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. Author manuscript; available in PMC: 2021 Jan 25.
Published in final edited form as: Psychol Sci Public Interest. 2020 Aug;21(1):1–5. doi: 10.1177/1529100620941808

How do cognitively stimulating activities affect cognition and the brain throughout life?

Mara Mather 1
PMCID: PMC7831356  NIHMSID: NIHMS1662614  PMID: 32772802

The effects of mental stimulation can extend for many years past the initial learning phase. Cognitively stimulating experiences early in life, such as extra years of education or becoming fluent in a second language, are associated with cognitive benefits years later. Those who had those earlier cognitively stimulating experiences perform better at the same level of Alzheimer’s pathology than those who did not get extra education or regularly switch between languages. Consider, for example, a post-mortem sample of 2372 participants who met neuropathological criteria for an Alzheimer’s diagnosis (Roe, Xiong, Miller, & Morris, 2007). Within this large cohort who suffered from Alzheimer’s disease, there were no significant correlations between education and neuropathology diagnosis stage. Thus, education did not have a detectable impact on levels of brain pathology. However, clinical diagnoses before death were associated with education. On average, those who had received a diagnosis of Alzheimer’s had 2–3 years less education than those who had been clinically diagnosed as not having dementia. Thus, actual levels of brain pathology were not significantly related to education, whereas late life cognitive functioning was.

The concept of ‘cognitive reserve’ was formulated to account for the fascinating phenomenon in which education and stimulating occupations somehow allow the aging brain to cope better even as Alzheimer’s pathology accumulates (Stern et al., 1994). Over the years since it was proposed, the concept of cognitive reserve has generated a lot of interest as well as debate (Anthony & Lin, 2017; Cabeza et al., 2018; Jones et al., 2011; Medaglia, Pasqualetti, Hamilton, Thompson-Schill, & Bassett, 2017; Satz, Cole, Hardy, & Rassovsky, 2011; Stern et al., 2019; Stern et al., 2018).

Does education create cognitive reserve or just a higher cognitive peak?

In their paper, Lövdén et al. review existing findings to examine how education is associated with cognitive functioning across the life span. They first confirm that the association between education attainment and cognitive function holds up across all adult ages as well as across birth cohorts, cultural contexts, races and genders. They then proceed to address the question of whether this association results from education attainment reflecting peak cognitive ability achieved in the process of that education or from a relationship between education attainment and the rate of age-related decline in cognition.

This is a critical question for the concept of cognitive reserve. In general, the concept of a reserve is something that is not necessary or influential initially but can play an important role later on. If educational attainment creates reserve capacity, that should be a capacity that is not needed initially but does become useful later on. Thus, if increasing education creates cognitive reserve, this should be reflected in more than just differences in the peak cognitive ability achieved. It should change the pattern of cognitive decline seen across adulthood. This altered pattern could take multiple forms. Obviously, cognitive reserve could slow the rate of cognitive decline, as reserve capacity progressively fills in to compensate for loss in function. However, it could also lead to non-linear effects. The reserve capacity might be most useful early or late in the progression of cognitive decline and may itself fade in strength.

In general, the non-linear nature of cognitive decline (Verhaeghen & Salthouse, 1997) poses a particular challenge for examining the relationship between educational attainment and age-related decline (see Lövdén et al.’s Fig. 4 for an illustration of this). The authors make the important point that findings that more highly educated people show steeper cognitive decline after dementia diagnosis can be entirely accounted for by methodological biases. Longitudinal data across the whole adult life course would be ideal for assessing this issue appropriately, but such studies are lacking. Based on their careful review of the literature that more heavily weights studies that have longitudinal data and use appropriate statistical approaches, Lövdén et al. conclude that the association between educational attainment and late-life cognitive decline is small and inconsistent, and that effect size estimates suggest it is at least 10 times smaller than the relationship between educational attainment and level of cognitive function. Thus, Lövdén et al.’s review suggests that ‘cognitive reserve’ is somewhat of a misnomer, as the benefits from education are not kept in reserve but instead exert their impact mainly by influencing the level of peak cognitive performance achieved before cognitive decline. Rates of decline from one’s peak cognitive ability are similar across levels of education.

A paper published after Lövdén et al.’s literature search was completed illustrates their point in a large cohort (Wilson et al., 2019). In this study, over 2000 older adults with at least four years of annual follow-up showed a significant relationship between education level and baseline cognitive function at entry in the study, but no relationship between education and the rate of cognitive decline.

The similar rates of decline across those with different education levels not only suggests that reserve capacity plays a minimal role in rates of cognitive decline, but also contrasts with theoretical predictions that education should attenuate rates of cognitive decline because it is associated with favorable life conditions such as increased occupational status.

What can be done after formal education ends?

For researchers (and individuals) interested in how to optimize late-life cognition, the robust association between education attainment and cognitive functioning throughout life is yet another reason among many to push for increasing early-life educational opportunities. However, the lack of a clear link between education and rates of cognitive decline raises questions about best approaches to protect or even enhance late-life cognition after full-time education has ended. While it is clear that education in childhood and early adulthood has lifelong benefits, does education (or similarly mentally stimulating activities) also help later in life? In particular, how can we best design effective interventions to benefit cognition in those who are already older?

Some aspects of these difficult questions about intervening during adulthood have already been tackled in previous Psychological Science in the Public Interest (PSPI) reviews. Hertzog et al. (2008) make the case that the potential for positive plasticity is maintained in older adult cognition. They review studies indicating that older adults’ cognition can be enhanced through training (especially when the training requires executive coordination, such as complex video games, task-switching and divided-attention tasks) and that a lifestyle that is more intellectually stimulating is associated with better maintenance of cognition. In contrast with this positive outlook, a more recent PSPI review concluded that, in general (across age groups), brain-training programs clearly improve performance on the brain-training tasks themselves but beyond that show quite limited efficacy, especially when it comes to everyday cognitive performance (Simons et al., 2016).

Why are later life interventions apparently less successful than early life education?

Thus, these previous PSPI reviews indicate that, although there is documented potential to intervene to enhance cognition in adulthood, most of the current approaches to enhancing adult cognition – unlike the robust effects of education on peak cognition reviewed by Lövdén et al. – have yielded underwhelming results. This is likely due to multiple reasons. One, of course, is that brain training is simply not as intensive nor as engaging as full-time schooling during childhood, adolescence and early adulthood. Another is that the potential for plasticity may decrease with age (Kühn & Lindenberger, 2016; Power & Schlaggar, 2017).

Yet another possibility suggested by emerging neuroscience research is that, while mental stimulation has many benefits for the brain, it also has metabolic costs and the cost-benefit equation may shift in aging. The benefits of education and cognitive activity have been posited to accrue in part due to the involvement of the locus coeruleus-norepinephrine system (Robertson, 2014, 2013; see also Clewett et al., 2016; Mather, 2020; Mather & Harley, 2016). Education and cognitive activity involve novelty, cognitive effort, and motivation – all of which activate the locus coeruleus, which serves as the brain’s arousal hub region (Mather, 2020), and which focuses attention and working memory in the moment (Mather, Clewett, Sakaki, & Harley, 2016) while also enhancing the likelihood of synaptic changes (Inoue et al., 2013; Maity, Rah, Sonenberg, Gkogkas, & Nguyen, 2015; Palacios-Filardo & Mellor, 2019; Salgado, Kohr, & Trevino, 2012; Salgado, Treviño, & Atzori, 2016). But the synaptic activity involved in this enhanced attentional focus and that is required for synaptic change also has a metabolic cost (Holroyd, 2016; Oyarzabal & Marin-Valencia, 2019). Synaptic activity creates amyloid-β as well as other metabolic waste (Cirrito et al., 2005). In younger adults, this metabolic waste is efficiently disposed of, but in aging the brain’s waste disposal system tends to be less effective (Benveniste et al., 2019). In particular, deep sleep seems to be important – either because it enhances waste clearance or it temporarily reduces the level of synaptic activity. Older adults who have poor deep sleep tend to have greater amyloid PET amyloid binding and tau protein aggregates (Lucey et al., 2019; Mander et al., 2015) and disrupting sleep increases cerebrospinal levels of amyloid-β and tau (Ju et al., 2017; Holth et al., 2019).

Does education slow the pace of Alzheimer’s pathology accumulation?

Intriguingly, positron emission tomography (PET) amyloid imaging suggests education and cognitive activity may affect the dynamics of amyloid-β in the brain. Those with more education show less PET amyloid binding than those with less education at the same age (Yasuno et al., 2015), an effect that may be specific for those with the APOE genetic risk factor for Alzheimer’s disease (Vemuri et al., 2016). Likewise, those reporting higher lifetime cognitive activity showed diminished PET amyloid binding (Landau et al., 2012; Lyons et al., 2018; Wirth, Villeneuve, La Joie, Marks, & Jagust, 2014; but see mixed effects by brain region in Arenaza-Urquijo et al., 2017; and lack of effect in Vemuri et al., 2012). However, because postmortem analyses of amyloid-β do not tend to find a significant relationship between education and levels of amyloid-β (Farfel et al., 2013; Roe et al., 2007; Wilson et al., 2019), further studies are needed to get a clear and consistent picture of how education affects amyloid-β. One possibility is that the imaging studies reflect something more temporary than the postmortem studies, as a recent study suggests that amyloid PET binding can be affected by just one night’s sleep deprivation (Shokri-Kojori et al., 2018), as is the case for cerebrospinal levels of amyloid-β (Ooms et al., 2014). In particular, as in the context of cognitive ability discussed by Lövdén et al., it will be important to see whether the levels PET amyloid binding show a different rate of longitudinal change depending on education or show a consistent relationship with education level regardless of age. Nevertheless, the findings suggest a fascinating possibility: it could be that those with higher education require less brain activity to get the same cognitive task done, which in turns means that those with higher education produce less metabolic waste during their mental activity, including amyloid-β (Jagust & Mormino, 2011; Karim et al., 2019).

Conclusions

Education is both a life and brain altering process that has lifelong effects. Higher levels of education are associated with higher levels of cognitive ability throughout life. In contrast, postmortem findings show little relationship between levels of Alzheimer’s pathology and education attainment. Lövdén et al.’s review indicates that there is no need to invoke cognitive reserve to explain the discrepancy between brain pathology levels and cognitive function in those with higher education attainment, as a simple effect of education on peak cognition is sufficient to explain why those with more education are able to forestall diagnoses of Alzheimer’s for longer. Many critical questions remain about the brain mechanisms involved. How exactly does education affect brain function through mechanisms seemingly unrelated to Alzheimer’s pathology? Does education affect the efficiency of cognitive processes in the brain? Does the cost/benefit ratio involved in the brain arousal systems activated by mentally stimulating experiences shift across adulthood? As the techniques to examine brain function and structure in vivo continue to improve hopefully we will gain insight into these processes that in turn will lead to improved targeted interventions that enhance cognition throughout adulthood.

References

  1. Anthony M, & Lin F (2017). A Systematic Review for Functional Neuroimaging Studies of Cognitive Reserve Across the Cognitive Aging Spectrum. Archives of Clinical Neuropsychology, 33(8), 937–948. doi: 10.1093/arclin/acx125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arenaza-Urquijo EM, Bejanin A, Gonneaud J, Wirth M, La Joie R, Mutlu J, … Eustache F (2017). Association between educational attainment and amyloid deposition across the spectrum from normal cognition to dementia: neuroimaging evidence for protection and compensation. Neurobiology of Aging, 59, 72–79. [DOI] [PubMed] [Google Scholar]
  3. Benveniste H, Liu X, Koundal S, Sanggaard S, Lee H, & Wardlaw J (2019). The glymphatic system and waste clearance with brain aging: a review. Gerontology, 65(2), 106–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cabeza R, Albert M, Belleville S, Craik FI, Duarte A, Grady CL, … Reuter-Lorenz PA (2018). Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nature Reviews Neuroscience, 19(11), 701–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cirrito JR, Yamada KA, Finn MB, Sloviter RS, Bales KR, May PC, … Holtzman DM (2005). Synaptic activity regulates interstitial fluid amyloid-β levels in vivo. Neuron, 48(6), 913–922. [DOI] [PubMed] [Google Scholar]
  6. Clewett D, Lee TH, Greening SG, Ponzio A, Margalit E, & Mather M (2016). Neuromelanin marks the spot: Identifying a locus coeruleus biomarker of cognitive reserve in healthy aging. Neurobiology of Aging, 37, 117–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Farfel JM, Nitrini R, Suemoto CK, Grinberg LT, Ferretti REL, Leite REP, … Neves RC (2013). Very low levels of education and cognitive reserve: a clinicopathologic study. Neurology, 81(7), 650–657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Hertzog C, Kramer AF, Wilson RS, & Lindenberger U (2008). Enrichment effects on adult cognitive development: can the functional capacity of older adults be preserved and enhanced? Psychological science in the public interest, 9(1), 1–65. [DOI] [PubMed] [Google Scholar]
  9. Holroyd CB (2016). The waste disposal problem of effortful control In Braver T (Ed.), Motivation and Cognitive Control (pp. 235–260). New York: Psychology Press. [Google Scholar]
  10. Holth JK, Fritschi SK, Wang C, Pedersen NP, Cirrito JR, Mahan TE, … Fuller PM (2019). The sleep-wake cycle regulates brain interstitial fluid tau in mice and CSF tau in humans. Science, 363(6429), 880–884. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Inoue W, Baimoukhametova DV, Füzesi T, Cusulin JIW, Koblinger K, Whelan PJ, … Bains JS (2013). Noradrenaline is a stress-associated metaplastic signal at GABA synapses. Nature Neuroscience, 16(5), 605. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Jagust WJ, & Mormino EC (2011). Lifespan brain activity, β-amyloid, and Alzheimer’s disease. Trends in Cognitive Sciences, 15(11), 520–526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Jones RN, Manly J, Glymour MM, Rentz DM, Jefferson AL, & Stern Y (2011). Conceptual and measurement challenges in research on cognitive reserve. Journal of the International Neuropsychological Society: JINS, 17(4), 593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Ju Y-ES, Ooms SJ, Sutphen C, Macauley SL, Zangrilli MA, Jerome G, … Holtzman DM (2017). Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain, 140(8), 2104–2111. doi: 10.1093/brain/awx148 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Karim HT, Tudorascu DL, Cohen A, Price JC, Lopresti B, Mathis C, … Aizenstein HJ (2019). Relationships between executive control circuit activity, amyloid burden, and education in cognitively healthy older adults. The American Journal of Geriatric Psychiatry, 27(12), 1360–1371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kühn S, & Lindenberger U (2016). Research on human plasticity in adulthood: A lifespan agenda Handbook of the psychology of aging (pp. 105–123): Elsevier. [Google Scholar]
  17. Landau SM, Marks SM, Mormino EC, Rabinovici GD, Oh H, O’Neil JP, … Jagust WJ (2012). Association of lifetime cognitive engagement and low β-amyloid deposition. Archives of Neurology, 69(5), 623–629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lucey BP, McCullough A, Landsness EC, Toedebusch CD, McLeland JS, Zaza AM, … Morris JC (2019). Reduced non–rapid eye movement sleep is associated with tau pathology in early Alzheimer’s disease. Science translational medicine, 11(474), eaau6550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lyons CE, Tudorascu D, Snitz BE, Price J, Aizenstein H, Lopez O, … Kamboh I (2018). The relationship of current cognitive activity to brain amyloid burden and glucose metabolism. The American Journal of Geriatric Psychiatry, 26(9), 977–984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Maity S, Rah S, Sonenberg N, Gkogkas CG, & Nguyen PV (2015). Norepinephrine triggers metaplasticity of LTP by increasing translation of specific mRNAs. Learning and Memory, 22(10), 499–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mander BA, Marks SM, Vogel JW, Rao V, Lu B, Saletin JM, … Walker MP (2015). β-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nature Neuroscience, 18(7), 1051–1057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mather M (2020). 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 (pp. 91–101). Cambridge, MA: MIT Press. [Google Scholar]
  23. Mather M, Clewett D, Sakaki M, & Harley CW (2016). Norepinephrine ignites local hotspots of neuronal excitation: How arousal amplifies selectivity in perception and memory. Behavioral and Brain Sciences, 39, e200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Mather M, & Harley CW (2016). The locus coeruleus: Essential for maintaining cognitive function and the aging brain. Trends in Cognitive Sciences, 20, 214–226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Medaglia JD, Pasqualetti F, Hamilton RH, Thompson-Schill SL, & Bassett DS (2017). Brain and cognitive reserve: Translation via network control theory. Neuroscience and Biobehavioral Reviews, 75, 53–64. doi: 10.1016/j.neubiorev.2017.01.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Ooms S, Overeem S, Besse K, Rikkert MO, Verbeek M, & Claassen JA (2014). Effect of 1 night of total sleep deprivation on cerebrospinal fluid β-amyloid 42 in healthy middle-aged men: a randomized clinical trial. JAMA neurology, 71(8), 971–977. [DOI] [PubMed] [Google Scholar]
  27. Oyarzabal A, & Marin-Valencia I (2019). Synaptic energy metabolism and neuronal excitability, in sickness and health. Journal of Inherited Metabolic Disease, 42(2), 220–236. doi: 10.1002/jimd.12071 [DOI] [PubMed] [Google Scholar]
  28. Palacios-Filardo J, & Mellor JR (2019). Neuromodulation of hippocampal long-term synaptic plasticity. Current Opinion in Neurobiology, 54, 37–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Power JD, & Schlaggar BL (2017). Neural plasticity across the lifespan. Wiley Interdisciplinary Reviews: Developmental Biology, 6(1), e216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Robertson IH (2013). A noradrenergic theory of cognitive reserve: implications for Alzheimer’s disease. Neurobiology of Aging, 34(1), 298–308. [DOI] [PubMed] [Google Scholar]
  31. Robertson IH (2014). A right hemisphere role in cognitive reserve. Neurobiology of Aging, 35(6), 1375–1385. [DOI] [PubMed] [Google Scholar]
  32. Roe CM, Xiong C, Miller JP, & Morris JC (2007). Education and Alzheimer disease without dementia: support for the cognitive reserve hypothesis. Neurology, 68(3), 223–228. [DOI] [PubMed] [Google Scholar]
  33. Salgado H, Kohr G, & Trevino M (2012). Noradrenergic ‘tone’ determines dichotomous control of cortical spike-timing-dependent plasticity. Scientific Reports, 2, 7. doi:41710.1038/srep00417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Salgado H, Treviño M, & Atzori M (2016). Layer-and area-specific actions of norepinephrine on cortical synaptic transmission. Brain Research, 1641, 163–176. [DOI] [PubMed] [Google Scholar]
  35. Satz P, Cole MA, Hardy DJ, & Rassovsky Y (2011). Brain and cognitive reserve: mediator (s) and construct validity, a critique. Journal of Clinical and Experimental Neuropsychology, 33(1), 121–130. [DOI] [PubMed] [Google Scholar]
  36. Shokri-Kojori E, Wang G-J, Wiers CE, Demiral SB, Guo M, Kim SW, … Volkow ND (2018). β-Amyloid accumulation in the human brain after one night of sleep deprivation. Proceedings of the National Academy of Sciences, 115(17), 4483–4488. doi: 10.1073/pnas.1721694115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Simons DJ, Boot WR, Charness N, Gathercole SE, Chabris CF, Hambrick DZ, & Stine-Morrow EA (2016). Do “brain-training” programs work? Psychological science in the public interest, 17(3), 103–186. [DOI] [PubMed] [Google Scholar]
  38. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, … Kremen WS (2018). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Stern Y, Chételat G, Habeck C, Arenaza-Urquijo EM, Vemuri P, Estanga A, … Elman JA (2019). Mechanisms underlying resilience in ageing. Nature Reviews Neuroscience, 20(4), 246–246. [DOI] [PubMed] [Google Scholar]
  40. Stern Y, Gurland B, Tatemichi TK, Tang MX, Wilder D, & Mayeux R (1994). Influence of education and occupation on the incidence of Alzheimer’s disease. JAMA, 271(13), 1004–1010. [PubMed] [Google Scholar]
  41. Vemuri P, Lesnick TG, Przybelski SA, Knopman DS, Machulda M, Lowe VJ, … Senjem ML (2016). Effect of intellectual enrichment on AD biomarker trajectories: longitudinal imaging study. Neurology, 86(12), 1128–1135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Vemuri P, Lesnick TG, Przybelski SA, Knopman DS, Roberts RO, Lowe VJ, … Boeve BF (2012). Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Annals of Neurology, 72(5), 730–738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Verhaeghen P, & Salthouse TA (1997). Meta-analyses of age–cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models. Psychological Bulletin, 122(3), 231. [DOI] [PubMed] [Google Scholar]
  44. Wilson RS, Yu L, Lamar M, Schneider JA, Boyle PA, & Bennett DA (2019). Education and cognitive reserve in old age. Neurology, 92(10), e1041–e1050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wirth M, Villeneuve S, La Joie R, Marks SM, & Jagust WJ (2014). Gene–environment interactions: lifetime cognitive activity, APOE genotype, and beta-amyloid burden. Journal of Neuroscience, 34(25), 8612–8617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Yasuno F, Kazui H, Morita N, Kajimoto K, Ihara M, Taguchi A, … Kudo T (2015). Low amyloid-β deposition correlates with high education in cognitively normal older adults: a pilot study. International Journal of Geriatric Psychiatry, 30(9), 919–926. [DOI] [PubMed] [Google Scholar]

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