Brain Imaging and Behavior

, Volume 11, Issue 2, pp 318–332 | Cite as

Differential age-related gray and white matter impact mediates educational influence on elders’ cognition

  • Lídia Vaqué-Alcázar
  • Roser Sala-Llonch
  • Cinta Valls-Pedret
  • Dídac Vidal-Piñeiro
  • Sara Fernández-Cabello
  • Núria Bargalló
  • Emilio Ros
  • David Bartrés-Faz
SI: Resilience/Reserve in AD

Abstract

High education, as a proxy of cognitive reserve (CR), has been associated with cognitive advantage amongst old adults and may operate through neuroprotective and/or compensation mechanisms. In neuromaging studies, indirect evidences of neuroprotection can be inferred from positive relationships between CR and brain integrity measures. In contrast, compensation allows high CR elders to sustain greater brain damage. We included 100 cognitively normal old-adults and investigated the associations and interactions between education, speed of processing (SP), memory and two brain integrity measures: cortical thickness (CTh) of gray matter (GM) and fractional anisotropy (FA) in the white matter (WM). High education was associated with better cognitive performance, enlarged CTh in frontal lobe areas and reduced measures of FA in several areas. Better SP performance in higher educated subjects was related to more preserved GM and WM, while memory status amongst high educated elders was better explained by a putative compensatory mechanism and independently from cerebrovascular risk indicators. Moreover, we analyzed the direct effect of age on measures of brain integrity and found a stronger negative effect on WM than in CTh, which was accentuated amongst the high CR sample. Our study suggests that the cognitive advantage associated to high education among healthy aging is related to the coexistence of both neuroprotective and compensatory mechanisms. In particular, high educated elders seem to have greater capacity to counteract a more abrupt age impact on WM integrity.

Keywords

Aging Education Cognitive reserve Neuroprotection Compensation Structural changes Speed of processing Memory 

Notes

Compliance with ethical standards

Funding

Partially funded by Spanish Ministry of Economy and Competitiveness (MINECO) grant to D-BF (PSI2015-64227-R) and the Walnuts and Healthy Aging (WAHA) study (http://www.clinicaltrials.gov NCT01634841) funded by the California Walnut Commission, Sacramento, California, USA. CIBEROBN is an initiative of ISCIII, Spain.

Conflict of interest

Lídia Vaqué-Alcázar declares that she has no conflict of interest. Roser Sala-Llonch declares that she has no conflict of interest. Cinta Valls declares that she has no conflict of interest. Dídac Vidal-Piñeiro declares that he has no conflict of interest. Sara Fernández-Cabello declares that she has no conflict of interest. Núria Bargalló declares that she has no conflict of interest. Emilio Ros declares that he has no conflict of interest. David Bartrés-Faz declares that he has 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.

References

  1. Almeida, R. P., Schultz, S. A., Austin, B. P., Boots, E. A., Dowling, N. M., Gleason, C. E., et al. (2015). Effect of cognitive reserve on age-related changes in cerebrospinal fluid biomarkers of Alzheimer disease. JAMA Neurology, 53792(6), 699–706. doi: 10.1001/jamaneurol.2015.0098.CrossRefGoogle Scholar
  2. Arenaza-Urquijo, E. M., Bosch, B., Sala-Llonch, R., Solé-Padullés, C., Junqué, C., Fernández-Espejo, D., et al. (2011). Specific anatomic associations between white matter integrity and cognitive reserve in normal and cognitively impaired elders. American Association for Geriatric Psychiatry, 19(1), 33–42. doi: 10.1097/JGP.0b013e3181e448e1.CrossRefGoogle Scholar
  3. Arenaza-Urquijo, E. M., Landeau, B., La Joie, R., Mevel, K., Mézenge, F., Perrotin, A., & Chételat, G. (2013a). Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. NeuroImage, 83, 450–457. doi: 10.1016/j.neuroimage.2013.06.053.CrossRefPubMedGoogle Scholar
  4. Arenaza-Urquijo, E. M., Molinuevo, J. L., Sala-Llonch, R., Solé-Padullés, C., Balasa, M., Bosch, B., Olives, J., et al. (2013b). Cognitive reserve proxies relate to gray matter loss in cognitively healthy elderly with abnormal cerebrospial fluid amyloid-β levels. Journal of Alzheimers Disease, 35(4), 715–726.Google Scholar
  5. Arenaza-Urquijo, E. M., Wirth, M., & Chételat, G. (2015). Cognitive reserve and lifestyle: moving towards preclinical Alzheimer’s disease. Frontiers in Aging Neuroscience, 7, 134. doi: 10.3389/fnagi.2015.00134.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Baker, L. M., Laidlaw, D. H., Cabeen, R., Akbudak, E., Conturo, T. E., Correia, S., et al. (2016). Cognitive reserve moderates the relationship between neuropsychological performance and white matter fiber bundle length in healthy older adults. Brain Imaging and Behavior. doi: 10.1007/s11682-016-9540-7.PubMedCentralGoogle Scholar
  7. Barnes, D. E., & Yaffe, K. (2011). The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurology, 10(9), 819–828. doi: 10.1016/S1474-4422(11)70072-2.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bartrés-Faz, D., & Arenaza-Urquijo, E. M. (2011). Structural and functional imaging correlates of cognitive and brain reserve hypotheses in healthy and pathological aging. Brain Topography, 24(3–4), 340–357. doi: 10.1007/s10548-011-0195-9.CrossRefPubMedGoogle Scholar
  9. Bartrés-Faz, D., Solé-Padullés, C., Junqué, C., Rami, L., Bosch, B., Bargalló, N., et al. (2009). Interactions of cognitive reserve with regional brain anatomy and brain function during a working memory task in healthy elders. Biological Psychology, 80, 256–259. doi: 10.1016/j.biopsycho.2008.10.005.CrossRefPubMedGoogle Scholar
  10. Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. doi: 10.1016/j.tics.2013.08.012.CrossRefPubMedGoogle Scholar
  11. Bender, A. R., Prindle, J. J., Brandmaier, A. M., & Raz, N. (2015). White matter and memory in healthy adults: coupled changes over two years. NeuroImage, 1(131), 193–204. doi: 10.1016/j.neuroimage.2015.10.085.Google Scholar
  12. Bennett, I. J., & Madden, D. J. (2014). Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience, 276, 187–205. doi: 10.1016/j.neuroscience.2013.11.026.CrossRefPubMedGoogle Scholar
  13. Bennett, I., Madden, D., & Vaidya, C. (2010). Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. Human Brain, 31(3), 378–390. doi: 10.1002/hbm.20872.Age-Related.Google Scholar
  14. Bosch, B., Bartrés-Faz, D., Rami, L., Arenaza-Urquijo, EM., Fernández-Espejo, D., Junqué, C., et al. (2010). Cognitive reserve modulates task-induced activations and deactivations in healthy elders, amnestic mild cognitive impairment and mild Alzheimer's disease. Cortex, 46(4), 451–61. doi: 10.1016/j.cortex.2009.05.006.
  15. Brooks-Wilson, A. R. (2013). Genetics of healthy aging and longevity. Human Genetics, 132(12), 1323–1338. doi: 10.1007/s00439-013-1342-z.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Chao, L. L., DeCarli, C., Kriger, S., Truran, D., Zhang, Y., Laxamana, J., et al. (2013). Associations between white matter Hyperintensities and β amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PloS One, 8(6). doi: 10.1371/journal.pone.0065175.
  17. Chételat, G., Villemagne, V. L., Pike, K. E., Baron, J. C., Bourgeat, P., Jones, G., et al. (2010). Larger temporal volume in elderly with high versus low beta-amyloid deposition. Brain, 133(11), 3349–3358. doi: 10.1093/brain/awq187.CrossRefPubMedGoogle Scholar
  18. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194. doi: 10.1006/nimg.1998.0395.CrossRefPubMedGoogle Scholar
  19. Dufouil, C., Alpérovitch, A., & Tzourio, C. (2003). Influence of education on the relationship between white matter lesions and cognition. Neurology, 60(5), 831–836. doi: 10.1212/01.WNL.0000049456.33231.96.CrossRefPubMedGoogle Scholar
  20. Ewers, M., Insel, P. S., & Stern, Y. (2013). Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology, 80(13), 1194–1201. doi: 10.1212/WNL.0b013e31828970c2.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Ferreira, D., Bartrés-Faz, D., Nygren, L., Rundkvist, L. J., Molina, Y., Machado, A., Junqué, C., Barroso, J., & Westman, E. (2016). Different reserve proxies confer overlapping and unique endurance to cortical thinning in healthy middle-aged adults. Behavioural Brain Research, 311, 375–383. doi: 10.1016/j.bbr.2016.05.061.CrossRefPubMedGoogle Scholar
  22. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055. doi: 10.1073/pnas.200033797.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2013). Brain changes in older adults at very low risk for Alzheimer’s disease. The Journal of Neuroscience, 33(19), 8237–8242. doi: 10.1523/JNEUROSCI.5506-12.2013.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014a). What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Progress in Neurobiology, 117, 20–40. doi: 10.1016/j.pneurobio.2014.02.004.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., et al. (2014b). Accelerating cortical thinning: unique to dementia or universal in aging? Cerebral Cortex, 24(4), 919–934. doi: 10.1093/cercor/bhs379.CrossRefPubMedGoogle Scholar
  26. Foubert-Samier, A., Catheline, G., Amieva, H., Dilharreguy, B., Helmer, C., Allard, M., & Dartigues, J.-F. (2012). Education, occupation, leisure activities, and brain reserve: a population-based study. Neurobiology of Aging, 33(2), 423.e15–423.e25. doi: 10.1016/j.neurobiolaging.2010.09.023.CrossRefGoogle Scholar
  27. Gazes, Y., Bowman, F. D., Razlighi, Q. R., O’Shea, D., Stern, Y., & Habeck, C. (2016). White matter tract covariance patterns predict age-declining cognitive abilities. NeuroImage, 125, 53–60. doi: 10.1016/j.neuroimage.2015.10.016.CrossRefPubMedGoogle Scholar
  28. Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., De Stefano, N., & Johansen-Berg, H. (2010). Age-related changes in grey and white matter structure throughout adulthood. NeuroImage, 51(3), 943–951. doi: 10.1016/j.neuroimage.2010.03.004.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Grau, M., Elosua, R., Cabrera De León, A., Guembe, M. J., Baena-Díez, J. M., Vega Alonso, T., et al. (2011). Factores de riesgo cardiovascular en España en la primera década del siglo XXI: análisis agrupado con datos individuales de 11 estudios de base poblacional, estudio DARIOS. Revista Española de Cardiología, 64(4), 295–304. doi: 10.1016/j.recesp.2010.11.005.CrossRefPubMedGoogle Scholar
  30. Hayes, A. F. (2009). Beyond baron and Kenny: statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420. doi: 10.1080/03637750903310360.CrossRefGoogle Scholar
  31. Hogstrom, L. J., Westlye, L. T., Walhovd, K. B., & Fjell, A. M. (2013). The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cerebral Cortex, 23(11), 2521–2530. doi: 10.1093/cercor/bhs231.CrossRefPubMedGoogle Scholar
  32. Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Graña, M., & Behrens, T. E. J. (2012). Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 68(6), 1846–1855. doi: 10.1002/mrm.24204.CrossRefGoogle Scholar
  33. Johansen-Berg, H., Baptista, C. S., & Thomas, A. G. (2012). Human structural plasticity at record speed. Neuron, 73(6), 1058–1060. doi: 10.1016/j.neuron.2012.03.001.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Kemppainen, N. M., Aalto, S., Karrasch, M., Någren, K., Savisto, N., Oikonen, V., et al. (2008). Cognitive reserve hypothesis: Pittsburgh compound B and fluorodeoxyglucose positron emission tomography in relation to education in mild Alzheimer’s disease. Annals of Neurology, 63(1), 112–118. doi: 10.1002/ana.21212.CrossRefPubMedGoogle Scholar
  35. Kerchner, G. A., Racine, C. A., Hale, S., Wilheim, R., Laluz, V., Miller, B. L., & Kramer, J. H. (2012). Cognitive processing speed in older adults: relationship with white matter integrity. PLoS ONE, 7(11). doi: 10.1371/journal.pone.0050425.
  36. Kim, J. P., Seo, S. W., Shin, H. Y., Ye, B. S., Yang, J.-J., Kim, C., et al. (2015). Effects of education on aging-related cortical thinning among cognitively normal individuals. Neurology, 85(9), 806–812. doi: 10.1212/WNL.0000000000001884.CrossRefPubMedGoogle Scholar
  37. Landau, S. M., Marks, S. M., Mormino, E. C., Rabinovici, G. D., Oh, H., O’Neil, J. P., Wilson, R. S., & Jagust, W. J. (2012). Association of lifetime cognitive engagementa dn low β-amyloid deposition. Archives of Neurology, 69(5), 623–629.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Laukka, E. J., Lövdén, M., Kalpouzos, G., Li, T.-Q., Jonsson, T., Wahlund, L.-O., et al. (2013). Associations between white matter microstructure and cognitive performance in old and very old age. PloS One, 8(11), e81419. doi: 10.1371/journal.pone.0081419.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Liu, Y., Julkunen, V., Paajanen, T., Westman, E., Wahlund, L. O., Aitken, A., et al. (2012). Education increases reserve against Alzheimer’s disease-evidence from structural MRI analysis. Neuroradiology, 54(9), 929–938. doi: 10.1007/s00234-012-1005-0.CrossRefPubMedPubMedCentralGoogle Scholar
  40. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. doi: 10.1146/annurev.psych.58.110405.085542.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Madden, D. J., Whiting, W. L., Huettel, S. A., White, L. E., MacFall, J. R., & Provenzale, J. M. (2004). Diffusion tensor imaging of adult age differences in cerebral white matter: relation to response time. NeuroImage, 21(3), 1174–1181. doi: 10.1016/j.neuroimage.2003.11.004.CrossRefPubMedGoogle Scholar
  42. Marrugat, J., D’Agostino, R., Sullivan, L., Elosua, R., Wilson, P., Ordovas, J., et al. (2003). An adaptation of the Framingham coronary heart disease risk function to European Mediterranean areas. Journal of Epidemiology and Community Health, 57(8), 634–638. doi: 10.1136/jech.57.8.634.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Nebes, R., Meltzer, C., Whyte, E., Scanlon, J., Halligan, E., Saxton, J., et al. (2006). The relation of white matter hyperintensities to cognitive performance in the normal old: education matters. Aging, Neuropsychology, and Cognition, 13(3–4), 326–340. doi: 10.1080/138255890969294.CrossRefGoogle Scholar
  44. Nyberg, L., Maitland, S. B., Rönnlund, M., Bäckman, L., Dixon, R. A., Wahlin, A., & Nilsson, L.-G. (2003). Selective adult age differences in an age-invariant multifactor model of declarative memory. Psychology and Aging, 18(1), 149–160. doi: 10.1037/0882-7974.18.1.149.CrossRefPubMedGoogle Scholar
  45. Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292–305. doi: 10.1016/j.tics.2012.04.005.CrossRefPubMedGoogle Scholar
  46. Okonkwo, O. C., Schultz, S. A., Oh, J. M., Larson, J., Edwards, D., Cook, D., et al. (2014). Physical activity attenuates age-related biomarker alterations in preclinical AD. Neurology, 83(19), 1753–1760. doi: 10.1212/WNL.0000000000000964.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Opdebeeck, C., Martyr, A., & Clare, L. (2016). Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 23(1), 40–60. doi: 10.1080/13825585.2015.1041450.
  48. Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. doi: 10.1146/annurev.psych.59.103006.093656.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Perneczky, R., Drzezga, A., Diehl-Schmid, J., Schmid, G., Wohlschläger, A., Kars, S., et al. (2006). Schooling mediates brain reserve in Alzheimer’s disease: findings of fluoro-deoxy-glucose-positron emission tomography. Journal of Neurology, Neurosurgery, and Psychiatry, 77(9), 1060–1063. doi: 10.1136/jnnp.2006.094714.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Persson, N., Ghisletta, P., Dahle, C. L., Bender, A. R., Yang, Y., Yuan, P., et al. (2016). Regional brain shrinkage and change in cognitive performance over two years: the bidirectional influences of the brain and cognitive reserve factors. NeuroImage, 126, 15–26. doi: 10.1016/j.neuroimage.2015.11.028.CrossRefPubMedGoogle Scholar
  51. Petersen, R. C., & Morris, J. C. (2005). Mild cognitive impairment as a clinical entity and treatment target. Archives of Neurology, 62(7), 1160–1163 discussion 1167. doi: 10.1001/archneur.62.7.1160.CrossRefPubMedGoogle Scholar
  52. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.CrossRefPubMedGoogle Scholar
  53. Rönnlund, M., Nyberg, L., Bäckman, L., & Nilsson, L. G. (2005). Stability, growth, and decline in adult life span development of declarative memory: cross-sectional and longitudinal data from a population-based study. Psychology and Aging, 20(1), 3–18. doi: 10.1037/0882-7974.20.1.3.CrossRefPubMedGoogle Scholar
  54. Sala, S., Agosta, F., Pagani, E., Copetti, M., Comi, G., & Filippi, M. (2012). Microstructural changes and atrophy in brain white matter tracts with aging. Neurobiology of Aging, 33(3), 488–498. doi: 10.1016/j.neurobiolaging.2010.04.027.CrossRefPubMedGoogle Scholar
  55. Salat, D. H. (2011). The declining infrastructure of the aging brain. Brain Connectivity, 1(4), 279–293. doi: 10.1089/brain.2011.0056.CrossRefPubMedPubMedCentralGoogle Scholar
  56. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. doi: 10.1037/0033-295X.103.3.403.CrossRefPubMedGoogle Scholar
  57. Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54(1–3), 35–54.CrossRefPubMedGoogle Scholar
  58. Salthouse, T. A. (2011). Neuroanatomical substrates of age-related cognitive decline. Psychological Bulletin, 137(5), 753–784. doi: 10.1037/a0023262.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Sasson, E., Doniger, G. M., Pasternak, O., Tarrasch, R., & Assaf, Y. (2013). White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Frontiers in Neuroscience, 7(March), 1–13. doi: 10.3389/fnins.2013.00032.Google Scholar
  60. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., et al. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage, 59(4), 3774–3783. doi: 10.1016/j.neuroimage.2011.11.032.CrossRefPubMedGoogle Scholar
  61. Ségonne, F., Pacheco, J., & Fischl, B. (2007). Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Transactions on Medical Imaging, 26(4), 518–529. doi: 10.1109/TMI.2006.887364.CrossRefPubMedGoogle Scholar
  62. Sexton, C. E., Walhovd, K., Storsve, A. B., Tamnes, C. K., Westlye, L. T., Johansen-Berg, H., & Fjell, A. M. (2014). Accelerated changes in white matter microstructure during ageing: a longitudinal diffusion tensor imaging study. Journal of Neuroscience, 34(46), 15425–15436. doi: 10.1523/JNEUROSCI.0203-14.2014.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97. doi: 10.1109/42.668698.CrossRefPubMedGoogle Scholar
  64. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. doi: 10.1002/hbm.10062.CrossRefPubMedGoogle Scholar
  65. Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505. doi: 10.1016/j.neuroimage.2006.02.024.CrossRefPubMedGoogle Scholar
  66. Solé-Padullés, C., Bartrés-Faz, D., Junqué, C., Vendrell, P., Rami, L., Clemente, I. C., et al. (2009). Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 30(7), 1114–1124. doi: 10.1016/j.neurobiolaging.2007.10.008.CrossRefPubMedGoogle Scholar
  67. Staff, R. T., Murray, A. D., Ahearn, T. S., Mustafa, N., Fox, H. C., & Whalley, L. J. (2012). Childhood socioeconomic status and adult brain size: childhood socioeconomic status influences adult hippocampal size. Annals of Neurology, 71(5), 653–660. doi: 10.1002/ana.22631.CrossRefPubMedGoogle Scholar
  68. Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society: JINS, 8(3), 448–460.CrossRefPubMedGoogle Scholar
  69. Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. doi: 10.1016/j.neuropsychologia.2009.03.004.CrossRefPubMedPubMedCentralGoogle Scholar
  70. Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 1006–1012. doi: 10.1016/S1474-4422(12)70191-6.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(25), 8488–8498. doi: 10.1523/JNEUROSCI.0391-14.2014.CrossRefGoogle Scholar
  72. Suo, C., León, I., Brodaty, H., Trollor, J., Wen, W., Sachdev, P., et al. (2012). Supervisory experience at work is linked to low rate of hippocampal atrophy in late life. NeuroImage, 63, 1542–1551.CrossRefPubMedGoogle Scholar
  73. Then, F. S., Luck, T., Angermeyer, M. C., & Riedel-Heller, S. G. (2016). Education as protector against dementia, but what exactly do we mean by education? Age and Ageing . doi: 10.1093/ageing/afw049.afw049PubMedGoogle Scholar
  74. Valenzuela, M. J., & Sachdev, P. (2006). Brain reserve and dementia: a systematic review. Psychological Medicine, 36(4), 441–454. doi: 10.1017/S0033291705006264.CrossRefPubMedGoogle Scholar
  75. Valenzuela, M. J., Sachdev, P., Wen, W., Chen, X., & Brodaty, H. (2008). Lifespan mental activity predicts diminished rate of hippocampal atrophy. PLoS ONE, 3(7), 1–6. doi: 10.1371/journal.pone.0002598.CrossRefGoogle Scholar
  76. Vemuri, P., Przybelski, S. A., Knopman, D. S., Machulda, M., Lowe, V. J., Mielke, M. M., et al. (2016). Effect of intellectual enrichment on AD biomarker trajectories. Neurology, 86, 1128–1135. doi: 10.1212/WNL.0000000000002490.CrossRefPubMedPubMedCentralGoogle Scholar
  77. Vidal-Piñeiro, D., Valls-Pedret, C., Fernández-Cabello, S., Arenaza-Urquijo, E. M., Sala-Llonch, R., Solana, E., et al. (2014). Decreased default mode network connectivity correlates with age-associated structural and cognitive changes. Frontiers in Aging Neuroscience, 6, 1–17. doi: 10.3389/fnagi.2014.00256.Google Scholar
  78. Westlye, L. T., Walhovd, K. B., Dale, A. M., Bjørnerud, A., Due-Tønnessen, P., Engvig, A., et al. (2010). Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cerebral Cortex, 20(9), 2055–2068. doi: 10.1093/cercor/bhp280.CrossRefPubMedGoogle Scholar
  79. Wirth, M., Hasse, C. M., Villeneuve, S., Vogel, J., & Jagust, W. J. (2014). Neuroprotective pathways: lifestyle activity, brain pathology, and cognition in cognitively normal older adults. Neurobiology of Aging, 35(8), 1873–1882. doi: 10.1016/j.neurobiolaging.2014.02.015.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Lídia Vaqué-Alcázar
    • 1
    • 2
  • Roser Sala-Llonch
    • 3
  • Cinta Valls-Pedret
    • 2
    • 4
    • 5
  • Dídac Vidal-Piñeiro
    • 3
  • Sara Fernández-Cabello
    • 6
    • 7
  • Núria Bargalló
    • 2
    • 8
  • Emilio Ros
    • 2
    • 4
    • 5
  • David Bartrés-Faz
    • 1
    • 2
    • 9
  1. 1.Medical Psychology Unit, Department of Medicine, Faculty of Medicine and Health SciencesUniversity of BarcelonaBarcelonaSpain
  2. 2.Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
  3. 3.Research Group for Lifespan Changes in Brain and Cognition, Department of PsychologyUniversity of OsloOsloNorway
  4. 4.Lipid Clinic, Endocrinology and Nutrition ServiceHospital ClínicBarcelonaSpain
  5. 5.Ciber Fisiopatología de la Obesidad y nutrición (CIBEROBN)Insituto de Salud Carlos IIIMadridSpain
  6. 6.Department of PsychologyUniversity of SalzburgSalzburgAustria
  7. 7.Center for Neurocognitive ResearchUniversity of SalzburgSalzburgAustria
  8. 8.Neuroradiology Section, Radiology Service, Centre de Diagnòstic per la ImatgeHospital ClínicBarcelonaSpain
  9. 9.Institute of NeurosciencesUniversity of BarcelonaBarcelonaSpain

Personalised recommendations