Models of Cognitive Aging

Part I - Models of Cognitive Aging

Published online by Cambridge University Press: 28 May 2020

Edited by

Ayanna K. Thomas and

Angela Gutchess

Show author details

Ayanna K. Thomas: Affiliation:
Tufts University, Massachusetts
Angela Gutchess: Affiliation:
Brandeis University, Massachusetts

Book contents

Get access

Type: Chapter
Information: The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
, pp. 3 - 124

DOI: https://doi.org/10.1017/9781108552684 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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/10.1037/0096-3445.132.3.470 Google 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/10.1037/0882-7974.12.1.137 CrossRef Google Scholar PubMed

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.p3 Google 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//0882-7974.8.4.562 Google 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.p225 CrossRef Google Scholar PubMed

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/10.1037/0882-7974.12.1.12 Google 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/awr037 Google 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.0011537 Google 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.2022-18.2019 Google 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/10.1037/0278-7393.32.2.183 Google 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.2013.06.016 Google 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//0882-7974.17.1.85 CrossRef Google Scholar PubMed

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/s41583-018-0068-2 Google 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.17-01-00391.1997 Google 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.2009.11.009 Google 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.2010.04.267 Google 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/bf03200930 Google 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.0139897 Google 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)90312-6 CrossRef Google 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/bhj112 Google 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/bhm155 Google 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/10.1177/0956797613486487 CrossRef Google Scholar PubMed

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.2010.04.004 Google 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//0278-7393.24.5.1121 Google 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//0278-7393.18.5.972 Google 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/10.1037/0882-7974.7.3.443 Google 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/10.1037/0096-3445.133.1.101 Google 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//0278-7393.27.5.1131 Google 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/a0021072 Google 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.14-03-01450.1994 Google 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.7618082 Google 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/bhp207 Google 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/10.1037/0278-7393.23.2.259 Google 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.1254604 Google 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/10.1162/0898929052880048 Google Scholar

Hamm, V. P., & Hasher, L. (1992). Age and the availability of inferences. Psychology and Aging, 7(1), 56–64. https://dx.doi.org/10.1037/0882-7974.7.1.56 Google 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/a0027545 CrossRef Google Scholar PubMed

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)60041-9 Google 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//0882-7974.4.1.106 Google 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/10.1037/0096-3445.132.1.93 Google 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.1994.1001 Google 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-x Google 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.2015.04.016 Google 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-f CrossRef Google 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//0278-7393.25.1.3 Google 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/10.1037/0096-3445.134.2.131 CrossRef Google Scholar PubMed

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//0022-3514.56.3.326 Google 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//0882-7974.9.1.103 Google 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/36.1.44 Google 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/10.1080/03610738108259807 Google 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)00504-1 Google 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.2012.06.017 Google 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/10.1101/345181 Google 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/10.1111/1467-9280.00341 Google 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//0278-7393.18.6.1284 Google 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//0012-1649.19.6.901 Google 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//0882-7974.9.3.339 Google 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/a0014986 Google 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/10.1037/0882-7974.15.3.417 Google 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-z Google 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/10.1037/11587-008 Google 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.2235925100 Google 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/bf03198529 Google 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/10.1037/0278-7393.25.3.563 Google 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/h0084154 Google 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/41.1.79 Google 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//0882-7974.15.3.527 Google 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/bf03200462 Google 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.2018.05.016 Google 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//0278-7393.26.5.1170 Google 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/10.1037/0882-7974.22.1.202 Google 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.1735487100 Google 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/10.1080/03610730109342353 Google 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/10.1037/0882-7974.23.2.467 Google 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.0405148101 Google 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/38.5.582 Google 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.59.103006.093656 Google 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.4494-11.2012 Google 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.2007.19.6.1021 Google 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//0012-1649.19.6.895 Google Scholar

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

Rabbitt, P. (1991). Management of the working population. Ergonomics, 34(6), 775–790. https://dx.doi.org/10.1080/00140139108967350 Google 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/s13423-012-0308-9 Google 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.x CrossRef Google 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/10.1162/089892900561814 Google 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/s11065-014-9270-9 Google 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/10.1037/0882-7974.23.2.239 Google 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/10.1097/00001756-200212200-00010 Google 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.0703-04.2004 Google 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/10.1037/0882-7974.23.1.79 Google Scholar

Salthouse, T. A. (1979). Adult age and the speed-accuracy trade-off. Ergonomics, 22(7), 811–821. https://dx.doi.org/10.1080/00140137908924659 Google 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//0021-9010.70.2.264 Google 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.x Google 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.1994.1016 Google 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.103.3.403 Google 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)00052-1 Google 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/10.1037/0882-7974.6.1.118 Google 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/10.1037/0882-7974.5.1.9 Google 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/10.1037/0012-1649.26.5.845 Google 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/10.1037/0882-7974.12.2.203 Google 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//0882-7974.10.4.527 Google 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/10.1177/1745691619827511 Google 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/10.1037/0882-7974.21.2.379 Google 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/bf03196103 Google Scholar

Tipper, S. P. (1991). Mechanisms of visual selective attention. Canadian Psychology/Psychologie Canadienne, 32(4), 640–642. http://dx.doi.org/10.1037/h0084644 Google 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/a0021361 Google 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/bul0000179 Google 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_00869 Google 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/a0027686 Google 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.1996.0008 Google 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//0096-3445.125.4.422 Google 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/s13421-012-0205-5 Google Scholar

References

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.154.2.165 Google 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.0000069923.64550 Google 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/10.1186/1471-2458-14-643 Google 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-141824 Google 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)00062-6 Google 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.2009.10.013 Google 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/10.1177/1533317516653826 Google 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.2016.11.013 Google 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/s11682-016-9599-1 Google 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/brainsci7020022 Google 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/10.1080/13803395.2013.847906 Google 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/10.1080/13825585.2013.874522 Google 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.0000278163.82636.30 Google 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.0b013e3181b04ae3 Google 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/s00259-008-0848-4 Google 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/10.1037/0882-7974.14.2.245 Google 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.x Google 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_1.13 Google 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.410230206 Google 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.21212 Google 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-160665 Google 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.25.5.671.14584 Google 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.2012.04.005 Google 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.2016.06.1941 Google 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/10.1080/13854046.2015.1005676 Google 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/s11065-014-9270-9 Google 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.25.5.614.14581 Google 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/10.1080/09084282.2011.595458 Google 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/10.1037/0894-4105.7.3.273 Google 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.57.12.2236 Google 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.25.5.625.14576 Google 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.60.3.359 Google 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.49.1.30 Google 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/37.2.150 Google 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.x Google 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.2007.10.008 Google 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.2014.12.025 Google 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/awh144 Google 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/s11682-011-9125-4 Google 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/S1355617702813248 Google 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.53.9.1942 Google 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.410320311 Google 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.2018.05.033 Google 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.1994.03510370056032 Google 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.287.6.742 Google 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/s12035-015-9211-5 Google 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/S1355617711001044 Google 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/neu0000141 Google Scholar

References

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.2010.100 Google 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/nrn3818 Google 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/10.1037/0012-1649.28.1.121 Google 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.2010.12.005 Google 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.0742-08.2008 Google 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.1718793115 Google 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-z Google 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/10.1037/0012-1649.43.2.465 Google 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.2015.11.074 Google 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.2012.00063 Google 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/s00426-014-0559-3 Google 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/10.1027/1618-3169.54.4.311 Google 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.0000000000002939 Google 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.2004.07.010 Google 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.8346443 Google 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/a0014296 Google 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.2016.07.007 Google 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.23756 Google 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/bhy003 Google 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/10.1177/0956797613486487 Google 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.410040410 Google 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/10.1038/427311a Google 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.2010.05.041 Google 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.21370 Google 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.2012.02.072 Google 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-131889 Google 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.1508831112 Google 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.2010.21.3.187 Google 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/10.1037/0033-2909.101.2.171 Google Scholar

Grady, C. (2012). The cognitive neuroscience of ageing. Nature Reviews Neuroscience, 13(7), 491. http://dx.doi.org/10.1038/nrn3256 Google 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/sbt005 Google 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/bhs231 Google 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/a0039260 Google 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.2018.02.018 Google 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.1609720113 Google 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.2013.64 Google 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-161097 Google 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.2012.09.012 Google 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/10.1037/0012-1649.26.6.894 Google 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/bhs401 Google 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.2014.03.045 Google 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)31363-6 Google 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/a0020080 Google 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.2010.08.026 Google 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.2011.02.013 Google 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/bhu027 Google 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.x Google 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/dds504 Google 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.12306 Google 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.2014-1108 Google 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)60461-5 Google Scholar

Nyberg, L., & Pudas, S. (2019). Successful memory aging. Annual Review of Psychology, 70, 219–243. http://dx.doi.org/10.1146/annurev-psych-010418-103052 Google 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.1735487100 Google 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/10.1177/1745691615577701 Google 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.2010.03.020 Google 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/bhi044 Google 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.1203350109 Google 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.0050354 Google 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.23-08-03295.2003 Google 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/s13423-015-0910-8 Google 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.49.4.304 Google 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.2010.00027 Google 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.2412 Google 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.0203-14.2014 Google 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.0012244 Google 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.0391-14.2014 Google 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.1520702112 Google 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/a0021361 Google 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/bul0000179 Google 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.23665 Google 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.2014.11.008 Google 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.1208180109 Google 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.12120 Google 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.1524259113 Google 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.2007.03.070 Google 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-c1 Google 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.2013.11.011 Google 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/s00787-012-0241-5 Google 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.2012.04.008 CrossRef Google Scholar PubMed

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.2011.11.015 Google 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.0b013e3182735c9c Google 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.3045 Google Scholar

References

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.1099 Google 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.2013.08.012 Google 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.23-09-03807.2003 Google 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.2036-08.2008 Google 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.3258-12.2013 Google 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.2010.00140 Google 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/nrn1809 Google 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)90023-5 Google 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.0033249 Google 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.x Google 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.2014.08.007 Google 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-I Google 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/y97-033 Google 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.2003.12.001 Google 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.2015.08.001 Google 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.1254604 Google 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.20-11-03993.2000 Google 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.2009.10.005 Google 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.2016.12.021 Google 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.21291 Google 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.0605-12.2012 Google 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.2016.10.012 Google 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.2018.06.028 Google 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.2017.00383 Google 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.2016.00113 Google 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)00213-8 Google 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.aad3267 Google 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/nn1586 Google Scholar

Lindenberger, U. (2014). Human cognitive aging: Corriger la fortune? Science, 346(6209), 572–578. https://dx.doi.org/10.1126/science.1254403 Google 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.2013.07.003 Google 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.2531 Google 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/nrn3200 Google 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.1725-04.2004 Google 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/BF03327247 Google 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.93.18.9926 Google 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.x Google 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.22828 Google 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)00126-0 Google 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.4163-06.2007 Google 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.2009.12.002 Google 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.1998.79.1.334 Google 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.2006.08.034 Google 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;2-1 Google 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.23367 Google 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.033506.113 Google 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.x Google 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/10.1080/016502599383739 Google Scholar

Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. https://doi.org/10.1016/j.neuropsychologia.2009.03.004 Google 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.050059 Google 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.2015.160 Google 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.2015.07.024 Google 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.2018.06.008 Google 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.68204 Google 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/gls140 Google 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.2008.05.014 Google 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.1744-05.2005 Google 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.2015.02.030 Google 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.2009.05.018 Google Scholar

References

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.2097-04.2005 Google 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/10.1037/0735-7044.118.1.223 Google 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.157.4.635 Google 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.2009.10.018 Google 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.2009.12.008 Google 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.2006.06.005 Google 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.7046051 Google 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.2011.06.002 Google 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.2017 Google 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_01286 Google 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.2007.03.006 Google 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.x Google 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.0b013e318232a379 Google 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.112679 Google 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/BF01249444 Google 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.2015.11.022 Google Scholar

Chowdhury, R., Guitart-Masip, M., Bunzeck, N., Dolan, R. J., & Düzel, E. (2012). Dopamine modulates episodic memory persistence in old age. Journal of Neuroscience, 32(41), 14193–14204. https://doi.org/10.1523/JNEUROSCI.1278-12.2012 Google Scholar

Clewett, D. V., Lee, T. H., Greening, S., et al. (2016). Neuromelanin marks the spot: Identifying a locus coeruleus biomarker of cognitive reserve in healthy aging. Neurobiology of Aging, 37, 117–126. https://doi.org/10.1016/j.neurobiolaging.2015.09.019 Google Scholar

Cools, R., & D’Esposito, M. (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biological Psychiatry, 69(12), 113–125. https://doi.org/10.1016/j.biopsych.2011.03.028 Google Scholar

Decker, M. W. (1987). The effects of aging on hippocampal and cortical projections of the forebrain cholinergic system. Brain Research Reviews, 12(4), 423–438. https://doi.org/10.1016/0165-0173(87)90007-5 Google Scholar

Dewey, S. L., Volkow, N. D., Logan, J., et al. (1990). Age-related decreases in muscarinic cholinergic receptor binding in the human brain measured with positron emission tomography (PET). Journal of Neuroscience Research, 27(4), 569–575. https://doi.org/10.1002/jnr.490270418 Google Scholar

Drachman, D. A., & Leavitt, J. (1974). Human memory and the cholinergic system: A relationship to aging? Archives of Neurology, 30(2), 113–121. https://doi.org/10.1001/archneur.1974.00490320001001 Google Scholar

Dumas, J. A., Saykin, A. J., McDonald, B. C., et al. (2008). Nicotinic versus muscarinic blockade alters verbal working memory-related brain activity in older women. American Journal of Geriatric Psychiatry, 16(4), 272–282. https://doi.org/10.1097/JGP.0b013e3181602a2b Google Scholar

Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. Journal of Neurophysiology, 83(3), 1733–1750. https://doi.org/10.1152/jn.2000.83.3.1733 Google Scholar

Eriksson, J., Vogel, E. K., Lansner, A., Bergström, F., & Nyberg, L. (2015). Neurocognitive architecture of working memory. Neuron, 88(1), 33–46. https://doi.org/10.1016/j.neuron.2015.09.020 Google Scholar

Fearnley, J. M., & Lees, A. J. (1991). Ageing and Parkinson’s disease: Substantia nigra regional selectivity. Brain, 114(5), 2283–2301. https://doi.org/10.1093/brain/114.5.2283 Google Scholar

Fischer, H., Nyberg, L., Karlsson, S., et al. (2010). Simulating neurocognitive aging: Effects of a dopaminergic antagonist on brain activity during working memory. Biological Psychiatry, 67(6), 575–580. https://doi.org/10.1016/j.biopsych.2009.12.013 Google Scholar

Floel, A., Garraux, G., Xu, B., et al. (2008). Levodopa increases memory encoding and dopamine release in the striatum in the elderly. Neurobiology of Aging, 29, 267–279. https://doi.org/10.1016/j.neurobiolaging.2006.10.009 Google Scholar

Garrett, D. D., Nagel, I. E., Preuschhof, C., et al. (2015). Amphetamine modulates brain signal variability and working memory in younger and older adults. Proceedings of the National Academy of Sciences USA, 112(24), 7593–7598. https://doi.org/10.1073/pnas.1504090112 Google Scholar

Hall, H., Sedvall, G., Magnusson, O., et al. (1994). Distribution of D1- and D2-dopamine receptors, and dopamine and its metabolites in the human brain. Neuropsychopharmacology, 11(4), 245–256. https://doi.org/10.1038/sj.npp.1380111 Google Scholar

Hämmerer, D., Callaghan, M. F., Hopkins, A., et al. (2018). Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events. Proceedings of the National Academy of Sciences USA, 115(9), 2228–2233. https://doi.org/10.1073/pnas.1712268115 Google Scholar

Hasselmo, M. E. (2006). The role of acetylcholine in learning and memory. Current Opinion in Neurobiology, 16(6), 710–715. https://doi.org/10.1016/j.conb.2006.09.002 Google Scholar

Hasselmo, M. E., & Sarter, M. (2011). Modes and models of forebrain cholinergic neuromodulation of cognition. Neuropsychopharmacology, 36(1), 52–73. https://doi.org/10.1038/npp.2010.104 Google Scholar

Karlsson, S., Nyberg, L., Karlsson, P., et al. (2009). Modulation of striatal dopamine D1 binding by cognitive processing. NeuroImage, 48(2), 398–404. https://doi.org/10.1016/j.neuroimage.2009.06.030 Google Scholar

Karrer, T. M., Josef, A. K., Mata, R., Morris, E. D., & Samanez-Larkin, G. R. (2017). Reduced dopamine receptors and transporters but not synthesis capacity in normal aging adults: A meta-analysis. Neurobiology of Aging, 57, 36–46. https://doi.org/10.1016/j.neurobiolaging.2017.05.006 Google Scholar

Koepp, M. J., Gunn, R. N., Lawrence, A. D., et al. (1998). Evidence for striatal dopamine release during a video game. Nature, 393(6682), 266–268. https://doi.org/10.1038/30498 Google Scholar

Kuhl, D. E., Minoshima, S., Fessler, J. A., et al. (1996). In vivo mapping of cholinergic terminals in normal aging, Alzheimer’s disease, and Parkinson’s disease. Annals of Neurology, 40(3), 399–410. https://doi.org/10.1002/ana.410400309 Google Scholar

Landau, S. M., Lal, R., O’Neil, J. P., Baker, S., & Jagust, W. J. (2009). Striatal dopamine and working memory. Cerebral Cortex, 19(2), 445–454. https://doi.org/10.1093/cercor/bhn095 Google Scholar

Laruelle, M. (2000). Imaging synaptic neurotransmission with in vivo binding competition techniques: A critical review. Journal of Cerebral Blood Flow and Metabolism, 20(3), 423–451. https://doi.org/10.1097/00004647-200003000-00001 Google Scholar

Lee, T. H., Greening, S. G., Ueno, T., et al. (2018). Arousal increases neural gain via the locus coeruleus–noradrenaline system in younger adults but not in older adults. Nature Human Behaviour, 2(5), 356–366. https://doi.org/10.1038/s41562-018-0344-1 Google Scholar

Lisman, J., Grace, A. A., & Düzel, E. (2011). A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP. Trends in Neurosciences, 34(10), 536–547. https://doi.org/10.1016/j.tins.2011.07.006 Google Scholar

Mather, M., & Carstensen, L. L. (2005). Aging and motivated cognition: The positivity effect in attention and memory. Trends in Cognitive Sciences, 9(10), 496–502. https://doi.org/10.1016/j.tics.2005.08.005 Google Scholar

Mather, M., & Harley, C. W. (2016). The locus coeruleus: Essential for maintaining cognitive function and the aging brain. Trends in Cognitive Sciences, 20(3), 214–226. https://doi.org/10.1016/j.tics.2016.01.001 Google Scholar

Mattay, V. S., Fera, F., Tessitore, A., et al. (2006). Neurophysiological correlates of age-related changes in working memory capacity. Neuroscience Letters, 392(1–2), 32–37. https://doi.org/10.1016/j.neulet.2005.09.025 Google Scholar

McNamara, C. G., & Dupret, D. (2017). Two sources of dopamine for the hippocampus. Trends in Neurosciences, 40(7), 383–384. https://doi.org/10.1016/j.tins.2017.05.005 Google Scholar

Mitsis, E. M., Cosgrove, K. P., Staley, J. K., et al. (2009). Age-related decline in nicotinic receptor availability with [123I]5-IA-85380 SPECT. Neurobiology of Aging, 30(9), 1490–1497. https://doi.org/10.1016/j.neurobiolaging.2007.12.008 Google Scholar

Monchi, O., Hyun Ko, J., & Strafella, A. P. (2006). Striatal dopamine release during performance of executive functions: A [11C] raclopride PET study. NeuroImage, 33(3), 907–912. https://doi.org/10.1016/j.neuroimage.2006.06.058 Google Scholar

Morcom, A. M., Bullmore, E. T., Huppert, F. A., et al. (2009). Memory encoding and dopamine in the aging brain: A psychopharmacological neuroimaging study. Cerebral Cortex, 20(3), 743–757. https://doi.org/10.1093/cercor/bhp139 Google Scholar

Moriguchi, S., Yamada, M., Takano, H., et al. (2017). Norepinephrine transporter in major depressive disorder: A PET study. American Journal of Psychiatry, 174(1), 36–41. https://doi.org/10.1176/appi.ajp.2016.15101334 Google Scholar

Nordberg, A. (1999). PET studies and cholinergic therapy in Alzheimer’s disease. Revue Neurologique, 155(Suppl. 4), 53–63. https://doi.org/10.1016/S0338-9898(99)80366-7 Google Scholar

Nyberg, L., Andersson, M., Kauppi, K., et al. (2014). Age-related and genetic modulation of frontal cortex efficiency. Journal of Cognitive Neuroscience, 26(4), 746–754. https://doi.org/10.1162/jocn_a_00521 Google Scholar

Nyberg, L., Karalija, N., Salami, A., et al. (2016). Dopamine D2 receptor availability is linked to hippocampal-caudate functional connectivity and episodic memory. Proceedings of the National Academy of Sciences USA, 113(28), 7918–7923. https://doi.org/10.1073/pnas.1606309113 Google Scholar

Onur, Ö. A., Piefke, M., Lie, C. H., et al. (2011). Modulatory effects of levodopa on cognitive control in young but not in older subjects: A pharmacological fMRI study. Journal of Cognitive Neuroscience, 23(10), 2797–2810. https://doi.org/10.1162/jocn.2011.21603 Google Scholar

O’Reilly, R. C., & Frank, M. J. (2006). Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18(2), 283–328. https://doi.org/10.1162/089976606775093909 Google Scholar

Persson, J., Kalpouzos, G., Nilsson, L. G., Ryberg, M., & Nyberg, L. (2011). Preserved hippocampus activation in normal aging as revealed by fMRI. Hippocampus, 21(7), 753–766. https://doi.org/10.1002/hipo.20794 Google Scholar

Picciotto, M. R., Higley, M. J., & Mineur, Y. S. (2012). Acetylcholine as a neuromodulator: Cholinergic signaling shapes nervous system function and behavior. Neuron, 76(1), 116–129. https://doi.org/10.1016/j.neuron.2012.08.036 Google Scholar

Reeves, S., Bench, C., & Howard, R. (2002). Ageing and the nigrostriatal dopaminergic system. International Journal of Geriatric Psychiatry, 17(4), 359–370. https://doi.org/10.1002/gps.606 Google Scholar

Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17(3), 177–182. https://doi.org/10.1111/j.1467-8721.2008.00570.x Google Scholar

Rieckmann, A., Buckner, R. L., & Hedden, T. (2016). Molecular imaging of aging and neurodegenerative disease. In Cabeza, R., Nyberg, L., & Park, D. C. (Eds.), Cognitive neuroscience of aging (2nd ed., pp. 35–69). New York: Oxford University Press.Google Scholar

Rieckmann, A., Karlsson, S., Karlsson, P., et al. (2011). Dopamine D1 receptor associations within and between dopaminergic pathways in younger and elderly adults: Links to cognitive performance. Cerebral Cortex, 21(9), 2023–2032. https://doi.org/10.1093/cercor/bhq266 Google Scholar

Robertson, I. H. (2013). A noradrenergic theory of cognitive reserve: Implications for Alzheimer’s disease. Neurobiology of Aging, 34(1), 298–308. https://doi.org/10.1016/j.neurobiolaging.2012.05.019 Google Scholar

Sara, S. J., & Bouret, S. (2012). Orienting and reorienting: The locus coeruleus mediates cognition through arousal. Neuron, 76(1), 130–141. https://doi.org/10.1016/j.neuron.2012.09.011 Google Scholar

Sawaguchi, T., & Goldman-Rakic, P. (1991). D1 dopamine receptors in prefrontal cortex: Involvement in working memory. Science, 251(4996), 947–950. https://doi.org/10.1126/science.1825731 Google Scholar

Schliebs, R., & Arendt, T. (2011). The cholinergic system in aging and neuronal degeneration. Behavioural Brain Research, 221(2), 555–563. https://doi.org/10.1016/j.bbr.2010.11.058 Google Scholar

Schwarz, L. A., & Luo, L. (2015). Organization of the locus coeruleus-norepinephrine system. Current Biology, 25(21), R1051–R1056. https://doi.org/10.1016/j.cub.2015.09.039 Google Scholar

Segal, S. K., Stark, S. M., Kattan, D., Stark, C. E., & Yassa, M. A. (2012). Norepinephrine-mediated emotional arousal facilitates subsequent pattern separation. Neurobiology of Learning and Memory, 97(4), 465–469. https://doi.org/10.1016/j.nlm.2012.03.010 Google Scholar

Servan-Schreiber, D., Printz, H., & Cohen, J. (1990). A network model of catecholamine effects: Gain, signal-to-noise ratio, and behavior. Science, 249(4971), 892–895. https://doi.org/10.1126/science.2392679 Google Scholar

Shibata, E., Sasaki, M., Tohyama, K., et al. (2006). Age-related changes in locus ceruleus on neuromelanin magnetic resonance imaging at 3 Tesla. Magnetic Resonance in Medical Sciences, 5(4), 197–200. https://doi.org/10.2463/mrms.5.197 Google Scholar

Shohamy, D., & Adcock, R. A. (2010). Dopamine and adaptive memory. Trends in Cognitive Sciences, 14(10), 464–472. https://doi.org/10.1016/j.tics.2010.08.002 Google Scholar

Sperling, R. A., Bates, J. F., Chua, E. F., et al. (2003). fMRI studies of associative encoding in young and elderly controls and mild Alzheimer’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 74(1), 44–50. http://dx.doi.org/10.1136/jnnp.74.1.44 Google Scholar

Sperling, R. A., Greve, D., Dale, A., et al. (2002). Functional MRI detection of pharmacologically induced memory impairment. Proceedings of the National Academy of Sciences USA, 99(1), 455–460. https://doi.org/10.1073/pnas.012467899 Google Scholar

Strange, B. A., & Dolan, R. J. (2004). Beta-adrenergic modulation of emotional memory-evoked human amygdala and hippocampal responses. Proceedings of the National Academy of Sciences USA, 101(31), 11454–11458. https://doi.org/10.1073/pnas.0404282101 Google Scholar

Takahashi, H., Kato, M., Takano, H., et al. (2008). Differential contributions of prefrontal and hippocampal dopamine D(1) and D(2) receptors in human cognitive functions. Journal of Neuroscience, 28(46), 12032–12038. https://doi.org/10.1523/JNEUROSCI.3446-08.2008 Google Scholar

Tully, K., & Bolshakov, V. Y. (2010). Emotional enhancement of memory: How norepinephrine enables synaptic plasticity. Molecular Brain, 3(1), p. 15. https://doi.org/10.1186/1756-6606-3-15 Google Scholar

Vijayashankar, N., & Brody, H. (1979). A quantitative study of the pigmented neurons in the nuclei locus coeruleus and subcoeruleus in man as related to aging. Journal of Neuropathology and Experimental Neurology, 38(5), 490–497. https://doi.org/10.1097/00005072-197909000-00004 Google Scholar

Voss, B., Thienel, R., Reske, M., et al. (2012). Cholinergic blockade under working memory demands encountered by increased rehearsal strategies: Evidence from fMRI in healthy subjects. European Archives of Psychiatry and Clinical Neuroscience, 262(4), 329–339. https://doi.org/10.1007/s00406-011-0267-6 Google Scholar

Whitehouse, P. J., Price, D. L., Struble, R. G., et al. (1982). Alzheimer’s disease and senile dementia: Loss of neurons in the basal forebrain. Science, 215(4537), 1237–1239. https://doi.org/10.1126/science.7058341 Google Scholar

Wilson, R. S., Nag, S., Boyle, P. A., et al. (2013). Neural reserve, neuronal density in the locus ceruleus, and cognitive decline. Neurology, 80(13), 1202–1208. https://doi.org/10.1212/WNL.0b013e3182897103 Google Scholar

Wittmann, B. C., Schott, B. H., Guderian, S., et al. (2005). Reward-related FMRI activation of dopaminergic midbrain is associated with enhanced hippocampus-dependent long-term memory formation. Neuron, 45(3), 459–467. https://doi.org/10.1016/j.neuron.2005.01.010 Google Scholar

Yassa, M. A., & Stark, C. E. L. (2011). Pattern separation in the hippocampus. Trends in Neurosciences, 34(10), 515–525. https://doi.org/10.1016/j.tins.2011.06.006 Google Scholar

References

Abdulrahman, H., Fletcher, P. C., Bullmore, E., & Morcom, A. M. (2017). Dopamine and memory dedifferentiation in aging. NeuroImage, 153, 211–220. https://doi.org/10.1016/j.neuroimage.2015.03.031 Google Scholar

Abercrombie, E. D., & Zigmond, M. J. (1989). Partial injury to central noradrenergic neurons: Reduction of tissue norepinephrine content is greater than reduction of extracellular norepinephrine measured by microdialysis. Journal of Neuroscience, 9(11), 4062–4067. https://doi.org/10.1523/JNEUROSCI.09-11-04062.1989 Google Scholar

Acheson, A., & Zigmond, M. J. (1981). Short and long term changes in tyrosine hydroxylase activity in rat brain after subtotal destruction of central noradrenergic neurons. Journal of Neuroscience, 1(5), 493–504. https://doi.org/10.1523/JNEUROSCI.01-05-00493.1981 Google Scholar

Acheson, A. L., Zigmond, M. J., & Stricker, E. M. (1980). Compensatory increase in tyrosine hydroxylase activity in rat brain after intraventricular injections of 6-hydroxydopamine. Science, 207(4430), 537–540. https://doi.org/10.1126/science.6101509 Google Scholar

Adolfsson, R., Gottfries, C. G., Roos, B. E., & Winblad, B. (1979). Postmortem distribution of dopamine and homovanillic acid in human brain, variations related to age, and a review of the literature. Journal of Neural Transmission, 45(2), 81–105. https://doi.org/10.1007/BF01250085 Google Scholar

Alexandre, C., Andermann, M. L., & Scammell, T. E. (2013). Control of arousal by the orexin neurons. Current Opinion in Neurobiology, 23(5), 752–759. https://doi.org/10.1016/j.conb.2013.04.008 Google Scholar

Almela, M., Hidalgo, V., Villada, C., et al. (2011). Salivary alpha-amylase response to acute psychosocial stress: The impact of age. Biological Psychology, 87(3), 421–429. https://doi.org/10.1016/j.biopsycho.2011.05.008 Google Scholar

Arendt, T., Stieler, J. T., & Holzer, M. (2016). Tau and tauopathies. Brain Research Bulletin, 126, 238–292. https://doi.org/10.1016/j.brainresbull.2016.08.018 Google Scholar

Arranz, B., Blennow, K., Ekman, R., et al. (1996). Brain monoaminergic and neuropeptidergic variations in human aging. Journal of Neural Transmission, 103(1–2), 101–115. https://doi.org/10.1007/BF01292620 Google Scholar

Birditt, K. S., Tighe, L. A., Nevitt, M. R., & Zarit, S. H. (2017). Daily social interactions and the biological stress response: Are there age differences in links between social interactions and alpha-amylase? Gerontologist, 6, 1114–1125. https://doi.org/10.1093/geront/gnx168 Google Scholar

Birren, J. E. (1960). Behavioral theories of aging. In Shock, N. W. (Ed.), Aging: Some social and biological aspects. Washington: American Association for the Advancement of Science.Google Scholar

Birren, J. E., Cunningham, W. R., & Yamamoto, K. (1983). Psychology of adult development and aging. Annual Review of Psychology, 34(1), 543–575. https://doi.org/10.1146/annurev.ps.34.020183.002551 Google Scholar

Blouin, A. M., Fried, I., Wilson, C. L., et al. (2013). Human hypocretin and melanin-concentrating hormone levels are linked to emotion and social interaction. Nature Communications, 4, p. 1547. https://doi.org/10.1038/ncomms2461 Google Scholar

Boureau, Y. L., & Dayan, P. (2011). Opponency revisited: Competition and cooperation between dopamine and serotonin. Neuropsychopharmacology, 36(1), 74–97. https://doi.org/10.1038/npp.2010.151 Google Scholar

Brewerton, T. D., Putnam, K. T., Lewine, R. R. J., & Risch, S. C. (2018). Seasonality of cerebrospinal fluid monoamine metabolite concentrations and their associations with meteorological variables in humans. Journal of Psychiatric Research, 99, 76–82. https://doi.org/10.1016/j.jpsychires.2018.01.004 Google Scholar

Britt, D. M., & Day, G. S. (2016). Over-prescribed medications, under-appreciated risks: A review of the cognitive effects of anticholinergic medications in older adults. Missouri Medicine, 113(3), 207–214.Google Scholar

Cason, A. M., Smith, R. J., Tahsili-Fahadan, P., et al. (2010). Role of orexin/hypocretin in reward-seeking and addiction: Implications for obesity. Physiology and Behavior, 100(5), 419–428. https://doi.org/10.1016/j.physbeh.2010.03.009 Google Scholar

Charles, S. T. (2010). Strength and vulnerability integration: A model of emotional well-being across adulthood. Psychological Bulletin, 136(6), 1068–1091. https://doi.org/10.1037/a0021232 Google Scholar

Chiodo, L. A., Acheson, A. L., Zigmond, M. J., & Stricker, E. M. (1983). Subtotal destruction of central noradrenergic projections increases the firing rate of locus coeruleus cells. Brain Research, 264(1), 123–126. https://doi.org/10.1016/0006-8993(83)91128-9 Google Scholar

Cho, J. R., Treweek, J. B., Robinson, J. E., et al. (2017). Dorsal raphe dopamine neurons modulate arousal and promote wakefulness by salient stimuli. Neuron, 94(6), 1205–1219. https://doi.org/10.1016/j.neuron.2017.05.020 Google Scholar

Cools, R., Nakamura, K., & Daw, N. D. (2011). Serotonin and dopamine: Unifying affective, activational, and decision functions. Neuropsychopharmacology, 36(1), 98–113. https://doi.org/10.1038/npp.2010.121 Google Scholar

Davies, P., & Maloney, A. (1976). Selective loss of central cholinergic neurons in Alzheimer’s disease. Lancet, 308(8000), p. 1403. https://doi.org/10.1016/s0140-6736(76)91936-x Google Scholar

Davis, K. L., Mohs, R. C., Marin, D., et al. (1999). Cholinergic markers in elderly patients with early signs of Alzheimer disease. JAMA, 281(15), 1401–1406. https://doi.org/10.1001/jama.281.15.1401 Google Scholar

Ding, Y. S., Singhal, T., Planeta‐Wilson, B., et al. (2010). PET imaging of the effects of age and cocaine on the norepinephrine transporter in the human brain using (S, S)‐[11C] O‐methylreboxetine and HRRT. Synapse, 64(1), 30–38. https://doi.org/10.1002/syn.20696 Google Scholar

Downs, J. L., Dunn, M. R., Borok, E., et al. (2007). Orexin neuronal changes in the locus coeruleus of the aging rhesus macaque. Neurobiology of Aging, 28(8), 1286–1295. https://doi.org/10.1016/j.neurobiolaging.2006.05.025 Google Scholar

Eisdorfer, C. (1968). Arousal and performance: Experiments in verbal learning and a tentative theory. In Talland, G. A. (Ed.), Human Aging and Behavior (pp. 189–216). Cambridge, MA: Academic Press.Google Scholar

Elman, J. A., Panizzon, M. S., Hagler, D. J. Jr., et al. (2017). Task-evoked pupil dilation and BOLD variance as indicators of locus coeruleus dysfunction. Cortex, 97, 60–69. https://doi.org/10.1016/j.cortex.2017.09.025 Google Scholar

Elrod, R., Peskind, E. R., DiGiacomo, L., et al. (1997). Effects of Alzheimer’s disease severity on cerebrospinal fluid norepinephrine concentration. American Journal of Psychiatry, 154(1), 25–30. https://doi.org/10.1176/ajp.154.1.25 Google Scholar

El‐Sedeek, M., Korish, A., & Deef, M. (2010). Plasma orexin‐A levels in postmenopausal women: Possible interaction with estrogen and correlation with cardiovascular risk status. BJOG: An International Journal of Obstetrics and Gynaecology, 117(4), 488–492. https://doi.org/10.1111/j.1471-0528.2009.02474.x Google Scholar

Fagius, J., & Wallin, B. G. (1993). Long-term variability and reproducibility of resting human muscle nerve sympathetic activity at rest, as reassessed after a decade. Clinical Autonomic Research, 3(3), 201–205. https://doi.org/10.1007/BF01826234 Google Scholar

Falk, J. L., & Kline, D. W. (1978). Stimulus persistence in CFF: Overarousal or underactivation? Experimental Aging Research, 4(2), 109–123. https://psycnet.apa.org/doi/10.1080/03610737808257134 Google Scholar

Fearnley, J. M., & Lees, A. J. (1991). Aging and Parkinson’s disease: Substantia nigra regional selectivity. Brain, 114, 2283–2301. https://doi.org/10.1093/brain/114.5.2283 Google Scholar

Fritschy, J. M., & Grzanna, R. (1992). Restoration of ascending noradrenergic projections by residual locus coeruleus neurons: Compensatory response to neurotoxin‐induced cell death in the adult rat brain. Journal of Comparative Neurology, 321(3), 421–441. https://doi.org/10.1002/cne.903210309 Google Scholar

Fronczek, R., van Geest, S., Frölich, M., et al. (2012). Hypocretin (orexin) loss in Alzheimer’s disease. Neurobiology of Aging, 33(8), 1642–1650. https://doi.org/10.1016/j.neurobiolaging.2011.03.014 Google Scholar

Gabelle, A., Jaussent, I., Hirtz, C., et al. (2017). Cerebrospinal fluid levels of orexin-A and histamine, and sleep profile within the Alzheimer process. Neurobiology of Aging, 53, 59–66. https://doi.org/10.1016/j.neurobiolaging.2017.01.011 Google Scholar

Gannon, M., & Wang, Q. (2018). Complex noradrenergic dysfunction in Alzheimer’s disease: Low norepinephrine input is not always to blame. Brain Research, 1702(1), 12–16. https://doi.org/10.1016/j.brainres.2018.01.001 Google Scholar

Gilmor, M. L., Erickson, J. D., Varoqui, H., et al. (1999). Preservation of nucleus basalis neurons containing choline acetyltransferase and the vesicular acetylcholine transporter in the elderly with mild cognitive impairment and early Alzheimer’s disease. Journal of Comparative Neurology, 411(4), 693–704. https://doi.org/10.1002/(SICI)1096-9861(19990906)411:4<693::AID-CNE13>3.0.CO;2-D Google Scholar

Gottfries, C. G., Gottfries, I., Johansson, B., et al. (1971). Acid monoamine metabolites in human cerebrospinal fluid and their relations to age and sex. Neuropharmacology, 10(6), 665–672. https://doi.org/10.1016/0028-3908(71)90081-5 Google Scholar

Gray, S. L., Anderson, M. L., Dublin, S., et al. (2015). Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study. JAMA Internal Medicine, 175(3), 401–407. https://doi.org/10.1001/jamainternmed.2014.7663 Google Scholar

Grothe, M., Heinsen, H., & Teipel, S. J. (2012). Atrophy of the cholinergic basal forebrain over the adult age range and in early stages of Alzheimer’s disease. Biological Psychiatry, 71(9), 805–813. https://doi.org/10.1016/j.biopsych.2011.06.019 Google Scholar

Haas, H., & Panula, P. (2003). The role of histamine and the tuberomamillary nucleus in the nervous system. Nature Reviews Neuroscience, 4(2), 121–130. https://doi.org/10.1038/nrn1034 Google Scholar

Hart, E., & Charkoudian, N. (2014). Sympathetic neural regulation of blood pressure: Influences of sex and aging. Physiology, 29(1), 8–15. https://doi.org/10.1152/physiol.00031.2013 Google Scholar

Higuchi, M., Yanai, K., Okamura, N., et al. (2000). Histamine H1 receptors in patients with Alzheimer’s disease assessed by positron emission tomography. Neuroscience, 99(4), 721–729. https://doi.org/10.1016/s0306-4522(00)00230-x Google Scholar

Hoogendijk, W. J., Feenstra, M. G., Botterblom, M. H., et al. (1999). Increased activity of surviving locus ceruleus neurons in Alzheimer’s disease. Annals of Neurology, 45(1), 82–91. https://doi.org/10.1002/1531-8249(199901)45:1<82::AID-ART14>3.0.CO;2-T Google Scholar

Hunt, N. J., Rodriguez, M. L., Waters, K. A., & Machaalani, R. (2015). Changes in orexin (hypocretin) neuronal expression with normal aging in the human hypothalamus. Neurobiology of Aging, 36(1), 292–300. https://doi.org/10.1016/j.neurobiolaging.2014.08.010 Google Scholar

Iqbal, K., Liu, F., & Gong, C.-X. (2016). Tau and neurodegenerative disease: The story so far. Nature Reviews Neurology, 12(1), 15–27. https://doi.org/10.1038/nrneurol.2015.225 Google Scholar

Javier Meana, J., Barturen, F., Asier Garro, M., et al. (1992). Decreased density of presynaptic α2‐adrenoceptors in postmortem brains of patients with Alzheimer’s disease. Journal of Neurochemistry, 58(5), 1896–1904. https://doi.org/10.1038/nrneurol.2015.225 Google Scholar

Jogeshwar, M., Lao, P. J., Betthauser, T. J., et al. (2018). Human brain imaging of nicotinic acetylcholine α4β2* receptors using [18F]Nifene: Selectivity, functional activity, toxicity, aging effects, gender effects, and extrathalamic pathways. Journal of Comparative Neurology, 526(1), 80–95. https://doi.org/10.1002/cne.24320 Google Scholar

Joshi, S., Li, Y., Kalwani, R. M., & Gold, J. I. (2015). Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron, 89, 1–14. https://doi.org/10.1016/j.neuron.2015.11.028 Google Scholar

Kalaria, R., & Andorn, A. (1991). Adrenergic receptors in aging and Alzheimer’s disease: Decreased α2-receptors demonstrated by [3H] p-aminoclonidine binding in prefrontal cortex. Neurobiology of Aging, 12(2), 131–136. https://doi.org/10.1016/0197-4580(91)90051-K Google Scholar

Kalaria, R., Andorn, A., Tabaton, M., et al. (1989). Adrenergic receptors in aging and Alzheimer’s disease: Increased β2‐receptors in prefrontal cortex and hippocampus. Journal of Neurochemistry, 53(6), 1772–1781. https://doi.org/10.1111/j.1471-4159.1989.tb09242.x Google Scholar

Karrer, T. M., Josef, A. K., Mata, R., et al. (2017). Reduced dopamine receptors and transporters but not synthesis capacity in normal aging adults: A meta-analysis. Neurobiology of Aging, 57, 36–46. https://doi.org/10.1016/j.neurobiolaging.2017.05.006 Google Scholar

Kish, S. J., Shannak, K., Rajput, A., Deck, J. H., & Hornykiewicz, O. (1992). Aging produces a specific pattern of striatal dopamine loss: Implications for the etiology of idiopathic Parkinson’s disease. Journal of Neurochemistry, 58(2), 642–648. https://doi.org/10.1111/j.1471-4159.1992.tb09766.x Google Scholar

Klinkenberg, I., Sambeth, A., & Blokland, A. (2011). Acetylcholine and attention. Behavioural Brain Research, 221(2), 430–442. https://doi.org/10.1016/j.bbr.2010.11.033 Google Scholar

Koss, M. C. (1986). Pupillary dilation as an index of central nervous system α2-adrenoceptor activation. Journal of Pharmacological Methods, 15(1), 1–19. https://doi.org/10.1016/0160-5402(86)90002-1 Google Scholar

Lavi, S., Nevo, O., Thaler, I., et al. (2007). Effect of aging on the cardiovascular regulatory systems in healthy women. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 292(2), R788–R793. https://doi.org/10.1152/ajpregu.00352.2006 Google Scholar

Lee, T.-H., Greening, S. G., Ueno, T., et al. (2018). Arousal increases neural gain via the locus coeruleus–noradrenaline system in younger adults but not in older adults. Nature Human Behaviour, 2(5), 356–366. https://dx.doi.org/10.1038%2Fs41562-018-0344-1 Google Scholar

Lemstra, A. W., Eikelenboom, P., & van Gool, W. A. (2003). The cholinergic deficiency syndrome and its therapeutic implications. Gerontology, 49(1), 55–60. https://doi.org/10.1159/000066508 Google Scholar

Li, S.-C., & Rieckmann, A. (2014). Neuromodulation and aging: Implications of aging neuronal gain control on cognition. Current Opinion in Neurobiology, 29, 148–158. https://doi.org/10.1016/j.conb.2014.07.009 Google Scholar

Liguori, C., Nuccetelli, M., Izzi, F., et al. (2016). Rapid eye movement sleep disruption and sleep fragmentation are associated with increased orexin-A cerebrospinal-fluid levels in mild cognitive impairment due to Alzheimer’s disease. Neurobiology of Aging, 40, 120–126. https://doi.org/10.1016/j.neurobiolaging.2016.01.007 Google Scholar

Lipsitz, L. A., Mietus, J., Moody, G. B., & Goldberger, A. L. (1990). Spectral characteristics of heart rate variability before and during postural tilt. Relations to aging and risk of syncope. Circulation, 81(6), 1803–1810. https://doi.org/10.1161/01.cir.81.6.1803 Google Scholar

Ma, S., Hangya, B., Leonard, C. S., Wisden, W., & Gundlach, A. L. (2018). Dual-transmitter systems regulating arousal, attention, learning and memory. Neuroscience and Biobehavioral Reviews, 85, 21–33. https://doi.org/10.1016/j.neubiorev.2017.07.009 Google Scholar

Mather, M. (in press). The locus coeruleus-norepinephrine system role in cognition and how it changes with aging. In Poeppel, D., Mangun, G., & Gazzaniga, M. (Eds.), The cognitive neurosciences. Cambridge, MA: MIT Press.Google Scholar

Mather, M., & Harley, C. W. (2016). The locus coeruleus: Essential for maintaining cognitive function and the aging brain. Trends in Cognitive Sciences, 20, 214–226. https://doi.org/10.1016/j.tics.2016.01.001 Google Scholar

Matsumura, T., Nakayama, M., Nomura, A., et al. (2002). Age-related changes in plasma orexin-A concentrations. Experimental Gerontology, 37(8), 1127–1130. https://doi.org/10.1016/s0531-5565(02)00092-x Google Scholar

McEwen, B. S. (2004). Protection and damage from acute and chronic stress – allostasis and allostatic overload and relevance to the pathophysiology of psychiatric disorders. Annals of the New York Academy of Sciences, 1032, 1–7. https://doi.org/10.1196/annals.1314.001 Google Scholar

McGeer, P. L., McGeer, E. G., & Suzuki, J. S. (1977). Aging and extrapyramidal function. Archives of Neurology, 34(1), 33–35. http://dx.doi.org/10.1001/archneur.1977.00500130053010 Google Scholar

Meltzer, C. C., Smith, G., DeKosky, S. T., et al. (1998). Serotonin in aging, late-life depression, and Alzheimer’s disease: The emerging role of functional imaging. Neuropsychopharmacology, 18(6), 407–430. https://doi.org/10.1016/S0893-133X(97)00194-2 Google Scholar

Motawaj, M., Peoc’h, K., Callebert, J., & Arrang, J.-M. (2010). CSF levels of the histamine metabolite tele-methylhistamine are only slightly decreased in Alzheimer’s disease. Journal of Alzheimer’s Disease, 22(3), 861–871. https://doi.org/10.3233/JAD-2010-100381 Google Scholar

Mouton, P. R., Pakkenberg, B., Gundersen, H. J., & Price, D. L. (1994). Absolute number and size of pigmented locus coeruleus neurons in young and aged individuals. Journal of Chemical Neuroanatomy, 7(3), 185–190. https://doi.org/10.1016/0891-0618(94)90028-0 Google Scholar

Nater, U. M., Hoppmann, C. A., & Scott, S. B. (2013). Diurnal profiles of salivary cortisol and alpha-amylase change across the adult lifespan: Evidence from repeated daily life assessments. Psychoneuroendocrinology, 38(12), 3167–3171. https://doi.org/10.1016/j.psyneuen.2013.09.008 Google Scholar

Nater, U. M., & Rohleder, N. (2009). Salivary alpha-amylase as a non-invasive biomarker for the sympathetic nervous system: Current state of research. Psychoneuroendocrinology, 34(4), 486–496. https://doi.org/10.1016/j.psyneuen.2009.01.014 Google Scholar

Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007). Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology, 191(3), 507–520. https://doi.org/10.1007/s00213-006-0502-4 Google Scholar

Nixon, J. P., Mavanji, V., Butterick, T. A., et al. (2015). Sleep disorders, obesity, and aging: The role of orexin. Ageing Research Reviews, 20, 63–73. https://doi.org/10.1016/j.arr.2014.11.001 Google Scholar

Ohm, T., Busch, C., & Bohl, J. (1997). Unbiased estimation of neuronal numbers in the human nucleus coeruleus during aging. Neurobiology of Aging, 18(4), 393–399. https://doi.org/10.1016/s0197-4580(97)00034-1 Google Scholar

Perry, E. K., Gibson, P. H., Blessed, G., Perry, R. H., & Tomlinson, B. E. (1977). Neurotransmitter enzyme abnormalities in senile dementia: Choline acetyltransferase and glutamic acid decarboxylase activities in necropsy brain tissue. Journal of the Neurological Sciences, 34(2), 247–265. https://doi.org/10.1016/0022-510x(77)90073-9 Google Scholar

Peskind, E. R., Wingerson, D., Murray, S., et al. (1995). Effects of Alzheimer’s disease and normal aging on cerebrospinal fluid norepinephrine responses to yohimbine and clonidine. Archives of General Psychiatry, 52(9), 774–782. https://doi.org/10.1001/archpsyc.1995.03950210068012 Google Scholar

Pfaff, D. W. (2006). Brain arousal and information theory. Cambridge, MA: Harvard University Press.Google Scholar

Prell, G. D., Khandelwal, J. K., Burns, R. S., LeWitt, P. A., & Green, J. P. (1990). Influence of age and gender on the levels of histamine metabolites and pros-methylimidazoleacetic acid in human cerebrospinal fluid. Archives of Gerontology and Geriatrics, 11(1), 85–95. https://doi.org/10.1016/0167-4943(90)90059-F Google Scholar

Rapoport, S. I., Schapiro, M. B., & May, C. (2004). Reduced brain delivery of homovanillic acid to cerebrospinal fluid during human aging. Archives of Neurology, 61(11), 1721–1724. https://doi.org/10.1001/archneur.61.11.1721 Google Scholar

Raskind, M. A., Peskind, E. R., Holmes, C., & Goldstein, D. S. (1999). Patterns of cerebrospinal fluid catechols support increased central noradrenergic responsiveness in aging and Alzheimer’s disease. Biological Psychiatry, 46(6), 756–765. https://doi.org/10.1016/s0006-3223(99)00008-6 Google Scholar

Raskind, M. A., Peskind, E. R., Veith, R. C., et al. (1988). Increased plasma and cerebrospinal fluid norepinephrine in older men: Differential suppression by clonidine. Journal of Clinical Endocrinology and Metabolism, 66(2), 438–443. https://doi.org/10.1210/jcem-66-2-438 Google Scholar

Robinson, D. S. (1975). Changes in monoamine oxidase and monoamines with human development and aging. In Thorbecke, G. J. (Ed.), Biology of Aging and Development (pp. 203–212). Boston, MA: Springer US.Google Scholar

Rodríguez, J. J., Noristani, H. N., & Verkhratsky, A. (2012). The serotonergic system in ageing and Alzheimer’s disease. Progress in Neurobiology, 99(1), 15–41. https://doi.org/10.1016/j.pneurobio.2012.06.010 Google Scholar

Satpute, A. B., Kragel, P. A., Barrett, L. F., Wager, T. D., & Bianciardi, M. (2018). Deconstructing arousal into wakeful, autonomic and affective varieties. Neuroscience Letters, 693, 19–28. https://doi.org/10.1016/j.neulet.2018.01.042 Google Scholar

Schliebs, R., & Arendt, T. (2011). The cholinergic system in aging and neuronal degeneration. Behavioural Brain Research, 221(2), 555–563. https://doi.org/10.1016/j.bbr.2010.11.058 Google Scholar

Seals, D. R., & Esler, M. D. (2000). Human ageing and the sympathoadrenal system. Journal of Physiology, 528(3), 407–417. https://dx.doi.org/10.1111%2Fj.1469-7793.2000.00407.x Google Scholar

Spector, R., Snodgrass, S. R., & Johanson, C. E. (2015). A balanced view of the cerebrospinal fluid composition and functions: Focus on adult humans. Experimental Neurology, 273, 57–68. https://doi.org/10.1016/j.expneurol.2015.07.027 Google Scholar

Strahler, J., Berndt, C., Kirschbaum, C., & Rohleder, N. (2010). Aging diurnal rhythms and chronic stress: Distinct alteration of diurnal rhythmicity of salivary α-amylase and cortisol. Biological Psychology, 84(2), 248–256. https://doi.org/10.1016/j.biopsycho.2010.01.019 Google Scholar

Szot, P., Leverenz, J. B., Peskind, E. R., et al. (2000). Tyrosine hydroxylase and norepinephrine transporter mRNA expression in the locus coeruleus in Alzheimer’s disease. Molecular Brain Research, 84(1), 135–140. https://doi.org/10.1016/S0169-328X(00)00168-6 Google Scholar

Szot, P., White, S. S., Greenup, J. L., et al. (2006). Compensatory changes in the noradrenergic nervous system in the locus ceruleus and hippocampus of postmortem subjects with Alzheimer’s disease and dementia with Lewy bodies. Journal of Neuroscience, 26(2), 467–478. https://doi.org/10.1523/JNEUROSCI.4265-05.2006 Google Scholar

Takagi, H., Morishima, Y., Matsuyama, T., et al. (1986). Histaminergic axons in the neostriatum and cerebral cortex of the rat: A correlated light and electron microscopic immunocytochemical study using histidine decarboxylase as a marker. Brain Research, 364(1), 114–123. https://doi.org/10.1016/0006-8993(86)90992-3 Google Scholar

Theofilas, P., Ehrenberg, A. J., Dunlop, S., et al. (2017). Locus coeruleus volume and cell population changes during Alzheimer’s disease progression: A stereological study in human postmortem brains with potential implication for early-stage biomarker discovery. Alzheimer’s and Dementia, 13(3), 236–246. https://doi.org/10.1016/j.jalz.2016.06.2362 Google Scholar

Tyree, S. M., & de Lecea, L. (2017). Optogenetic investigation of arousal circuits. International Journal of Molecular Sciences, 18(8), 1773. https://doi.org/10.3390/ijms18081773 Google Scholar

Ursin, R. (2002). Serotonin and sleep. Sleep Medicine Reviews, 6(1), 55–67. https://doi.org/10.1053/smrv.2001.0174 Google Scholar

Volkow, N. D., Wise, R. A., & Baler, R. (2017). The dopamine motive system: Implications for drug and food addiction. Nature Reviews Neuroscience, 18, 741–752. https://doi.org/10.1038/nrn.2017.130 Google Scholar

Wada, H., Inagaki, N., Yamatodani, A., & Watanabe, T. (1991). Is the histaminergic neuron system a regulatory center for whole-brain activity? Trends in Neurosciences, 14(9), 415–418. https://doi.org/10.1016/0166-2236(91)90034-r Google Scholar

Weinshenker, D. (2018). Long road to ruin: Noradrenergic dysfunction in neurodegenerative disease. Trends in Neurosciences, 41(4), 211–223. https://doi.org/10.1016/j.tins.2018.01.010.Google Scholar

White, M., Courtemanche, M., Stewart, D. J., et al. (1997). Age-and gender-related changes in endothelin and catecholamine release, and in autonomic balance in response to head-up tilt. Clinical Science, 93(4), 309–316. http://doi.org/10.1042/cs0930309 Google Scholar

Winblad, B., Hardy, J., Bäckman, L., & Nilsson, L. G. (1985). Memory function and brain biochemistry in normal aging and in senile dementia. Annals of the New York Academy of Sciences, 444(1), 255–268. https://doi.org/10.1111/j.1749-6632.1985.tb37595.x Google Scholar

Wisor, J. P. (2018). Dopamine and wakefulness: Pharmacology, genetics, and circuitry. In Handbook of experimental psychology (pp. 1–15). Berlin: Springer.Google Scholar

Yanai, K., Watanabe, T., Meguro, K., et al. (1992). Age-dependent decrease in histamine H1 receptor in human brains revealed by PET. NeuroReport, 3(5), 433–436. https://doi.org/10.1097/00001756-199205000-00014 Google Scholar

Yo, Y., Nagano, M., Nagano, N., et al. (1994). Effects of age and hypertension on autonomic nervous regulation during passive head-up tilt. Hypertension, 23(Suppl.I), 82–86. https://doi.org/10.1161/01.hyp.23.1_suppl.i82 Google Scholar

Yoon, H. S., Hattori, K., Ogawa, S., et al. (2017). Relationships of cerebrospinal fluid monoamine metabolite levels with clinical variables in major depressive disorder. Journal of Clinical Psychiatry, 78(8), e947–e956. https://doi.org/10.4088/JCP.16m11144 Google Scholar

Yu, X., Ye, Z., Houston, C. M., et al. (2015). Wakefulness is governed by GABA and histamine cotransmission. Neuron, 87(1), 164–178. https://doi.org/10.1016/j.neuron.2015.06.003 Google Scholar

References

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/s11065-014-9270-9 Google Scholar

Book contents

Part I - Models of Cognitive Aging

Access options

References

References

References

References

References

References

References

References

Save book to Kindle

Save book to Dropbox

Save book to Google Drive