Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-26T13:18:06.248Z Has data issue: false hasContentIssue false

4 - Neuroadaptive Trajectories of Healthy Mindspan: From Genes to Neural Networks

from Part I - Models of Cognitive Aging

Published online by Cambridge University Press:  28 May 2020

By
Peter R. Rapp ,
Cristina Bañuelos  and
Craig Myrum
Edited by
Ayanna K. Thomas  and
Angela Gutchess
Ayanna K. Thomas
Affiliation:
Tufts University, Massachusetts
Angela Gutchess
Affiliation:
Brandeis University, Massachusetts
Get access

Summary

Decline and deterioration are prominent features of cognitive aging. Against this background, successful cognitive aging is usually conceptualized as buffering, protecting against, or compensating for disrupted neural integrity in the aged brain. Here we review evidence for a parallel dynamic, comprising a life course trajectory of neuroadaptive plasticity, extending from gene expression to cognitive organization. The encouraging implication is that, alongside the search for treatments that target mechanisms of decline, designing interventions to promote neuroadaptive aging may be a feasible alternative.

Keywords

Arcepigeneticsgene expressionglutamate receptorshippocampusindividual variabilitylong-term depressionlong-term potentiationneurocognitive agingprefrontal cortex
Type
Chapter
Information
The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
 , pp. 62 - 81
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

Adams, M. M., Smith, T. D., Moga, D., et al. (2001). Hippocampal dependent learning ability correlates with N-methyl-D-aspartate (NMDA) receptor levels in CA3 neurons of young and aged rats. Journal of Computational Neurology, 432(2), 230–243. https://doi.org/10.1002/cne.1099Google Scholar
Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. https://doi.org/10.1016/j.tics.2013.08.012Google Scholar
Blalock, E. M., Chen, K. C., Sharrow, K., et al. (2003). Gene microarrays in hippocampal aging: Statistical profiling identifies novel processes correlated with cognitive impairment. Journal of Neuroscience, 23(9), 3807–3819. https://dx.doi.org/10.1523/JNEUROSCI.23-09-03807.2003CrossRefGoogle ScholarPubMed
Boric, K., Munoz, P., Gallagher, M., & Kirkwood, A. (2008). Potential adaptive function for altered long-term potentiation mechanisms in aging hippocampus. Journal of Neuroscience, 28(32), 8034–8039. https://dx.doi.org/10.1523/JNEUROSCI.2036-08.2008Google Scholar
Bories, C., Husson, Z., Guitton, M. J., & De Koninck, Y. (2013). Differential balance of prefrontal synaptic activity in successful versus unsuccessful cognitive aging. Journal of Neuroscience, 33(4), 1344–1356. https://dx.doi.org/10.1523/JNEUROSCI.3258-12.2013CrossRefGoogle ScholarPubMed
Burger, C. (2010). Region-specific genetic alterations in the aging hippocampus: Implications for cognitive aging. Frontiers in Aging Neuroscience, 2, P. 140. https://doi.org/10.3389/fnagi.2010.00140.Google Scholar
Burger, C., Lopez, M. C., Baker, H. V., Mandel, R. J., & Muzyczka, N. (2008). Genome-wide analysis of aging and learning-related genes in the hippocampal dentate gyrus. Neurobiology of Learning and Memory, 89(4), 379–396. https://doi.org/10.3389/fnagi.2010.00140Google Scholar
Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 7(1), 30–40. https://doi.org/10.1038/nrn1809Google Scholar
Burwell, R. D., & Gallagher, M. (1993). A longitudinal study of reaction time performance in Long-Evans rats. Neurobiology of Aging, 14(1), 57–64. https://doi.org/10.1016/0197-4580(93)90023-5CrossRefGoogle ScholarPubMed
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.0033249CrossRefGoogle ScholarPubMed
Fabiani, M. (2012). It was the best of times, it was the worst of times: A psychophysiologist’s view of cognitive aging. Psychophysiology, 49(3), 283–304. https://doi.org/10.1111/j.1469-8986.2011.01331.xGoogle Scholar
Fletcher, B. R., Hill, G. S., Long, J. M., et al. (2014). A fine balance: Regulation of hippocampal Arc/Arg3.1 transcription, translation and degradation in a rat model of normal cognitive aging. Neurobiology of Learning and Memory, 115, 58–67. https://doi.org/10.1016/j.nlm.2014.08.007CrossRefGoogle 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-I3.0.CO;2-I>CrossRefGoogle ScholarPubMed
Freund, T. F., & Gulyas, A. I. (1997). Inhibitory control of GABAergic interneurons in the hippocampus. Canadian Journal of Physiology and Pharmacology, 75(5), 479–487. https://doi.org/10.1139/y97-033Google Scholar
Geinisman, Y., Ganeshina, O., Yoshida, R., et al. (2004). Aging, spatial learning, and total synapse number in the rat CA1 stratum radiatum. Neurobiology of Aging, 25(3), 407–416. https://doi.org/10.1016/j.neurobiolaging.2003.12.001Google Scholar
Gray, D. T., & Barnes, C. A. (2015). Distinguishing adaptive plasticity from vulnerability in the aging hippocampus. Neuroscience, 309, 17–28. https://doi.org/10.1016/j.neuroscience.2015.08.001Google Scholar
Gutchess, A. (2014). Plasticity of the aging brain: New directions in cognitive neuroscience. Science, 346(6209), 579–582. https://dx.doi.org/10.1126/science.1254604Google Scholar
Guzowski, J. F., Lyford, G. L., Stevenson, G. D., et al. (2000). Inhibition of activity-dependent arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and the consolidation of long-term memory. Journal of Neuroscience, 20(11), 3993–4001. https://dx.doi.org/10.1523/jneurosci.20-11-03993.2000Google Scholar
Haberman, R. P., Colantuoni, C., Stocker, A. M., et al. (2011). Prominent hippocampal CA3 gene expression profile in neurocognitive aging. Neurobiology of Aging, 32(9), 1678–1692. https://dx.doi.org/10.1016/j.neurobiolaging.2009.10.005Google Scholar
Haberman, R. P., Koh, M. T., & Gallagher, M. (2017). Heightened cortical excitability in aged rodents with memory impairment. Neurobiology of Aging, 54, 144–151. https://doi.org/10.1016/j.neurobiolaging.2016.12.021CrossRefGoogle ScholarPubMed
Haberman, R. P., Quigley, C. K., & Gallagher, M. (2012). Characterization of CpG island DNA methylation of impairment-related genes in a rat model of cognitive aging. Epigenetics, 7(9), 1008–1019. https://doi.org/10.4161/epi.21291Google Scholar
Hara, Y., Punsoni, M., Yuk, F., et al. (2012). Synaptic distributions of GluA2 and PKMzeta in the monkey dentate gyrus and their relationships with aging and memory. Journal of Neuroscience, 32(21), 7336–7344. https://doi.org/10.1523/JNEUROSCI.0605-12.2012CrossRefGoogle ScholarPubMed
Hernandez, A. R., Reasor, J. E., Truckenbrod, L. M., et al. (2017). Medial prefrontal-perirhinal cortical communication is necessary for flexible response selection. Neurobiology of Learning and Memory, 137, 36–47. https://dx.doi.org/10.1016/j.nlm.2016.10.012Google Scholar
Hernandez, A. R., Reasor, J. E., Truckenbrod, L. M., et al. (2018). Dissociable effects of advanced age on prefrontal cortical and medial temporal lobe ensemble activity. Neurobiology of Aging, 70, 217–232. https://doi.org/10.1016/j.neurobiolaging.2018.06.028CrossRefGoogle ScholarPubMed
Ianov, L., De Both, M., Chawla, M. K., et al. (2017). Hippocampal transcriptomic profiles: Subfield vulnerability to age and cognitive impairment. Frontiers in Aging Neuroscience, 9, p. 383. https://dx.doi.org/10.3389/fnagi.2017.00383Google Scholar
Ianov, L., Rani, A., Beas, B. S., Kumar, A., & Foster, T. C. (2016). Transcription profile of aging and cognition-related genes in the medial prefrontal cortex. Frontiers in Aging Neuroscience, 8, p. 113. https://dx.doi.org/10.3389/fnagi.2016.00113Google Scholar
Jeune, H. L., Cécyre, D., Rowe, W., Meaney, M. J., & Quirion, R. (1996). Ionotropic glutamate receptor subtypes in the aged memory-impaired and unimpaired Long–Evans rat. Neuroscience, 74(2), 349–363. https://dx.doi.org/10.1016/0306-4522(96)00213-8CrossRefGoogle ScholarPubMed
Kaeberlein, M., Rabinovitch, P. S., & Martin, G. M. (2015). Healthy aging: The ultimate preventative medicine. Science, 350(6265), 1191–1193. https://dx.doi.org/10.1126/science.aad3267Google Scholar
Lee, H. K., Min, S. S., Gallagher, M., & Kirkwood, A. (2005). NMDA receptor-independent long-term depression correlates with successful aging in rats. Nature Neuroscience, 8(12), 1657–1659. https://dx.doi.org/10.1038/nn1586Google Scholar
Lindenberger, U. (2014). Human cognitive aging: Corriger la fortune? Science, 346(6209), 572–578. https://dx.doi.org/10.1126/science.1254403Google Scholar
McQuail, J. A., Johnson, S. A., Burke, S. N., & Bizon, J. L. (2018). Rat models of cognitive aging. In Ram, J. and Conn, P.M. (Eds.), Conn’s handbook of models for human aging (2nd ed., pp. 211–230). Cambridge, MA: Academic Press.Google Scholar
Meunier, D., Stamatakis, E. A., & Tyler, L. K. (2014). Age-related functional reorganization, structural changes, and preserved cognition. Neurobiology of Aging, 35(1), 42–54. https://doi.org/10.1016/j.neurobiolaging.2013.07.003Google Scholar
Migues, P. V., Hardt, O., Wu, D. C., et al. (2010). PKMzeta maintains memories by regulating GluR2-dependent AMPA receptor trafficking. Nature Neuroscience, 13(5), 630–634. https://dx.doi.org/10.1038/nn.2531Google Scholar
Morrison, J. H., & Baxter, M. G. (2012). The ageing cortical synapse: Hallmarks and implications for cognitive decline. Nature Reviews Neuroscience, 13(4), 240–250. https://dx.doi.org/10.1038/nrn3200Google Scholar
Nicholson, D. A., Yoshida, R., Berry, R. W., Gallagher, M., & Geinisman, Y. (2004). Reduction in size of perforated postsynaptic densities in hippocampal axospinous synapses and age-related spatial learning impairments. Journal of Neuroscience, 24(35), 7648–7653. https://dx.doi.org/10.1523/JNEUROSCI.1725-04.2004CrossRefGoogle ScholarPubMed
Pelleymounter, M., Beatty, G., & Gallagher, M. (1990). Hippocampal 3H-CPP binding and spatial learning deficits in aged rats. Psychobiology, 18(3), 298–304. https://dx.doi.org/10.3758/BF03327247Google Scholar
Rapp, P. R. (2009). Aging and memory in animals. In Squire, L. R. (Ed.), Encyclopedia of neuroscience (Vol. 1, pp. 167–174). Oxford: Academic Press.CrossRefGoogle Scholar
Rapp, P. R., & Gallagher, M. (1996). Preserved neuron number in the hippocampus of aged rats with spatial learning deficits. Proceedings of the National Academy of Sciences USA, 93, 9926–9930. https://dx.doi.org/10.1073/pnas.93.18.9926Google Scholar
Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Sciences, 17(3), 177–182.Google Scholar
Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing discoveries about the aging mind. Current Opinion in Neurobiology, 15(2), 245–251. https://dx.doi.org/10.1111/j.1467-8721.2008.00570.xGoogle Scholar
Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355–370. https://dx.doi.org/10.1007/s11065-014-9270-9Google Scholar
Rogalski, E., Gefen, T., Mao, Q., et al. (2018). Cognitive trajectories and spectrum of neuropathology in SuperAgers: The first 10 cases. Hippocampus, 29(5), 458–467. https://dx.doi.org/10.1002/hipo.22828Google Scholar
Rosenzweig, E. S., & Barnes, C. A. (2003). Impact of aging on hippocampal function: Plasticity, network dynamics, and cognition. Progress in Neurobiology, 69(3), 143–179. https://doi.org/10.1016/S0301-0082(02)00126-0Google Scholar
Rowe, W. B., Blalock, E. M., Chen, K. C., et al. (2007). Hippocampal expression analyses reveal selective association of immediate-early, neuroenergetic, and myelinogenic pathways with cognitive impairment in aged rats. Journal of Neuroscience, 27(12), 3098–3110. https://dx.doi.org/10.1523/JNEUROSCI.4163-06.2007Google Scholar
Schulte, T., Muller-Oehring, E. M., Chanraud, S., et al. (2011). Age-related reorganization of functional networks for successful conflict resolution: A combined functional and structural MRI study. Neurobiology of Aging, 32(11), 2075–2090. https://doi.org/10.1016/j.neurobiolaging.2009.12.002Google Scholar
Shankar, S., Teyler, T. J., & Robbins, N. (1998). Aging differentially alters forms of long-term potentiation in rat hippocampal area CA1. Journal of Neurophysiology, 79(1), 334–341. https://doi.org/10.1152/jn.1998.79.1.334Google Scholar
Shepherd, J. D., Rumbaugh, G., Wu, J., et al. (2006). Arc/Arg3.1 mediates homeostatic synaptic scaling of AMPA receptors. Neuron, 52(3), 475–484. https://doi.org/10.1016/j.neuron.2006.08.034Google Scholar
Shetty, A. K., & Turner, D. A. (1998). Hippocampal interneurons expressing glutamic acid decarboxylase and calcium-binding proteins decrease with aging in Fischer 344 rats. Journal of Computational Neurology, 394(2), 252–269. https://doi.org/10.1002/(SICI)1096-9861(19980504)394:2<252::AID-CNE9>3.0.CO;2-1Google Scholar
Spiegel, A. M., Koh, M. T., Vogt, N. M., et al. (2013). Hilar interneuron vulnerability distinguishes aged rats with memory impairment. Journal of Computational Neurology, 521(15), 3508–3523. https://doi.org/10.1002/cne.23367Google Scholar
Spiegel, A. M., Perez, E. J., Long, J. M., Park, P., & Rapp, P. R. (2014a). Regionally selective decline in hippocampal somatostatin-immunoreactive neuron number in aged rhesus monkeys with memory impairment. Presented at the Society for Neuroscience Annual Meeting, Chicago, IL, November 19.Google Scholar
Spiegel, A. M., Sewal, A. S., & Rapp, P. R. (2014b). Epigenetic contributions to cognitive aging: Disentangling mindspan and lifespan. Learning and Memory, 21(10), 569–574. https://doi.org/10.1101/lm.033506.113Google Scholar
Stanley, D. P., & Shetty, A. K. (2004). Aging in the rat hippocampus is associated with widespread reductions in the number of glutamate decarboxylase-67 positive interneurons but not interneuron degeneration. Journal of Neurochemistry, 89(1), 204–216. https://dx.doi.org/10.1111/j.1471-4159.2004.02318.xGoogle Scholar
Staudinger, U. M. (1999). Older and wiser? Integrating results on the relationship between age and wisdom-related performance. International Journal of Behavioral Development, 23(3), 641–664. https://doi.org/10.1080/016502599383739Google Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. https://doi.org/10.1016/j.neuropsychologia.2009.03.004Google Scholar
Tannenbaum, C., Mayo, N., & Ducharme, F. (2005). Older women’s health priorities and perceptions of care delivery: Results of the WOW health survey. Canadian Medical Association Journal, 173(2), 153–159. https://dx.doi.org/10.1503/cmaj.050059CrossRefGoogle ScholarPubMed
Thome, A., Gray, D. T., Erickson, C. A., Lipa, P., & Barnes, C. A. (2016). Memory impairment in aged primates is associated with region-specific network dysfunction. Molecular Psychiatry, 21(9), 1257–1262. https://dx.doi.org/10.1038/mp.2015.160Google Scholar
Tomas Pereira, I., Gallagher, M., & Rapp, P. R. (2015). Head west or left, east or right: Interactions between memory systems in neurocognitive aging. Neurobiology of Aging, 36(11), 3067–3078. https://doi.org/10.1016/j.neurobiolaging.2015.07.024Google Scholar
Tran, T., Gallagher, M., & Kirkwood, A. (2018). Enhanced postsynaptic inhibitory strength in hippocampal principal cells in high-performing aged rats. Neurobiology of Aging 70, 92–101. https://doi.org/10.1016/j.neurobiolaging.2018.06.008Google Scholar
Verbitsky, M., Yonan, A. L., Malleret, G., et al. (2004). Altered hippocampal transcript profile accompanies an age-related spatial memory deficit in mice. Learning and Memory, 11(3), 253–260. https://doi.org/10.1101/lm.68204Google Scholar
Wagster, M. V., King, J. W., Resnick, S. M., & Rapp, P. R. (2012). The 87%. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 67(7), 739–740. https://doi.org/10.1093/gerona/gls140Google Scholar
Waung, M. W., Pfeiffer, B. E., Nosyreva, E. D., Ronesi, J. A., & Huber, K. M. (2008). Rapid translation of Arc/Arg3.1 selectively mediates mGluR-dependent LTD through persistent increases in AMPAR endocytosis rate. Neuron, 59(1), 84–97. https://doi.org/10.1016/j.neuron.2008.05.014Google Scholar
Wilson, I. A., Ikonen, S., Gallagher, M., Eichenbaum, H., & Tanila, H. (2005). Age-associated alterations of hippocampal place cells are subregion specific. Journal of Neuroscience, 25(29), 6877–6886. https://dx.doi.org/10.1523/JNEUROSCI.1744-05.2005CrossRefGoogle ScholarPubMed
Witter, M. P., & Amaral, D. G. (2004). Hippocampal formation. In Paxinos, G. (Ed.), The rat nervous system (pp. 635–704). Cambridge, MA: Academic Press.Google Scholar
Yang, Y. J., Chen, H. B., Wei, B., et al. (2015). Cognitive decline is associated with reduced surface GluR1 expression in the hippocampus of aged rats. Neuroscience Letters, 591, 176–181. https://doi.org/10.1016/j.neulet.2015.02.030Google Scholar
Zhao, X., Rosenke, R., Kronemann, D., et al. (2009). The effects of aging on N-methyl-D-aspartate receptor subunits in the synaptic membrane and relationships to long-term spatial memory. Neuroscience, 162(4), 933–945. https://doi.org/10.1016/j.neuroscience.2009.05.018Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×