106:22552–22557.  https://doi.org/10.1073/pnas.0908238106 CrossRefPubMedGoogle Scholar
  34. Nyberg L, Pudas S (2019) Successful memory aging. Annu Rev Psychol 70:1.  https://doi.org/10.1146/annurev-psych-010418-103052 CrossRefGoogle Scholar
  35. Nyberg L, Dahlin E, Stigsdotter Neely A, Bäckman L (2009) Neural correlates of variable working memory load across adult age and skill: dissociative patterns within the fronto-parietal network: Cognition and Neurosciences. Scand J Psychol 50:41–46.  https://doi.org/10.1111/j.1467-9450.2008.00678.x CrossRefPubMedGoogle Scholar
  36. Nyberg L, Salami A, Andersson M et al (2010) Longitudinal evidence for diminished frontal cortex function in aging. Proc Natl Acad Sci 107:22682–22686.  https://doi.org/10.1097/01.blo.0000174688.08121.29 CrossRefPubMedGoogle Scholar
  37. Nyberg L, Lövdén M, Riklund K et al (2012) Memory aging and brain maintenance. Trends Cogn Sci 16:292–305.  https://doi.org/10.1016/j.tics.2012.04.005 CrossRefPubMedGoogle Scholar
  38. Park DC, Reuter-Lorenz P (2009) The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol 60:173–196.  https://doi.org/10.1146/annurev.psych.59.103006.093656 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Park DC, Polk TA, Park R et al (2004) Aging reduces neural specialization in ventral visual cortex. Proc Natl Acad Sci USA 101:13091–13095.  https://doi.org/10.1073/pnas.0405148101 CrossRefPubMedGoogle Scholar
  40. Persson J, Nyberg L, Lind J et al (2006) Structure-function correlates of cognitive decline in aging. Cereb Cortex 16:907–915.  https://doi.org/10.1093/cercor/bhj036 CrossRefPubMedGoogle Scholar
  41. Persson J, Pudas S, Lind J et al (2012) Longitudinal structure-function correlates in elderly reveal MTL dysfunction with cognitive decline. Cereb Cortex 22:2297–2304.  https://doi.org/10.1093/cercor/bhr306 CrossRefPubMedGoogle Scholar
  42. Petersen RC, Morris JC (2005) Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol 62:1160–1163.  https://doi.org/10.1001/archneur.62.7.1160 CrossRefPubMedGoogle Scholar
  43. Pudas S, Josefsson M, Rieckmann A, Nyberg L (2017) Longitudinal evidence for increased functional response in frontal cortex for older adults with hippocampal atrophy and memory decline. Cereb Cortex.  https://doi.org/10.1093/cercor/bhw418 CrossRefGoogle Scholar
  44. Rajah MN, D’Esposito M (2005) Region-specific changes in prefrontal function with age: a review of PET and fMRI studies on working and episodic memory. Brain 128:1964–1983.  https://doi.org/10.1093/brain/awh608 CrossRefPubMedGoogle Scholar
  45. Rajaram S, Valls-Pedret C, Cofán M et al (2017) The Walnuts and Healthy Aging Study (WAHA): protocol for a nutritional intervention trial with walnuts on brain aging. Front Aging Neurosci 8:1–12.  https://doi.org/10.3389/fnagi.2016.00333 CrossRefGoogle Scholar
  46. Reuter M, Fischl B (2011) Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage 57:19–21.  https://doi.org/10.1016/j.neuroimage.2011.02.076 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Reuter M, Schmansky N, Rosas H, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61:1402–1418CrossRefGoogle Scholar
  48. Reuter-Lorenz PA, Cappell KA (2008) Neurocognitive aging and the compensation hypothesis. Curr Dir Psychol Sci 17:177–182.  https://doi.org/10.1111/j.1467-8721.2008.00570.x CrossRefGoogle Scholar
  49. Reuter-Lorenz PA, Park DC (2014) How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol Rev 24:355–370.  https://doi.org/10.1007/s11065-014-9270-9 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Rieckmann A, Pudas S, Nyberg L (2017) Longitudinal changes in component processes of working memory. Eneuro 4:52.  https://doi.org/10.1523/ENEURO.0052-17.2017 CrossRefGoogle Scholar
  51. Rosano C, Aizenstein HJ, Newman AB et al (2012) Neuroimaging differences between older adults with maintained versus declining cognition over a 10-year period. Neuroimage 62:307–313.  https://doi.org/10.1016/j.neuroimage.2012.04.033 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Sala-Llonch R, Peña-Gómez C, Arenaza-Urquijo EM et al (2012) Brain connectivity during resting state and subsequent working memory task predicts behavioural performance. Cortex 48:1187–1196.  https://doi.org/10.1016/j.cortex.2011.07.006 CrossRefPubMedGoogle Scholar
  53. Salat DH (2004) Thinning of the Cerebral Cortex in Aging. Cereb Cortex 14:721–730.  https://doi.org/10.1093/cercor/bhh032 CrossRefPubMedGoogle Scholar
  54. Salat DH (2011) The declining infrastructure of the aging brain. Brain Connect 1:279–293.  https://doi.org/10.1089/brain.2011.0056 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Salthouse TA (2010) Selective review of cognitive aging. J Int Neuropsychol Soc 16:754–760.  https://doi.org/10.1017/S1355617710000706 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Spreng RN, Wojtowicz M, Grady CL (2010) Reliable differences in brain activity between young and old adults: A quantitative meta-analysis across multiple cognitive domains. Neurosci Biobehav Rev 34:1178–1194.  https://doi.org/10.1016/j.neubiorev.2010.01.009 CrossRefPubMedGoogle Scholar
  57. Spreng RN, Stevens WD, Viviano JD, Schacter DL (2016) Neurobiology of aging attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest. Neurobiol Aging 45:149–160.  https://doi.org/10.1016/j.neurobiolaging.2016.05.020 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Steffener J, Stern Y (2012) Exploring the neural basis of cognitive reserve in aging. Biochim Biophys Acta Mol Basis Dis 1822:467–473.  https://doi.org/10.1016/j.bbadis.2011.09.012 CrossRefGoogle Scholar
  59. Steffener J, Habeck CG, Stern Y (2012) Age-related changes in task related functional network connectivity. PLoS ONE.  https://doi.org/10.1371/journal.pone.0044421 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Stern Y (2002) What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 8:448–460CrossRefGoogle Scholar
  61. Stern Y (2009) Cognitive reserve. Neuropsychologia 47:2015–2028.  https://doi.org/10.1016/j.neuropsychologia.2009.03.004 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Storsve AB, Fjell AM, Tamnes CK et al (2014) Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci 34:8488–8498.  https://doi.org/10.1523/JNEUROSCI.0391-14.2014 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Sun FW, Stepanovic MR, Andreano J et al (2016) Youthful brains in older adults: preserved neuroanatomy in the default mode and salience networks contributes to youthful memory in superaging. J Neurosci 36:9659–9668.  https://doi.org/10.1523/JNEUROSCI.1492-16.2016 CrossRefPubMedPubMedCentralGoogle Scholar
  64. Unsworth N, Fukuda K, Awh E, Vogel EK (2014) Working memory and fluid intelligence: capacity, attention control, and secondary memory retrieval. Cogn Psychol 71:1–26.  https://doi.org/10.1016/j.cogpsych.2014.01.003 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Vemuri P, Lesnick TG, Przybelski SA et al (2014) Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurol 71:1017–1024.  https://doi.org/10.1001/jamaneurol.2014.963 CrossRefPubMedPubMedCentralGoogle Scholar
  66. Vidal-Piñeiro D, Valls-Pedret C, Fernández-Cabello S et al (2014) Decreased default mode network connectivity correlates with age-associated structural and cognitive changes. Front Aging Neurosci 6:1–17.  https://doi.org/10.3389/fnagi.2014.00256 CrossRefGoogle Scholar
  67. Wilson RS, Beckett LA, Barnes LL et al (2002) Individual differences in rates of change in cognitive abilities of older persons. Psychol Aging 17:179–193.  https://doi.org/10.1037/0882-7974.17.2.179 CrossRefPubMedGoogle Scholar
  68. Wilson RS, Yu L, Lamar M et al (2019) Education and cognitive reserve in old age. Neurology.  https://doi.org/10.1212/wnl.0000000000007036 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Woolrich MW, Ripley BD, Brady M, Smith SM (2001) Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14:1370–1386.  https://doi.org/10.1006/nimg.2001.0931 CrossRefPubMedGoogle Scholar
  70. Woolrich MW, Behrens TEJ, Beckmann CF et al (2004) Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 21:1732–1747.  https://doi.org/10.1016/j.neuroimage.2003.12.023 CrossRefPubMedGoogle Scholar
  71. Yaffe K, Lindquist K, Vittinghoff E et al (2010) The effect of maintaining cognition on risk of disability and death. J Am Geriatr Soc 58:889–894.  https://doi.org/10.1111/j.1532-5415.2010.02818.x CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medicine, Faculty of Medicine and Health Sciences, Institute of NeurosciencesUniversity of BarcelonaBarcelonaSpain
  2. 2.Institute of Biomedical Research August Pi i Sunyer (IDIBAPS)BarcelonaSpain
  3. 3.Department of BiomedicineUniversity of BarcelonaBarcelonaSpain
  4. 4.Alzheimer Disease and Other Cognitive Disorders UnitNeurology Service, Hospital ClínicBarcelonaSpain
  5. 5.Endocrinology and Nutrition ServiceLipid Clinic, Hospital ClínicBarcelonaSpain
  6. 6.Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad Y Nutrición (CIBEROBN)Instituto de Salud Carlos III (ISCIII)MadridSpain
  7. 7.Neuroradiology Section, Radiology ServiceCentre de Diagnòstic Per La Imatge, Hospital ClínicBarcelonaSpain

Personalised recommendations