Anstey, K. J., Hofer, S. M., & Luszcz, M. A. (2003). Cross-sectional and longitudinal patterns of dedifferentiation in late-life cognitive and sensory function: The effects of age, ability, attrition, and occasion of measurement. Journal of Experimental Psychology: General, 132(3), 470–487. https://dx.doi.org/10.1037/0096-3445.132.3.470Google Scholar

Anstey, K. J., Lord, S. R., & Williams, P. (1997). Strength in the lower limbs, visual contrast sensitivity, and simple reaction time predict cognition in older women. Psychology and Aging, 12(1), 137–144. https://dx.doi.org/10.1037/0882-7974.12.1.137CrossRefGoogle ScholarPubMed

Anstey, K. J., Luszcz, M. A., & Sanchez, L. (2001). A reevaluation of the common factor theory of shared variance among age, sensory function, and cognitive function in older adults. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 56(1), 3–11. https://dx.doi.org/10.1093/geronb/56.1.p3Google Scholar

Anstey, K., Stankov, L., & Lord, S. (1993). Primary aging, secondary aging, and intelligence. Psychology and Aging, 8(4), 562–570. https://dx.doi.org/10.1037//0882-7974.8.4.562Google Scholar

Arbuckle, T. Y., & Gold, D. P. (1993). Aging, inhibition, and verbosity. Journals of Gerontology, 48(5), 225–232. https://dx.doi.org/10.1093/geronj/48.5.p225CrossRefGoogle ScholarPubMed

Atkinson, R. C., & Juola, J. F. (1974). Search and decision processes in recognition memory. In Krantz, D. H., Atkinson, R. C., Luce, R. D., & Suppes, P. (Eds.), Learning, memory and thinking (Contemporary developments in mathematical psychology, Vol. 1) (pp. 242–293). Oxford: W. H. Freeman.Google Scholar

Baltes, P. B., & Lindenberger, U. (1997). Emergence of a powerful connection between sensory and cognitive functions across the adult life span: A new window to the study of cognitive aging? Psychology and Aging, 12(1), 12–21. https://dx.doi.org/10.1037/0882-7974.12.1.12Google Scholar

Belleville, S., Clément, F., Mellah, S., et al. (2011). Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain, 134(Pt. 6), 1623–1634. https://dx.doi.org/10.1093/brain/awr037Google Scholar

Berry, A. S., Zanto, T. P., Clapp, W. C., et al. (2010). The influence of perceptual training on working memory in older adults. PLoS One, 5(7), e11537. https://dx.doi.org/10.1371/journal.pone.0011537Google Scholar

Bowman, C. R., Chamberlain, J. D., & Dennis, N. A. (2019). Sensory representations supporting memory specificity: Age effects on behavioral and neural discriminability. Journal of Neuroscience, 39(12), 2265–2275. https://dx.doi.org/10.1523/jneurosci.2022-18.2019Google Scholar

Bunting, M. (2006). Proactive interference and item similarity in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(2), 183–196. https://dx.doi.org/10.1037/0278-7393.32.2.183Google Scholar

Burianová, H., Lee, Y., Grady, C. L., & Moscovitch, M. (2013). Age-related dedifferentiation and compensatory changes in the functional network underlying face processing. Neurobiology of Aging, 34(12), 2759–2767. https://dx.doi.org/10.1016/j.neurobiolaging.2013.06.016Google Scholar

Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: The HAROLD model. Psychology and Aging, 17(1), 85–100. https://dx.doi.org/10.1037//0882-7974.17.1.85CrossRefGoogle ScholarPubMed

Cabeza, R., Albert, M., Belleville, S., et al. (2018). Maintenance, reserve, and compensation: The cognitive neuroscience of healthy aging. Nature Reviews Neuroscience, 19(11), 701–710. https://dx.doi.org/10.1038/s41583-018-0068-2Google Scholar

Cabeza, R., & Dennis, N. A. (2012). Frontal lobes and aging: Deterioration and Compensation. In Stuss, D. T. & Knight, R. T. (Eds.), Principles of frontal lobe function (2nd ed., pp. 628–652). New York: Oxford University Press.Google Scholar

Cabeza, R., Grady, C. L., Nyberg, L., et al. (1997). Age-related differences in neural activity during memory encoding and retrieval: A positron emission tomography study. Journal of Neuroscience, 17(1), 391–400. https://dx.doi.org/10.1523/jneurosci.17-01-00391.1997Google Scholar

Cappell, K. A., Gmeindl, L., & Reuter-Lorenz, P. A. (2010). Age differences in prefontal recruitment during verbal working memory maintenance depend on memory load. Cortex, 46(4), 462–473. https://dx.doi.org/10.1016/j.cortex.2009.11.009Google Scholar

Carp, J., Park, J., Polk, T. A., & Park, D. C. (2011). Age differences in neural distinctiveness revealed by multi-voxel pattern analysis. NeuroImage, 56(2), 736–743. https://dx.doi.org/10.1016/j.neuroimage.2010.04.267Google Scholar

Chalfonte, B. L., & Johnson, M. K. (1996). Feature memory and binding in young and older adults. Memory and Cognition, 24(4), 403–416. https://dx.doi.org/10.3758/bf03200930Google Scholar

Chevalier, N., Kurth, S., Doucette, M. R., et al. (2015). Myelination is associated with processing speed in early childhood: Preliminary insights. PLoS One, 10(10), e0139897. https://dx.doi.org/10.1371/journal.pone.0139897Google Scholar

Craik, F. I. M., & Byrd, M. (1982). Aging and cognitive deficits: The role of attentional resources. In Craik, F. I. M. & Trehub, S. E. (Eds.), Aging and Cognitive Processes (pp. 191–211). New York: Plenum.Google Scholar

Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19(4), 450–466. https://dx.doi.org/10.1016/S0022-5371(80)90312-6CrossRefGoogle Scholar

Daselaar, S. M., Fleck, M. S., Dobbins, I. G., Madden, D. J., & Cabeza, R. (2006). Effects of healthy aging on hippocampal and rhinal memory functions: An event-related fMRI study. Cerebral Cortex, 16, 1771–1782. https://dx.doi.org/10.1093/cercor/bhj112Google Scholar

Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S., & Cabeza, R. (2008). Que PASA? The posterior-anterior shift in aging. Cerebral Cortex, 18, 1201–1209. https://dx.doi.org/10.1093/cercor/bhm155Google Scholar

Deary, I. J., Pattie, A., & Starr, J. M. (2013). The stability of intelligence from age 11 to age 90 years: The Lothian birth cohort of 1921. Psychological Science, 24, 2361–2368. https://dx.doi.org/10.1177/0956797613486487CrossRefGoogle ScholarPubMed

DeCaro, R., & Thomas, A. K. (2019). How retrieval success and task demands drive age differences in self-regulated learning. Journal of Memory and Language.Google Scholar

Dennis, N. A., & Cabeza, R. (2008). Neuroimaging of health cognitive aging. In Craik, F. E. M. & Salthouse, T. (Eds.), Handbook of cognitive aging (3rd ed., pp. 1–54). New York: Psychological Press.Google Scholar

Dennis, N. A., & Cabeza, R. (2011). Age-related dedifferentiation of learning systems: An fMRI study of implicit and explicit learning. Neurobiology of Aging, 32, p. 2318.e17–2318.e30. https://dx.doi.org/10.1016/j.neurobiolaging.2010.04.004Google Scholar

Dodson, C. S., Holland, P. W., & Shimamura, A. P. (1998). On the recollection of specific- and partial-source information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(5), 1121–1136. https://dx.doi.org/10.1037//0278-7393.24.5.1121Google Scholar

Engle, R. W., Cantor, J., & Carullo, J. J. (1992). Individual differences in working memory and comprehension: A test of four hypotheses. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 972–992. https://dx.doi.org/10.1037//0278-7393.18.5.972Google Scholar

Ferguson, S. A., Hashtroudi, S., & Johnson, M. K. (1992). Age differences in using source-relevant cues. Psychology and Aging, 7(3), 443–452. https://dx.doi.org/10.1037/0882-7974.7.3.443Google Scholar

Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control functions: A latent-variable analysis. Journal of Experimental Psychology: General, 133(1), 101–135. https://dx.doi.org/10.1037/0096-3445.133.1.101Google Scholar

Gazzaley, A., Sheridan, M. A., Cooney, J. W., & D’Esposito, M. (2007). Age-related deficits in component processes of working memory. Neuropsychology, 21(5), 532–539.Google Scholar

Glisky, E. L., Rubin, S. R., & Davidson, P. S. (2001). Source memory in older adults: an encoding or retrieval problem? Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(5), 1131–1146. https://dx.doi.org/10.1037//0278-7393.27.5.1131Google Scholar

Gow, A. J., Johnson, W., Pattie, A., et al. (2011). Stability and change in intelligence from age 11 to ages 70, 79, and 87: The Lothian birth cohorts of 1921 and 1936. Psychology and Aging, 26, 232–240. https://dx.doi.org/10.1037/a0021072Google Scholar

Grady, C. L., Maisog, J. M., Horwitz, B., et al. (1994). Age-related changes in cortical blood flow activation during visual processing of faces and location. Journal of Neuroscience, 14, 1450–1462. https://dx.doi.org/10.1523/jneurosci.14-03-01450.1994Google Scholar

Grady, C. L., McIntosh, A. R., Horwitz, B., et al. (1995). Age-related reductions in human recognition memory due to impaired encoding. Science, 269, 218–221. https://dx.doi.org/10.1126/science.7618082Google Scholar

Grady, C. L., Protzner, A. B., Kovacevic, N., et al. (2010). A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cerebral Cortex, 20, 1432–1447. https://dx.doi.org/10.1093/cercor/bhp207Google Scholar

Gruppuso, V., Lindsay, D. S., & Kelley, C. M. (1997). The process-dissociation procedure and similarity: Defining and estimating recollection and familiarity in recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(2), 259–278. https://dx.doi.org/10.1037/0278-7393.23.2.259Google Scholar

Gutchess, A. H. (2014). Plasticity of the aging brain: New directions in cognitive neuroscience. Science, 346, 579–582. https://dx.doi.org/10.1126/science.1254604Google Scholar

Gutchess, A. H., Welsh, R. C., Hedden, T., et al. (2005). Aging and the neural correlates of successful picture encoding: Frontal activations compensate for decreased medial-temporal activity. Journal of Cognitive Neuroscience, 17, 84–96. https://dx.doi.org/10.1162/0898929052880048Google Scholar

Hamm, V. P., & Hasher, L. (1992). Age and the availability of inferences. Psychology and Aging, 7(1), 56–64. https://dx.doi.org/10.1037/0882-7974.7.1.56Google Scholar

Hampstead, B. M., Sathian, K., Phillips, P. A., et al. (2012). Mnemonic strategy training improves memory for object location associations in both healthy elderly and patients with amnestic mild cognitive impairment: A randomized, single-blind study. Neuropsychology, 26, 385–399. https://dx.doi.org/10.1037/a0027545CrossRefGoogle ScholarPubMed

Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 22, pp. 193–225). San Diego: Academic Press. https://doi.org/10.1016/S0079-7421(08)60041-9Google Scholar

Hashtroudi, S., Johnson, M. K., & Chrosniak, L. D. (1989). Aging and source monitoring. Psychology and Aging, 4(1), 106–112. https://dx.doi.org/10.1037//0882-7974.4.1.106Google Scholar

Hedden, T., & Park, D. C. (2003). Contributions of source and inhibitory mechanisms to age-related retroactive interference in verbal working memory. Journal of Experimental Psychology: General, 132(1), 93–112. https://dx.doi.org/10.1037/0096-3445.132.1.93Google Scholar

Hintzman, D. L., & Curran, T. (1994). Retrieval dynamics of recognition and frequency judgments: Evidence for separate processes of familiarity and recall. Journal of Memory and Language, 33(1), 1–18. https://dx.doi.org/10.1006/jmla.1994.1001Google Scholar

Horn, J. L., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107–129. https://dx.doi.org/10.1016/0001-6918(67)90011-xGoogle Scholar

Hsu, W. Y., Ku, Y., Zanto, T. P., & Gazzaley, A. (2015). Effects of noninvasive brain stimulation on cognitive function in healthy aging and Alzheimer’s disease: A systematic review and meta-analysis. Neurobiology of Aging, 36, 2348–2359. https://dx.doi.org/10.1016/j.neurobiolaging.2015.04.016Google Scholar

Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30(5), 513–541. https://dx.doi.org/10.1016/0749-596x(91)90025-fCrossRefGoogle Scholar

Jacoby, L. L. (1999). Ironic effects of repetition: Measuring age-related differences in memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(1), 3–22. https://dx.doi.org/10.1037//0278-7393.25.1.3Google Scholar

Jacoby, L. L., Bishara, A. J., Hessels, S., & Toth, J. P. (2005). Aging, subjective experience, and cognitive control: Dramatic false remembering by older adults. Journal of Experimental Psychology: General, 134(2), 131–148. https://dx.doi.org/10.1037/0096-3445.134.2.131CrossRefGoogle ScholarPubMed

Jacoby, L. L., Kelley, C., Brown, J., & Jasechko, J. (1989a). Becoming famous overnight: Limits on the ability to avoid unconscious influences of the past. Journal of Personality and Social Psychology, 56(3), 326–338. https://dx.doi.org/10.1037//0022-3514.56.3.326Google Scholar

Jacoby, L. L., Kelley, C. M., & Dywan, J. (1989b). Memory attributions. In H. L. Roediger III & F. I. M. Craik (Eds.), Varieties of memory and consciousness: Essays in honour of Endel Tulving (pp. 391–422). Lawrence Erlbaum Associates, Inc.Google Scholar

Jacoby, L. L., Yonelinas, A. P., & Jennings, J. M. (1997). The relation between conscious and unconscious (automatic) influences: A declaration of independence. In Cohen, J. D. & Schooler, J. W. (Eds.), Carnegie Mellon Symposia on cognition: Scientific approaches to consciousness (pp. 13–47). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar

Kane, M. J., Hasher, L., Stoltzfus, E. R., Zacks, R. T., & Connelly, S. (1994). Inhibitory attentional mechanisms and aging. Psychology and Aging, 9(1), 103–112. https://dx.doi.org/10.1037//0882-7974.9.1.103Google Scholar

Kaszniak, A. W., & Newman, M. C. (2000). Toward a neuropsychology of cognitive aging. In Honn Qualis, S. & Abeles, N. (Eds.), Psychology and the aging revolution: How we adapt to longer life (pp. 43–67). Washington: American Psychological Association, Washington, DC.Google Scholar

Kausler, D. H., & Puckett, J. M. (1981a). Adult age differences in memory for sex of voice. Journal of Gerontology, 36(1), 44–50. https://dx.doi.org/10.1093/geronj/36.1.44Google Scholar

Kausler, D. H., & Puckett, J. M. (1981b). Modality memory and frequency of occurrence memory for young and middle-aged adults. Experimental Aging Research, 7(3), 235–243. https://dx.doi.org/10.1080/03610738108259807Google Scholar

Kelley, C. M., & Sahakyan, L. (2003). Memory, monitoring, and control in the attainment of memory accuracy. Journal of Memory and Language, 48(4), 704–721. https://dx.doi.org/10.1016/s0749-596x(02)00504-1Google Scholar

Kiely, K. M., & Anstey, K. J. (2015). Common cause theory in aging. In Pachana, N. (Ed.), Encyclopedia of Geropsychology (pp. 559–569). Singapore: Springer.Google Scholar

Kirchhoff, B. A., Anderson, B. A., Smith, S. E., Barch, D. M., & Jacoby, L. L. (2012). Cognitive training-related changes in hippocampal activity associated with recollection in older adults. Neuroimage, 62(3), 1956–1964. https://dx.doi.org/10.1016/j.neuroimage.2012.06.017Google Scholar

Koen, J. D., Hauck, N., & Rugg, M. D. (2019). The relationship between age, neural differentiation, and memory performance. Journal of Neuroscience, 39, 149–162. https://dx.doi.org/10.1101/345181Google Scholar

Li, K. Z., Lindenberger, U., Freund, A. M., & Baltes, P. B. (2001). Walking while memorizing: Age-related differences in compensatory behavior. Psychological Science, 12(3), 230–237. https://dx.doi.org/10.1111/1467-9280.00341Google Scholar

Li, S. C., & Lindenberger, U. (1999). Cross-level unification: A computational exploration of the link between deterioration of neurotransmitter systems and dedifferentiation of cognitive abilities in old age. In Nilsson, L.-G. & Markowitsch, H. J., (Eds.), Cognitive Neuroscience of Memory (pp. 103–146). Seattle: Hogrefe & Huber.Google Scholar

Light, L. L. (2012). Dual-process theories of memory in old age: An update. In Naveh-Benjamin, M. & Ohta, N. (Eds.), Memory and aging: Current issues and future directions (pp. 97–124). New York: Psychology Press.Google Scholar

Light, L. L., LaVoie, D., Valencia-Laver, D., Albertson Owens, S. A., & Mead, G. (1992). Direct and indirect measures of memory for modality in young and older adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(6), 1284–1297. https://dx.doi.org/10.1037//0278-7393.18.6.1284Google Scholar

Light, L. L., & Zelinski, E. M. (1983). Memory for spatial information in young and old adults. Developmental Psychology, 19(6), 901–906. https://dx.doi.org/10.1037//0012-1649.19.6.901Google Scholar

Lindenberger, U., & Baltes, P. B. (1994). Sensory functioning and intelligence in old age: A strong connection. Psychology and Aging, 9, 339–355. https://dx.doi.org/10.1037//0882-7974.9.3.339Google Scholar

Lindenberger, U., & Ghisletta, P. (2009). Cognitive and sensory declines in old age: Gauging the evidence for a common cause. Psychology and Aging, 24(1), 1–16. https://dx.doi.org/10.1037/a0014986Google Scholar

Lindenberger, U., Marsiske, M., & Baltes, P. B. (2000). Memorizing while walking: Increase in dual-task costs from young adulthood to old age. Psychology and Aging, 15(3), 417–436. https://dx.doi.org/10.1037/0882-7974.15.3.417Google Scholar

Lindsay, D. S., Johnson, M. K., and Kwon, P. (1991). Developmental changes in memory source monitoring. Journal of Experimental Child Psychology, 52(3), 297–318. https://dx.doi.org/10.1016/0022-0965(91)90065-zGoogle Scholar

Lustig, C., Hasher, L., & Zacks, R. T. (2007). Inhibitory deficit theory: Recent developments in a “new view.” In Gorfein, D. S. & MacLeod, C. M. (Eds.), Inhibition in cognition (pp. 145–162). Washington: American Psychological Association.Google Scholar

Lustig, C., May, C. P., & Hasher, L. (2001). Working memory span and the role of proactive interference. Journal of Experimental Psychology: General, 130(2), 199–207. https://dx.doi.org/10.1037/11587-008Google Scholar

Lustig, C., Snyder, A. Z., Bhakta, M., et al. (2003). Functional deactivations: Change with age and dementia of the Alzheimer type. Proceedings of the National Academy of Sciences USA, 100, 14504–14509. https://dx.doi.org/10.1073/pnas.2235925100Google Scholar

May, C. P., Hasher, L., & Kane, M. J. (1999). The role of interference in memory span. Memory and Cognition, 27(5), 759–767. https://dx.doi.org/10.3758/bf03198529Google Scholar

McElree, B., Dolan, P. O., & Jacoby, L. L. (1999). Isolating the contributions of familiarity and source information to item recognition: A time course analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(3), 563–582. https://dx.doi.org/10.1037/0278-7393.25.3.563Google Scholar

McIntyre, J. S., & Craik, F. I. (1987). Age differences in memory for item and source information. Canadian Journal of Psychology/Revue canadienne de psychologie, 41(2), 175–192. https://dx.doi.org/10.1037/h0084154Google Scholar

Mitchell, D. B., Hunt, R. R., & Schmitt, F. A. (1986). The generation effect and reality monitoring: Evidence from dementia and normal aging. Journal of Gerontology, 41(1), 79–84. https://dx.doi.org/10.1093/geronj/41.1.79Google Scholar

Mitchell, K. J., Johnson, M. K., Raye, C. L., Mather, M., & D’Esposito, M. (2000). Aging and reflective processes of working memory: Binding and test load deficits. Psychology and Aging, 15(3), 527–541. https://dx.doi.org/10.1037//0882-7974.15.3.527Google Scholar

Mitchell, K. J., & Zaragoza, M. S. (2001). Contextual overlap and eyewitness suggestibility. Memory and Cognition, 29(4), 616–626. https://dx.doi.org/10.3758/bf03200462Google Scholar

Monge, Z. A., Stanley, M. L., Geib, B. R., Davis, S. W., & Cabeza, R. C. (2018). Functional networks underlying item and source memory: Shared and distinct network components and age-related differences. Neurobiology of Aging, 69, 140–150. https://dx.doi.org/10.1016/j.neurobiolaging.2018.05.016Google Scholar

Naveh-Benjamin, M. (2000). Adult age differences in memory performance: Tests of an associative deficit hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(5), 1170–1187. https://dx.doi.org/10.1037//0278-7393.26.5.1170Google Scholar

Naveh-Benjamin, M., Brav, T. K., & Levy, O. (2007). The associative memory deficit of older adults: The role of strategy utilization. Psychology and Aging, 22(1), 202–208. https://dx.doi.org/10.1037/0882-7974.22.1.202Google Scholar

Nyberg, L., Sandblom, J., Jones, S., et al. (2003). Neural correlates of training-related memory improvement in adulthood and aging. Proceedings of the National Academy of Sciences USA, 100, 13728–13733. https://dx.doi.org/10.1073/pnas.1735487100Google Scholar

O’Hanlon, L., Wilcox, K. A., & Kemper, S. (2001). Age differences in implicit and explicit associative memory: Exploring elaborative processing effects. Experimental Aging Research, 27(4), 341–359. https://dx.doi.org/10.1080/03610730109342353Google Scholar

Old, S. R., & Naveh-Benjamin, M. (2008). Memory for people and their actions: Further evidence for an age-related associative deficit. Psychology and Aging, 23(2), 467–472. https://dx.doi.org/10.1037/0882-7974.23.2.467Google Scholar

Park, D. C., & Payer, D. (2006). Working memory across the adult lifespan. In Bialystok, E. & Craik, F. I. M. (Eds.), Lifespan cognition: Mechanisms of change (pp. 128–142). New York: Oxford University Press.Google Scholar

Park, D. C., Polk, T. A., Park, R., et al. (2004). Aging reduces neural specialization in ventral visual cortex. Proceedings of the National Academy of Sciences USA, 101, 13091–13095. https://dx.doi.org/10.1073/pnas.0405148101Google Scholar

Park, D. C., Puglisi, J. T., & Sovacool, M. (1983). Memory for pictures, words, and spatial location in older adults: Evidence for pictorial superiority. Journal of Gerontology, 38(5), 582–588. https://dx.doi.org/10.1093/geronj/38.5.582Google Scholar

Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. https://dx.doi.org/10.1146/annurev.psych.59.103006.093656Google Scholar

Park, J., Carp, J., Kennedy, K. M., et al. (2012). Neural broadening or neural attenuation? Investigating age-related dedifferentiation in the face network in a large lifespan sample. Journal of Neuroscience, 32, 2154–2158. https://dx.doi.org/10.1523/jneurosci.4494-11.2012Google Scholar

Persson, J., Lustig, C., Nelson, J. K., & Reuter-Lorenz, P. A. (2007). Age differences in deactivation: A link to cognitive control? Journal of Cognitive Neuroscience, 19, 1021–1032. https://dx.doi.org/10.1162/jocn.2007.19.6.1021Google Scholar

Pezdek, K. (1983). Memory for items and their spatial locations by young and elderly adults. Developmental Psychology, 19(6), 895–900. https://dx.doi.org/10.1037//0012-1649.19.6.895Google Scholar

Rabbitt, P. M. (1968). Channel-capacity, intelligibility and immediate memory. Quarterly Journal of Experimental Psychology, 20(3), 241–248. https://dx.doi.org/10.1080/14640746808400158Google Scholar

Rabbitt, P. (1991). Management of the working population. Ergonomics, 34(6), 775–790. https://dx.doi.org/10.1080/00140139108967350Google Scholar

Reinitz, M. T., Séguin, J. A., Peria, W., & Loftus, G. R. (2012). Confidence–accuracy relations for faces and scenes: Roles of features and familiarity. Psychonomic Bulletin and Review, 19(6), 1085–1093. https://dx.doi.org/10.3758/s13423-012-0308-9Google Scholar

Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17, 177–182. https://dx.doi.org/10.1111/j.1467-8721.2008.00570.xCrossRefGoogle Scholar

Reuter-Lorenz, P. A., Jonides, J., Smith, E. E., et al. (2000). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. Journal of Cognitive Neuroscience, 12, 174–187. https://dx.doi.org/10.1162/089892900561814Google Scholar

Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24, 355–370. https://dx.doi.org/10.1007/s11065-014-9270-9Google Scholar

Rhodes, M. G., Castel, A. D., & Jacoby, L. L. (2008). Associative recognition of face pairs by younger and older adults: The role of familiarity-based processing. Psychology and Aging, 23(2), 239–249. https://dx.doi.org/10.1037/0882-7974.23.2.239Google Scholar

Rosen, A. C., Prull, M. W., O’Hara, R., et al. (2002). Variable effects of aging on frontal lobe contributions to memory. NeuroReport, 13, 2425–2428. https://dx.doi.org/10.1097/00001756-200212200-00010Google Scholar

Rossi, S., Miniussi, C., Pasqualetti, P., et al. (2004). Age-related functional changes of prefrontal cortex in long-term memory: A repetitive transcranial magnetic stimulation study. Journal of Neuroscience, 24(36), 7939–7944. https://dx.doi.org/10.1523/jneurosci.0703-04.2004Google Scholar

Rowe, G., Hasher, L., & Turcotte, J. (2008). Age differences in visuospatial working memory. Psychology and Aging, 23(1), 79–84. https://dx.doi.org/10.1037/0882-7974.23.1.79Google Scholar

Salthouse, T. A. (1979). Adult age and the speed-accuracy trade-off. Ergonomics, 22(7), 811–821. https://dx.doi.org/10.1080/00140137908924659Google Scholar

Salthouse, T. A. (1985). Speed of behavior and its implications for cognition. In J. E. Birren & K. W. Schaie (Eds.), The handbooks of aging. Handbook of the psychology of aging (pp. 400–426). Van Nostrand Reinhold Co.Google Scholar

Salthouse, T. A. (1985a). Anticipatory processing in transcription typing. Journal of Applied Psychology, 70(2), 264–271. https://dx.doi.org/10.1037//0021-9010.70.2.264Google Scholar

Salthouse, T. A. (1991a). Cognitive facets of aging well. Generations: Journal of the American Society on Aging, 15(1), 35–38.Google Scholar

Salthouse, T. A. (1991b). Mediation of adult age differences in cognition by reductions in working memory and speed of processing. Psychological Science, 2(3), 179–183. https://dx.doi.org/10.1111/j.1467-9280.1991.tb00127.xGoogle Scholar

Salthouse, T. A. (1994). How many causes are there of aging-related decrements in cognitive functioning? Developmental Review, 14, 413–437. https://dx.doi.org/10.1006/drev.1994.1016Google Scholar

Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. https://dx.doi.org/10.1037//0033-295x.103.3.403Google Scholar

Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 35–54. https://dx.doi.org/10.1016/s0301-0511(00)00052-1Google Scholar

Salthouse, T. A., Babcock, R. L., & Shaw, R. J. (1991). Effects of adult age on structural and operational capacities in working memory. Psychology and Aging, 6(1), 118–127. https://dx.doi.org/10.1037/0882-7974.6.1.118Google Scholar

Salthouse, T. A., Legg, S., Palmon, R., & Mitchell, D. R. (1990). Memory factors in age-related differences in simple reasoning. Psychology and Aging, 5(1), 9–15. https://dx.doi.org/10.1037/0882-7974.5.1.9Google Scholar

Salthouse, T. A., & Mitchell, D. R. (1990). Effects of age and naturally occurring experience on spatial visualization performance. Developmental Psychology, 26(5), 845–854. https://dx.doi.org/10.1037/0012-1649.26.5.845Google Scholar

Schacter, D. L., Koutstaal, W., Johnson, M. K., Gross, M. S., & Angell, K. E. (1997). False recollection induced by photographs: A comparison of older and younger adults. Psychology and Aging, 12(2), 203–215. https://dx.doi.org/10.1037/0882-7974.12.2.203Google Scholar

Spencer, W. D., & Raz, N. (1995). Differential effects of aging on memory for content and context: A meta-analysis. Psychology and Aging, 10(4), 527–539. https://dx.doi.org/10.1037//0882-7974.10.4.527Google Scholar

Spreng, R. N., & Turner, G. R. (2019). The shifting architecture of cognition and brain function in older adulthood. Perspectives on Psychological Science, 14(4), 523–542. https://doi.org/10.1177/1745691619827511Google Scholar

Stine, E. L., Wingfield, A., & Poon, L. W. (1989). Speech comprehension and memory through adulthood: The roles of time and strategy. In Poon, L. W., Rubin, D. C., & Wilson, B. A. (Eds.), Everyday cognition in adulthood and late life (pp. 195–221). New York: Cambridge University Press.Google Scholar

Thomas, A. K., & Bulevich, J. B. (2006). Effective cue utilization reduces memory errors in older adults. Psychology and Aging, 21(2), 379–389. https://dx.doi.org/10.1037/0882-7974.21.2.379Google Scholar

Thomas, A. K., Bulevich, J. B., & Loftus, E. F. (2003). Exploring the role of repetition and sensory elaboration in the imagination inflation effect. Memory and Cognition, 31(4), 630–640. https://dx.doi.org/10.3758/bf03196103Google Scholar

Tipper, S. P. (1991). Mechanisms of visual selective attention. Canadian Psychology/Psychologie Canadienne, 32(4), 640–642. http://dx.doi.org/10.1037/h0084644Google Scholar

Tucker-Drob, E. M. (2011). Global and domain-specific changes in cognition throughout adulthood. Developmental Psychology, 47, 331–343. https://dx.doi.org/10.1037/a0021361Google Scholar

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. https://dx.doi.org/10.1037/bul0000179Google Scholar

Turner, G. R., & Spreng, R. N. (2015). Prefrontal engagement and reduced default network suppression co-occur and are dynamically coupled in older adults: The default–executive coupling hypothesis of aging. Journal of Cognitive Neuroscience, 27, 2462–2476. https://dx.doi.org/10.1162/jocn_a_00869Google Scholar

Wong, J. T., Cramer, S. J., & Gallo, D. A. (2012). Age-related reduction of the confidence–accuracy relationship in episodic memory: Effects of recollection quality and retrieval monitoring. Psychology and Aging, 27(4), 1053–1065. https://dx.doi.org/10.1037/a0027686Google Scholar

Yonelinas, A. P., & Jacoby, L. L. (1996a). Noncriterial recollection: Familiarity as automatic, irrelevant recollection. Consciousness and Cognition: An International Journal, 5(1–2), 131–141. https://dx.doi.org/10.1006/ccog.1996.0008Google Scholar

Yonelinas, A. P., & Jacoby, L. L. (1996b). Response bias and the process-dissociation procedure. Journal of Experimental Psychology: General, 125(4), 422–434. https://dx.doi.org/10.1037//0096-3445.125.4.422Google Scholar

Yonelinas, A. P., & Jacoby, L. L. (2012). The process-dissociation approach two decades later: Convergence, boundary conditions, and new directions. Memory and Cognition, 40(5), 663–680. https://dx.doi.org/10.3758/s13421-012-0205-5Google Scholar

Alexander, G. E., Furey, M. L., Grady, C. L., et al. (1997). Association of premorbid intellectual function with cerebral metabolism in Alzheimer’s disease: Implications for the cognitive reserve hypothesis. American Journal of Psychiatry, 154(2), 165–172. https://dx.doi.org/10.1176/ajp.154.2.165Google Scholar

Bennett, D. A., Wilson, R. S., Schneider, J. A., et al. (2003). Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology, 60(12), 1909–1915. https://dx.doi.org/10.1212/01.wnl.0000069923.64550Google Scholar

Beydoun, M. A., Beydoun, H. A., Gamaldo, A. A., et al. (2014). Epidemiologic studies of modifiable factors associated with cognition and dementia: Systematic review and meta-analysis. BMC Public Health, 14, 643. https://dx.doi.org/10.1186/1471-2458-14-643Google Scholar

Bozzali, M., Dowling, C., Serra, L., et al. (2015). The impact of cognitive reserve on brain functional connectivity in Alzheimer’s disease. Journal of Alzheimer’s Disease, 44(1), 243–250. https://dx.doi.org/10.3233/JAD-141824Google Scholar

Braak, H., & Braak, E. (1997). Diagnostic criteria for neuropathologic assessment of Alzheimer’s disease. Neurobiology of Aging, 18(Suppl.4), 85–88. https://dx.doi.org/10.1016/s0197-4580(97)00062-6Google Scholar

Brickman, A. M., Siedlecki, K. L., Muraskin, J., et al. (2011). White matter hyperintensities and cognition: Testing the reserve hypothesis. Neurobiology of Aging, 32(9), 1588–1598. https://dx.doi.org/10.1016/j.neurobiolaging.2009.10.013Google Scholar

Colangeli, S., Boccia, M., Verde, P., et al. (2016). Cognitive reserve in healthy aging and Alzheimer’s disease: A meta-analysis of fMRI studies. American Journal of Alzheimer’s Disease and Other Dementias, 31(5), 443–449. https://dx.doi.org/10.1177/1533317516653826Google Scholar

Franzmeier, N., Buerger, K., Teipel, S., et al. (2017a). Cognitive reserve moderates the association between functional network anti-correlations and memory in MCI. Neurobiology of Aging, 50, 152–162. https://dx.doi.org/10.1016/j.neurobiolaging.2016.11.013Google Scholar

Franzmeier, N., Caballero, M. A. A., Taylor, A. N. W., et al. (2017b). Resting-state global functional connectivity as a biomarker of cognitive reserve in mild cognitive impairment. Brain Imaging and Behavior, 11(2), 368–382. https://dx.doi.org/10.1007/s11682-016-9599-1Google Scholar

Gallaway, P. J., Miyake, H., Buchowski, M. S., et al. (2017). Physical activity: A viable way to reduce the risks of mild cognitive impairment, Alzheimer’s disease, and vascular dementia in older adults. Brain Sciences, 7(2), 22. https://dx.doi.org/10.3390/brainsci7020022Google Scholar

Giogkaraki, E., Michaelides, M. P., & Constantinidou, F. (2013). The role of cognitive reserve in cognitive aging: Results from the neurocognitive study on aging. Journal of Clinical and Experimental Neuropsychology, 35(10), 1024–1035. https://dx.doi.org/10.1080/13803395.2013.847906Google Scholar

Guzzetti, S., & Daini, R. (2014). Inter-hemispheric recruitment as a function of task complexity, age and cognitive reserve. Aging, Neuropsychology, and Cognition, 21(6), 722–745. https://dx.doi.org/10.1080/13825585.2013.874522Google Scholar

Hall, C. B., Derby, C., LeValley, A., et al. (2007). Education delays accelerated decline on a memory test in persons who develop dementia. Neurology, 69(17), 1657–1664. https://dx.doi.org/10.1212/01.wnl.0000278163.82636.30Google Scholar

Hall, C. B., Lipton, R. B., Sliwinski, M., et al. (2009). Cognitive activities delay onset of memory decline in persons who develop dementia. Neurology, 73(5), 356–361. https://dx.doi.org/10.1212/WNL.0b013e3181b04ae3Google Scholar

Hanyu, H., Sato, T., Shimizu, S., et al. (2008). The effect of education on rCBF changes in Alzheimer’s disease: A longitudinal SPECT study. European Journal of Nuclear Medicine and Molecular Imaging, 35(12), 2182–2190. https://dx.doi.org/10.1007/s00259-008-0848-4Google Scholar

Hultsch, D. F., Hertzog, C., Small, B. J., & Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14(2), 245–263. https://dx.doi.org/10.1037/0882-7974.14.2.245Google Scholar

Jefferson, A. L., Gibbons, L. E., Rentz, D. M., et al. (2011). A life course model of cognitive activities, socioeconomic status, education, reading ability, and cognition. Journal of the American Geriatric Society, 59(8), 1403–1411. https://dx.doi.org/10.1111/j.1532-5415.2011.03499.xGoogle Scholar

Katzman, R. (1993). Education and the prevalence of dementia and Alzheimer’s disease. Neurology, 43(1), 13–20. https://dx.doi.org/10.1212/wnl.43.1_part_1.13Google Scholar

Katzman, R., Terry, R., DeTeresa, R., et al. (1988). Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques. Annals of Neurology, 23(2), 138–144. https://dx.doi.org/10.1002/ana.410230206Google Scholar

Kemppainen, N. M., Aalto, S., Karrasch, M., 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. https://dx.doi.org/10.1002/ana.21212Google Scholar

Kennedy, G., Hardman, R. J., Macpherson, H., Scholey, A. B., & Pipingas, A. (2017). How does exercise reduce the rate of age-associated cognitive decline? A review of potential mechanisms. Journal of Alzheimer’s Disease, 55(1), 1–18. https://dx.doi.org/10.3233/JAD-160665Google Scholar

Mortimer, J. A., Snowdon, D. A., & Markesbery, W. R. (2003). Head circumference, education and risk of dementia: Findings from the Nun Study. Journal of Clinical and Experimental Neuropsychology, 25(5), 671–679. https://dx.doi.org/10.1076/jcen.25.5.671.14584Google Scholar

Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Backman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292–305. https://dx.doi.org/10.1016/j.tics.2012.04.005Google Scholar

Oh, H., Razlighi, Q., Gazes, Y., Habeck, C., & Stern, Y. (2016). Protective mechanisms of cognitive reserve revealed by multimodal neuroimaging markers. Alzheimer’s and Dementia, 12(7), 81–82. https://doi.org/10.1016/j.jalz.2016.06.1941Google Scholar

Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. https://dx.doi.org/10.1146/annurev.psych.59.103006.093656Google Scholar

Puente, A. N., Lindbergh, C. A., & Miller, L. S. (2015). The relationship between cognitive reserve and functional ability is mediated by executive functioning in older adults. Clinical Neuropsychologist, 29(1), 67–81. https://dx.doi.org/10.1080/13854046.2015.1005676Google Scholar

Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355–370. https://dx.doi.org/10.1007/s11065-014-9270-9Google Scholar

Richards, M., & Sacker, A. (2003). Lifetime antecedents of cognitive reserve. Journal of Clinical and Experimental Neuropsychology, 25(5), 614–624. https://dx.doi.org/10.1076/jcen.25.5.614.14581Google Scholar

Roldan-Tapia, L., Garcia, J., Canovas, R., & Leon, I. (2012). Cognitive reserve, age, and their relation to attentional and executive functions. Applied Neuropsychology: Adult, 19(1), 2–8. https://dx.doi.org/10.1080/09084282.2011.595458Google Scholar

Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology, 7(3), 273–295. https://dx.doi.org/http://dx.doi.org/10.1037/0894-4105.7.3.273Google Scholar

Scarmeas, N., Levy, G., Tang, M. X., Manly, J., & Stern, Y. (2001). Influence of leisure activity on the incidence of Alzheimer’s disease. Neurology, 57(12), 2236–2242. https://dx.doi.org/10.1212/wnl.57.12.2236Google Scholar

Scarmeas, N., & Stern, Y. (2003). Cognitive reserve and lifestyle. Journal of Clinical and Experimental Neuropsychology, 25(5), 625–633. https://dx.doi.org/10.1076/jcen.25.5.625.14576Google Scholar

Scarmeas, N., Zarahn, E., Anderson, K. E., et al. (2003). Association of life activities with cerebral blood flow in Alzheimer disease: Implications for the cognitive reserve hypothesis. Archives of Neurology, 60(3), 359–365. https://dx.doi.org/10.1001/archneur.60.3.359Google Scholar

Schofield, P. W., Logroscino, G., Andrews, H. F., Albert, S., & Stern, Y. (1997). An association between head circumference and Alzheimer’s disease in a population-based study of aging and dementia. Neurology, 49(1), 30–37. https://dx.doi.org/10.1212/wnl.49.1.30Google Scholar

Snowdon, D. A. (1997). Aging and Alzheimer’s disease: Lessons from the Nun Study. Gerontologist, 37(2), 150–156. https://dx.doi.org/10.1093/geront/37.2.150Google Scholar

Snowdon, D. A., Greiner, L. H., & Markesbery, W. R. (2000). Linguistic ability in early life and the neuropathology of Alzheimer’s disease and cerebrovascular disease: Findings from the Nun Study. Annals of the New York Academy of Sciences, 903(1), 34–38. https://dx.doi.org/10.1111/j.1749-6632.2000.tb06347.xGoogle Scholar

Sole-Padulles, C., Bartres-Faz, D., Junque, 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. https://dx.doi.org/10.1016/j.neurobiolaging.2007.10.008Google Scholar

Speer, M. E., & Soldan, A. (2015). Cognitive reserve modulates ERPs associated with verbal working memory in healthy younger and older adults. Neurobiology of Aging, 36(3), 1424–1434. https://dx.doi.org/10.1016/j.neurobiolaging.2014.12.025Google Scholar

Staff, R. T., Murray, A. D., Deary, I. J., & Whalley, L. J. (2004). What provides cerebral reserve? Brain, 127(5), 1191–1199. https://dx.doi.org/10.1093/brain/awh144Google Scholar

Steffener, J., Reuben, A., Rakitin, B. C., & Stern, Y. (2011). Supporting performance in the face of age-related neural changes: Testing mechanistic roles of cognitive reserve. Brain Imaging and Behavior, 5(3), 212–221. https://dx.doi.org/10.1007/s11682-011-9125-4Google Scholar

Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–460. https://dx.doi.org/https://doi.org/10.1017/S1355617702813248Google Scholar

Stern, Y., Albert, S., Tang, M. X., & Tsai, W. Y. (1999). Rate of memory decline in AD is related to education and occupation: Cognitive reserve? Neurology, 53(9), 1942–1947. https://dx.doi.org/10.1212/wnl.53.9.1942Google Scholar

Stern, Y., Alexander, G. E., Prohovnik, I., & Mayeux, R. (1992). Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Annals of Neurology, 32(3), 371–375. https://dx.doi.org/10.1002/ana.410320311Google Scholar

Stern, Y., Gazes, Y., Razlighi, Q., Steffener, J., & Habeck, C. (2018). A task-invariant cognitive reserve network. NeuroImage, 178, 36–45. https://dx.doi.org/10.1016/j.neuroimage.2018.05.033Google Scholar

Stern, Y., Gurland, B., Tatemichi, T. K., et al. (1994). Influence of education and occupation on the incidence of Alzheimer’s disease. JAMA, 271(13), 1004–1010. https://dx.doi.org/10.1001/jama.1994.03510370056032Google Scholar

Stern, Y., Tang, M. X., Denaro, J., & Mayeux, R. (1995). Increased risk of mortality in Alzheimer’s disease patients with more advanced educational and occupational attainment. Annals of Neurology, 37(5), 590–595.Google Scholar

Wilson, R. S., Mendes de Leon, C. F., Barnes, L. L., et al. (2002). Participation in cognitively stimulating activities and risk of incident Alzheimer disease. JAMA, 287(6), 742–748. https://dx.doi.org/10.1001/jama.287.6.742Google Scholar

Xu, W., Tan, L., Wang, H. F., et al. (2016). Education and risk of dementia: Dose-response meta-analysis of prospective cohort studies. Molecular Neurobiology, 53(5), 3113–3123. https://dx.doi.org/10.1007/s12035-015-9211-5Google Scholar

Zahodne, L. B., Glymour, M. M., Sparks, C., et al. (2011). Education does not slow cognitive decline with aging: 12-year evidence from the Victoria Longitudinal Study. Journal of the International Neuropsychological Society, 17(6), 1039–1046. https://dx.doi.org/10.1017/S1355617711001044Google Scholar

Zahodne, L. B., Stern, Y., & Manly, J. J. (2015). Differing effects of education on cognitive decline in diverse elders with low versus high educational attainment. Neuropsychology, 29(4), 649–657. https://dx.doi.org/10.1037/neu0000141Google Scholar

Abel, K. M., Wicks, S., Susser, E. S., et al. (2010). Birth weight, schizophrenia, and adult mental disorder: Is risk confined to the smallest babies? Archives of General Psychiatry, 67(9), 923–930. http://dx.doi.org/10.1001/archgenpsychiatry.2010.100Google Scholar

Bale, T. L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nature Reviews Neuroscience, 16(6), 332–344. http://dx.doi.org/10.1038/nrn3818Google Scholar

Baltes, P. B., & Kliegl, R. (1992). Further testing of limits of cognitive plasticity: Negative age differences in a mnemonic skill are robust. Developmental Psychology, 28(1), 121–125. http://dx.doi.org/10.1037/0012-1649.28.1.121Google Scholar

Barnes, S. K., & Ozanne, S. E. (2011). Pathways linking the early environment to long-term health and lifespan. Progress in Biophysics and Molecular Biology, 106(1), 323–336. http://dx.doi.org/10.1016/j.pbiomolbio.2010.12.005Google Scholar

Boyke, J., Driemeyer, J., Gaser, C., Büchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28(28), 7031–7035. http://dx.doi.org/10.1523/JNEUROSCI.0742-08.2008Google Scholar

Bratsberg, B., & Rogeberg, O. (2018). Flynn effect and its reversal are both environmentally caused. Proceedings of the National Academy of Sciences USA, 115(26), 6674–6678. http://dx.doi.org/10.1073/pnas.1718793115Google Scholar

Brehmer, Y., Kalpouzos, G., Wenger, E., & Lövdén, M. (2014). Plasticity of brain and cognition in older adults. Psychological Research, 78(6), 790–802. http://dx.doi.org/10.1007/s00426-014-0587-zGoogle Scholar

Brehmer, Y., Li, S. C., Müller, V., von Oertzen, T., & Lindenberger, U. (2007). Memory plasticity across the life span: Uncovering children’s latent potential. Developmental Psychology, 43(2), 465–478. http://dx.doi.org/10.1037/0012-1649.43.2.465Google Scholar

Brehmer, Y., Shing, Y. L., Heekeren, H. R., Lindenberger, U., & Bäckman, L. (2016). Training-induced changes in subsequent-memory effects: No major differences among children, younger adults, and older adults. NeuroImage, 131, 214–225. http://dx.doi.org/10.1016/j.neuroimage.2015.11.074Google Scholar

Brehmer, Y., Westerberg, H., & Bäckman, L. (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6, p. 63. http://dx.doi.org/10.3389/fnhum.2012.00063Google Scholar

Bürki, C. N., Ludwig, C., Chicherio, C., & de Ribaupierre, A. (2014). Individual differences in cognitive plasticity: An investigation of training curves in younger and older adults. Psychological Research, 78(6), 821–835. http://dx.doi.org/10.1007/s00426-014-0559-3Google Scholar

Carretti, B., Borella, E., & De Beni, R. (2007). Does strategic memory training improve the working memory performance of younger and older adults? Experimental Psychology, 54(4), 311–320. http://dx.doi.org/10.1027/1618-3169.54.4.311Google Scholar

Chang, L., Douet, V., Bloss, C., et al. (2016). Gray matter maturation and cognition in children with different APOE ε genotypes. Neurology, 87(6), 585–594. http://dx.doi.org/10.1212/WNL.0000000000002939Google Scholar

Colom, R., Lluis-Font, J. M., & Andrés-Pueyo, A. (2005). The generational intelligence gains are caused by decreasing variance in the lower half of the distribution: Supporting evidence for the nutrition hypothesis. Intelligence, 33(1), 83–91. http://dx.doi.org/10.1016/j.intell.2004.07.010Google Scholar

Corder, E. H., Saunders, A. M., Strittmatter, W. J., et al. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261(5123), 921–923. http://dx.doi.org/10.1126/science.8346443Google Scholar

Dahlin, E., Nyberg, L., Bäckman, L., & Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: Immediate training gains, transfer, and long-term maintenance. Psychology and Aging, 23(4), 720–730. http://dx.doi.org/10.1037/a0014296Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Grydeland, H., et al. (2016). White matter integrity as a marker for cognitive plasticity in aging. Neurobiology of Aging, 47, 74–82. http://dx.doi.org/10.1016/j.neurobiolaging.2016.07.007Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Rohani, D. A., et al. (2017). The effects of memory training on behavioral and microstructural plasticity in young and older adults. Human Brain Mapping, 38(11), 5666–5680. http://dx.doi.org/10.1002/hbm.23756Google Scholar

de Lange, A. M. G., Bråthen, A. C. S., Rohani, D. A., Fjell, A. M., & Walhovd, K. B. (2018). The temporal dynamics of brain plasticity in aging. Cerebral Cortex, 28(5), 1857–1865. http://dx.doi.org/10.1093/cercor/bhy003Google Scholar

Deary, I. J., Pattie, A., & Starr, J. M. (2013). The stability of intelligence from age 11 to age 90 years: The Lothian birth cohort of 1921. Psychological Science, 24(12), 2361–2368. http://dx.doi.org/10.1177/0956797613486487Google Scholar

Dekaban, A. S., & Sadowsky, D. (1978). Changes in brain weights during the span of human life: Relation of brain weights to body heights and body weights. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 4(4), 345–356. https://doi.org/10.1002/ana.410040410Google Scholar

Draganski, B., Gaser, C., Busch, V., et al. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311–312. http://dx.doi.org/10.1038/427311aGoogle Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage, 52(4), 1667–1676. http://dx.doi.org/10.1016/j.neuroimage.2010.05.041Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2012a). Memory training impacts short‐term changes in aging white matter: A longitudinal diffusion tensor imaging study. Human Brain Mapping, 33(10), 2390–2406. http://dx.doi.org/10.1002/hbm.21370Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2012b). Hippocampal subfield volumes correlate with memory training benefit in subjective memory impairment. NeuroImage, 61(1), 188–194. http://dx.doi.org/10.1016/j.neuroimage.2012.02.072Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., et al. (2014). Effects of cognitive training on gray matter volumes in memory clinic patients with subjective memory impairment. Journal of Alzheimer’s Disease, 41(3), 779–791. http://dx.doi.org/10.3233/JAD-131889Google Scholar

Fjell, A. M., Grydeland, H., Krogsrud, S. K., et al. (2015). Development and aging of cortical thickness correspond to genetic organization patterns. Proceedings of the National Academy of Sciences USA, 112(50), 15462–15467. http://dx.doi.org/10.1073/pnas.1508831112Google Scholar

Fjell, A. M., & Walhovd, K. B. (2010). Structural brain changes in aging: Courses, causes and cognitive consequences. Reviews in the Neurosciences, 21(3), 187–222. https://doi.org/10.1515/REVNEURO.2010.21.3.187Google Scholar

Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171–191. http://dx.doi.org/10.1037/0033-2909.101.2.171Google Scholar

Grady, C. (2012). The cognitive neuroscience of ageing. Nature Reviews Neuroscience, 13(7), 491. http://dx.doi.org/10.1038/nrn3256Google Scholar

Haukvik, U. K., Rimol, L. M., Roddey, J. C., et al. (2014). Normal birth weight variation is related to cortical morphology across the psychosis spectrum. Schizophrenia Bulletin, 40(2), 410–419. http://dx.doi.org/10.1093/schbul/sbt005Google Scholar

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. http://dx.doi.org/10.1093/cercor/bhs231Google Scholar

Hülür, G., Ram, N., Willis, S. L., Schaie, K. W., & Gerstorf, D. (2015). Cognitive dedifferentiation with increasing age and proximity of death: Within-person evidence from the Seattle Longitudinal Study. Psychology and Aging, 30(2), 311–323. http://dx.doi.org/10.1037/a0039260Google Scholar

Jack, C. R., Bennett, D. A., Blennow, K., et al. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s and Dementia, 14(4), 535–562. http://dx.doi.org/10.1016/j.jalz.2018.02.018Google Scholar

Jagust, W. J. (2016). Early life sets the stage for aging. Proceedings of the National Academy of Sciences USA, 113(33), 9148–9150. http://dx.doi.org/10.1073/pnas.1609720113Google Scholar

Karama, S., Bastin, M. E., Murray, C., et al. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(5), 555–559. http://dx.doi.org/10.1038/mp.2013.64Google Scholar

Khan, W., Giampietro, V., Banaschewski, T., et al. (2017). A multi-cohort study of ApoE ɛ4 and Amyloid-β effects on the hippocampus in Alzheimer’s disease. Journal of Alzheimer’s Disease, 56(3), 1159–1174. http://dx.doi.org/10.3233/JAD-161097Google Scholar

Kivipelto, M., Solomon, A., Ahtiluoto, S., et al. (2013). The Finnish geriatric intervention study to prevent cognitive impairment and disability (FINGER): Study design and progress. Alzheimer’s and Dementia, 9(6), 657–665. http://dx.doi.org/10.1016/j.jalz.2012.09.012Google Scholar

Kliegl, R., Smith, J., & Baltes, P. B. (1990). On the locus and process of magnification of age differences during mnemonic training. Developmental Psychology, 26(6), 894–904. http://dx.doi.org/10.1037/0012-1649.26.6.894Google Scholar

Knickmeyer, R. C., Wang, J., Zhu, H., et al. (2013). Common variants in psychiatric risk genes predict brain structure at birth. Cerebral Cortex, 24(5), 1230–1246. http://dx.doi.org/10.1093/cercor/bhs401Google Scholar

Kovacs, G. G., Adle-Biassette, H., Milenkovic, I., et al. (2014). Linking pathways in the developing and aging brain with neurodegeneration. Neuroscience, 269, 152–172. http://dx.doi.org/10.1016/j.neuroscience.2014.03.045Google Scholar

Livingston, G., Sommerlad, A., Orgeta, V., et al. (2017). Dementia prevention, intervention, and care. Lancet, 390(10113), 2673–2734. http://dx.doi.org/10.1016/S0140-6736(17)31363-6Google Scholar

Lövdén, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010a). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659–676. http://dx.doi.org/10.1037/a0020080Google Scholar

Lövdén, M., Bodammer, N. C., Kühn, S., et al. (2010b). Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia, 48(13), 3878–3883. http://dx.doi.org/10.1016/j.neuropsychologia.2010.08.026Google Scholar

Lövdén, M., Schaefer, S., Noack, H., et al. (2012). Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiology of Aging, 33(3), 620e9–620e22. http://dx.doi.org/10.1016/j.neurobiolaging.2011.02.013Google Scholar

Lyall, A. E., Shi, F., Geng, X., et al. (2015). Dynamic development of regional cortical thickness and surface area in early childhood. Cerebral Cortex, 25(8), 2204–2212. http://dx.doi.org/10.1093/cercor/bhu027Google Scholar

Lyons, M. J., York, T. P., Franz, C. E., et al. (2009). Genes determine stability and the environment determines change in cognitive ability during 35 years of adulthood. Psychological Science, 20(9), 1146–1152. http://dx.doi.org/10.1111/j.1467-9280.2009.02425.xGoogle Scholar

Melka, M. G., Gillis, J., Bernard, M., et al. (2013). FTO, obesity and the adolescent brain. Human Molecular Genetics, 22(5), 1050–1058. http://dx.doi.org/10.1093/hmg/dds504Google Scholar

Mosing, M. A., Madison, G., Pedersen, N. L., & Ullén, F. (2016). Investigating cognitive transfer within the framework of music practice: Genetic pleiotropy rather than causality. Developmental Science, 19(3), 504–512. http://dx.doi.org/10.1111/desc.12306Google Scholar

Muller, M., Sigurdsson, S., Kjartansson, O., et al. (2014). Birth size and brain function 75 years later. Pediatrics, 134(4), 761–770. http://dx.doi.org/10.1542/peds.2014-1108Google Scholar

Ngandu, T., Lehtisalo, J., Solomon, A., et al. (2015). A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet, 385(9984), 2255–2263. http://dx.doi.org/10.1016/S0140-6736(15)60461-5Google Scholar

Nyberg, L., & Pudas, S. (2019). Successful memory aging. Annual Review of Psychology, 70, 219–243. http://dx.doi.org/10.1146/annurev-psych-010418-103052Google Scholar

Nyberg, L., Sandblom, J., Jones, S., et al. (2003). Neural correlates of training-related memory improvement in adulthood and aging. Proceedings of the National Academy of Sciences USA, 100(23), 13728–13733. http://dx.doi.org/10.1073/pnas.1735487100Google Scholar

Pietschnig, J., & Voracek, M. (2015). One century of global IQ gains: A formal meta-analysis of the Flynn effect (1909–2013). Perspectives on Psychological Science, 10(3), 282–306. http://dx.doi.org/10.1177/1745691615577701Google Scholar

Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010). Trajectories of brain aging in middle-aged and older adults: Regional and individual differences. NeuroImage, 51(2), 501–511. http://dx.doi.org/10.1016/j.neuroimage.2010.03.020Google Scholar

Raz, N., Lindenberger, U., Rodrigue, K. M., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. http://dx.doi.org/10.1093/cercor/bhi044Google Scholar

Raznahan, A., Greenstein, D., Lee, N. R., Clasen, L. S., & Giedd, J. N. (2012). Prenatal growth in humans and postnatal brain maturation into late adolescence. Proceedings of the National Academy of Sciences USA, 109(28), 11366–11371. http://dx.doi.org/10.1073/pnas.1203350109Google Scholar

Reitz, C., Tosto, G., Mayeux, R., & Luchsinger, J. A. (2012). Genetic variants in the fat and obesity associated (FTO) gene and risk of Alzheimer’s disease. PLoS One, 7(12), e50354. http://dx.doi.org/10.1371/journal.pone.0050354Google Scholar

Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain. Journal of Neuroscience, 23(8), 3295–3301. http://dx.doi.org/10.1523/JNEUROSCI.23-08-03295.2003Google Scholar

Salthouse, T. A. (2016). Continuity of cognitive change across adulthood. Psychonomic Bulletin and Review, 23(3), 932–939. http://dx.doi.org/10.3758/s13423-015-0910-8Google Scholar

Schaie, K. W. (1994). The course of adult intellectual development. American Psychologist, 49(4), 304–313. http://dx.doi.org/10.1037/0003-066X.49.4.304Google Scholar

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, p. 27. http://dx.doi.org/10.3389/fnagi.2010.00027Google Scholar

Scholz, J., Klein, M. C., Behrens, T. E., & Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature Neuroscience, 12(11), 1370–1371. http://dx.doi.org/10.1038/nn.2412Google Scholar

Sexton, C. E., Walhovd, K. B., Storsve, A. B., et al. (2014). Accelerated changes in white matter microstructure during aging: A longitudinal diffusion tensor imaging study. Journal of Neuroscience, 34(46), 15425–15436. http://dx.doi.org/10.1523/JNEUROSCI.0203-14.2014Google Scholar

Smith, A. D., Smith, S. M., De Jager, C. A., et al. (2010). Homocysteine-lowering by B vitamins slows the rate of accelerated brain atrophy in mild cognitive impairment: A randomized controlled trial. PLoS One, 5(9), e12244. http://dx.doi.org/10.1371/journal.pone.0012244Google Scholar

Storsve, A. B., Fjell, A. M., Tamnes, C. K., et al. (2014). Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. Journal of Neuroscience, 34(25), 8488–8498. http://dx.doi.org/10.1523/JNEUROSCI.0391-14.2014Google Scholar

Thompson, P. M. (2015). Cracking the brain’s genetic code. Proceedings of the National Academy of Sciences USA, 112(50), 15269–15270. http://dx.doi.org/10.1073/pnas.1520702112Google Scholar

Tucker-Drob, E. M. (2011). Global and domain-specific changes in cognition throughout adulthood. Developmental Psychology, 47(2), 331–343. http://dx.doi.org/10.1037/a0021361Google Scholar

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. http://dx.doi.org/10.1037/bul0000179Google Scholar

Vemuri, P., Lesnick, T. G., Przybelski, S. A., et al. (2012). Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Annals of Neurology, 72(5), 730–738. http://dx.doi.org/10.1002/ana.23665Google Scholar

Walhovd, K. B., Bjørnebekk, A., Haabrekke, K., et al. (2015). Child neuroanatomical, neurocognitive, and visual acuity outcomes with maternal opioid and polysubstance detoxification. Pediatric Neurology, 52(3), 326–332. http://dx.doi.org/10.1016/j.pediatrneurol.2014.11.008Google Scholar

Walhovd, K. B., Fjell, A. M., Brown, T. T., et al. (2012a). Long-term influence of normal variation in neonatal characteristics on human brain development. Proceedings of the National Academy of Sciences USA, 109(49), 20089–20094. http://dx.doi.org/10.1073/pnas.1208180109Google Scholar

Walhovd, K. B., Fjell, A. M., & Espeseth, T. (2014a). Cognitive decline and brain pathology in aging – need for a dimensional, lifespan and systems vulnerability view. Scandinavian Journal of Psychology, 55(3), 244–254. http://dx.doi.org/10.1111/sjop.12120Google Scholar

Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., et al. (2016). Neurodevelopmental origins of lifespan changes in brain and cognition. Proceedings of the National Academy of Sciences USA, 113(33), 9357–9362. http://dx.doi.org/10.1073/pnas.1524259113Google Scholar

Walhovd, K. B., Moe, V., Slinning, K., et al. (2007). Volumetric cerebral characteristics of children exposed to opiates and other substances in utero. NeuroImage, 36(4), 1331–1344. http://dx.doi.org/10.1016/j.neuroimage.2007.03.070Google Scholar

Walhovd, K. B., Moe, V., Slinning, K., et al. (2009). Effects of prenatal opiate exposure on brain development – a call for attention. Nature Reviews Neuroscience, 10(5), 390. http://dx.doi.org/10.1038/nrn2598-c1Google Scholar

Walhovd, K. B., Storsve, A. B., Westlye, L. T., et al. (2014b). Blood markers of fatty acids and vitamin D, cardiovascular measures, body mass index, and physical activity relate to longitudinal cortical thinning in normal aging. Neurobiology of Aging, 35(5), 1055–1064. http://dx.doi.org/10.1016/j.neurobiolaging.2013.11.011Google Scholar

Walhovd, K. B., Tamnes, C. K., Østby, Y., Due-Tønnessen, P., & Fjell, A. M. (2012b). Normal variation in behavioral adjustment relates to regional differences in cortical thickness in children. European Child and Adolescent Psychiatry, 21(3), 133–140. http://dx.doi.org/10.1007/s00787-012-0241-5Google Scholar

Walhovd, K. B., Watts, R., Amlien, I., & Woodward, L. J. (2012c). Neural tract development of infants born to methadone-maintained mothers. Pediatric Neurology, 47(1), 1–6. http://dx.doi.org/10.1016/j.pediatrneurol.2012.04.008CrossRefGoogle ScholarPubMed

Wenger, E., Schaefer, S., Noack, H., et al. (2012). Cortical thickness changes following spatial navigation training in adulthood and aging. NeuroImage, 59(4), 3389–3397. http://dx.doi.org/10.1016/j.neuroimage.2011.11.015Google Scholar

Westlye, L. T., Reinvang, I., Rootwelt, H., & Espeseth, T. (2012). Effects of APOE on brain white matter microstructure in healthy adults. Neurology, 79(19), 1961–1969. http://dx.doi.org/10.1212/WNL.0b013e3182735c9cGoogle Scholar

Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15(4), 528–536. http://dx.doi.org/10.1038/nn.3045Google Scholar

Adams, M. M., Smith, T. D., Moga, D., et al. (2001). Hippocampal dependent learning ability correlates with N-methyl-D-aspartate (NMDA) receptor levels in CA3 neurons of young and aged rats. Journal of Computational Neurology, 432(2), 230–243. https://doi.org/10.1002/cne.1099Google Scholar

Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. https://doi.org/10.1016/j.tics.2013.08.012Google Scholar

Blalock, E. M., Chen, K. C., Sharrow, K., et al. (2003). Gene microarrays in hippocampal aging: Statistical profiling identifies novel processes correlated with cognitive impairment. Journal of Neuroscience, 23(9), 3807–3819. https://dx.doi.org/10.1523/JNEUROSCI.23-09-03807.2003Google Scholar

Boric, K., Munoz, P., Gallagher, M., & Kirkwood, A. (2008). Potential adaptive function for altered long-term potentiation mechanisms in aging hippocampus. Journal of Neuroscience, 28(32), 8034–8039. https://dx.doi.org/10.1523/JNEUROSCI.2036-08.2008Google Scholar

Bories, C., Husson, Z., Guitton, M. J., & De Koninck, Y. (2013). Differential balance of prefrontal synaptic activity in successful versus unsuccessful cognitive aging. Journal of Neuroscience, 33(4), 1344–1356. https://dx.doi.org/10.1523/JNEUROSCI.3258-12.2013Google Scholar

Burger, C. (2010). Region-specific genetic alterations in the aging hippocampus: Implications for cognitive aging. Frontiers in Aging Neuroscience, 2, P. 140. https://doi.org/10.3389/fnagi.2010.00140.Google Scholar

Burger, C., Lopez, M. C., Baker, H. V., Mandel, R. J., & Muzyczka, N. (2008). Genome-wide analysis of aging and learning-related genes in the hippocampal dentate gyrus. Neurobiology of Learning and Memory, 89(4), 379–396. https://doi.org/10.3389/fnagi.2010.00140Google Scholar

Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 7(1), 30–40. https://doi.org/10.1038/nrn1809Google Scholar

Burwell, R. D., & Gallagher, M. (1993). A longitudinal study of reaction time performance in Long-Evans rats. Neurobiology of Aging, 14(1), 57–64. https://doi.org/10.1016/0197-4580(93)90023-5Google Scholar

Cabeza, R., & Dennis, N. A. (2013). Frontal lobes and aging: Deterioration and compensation. In Stuss, D. T. & Knight, R. T. (Eds.), Principles of frontal lobe function (pp. 628–652). Oxford: Oxford University Press.Google Scholar

Castellano, J. F., Fletcher, B. R., Kelley-Bell, et al. (2012). Age-related memory impairment is associated with disrupted multivariate epigenetic coordination in the hippocampus. PLoS One, 7(3), e33249. https://doi.org/10.1371/journal.pone.0033249Google Scholar

Fabiani, M. (2012). It was the best of times, it was the worst of times: A psychophysiologist’s view of cognitive aging. Psychophysiology, 49(3), 283–304. https://doi.org/10.1111/j.1469-8986.2011.01331.xGoogle Scholar

Fletcher, B. R., Hill, G. S., Long, J. M., et al. (2014). A fine balance: Regulation of hippocampal Arc/Arg3.1 transcription, translation and degradation in a rat model of normal cognitive aging. Neurobiology of Learning and Memory, 115, 58–67. https://doi.org/10.1016/j.nlm.2014.08.007Google Scholar

Fletcher, B. R., & Rapp, P. R. (2013). Normal neurocognitive aging. In Weiner, I. B., Nelson, R. J., & Mizumori, S. J. Y. (Eds.), Handbook of psychology (2nd ed., Vol. 3, pp. 643–664). Hoboken: Wiley & Sons.Google Scholar

Freund, T. F., & Buzsaki, G. (1996). Interneurons of the hippocampus. Hippocampus, 6(4), 347–470. https://dx.doi.org/10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-IGoogle Scholar

Freund, T. F., & Gulyas, A. I. (1997). Inhibitory control of GABAergic interneurons in the hippocampus. Canadian Journal of Physiology and Pharmacology, 75(5), 479–487. https://doi.org/10.1139/y97-033Google Scholar

Geinisman, Y., Ganeshina, O., Yoshida, R., et al. (2004). Aging, spatial learning, and total synapse number in the rat CA1 stratum radiatum. Neurobiology of Aging, 25(3), 407–416. https://doi.org/10.1016/j.neurobiolaging.2003.12.001Google Scholar

Gray, D. T., & Barnes, C. A. (2015). Distinguishing adaptive plasticity from vulnerability in the aging hippocampus. Neuroscience, 309, 17–28. https://doi.org/10.1016/j.neuroscience.2015.08.001Google Scholar

Gutchess, A. (2014). Plasticity of the aging brain: New directions in cognitive neuroscience. Science, 346(6209), 579–582. https://dx.doi.org/10.1126/science.1254604Google Scholar

Guzowski, J. F., Lyford, G. L., Stevenson, G. D., et al. (2000). Inhibition of activity-dependent arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and the consolidation of long-term memory. Journal of Neuroscience, 20(11), 3993–4001. https://dx.doi.org/10.1523/jneurosci.20-11-03993.2000Google Scholar

Haberman, R. P., Colantuoni, C., Stocker, A. M., et al. (2011). Prominent hippocampal CA3 gene expression profile in neurocognitive aging. Neurobiology of Aging, 32(9), 1678–1692. https://dx.doi.org/10.1016/j.neurobiolaging.2009.10.005Google Scholar

Haberman, R. P., Koh, M. T., & Gallagher, M. (2017). Heightened cortical excitability in aged rodents with memory impairment. Neurobiology of Aging, 54, 144–151. https://doi.org/10.1016/j.neurobiolaging.2016.12.021Google Scholar

Haberman, R. P., Quigley, C. K., & Gallagher, M. (2012). Characterization of CpG island DNA methylation of impairment-related genes in a rat model of cognitive aging. Epigenetics, 7(9), 1008–1019. https://doi.org/10.4161/epi.21291Google Scholar

Hara, Y., Punsoni, M., Yuk, F., et al. (2012). Synaptic distributions of GluA2 and PKMzeta in the monkey dentate gyrus and their relationships with aging and memory. Journal of Neuroscience, 32(21), 7336–7344. https://doi.org/10.1523/JNEUROSCI.0605-12.2012Google Scholar

Hernandez, A. R., Reasor, J. E., Truckenbrod, L. M., et al. (2017). Medial prefrontal-perirhinal cortical communication is necessary for flexible response selection. Neurobiology of Learning and Memory, 137, 36–47. https://dx.doi.org/10.1016/j.nlm.2016.10.012Google Scholar

Hernandez, A. R., Reasor, J. E., Truckenbrod, L. M., et al. (2018). Dissociable effects of advanced age on prefrontal cortical and medial temporal lobe ensemble activity. Neurobiology of Aging, 70, 217–232. https://doi.org/10.1016/j.neurobiolaging.2018.06.028Google Scholar

Ianov, L., De Both, M., Chawla, M. K., et al. (2017). Hippocampal transcriptomic profiles: Subfield vulnerability to age and cognitive impairment. Frontiers in Aging Neuroscience, 9, p. 383. https://dx.doi.org/10.3389/fnagi.2017.00383Google Scholar

Ianov, L., Rani, A., Beas, B. S., Kumar, A., & Foster, T. C. (2016). Transcription profile of aging and cognition-related genes in the medial prefrontal cortex. Frontiers in Aging Neuroscience, 8, p. 113. https://dx.doi.org/10.3389/fnagi.2016.00113Google Scholar

Jeune, H. L., Cécyre, D., Rowe, W., Meaney, M. J., & Quirion, R. (1996). Ionotropic glutamate receptor subtypes in the aged memory-impaired and unimpaired Long–Evans rat. Neuroscience, 74(2), 349–363. https://dx.doi.org/10.1016/0306-4522(96)00213-8Google Scholar

Kaeberlein, M., Rabinovitch, P. S., & Martin, G. M. (2015). Healthy aging: The ultimate preventative medicine. Science, 350(6265), 1191–1193. https://dx.doi.org/10.1126/science.aad3267Google Scholar

Lee, H. K., Min, S. S., Gallagher, M., & Kirkwood, A. (2005). NMDA receptor-independent long-term depression correlates with successful aging in rats. Nature Neuroscience, 8(12), 1657–1659. https://dx.doi.org/10.1038/nn1586Google Scholar

Lindenberger, U. (2014). Human cognitive aging: Corriger la fortune? Science, 346(6209), 572–578. https://dx.doi.org/10.1126/science.1254403Google Scholar

McQuail, J. A., Johnson, S. A., Burke, S. N., & Bizon, J. L. (2018). Rat models of cognitive aging. In Ram, J. and Conn, P.M. (Eds.), Conn’s handbook of models for human aging (2nd ed., pp. 211–230). Cambridge, MA: Academic Press.Google Scholar

Meunier, D., Stamatakis, E. A., & Tyler, L. K. (2014). Age-related functional reorganization, structural changes, and preserved cognition. Neurobiology of Aging, 35(1), 42–54. https://doi.org/10.1016/j.neurobiolaging.2013.07.003Google Scholar

Migues, P. V., Hardt, O., Wu, D. C., et al. (2010). PKMzeta maintains memories by regulating GluR2-dependent AMPA receptor trafficking. Nature Neuroscience, 13(5), 630–634. https://dx.doi.org/10.1038/nn.2531Google Scholar

Morrison, J. H., & Baxter, M. G. (2012). The ageing cortical synapse: Hallmarks and implications for cognitive decline. Nature Reviews Neuroscience, 13(4), 240–250. https://dx.doi.org/10.1038/nrn3200Google Scholar

Nicholson, D. A., Yoshida, R., Berry, R. W., Gallagher, M., & Geinisman, Y. (2004). Reduction in size of perforated postsynaptic densities in hippocampal axospinous synapses and age-related spatial learning impairments. Journal of Neuroscience, 24(35), 7648–7653. https://dx.doi.org/10.1523/JNEUROSCI.1725-04.2004Google Scholar

Pelleymounter, M., Beatty, G., & Gallagher, M. (1990). Hippocampal 3H-CPP binding and spatial learning deficits in aged rats. Psychobiology, 18(3), 298–304. https://dx.doi.org/10.3758/BF03327247Google Scholar

Rapp, P. R. (2009). Aging and memory in animals. In Squire, L. R. (Ed.), Encyclopedia of neuroscience (Vol. 1, pp. 167–174). Oxford: Academic Press.Google Scholar

Rapp, P. R., & Gallagher, M. (1996). Preserved neuron number in the hippocampus of aged rats with spatial learning deficits. Proceedings of the National Academy of Sciences USA, 93, 9926–9930. https://dx.doi.org/10.1073/pnas.93.18.9926Google Scholar

Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Sciences, 17(3), 177–182.Google Scholar

Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing discoveries about the aging mind. Current Opinion in Neurobiology, 15(2), 245–251. https://dx.doi.org/10.1111/j.1467-8721.2008.00570.xGoogle Scholar

Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355–370. https://dx.doi.org/10.1007/s11065-014-9270-9Google Scholar

Rogalski, E., Gefen, T., Mao, Q., et al. (2018). Cognitive trajectories and spectrum of neuropathology in SuperAgers: The first 10 cases. Hippocampus, 29(5), 458–467. https://dx.doi.org/10.1002/hipo.22828Google Scholar

Rosenzweig, E. S., & Barnes, C. A. (2003). Impact of aging on hippocampal function: Plasticity, network dynamics, and cognition. Progress in Neurobiology, 69(3), 143–179. https://doi.org/10.1016/S0301-0082(02)00126-0Google Scholar

Rowe, W. B., Blalock, E. M., Chen, K. C., et al. (2007). Hippocampal expression analyses reveal selective association of immediate-early, neuroenergetic, and myelinogenic pathways with cognitive impairment in aged rats. Journal of Neuroscience, 27(12), 3098–3110. https://dx.doi.org/10.1523/JNEUROSCI.4163-06.2007Google Scholar

Schulte, T., Muller-Oehring, E. M., Chanraud, S., et al. (2011). Age-related reorganization of functional networks for successful conflict resolution: A combined functional and structural MRI study. Neurobiology of Aging, 32(11), 2075–2090. https://doi.org/10.1016/j.neurobiolaging.2009.12.002Google Scholar

Shankar, S., Teyler, T. J., & Robbins, N. (1998). Aging differentially alters forms of long-term potentiation in rat hippocampal area CA1. Journal of Neurophysiology, 79(1), 334–341. https://doi.org/10.1152/jn.1998.79.1.334Google Scholar

Shepherd, J. D., Rumbaugh, G., Wu, J., et al. (2006). Arc/Arg3.1 mediates homeostatic synaptic scaling of AMPA receptors. Neuron, 52(3), 475–484. https://doi.org/10.1016/j.neuron.2006.08.034Google Scholar

Shetty, A. K., & Turner, D. A. (1998). Hippocampal interneurons expressing glutamic acid decarboxylase and calcium-binding proteins decrease with aging in Fischer 344 rats. Journal of Computational Neurology, 394(2), 252–269. https://doi.org/10.1002/(SICI)1096-9861(19980504)394:2<252::AID-CNE9>3.0.CO;2-1Google Scholar

Spiegel, A. M., Koh, M. T., Vogt, N. M., et al. (2013). Hilar interneuron vulnerability distinguishes aged rats with memory impairment. Journal of Computational Neurology, 521(15), 3508–3523. https://doi.org/10.1002/cne.23367Google Scholar

Spiegel, A. M., Perez, E. J., Long, J. M., Park, P., & Rapp, P. R. (2014a). Regionally selective decline in hippocampal somatostatin-immunoreactive neuron number in aged rhesus monkeys with memory impairment. Presented at the Society for Neuroscience Annual Meeting, Chicago, IL, November 19.Google Scholar

Spiegel, A. M., Sewal, A. S., & Rapp, P. R. (2014b). Epigenetic contributions to cognitive aging: Disentangling mindspan and lifespan. Learning and Memory, 21(10), 569–574. https://doi.org/10.1101/lm.033506.113Google Scholar

Stanley, D. P., & Shetty, A. K. (2004). Aging in the rat hippocampus is associated with widespread reductions in the number of glutamate decarboxylase-67 positive interneurons but not interneuron degeneration. Journal of Neurochemistry, 89(1), 204–216. https://dx.doi.org/10.1111/j.1471-4159.2004.02318.xGoogle Scholar

Staudinger, U. M. (1999). Older and wiser? Integrating results on the relationship between age and wisdom-related performance. International Journal of Behavioral Development, 23(3), 641–664. https://doi.org/10.1080/016502599383739Google Scholar

Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. https://doi.org/10.1016/j.neuropsychologia.2009.03.004Google Scholar

Tannenbaum, C., Mayo, N., & Ducharme, F. (2005). Older women’s health priorities and perceptions of care delivery: Results of the WOW health survey. Canadian Medical Association Journal, 173(2), 153–159. https://dx.doi.org/10.1503/cmaj.050059Google Scholar

Thome, A., Gray, D. T., Erickson, C. A., Lipa, P., & Barnes, C. A. (2016). Memory impairment in aged primates is associated with region-specific network dysfunction. Molecular Psychiatry, 21(9), 1257–1262. https://dx.doi.org/10.1038/mp.2015.160Google Scholar

Tomas Pereira, I., Gallagher, M., & Rapp, P. R. (2015). Head west or left, east or right: Interactions between memory systems in neurocognitive aging. Neurobiology of Aging, 36(11), 3067–3078. https://doi.org/10.1016/j.neurobiolaging.2015.07.024Google Scholar

Tran, T., Gallagher, M., & Kirkwood, A. (2018). Enhanced postsynaptic inhibitory strength in hippocampal principal cells in high-performing aged rats. Neurobiology of Aging 70, 92–101. https://doi.org/10.1016/j.neurobiolaging.2018.06.008Google Scholar

Verbitsky, M., Yonan, A. L., Malleret, G., et al. (2004). Altered hippocampal transcript profile accompanies an age-related spatial memory deficit in mice. Learning and Memory, 11(3), 253–260. https://doi.org/10.1101/lm.68204Google Scholar

Wagster, M. V., King, J. W., Resnick, S. M., & Rapp, P. R. (2012). The 87%. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 67(7), 739–740. https://doi.org/10.1093/gerona/gls140Google Scholar

Waung, M. W., Pfeiffer, B. E., Nosyreva, E. D., Ronesi, J. A., & Huber, K. M. (2008). Rapid translation of Arc/Arg3.1 selectively mediates mGluR-dependent LTD through persistent increases in AMPAR endocytosis rate. Neuron, 59(1), 84–97. https://doi.org/10.1016/j.neuron.2008.05.014Google Scholar

Wilson, I. A., Ikonen, S., Gallagher, M., Eichenbaum, H., & Tanila, H. (2005). Age-associated alterations of hippocampal place cells are subregion specific. Journal of Neuroscience, 25(29), 6877–6886. https://dx.doi.org/10.1523/JNEUROSCI.1744-05.2005Google Scholar

Witter, M. P., & Amaral, D. G. (2004). Hippocampal formation. In Paxinos, G. (Ed.), The rat nervous system (pp. 635–704). Cambridge, MA: Academic Press.Google Scholar

Yang, Y. J., Chen, H. B., Wei, B., et al. (2015). Cognitive decline is associated with reduced surface GluR1 expression in the hippocampus of aged rats. Neuroscience Letters, 591, 176–181. https://doi.org/10.1016/j.neulet.2015.02.030Google Scholar

Zhao, X., Rosenke, R., Kronemann, D., et al. (2009). The effects of aging on N-methyl-D-aspartate receptor subunits in the synaptic membrane and relationships to long-term spatial memory. Neuroscience, 162(4), 933–945. https://doi.org/10.1016/j.neuroscience.2009.05.018Google Scholar

Aalto, S., Brück, A., Laine, M., Någren, K., & Rinne, J. O. (2005). Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: A positron emission tomography study using the high-affinity dopamine D2 receptor ligand [11C]FLB 457. Journal of Neuroscience, 25(10), 2471–2477. https://doi.org/10.1523/JNEUROSCI.2097-04.2005Google Scholar

Atri, A., Sherman, S., Norman, K. A., et al. (2004). Blockade of central cholinergic receptors impairs new learning and increases proactive interference in a word paired-associate memory task. Behavioral Neuroscience, 118(1), 223–236. http://dx.doi.org/10.1037/0735-7044.118.1.223Google Scholar

Bäckman, L., Ginovart, N., Dixon, R. A., et al. (2000). Age-related cognitive deficits mediated by changes in the striatal dopamine system. American Journal of Psychiatry, 157(4), 635–637. https://doi.org/10.1176/ajp.157.4.635Google Scholar

Bäckman, L., Karlsson, S., Fischer, H., et al. (2011). Dopamine D(1) receptors and age differences in brain activation during working memory. Neurobiology of Aging, 32(10), 1849–1856. https://doi.org/10.1016/j.neurobiolaging.2009.10.018Google Scholar

Bäckman, L., Lindenberger, U., Li, S. C., & Nyberg, L. (2010). Linking cognitive aging to alterations in dopamine neurotransmitter functioning: Recent data and future avenues. Neuroscience and Biobehavioral Reviews, 34(5), 670–677. https://doi.org/10.1016/j.neubiorev.2009.12.008Google Scholar

Bäckman, L., Nyberg, L., Lindenberger, U., Li, S. C., & Farde, L. (2006). The correlative triad among aging, dopamine, and cognition: Current status and future prospects. Neuroscience and Biobehavioral Reviews, 30(6), 791–807. https://doi.org/10.1016/j.neubiorev.2006.06.005Google Scholar

Bartus, R. T., Dean, R. L. 3rd, Beer, B., & Lippa, A. S. (1982). The cholinergic hypothesis of geriatric memory dysfunction. Science, 217(4558), 408–414. https://doi.org/10.1126/science.7046051Google Scholar

Bentley, P., Driver, J., & Dolan, R. J. (2011). Cholinergic modulation of cognition: Insights from human pharmacological functional neuroimaging. Progress in Neurobiology, 94(4), 360–388. https://doi.org/10.1016/j.pneurobio.2011.06.002Google Scholar

Berry, A. S., Shah, V. D., Furman, D. J., et al. (2018a). Dopamine synthesis capacity is associated with D2/3 receptor binding but not dopamine release. Neuropsychopharmacology, 43(6), 1201–1211. https://doi.org/10.1038/npp.2017Google Scholar

Berry, A. S., Shah, V. D., & Jagust, W. J. (2018b). The influence of dopamine on cognitive flexibility is mediated by functional connectivity in young but not older adults. Journal of Cognitive Neuroscience, 30(9), 1–15. https://doi.org/10.1162/jocn_a_01286Google Scholar

Björklund, A., & Dunnett, S. B. (2007). Dopamine neuron systems in the brain: An update. Trends in Neurosciences, 30(5), 194–202. https://doi.org/10.1016/j.tins.2007.03.006Google Scholar

Bolam, J. P., Hanley, J. J., Booth, P. A. C., & Bevan, M. D. (2000). Synaptic organisation of the basal ganglia. Journal of Anatomy, 196(4), 527–542. https://doi.org/10.1046/j.1469-7580.2000.19640527.xGoogle Scholar

Braak, H., Thal, D. R., Ghebremedhin, E., & Del Tredici, K. (2011). Stages of the pathologic process in Alzheimer disease: Age categories from 1 to 100 years. Journal of Neuropathology and Experimental Neurology, 70(11), 960–969. https://doi.org/10.1097/NEN.0b013e318232a379Google Scholar

Brozoski, T. J., Brown, R. M., Rosvold, H. E., & Goldman, P. S. (1979). Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science, 205(4409), 929–932. https://doi.org/10.1126/science.112679Google Scholar

Carlsson, A., & Winblad, B. (1976). Influence of age and time interval between death and autopsy on dopamine and 3-methoxytyramine levels in human basal ganglia. Journal of Neural Transmission, 38(3–4), 271–276. https://doi.org/10.1007/BF01249444Google Scholar

Chandler, D. J. (2016). Evidence for a specialized role of the locus coeruleus noradrenergic system in cortical circuitries and behavioral operations. Brain Research, 1641(Pt. B), 197–206. https://doi.org/10.1016/j.brainres.2015.11.022Google Scholar