Increased cognitive activity (CA), both current and past, is known to be associated with reduced cognitive decline (Marquine et al., 2012; Wilson et al., 2012, 2013; Hughes et al., 2015; Arfanakis et al., 2016) and the occurrence of Alzheimer’s disease (AD) dementia (Wilson et al., 2002a, 2007; Sattler et al., 2012) in the elderly. However, the exact pathological process underlying this inverse association between CA and AD dementia remains unclear.

To explore the pathological process, several studies investigated the association between the degree of CA and both cerebral amyloid-beta (Aβ) pathology (Landau et al., 2012; Vemuri et al., 2012, 2016, 2017; Wirth et al., 2014; Gidicsin et al., 2015) and neurodegeneration (Valenzuela et al., 2008; Vemuri et al., 2012, 2016, 2017; Gidicsin et al., 2015) using in vivo AD neuroimaging biomarkers. The results from these studies are, however, controversial. One possible explanation for this controversy is that the brain has different physiological or pathological properties during different stages of life. The influence of a certain life experience, such as CA, on the brain may vary at different stages of life. Nevertheless, most previous studies exploring the association between CA and AD biomarkers did not take into account different life stages and simply classified all CA into simple categories, mainly current or past (Landau et al., 2012; Vemuri et al., 2012; Gidicsin et al., 2015), or focused only on either midlife CA (CA_mid) (Vemuri et al., 2016, 2017) or late-life CA (CA_late) (Valenzuela et al., 2008).

Early life (i.e., childhood and early adulthood) is a critical period for brain development characterized by neural plasticity (Chugani et al., 1987; Andersen, 2003; Dekhtyar et al., 2016). Previous studies have shown that early-life CA (CA_early) is associated with reduced late-life cognitive decline and progression to cognitive disorders in later life (Wilson et al., 2013, 2015; Dekhtyar et al., 2016), suggesting that CA_early is closely related to increases in cognitive reserve (CR). CR refers to functional rather than structural or quantitative aspects of the brain, and may explain why some people are more resilient to cognitive decline than others who present with the same level of pathology (Stern, 2012). In contrast, CA in mid or late life stages is less beneficial to individuals, given that brain plasticity is limited during mid- and late life (Leuner et al., 2007; Kolb and Gibb, 2011).

The accumulation of cerebral Aβ pathology begins 10–20 years prior to AD dementia (Villemagne et al., 2013) and its prevalence in non-demented persons typically increases from mid- to late life (Jansen et al., 2015). Thus, cerebral Aβ pathology is rarely observed in the early-life period. Therefore, it is more reasonable to assume that CA or other brain affecting activities may influence the occurrence of Aβ pathology when they are applied in mid or late life rather than in early life. Some studies have reported an association between CA_mid and Aβ deposition (Wirth et al., 2014; Vemuri et al., 2016). In the case of late life, however, about half of the cognitively healthy elderly already have amyloid or neurodegeneration abnormalities and the estimated frequency of normal AD biomarker status decreases continuously with age (Jack et al., 2014). Therefore, the accumulation of amyloid and/or neurodegeneration itself might reduce participation in CA in late life, although a few studies have reported a beneficial effect of congitive training or exercise in late life on brain function as well as cognive performance (Snowball et al., 2013; Shah et al., 2014; Lampit et al., 2015).

We hypothesized that CA during different stages of life is differentially associated with cerebral Aβ pathology and AD-related neurodegeneration in non-demented older adults. More specifically, we formulated three working hypotheses. First, CA_early is inversely associated with the degree of AD-related neurodegeneration, including neuronal or synaptic dysfunction in late life. Second, CA_mid is inversely associated with cerebral Aβ pathology in late life. Third, CA_late is inversely associated with both cerebral Aβ pathology and AD-related neurodegeneration in late life.

To test our hypotheses, we measured cerebral Aβ pathology using ¹¹C-Pittsburgh compound B (PiB)-positron emission tomography (PET) and AD-related neurodegeneration using ¹⁸F-fluorodeoxyglucose (FDG)-PET. We selected cerebral glucose metabolism (CMglu) on FDG-PET as a neurodegeneration marker because it is a reliable index of regional neuronal or synaptic function (Sokoloff, 1981; Jueptner and Weiller, 1995), and specific regional hypometabolism in the temporo-parietal cortices is a reliable and sensitive measure of AD-related neurodegeneration, which appears earlier than structural brain changes on magnetic resonance imaging (MRI) (Jack et al., 2014, 2015, 2016). CA in early, mid, and late life was assessed using a structured questionnaire (Wilson et al., 2005, 2007; Barnes et al., 2006). We further investigated the moderating effects of apolipoprotein E ε4 (APOE4) on the relationship between CA and AD-related brain changes, as CA is particularly protective in APOE4 carriers for the risk of dementia onset (Carlson et al., 2008) and Aβ accumulation (Wirth et al., 2014; Vemuri et al., 2016).

The characteristics of the study participants are shown in Table 1. Both global Aβ deposition and AD-CMglu were weakly correlated with clinical variables. Global Aβ deposition was inversely associated with CERAD total score (Kendall’s tau = -0.19, p < 0.001) and was positively associated with CDR sum of boxes (Kendall’s tau = 0.33, p < 0.001). AD-CMglu showed a similar association with CERAD total score (Kendall’s tau = 0.16, p < 0.001) and was inversely associated with CDR-SOB (Kendall’s tau = -0.26, p < 0.001). Global Aβ deposition and AD-CMglu were weakly correlated with each other (Kendall’s tau = -0.17, P < 0.001). CA_early was moderately correlated with CA_mid (Kendall’s tau = 0.52, P < 0.001) and CA_late (Kendall’s tau = 0.43, P < 0.001). CA_mid and CA_late were also moderately correlated (Kendall’s tau = 0.51, P < 0.001).

The results of this study generally support the hypothesis that CA during different life stages is differentially associated with cerebral Aβ pathology and AD-related neurodegeneration in non-demented older adults. With regard to the three working hypotheses, our findings supported the first hypothesis: CA_early was inversely associated with the degree of AD-related neurodegeneration in late life. In contrast, we could not accept the second hypothesis (i.e., an inverse association between CA_mid and cerebral Aβ pathology in late life) or the third (i.e., a significant inverse association between CA_late and both cerebral Aβ pathology and AD-related neurodegeneration in late life).

Our study is the first to verify the association between CA_early and AD-CMglu in late life, suggesting the presence of a potential mechanism underlying the inverse association between CA_early and AD dementia or cognitive decline. Previous studies have reported that childhood CA could reduce cognitive decline (Wilson et al., 2013) and music or foreign language training in early life was associated with a lower risk of MCI or AD dementia (Wilson et al., 2015). Another study showed that a complex occupation could not compensate for low school grades at a young age to prevent dementia, suggesting that early life is a critical period for increasing CR against dementia (Dekhtyar et al., 2016). To the best of our knowledge, no previous human studies have focused on the direct relationship between CA_early and brain changes in late life.

The association between CA_early and AD-CMglu in late life may be explained by the influence of CA_early on brain developmental processes (Chugani et al., 1987; Benes et al., 1994; Paus et al., 1999; Andersen, 2003), such as synaptogenesis and pruning during the early-life period in particular (Tau and Peterson, 2010). As activity-dependent mechanisms could modulate these processes, especially in early life (Bourgeois et al., 1989; Goodman and Shatz, 1993; Hata and Stryker, 1994; Kleim et al., 1996; Baker et al., 2017), it may be that CA_early promotes synaptogenesis and/or pruning in humans offers a plausible explanation. Metabolic changes measured by FDG-PET may reflect energy expenditures of these processes (Chugani et al., 1987). Other animal studies also suggest that early-life cognitive enrichment has various protective effects on the brain by increasing neurotrophic factors (Wolf et al., 2006) or gene/protein expression related to synaptic plasticity (Costa et al., 2007). However, the influence of common genetic predisposition cannot be completely excluded when addressing the association between CA_early and neurodegeneration in late life. A certain genetic factor may be related to both more CA participation in early life and less neurodegeneration in late life (Fox et al., 2010).

Educational level is associated with the level of CA, regardless of life period (Wilson et al., 2002b, 2013; Barnes et al., 2006; Gidicsin et al., 2015). Our data also show a similar association between years of education and CA_early (Kendall’s tau = 0.43, P < 0.001), CA_mid (Kendall’s tau = 0.48, P < 0.001), and CA_late (Kendall’s tau = 0.45, P < 0.001). A previous report showed that higher-level education, related to early-life enrichment, was associated with reduced age-related alterations of cerebrospinal fluid (CSF) neurodegeneration biomarkers (e.g., CSF total-tau, phosphorylated-tau), but not with amyloid biomarkers (CSF Aβ) (Almeida et al., 2015), similar to our observation for CA_early, Aβ pathology, and neurodegeneration. Nevertheless, because the aim of this study was to investigate the differential effect of CA during different life stages on in vivo AD pathology, we applied a lifetime CA questionnaire instead of simply using years of education as a measure of CA. In the current study, CA_early had a significant inverse relationship with AD-related neurodegeneration, while CA_mid and CA_late did not, after controlling for the level of education. This finding suggests that CA_early itself is potentially protective against late-life neurodegeneration or related cognitive decline, regardless of educational attainment.

An exploratory analysis to investigate the moderating effects of APOE4 revealed no significant interaction between CA_early and APOE4 on AD-CMglu. This finding may be explained by previous reports indicating that APOE4-related cognitive changes generally occur during mid or late life, as opposed to early life (Ruiz et al., 2010; Richter-Schmidinger et al., 2011; Wisdom et al., 2011). A previous meta-analysis of 20 studies also demonstrated that APOE4 was not associated with cognitive function in young adults, adolescents, or children (Ihle et al., 2012).

Midlife CA was not associated with late-life Aβ deposition, which did not support our second hypothesis. Similar to our current finding, Mayo investigators reported no association between CA_mid and late-life Aβ deposition, in general, in non-demented elderly (Vemuri et al., 2016, 2017). They also showed that high CA_mid was associated with lower Aβ deposition in highly educated APOE4 carriers (Vemuri et al., 2016). They proposed that a reverse causality may explain their finding: among highly educated APOE4 carriers, those with higher Aβ deposition in middle age are most likely to experience subtle cognitive symptoms at that time and, consequently, avoid intellectual activity (Vemuri et al., 2016). We conducted similar analyses for highly educated (>14 years) APOE4 carriers, but did not find any significant associations between CA_mid and Aβ deposition. Such discrepancies may be associated with the time frame for CA_mid. We defined CA_mid as CA at the age of 40 years, while Mayo investigators measured CA_mid at 50–65 years of age. Younger individuals are less likely to be influenced by the reverse causality issue. With respect to neurodegeneration, no association between CA_mid and AD-CMglu or AD-ND positivity was observed, which is consistent with previous reports (Vemuri et al., 2016, 2017).

Although not statistically significant, CA_late showed a trend association with global Aβ deposition and AD-CMglu. This may be explained by reverse causality: as previously mentioned in the section “Introduction”; elderly individuals with greater AD pathologies may participate in less CA (Jack et al., 2013; Villemagne et al., 2013). This explanation was further supported by the sensitivity analysis conducted for the CN subgroup. In the CN subgroup, no trend level association was observed between CA_late and AD-related brain changes, which is consistent with previous reports (Landau et al., 2012; Wirth et al., 2014; Gidicsin et al., 2015).

In a sensitivity analysis, we controlled for the effect of CA_early as well as education when analyzing the relationship between CA_mid or CA_late and global Aβ deposition and AD-CMglu, because CA_mid and CA_late were correlated with CA_early. Controlling for CA_early did not change the results, indicating that the negative findings for the relationship of CA_mid or CA_late with AD-related brain change were significant, regardless of the influence of CA_early.

There are several limitations to our study. First, although we used well-validated and reliable questionnaires, retrospective measurements of CA may have a recall bias. Current depression and memory impairment have the potential to affect retrospective measurements based on subjective recall. To mitigate the potential risk, we conducted two sensitivity analyses. We controlled for current depression using the GDS score. This did not change the overall results of our study. Furthermore, the same analyses conducted for the CN group revealed potential associations between CA_early and both AD-CMglu and AD-ND positivity, although not statistically significant. Future long-term prospective studies are required to confirm our findings. Second, as for AD-related neurodegeneration, we measured cerebral glucose metabolism by FDG-PET. Although we defined AD-CMglu or AD-ND positivity by applying AD-signature regions showing typical AD-pattern hypometabolism, glucose metabolism may be influenced by non-AD pathologies, such as vascular pathology and non-AD degenerative conditions (Kato et al., 2016). Tau-PET imaging (Saint-Aubert et al., 2016) or CSF phosphorylated tau measurements (Blennow and Hampel, 2003) may provide information to address this issue. Third, we did not consider the influence of potential confounding factors, which may affect the in vivo AD pathologies, such as physical activity (Shah et al., 2014), social interaction (Bennett et al., 2006), diet (Berti et al., 2015), oxidative stress (Markesbery, 1997), and various physical conditions, including hypertension, diabetes, obesity, and other chronic illnesses (Chui et al., 2012), although we excluded individuals with serious medical or neurological disorders that may influence mental functioning.

Our results support that CA in early life is probably protective against late-life AD-related neurodegeneration, independently of cerebral Aβ pathology. In contrast, CA in midlife and late life appears to have no or limited association with AD-related brain changes, including amyloid pathology and neurodegeneration. With respect to prevention of dementia and cognitive impairment in late life, a cognitively active lifestyle in childhood and early adulthood needs to be more emphasized.

This study protocol was approved by the Institutional Review Boards of Seoul National University Hospital (C-1401-027-547) and SNU-SMG Boramae Center, Seoul, South Korea (26-2015-60), and was conducted in accordance with the recommendations of the current version of the Declaration of Helsinki. All subjects provided written informed consents.

KK and DL designed the study, acquired and interpreted the data, and were major contributors to the writing of the manuscript and critically revising the manuscript for intellectual content. MB, DY, JL, and CK acquired and analyzed the data and helped to draft the manuscript. KK and DY analyzed the imaging data. DL served as the principal investigator and supervised the study. All authors read and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.