Rethinking the Reserve with a Translational Approach: Novel Ideas on the Construct and the Interventions

Abstract

The concept of brain, cognitive, and neural reserves has been introduced to account for the apparent discrepancies between neurological damage and clinical manifestations. However, these ideas are yet theoretical suggestions that are not completely assimilated in the clinical routine. The mechanisms of the reserves have been extensively studied in neurodegenerative pathologies, in particular in Alzheimer’s disease. Both human and animal studies addressed this topic by following two parallel pathways. The specific aim of the present review is to attempt to combine the suggestions derived from the two different research fields to deepen the knowledge about reserves. In fact, the achievement of a comprehensive theoretical framework on reserve mechanisms is an essential step to propose well-timed interventions tailored to the clinical characteristics of patients. The present review highlights the importance of addressing three main aspects: the definition of reserve proxy measures, the interaction between reserve level and therapeutic interventions, and the specific time-window of reserve efficacy.

Keywords

Alzheimer’s disease animal studies environmental enrichment human studies MRI reserve mechanisms

Introduction

In Alzheimer’s disease (AD) [1], there is not a clear correlation between neuropathology and cognitive decline [2, 3]. Indeed, patients with an apparent same level of neurological damage (e.g., hippocampal atrophy, global grey matter atrophy, etc.) can show different levels of cognitive impairment. Several years ago, the “reserve” concept [2, 3] was introduced to account for this contradiction.

Brain reserve (BR) is related to the quantitative aspects of the brain (e.g., number of neurons/synapses, brain volumetrics) that may account for differences in the cognitive, behavioral, and functional decline observed in patients [4]. Classical studies of Katzman et al. [5] and Mortimer [6] revealed that patients with larger brains were less affected by dementia, or showed a lower level of cognitive decline. The BR implies that a larger brain can support more neurological damage before it shows clinical and cognitive decline. It is likely that a “clinical threshold” has to be achieved before the appearance of clinical symptoms [4].

Conversely, the cognitive reserve (CR) refers to the differences in cognitive processing, and it is related to the lifetime physical, intellectual, and social activities [4]. The CR implies that the brain actively copes with neurological damages by using compensatory mechanisms or some still available cognitive abilities (such as memory, executive functions, or general intellectual abilities) [3 , 8].

The concepts of BR and CR have been extensively studied both in humans and in animal models. Specifically, Stern and co-workers [9] addressed CR in a study involving patients with AD and subjects without AD symptomatology. The authors showed that individuals with greater reserves are able to tolerate AD pathology better than those with a lower level of CR. As a consequence, in subjects with high reserve, the clinical manifestation of AD appear to be delayed, although their cognitive decline is faster after the clinical diagnosis. A classical postmortem study showed that CR is more able than BR to resist the AD neuropathological process [6]. However, more recently it is becoming clear that the CR depends on the efficiency of neural activity in terms of brain network functioning [4, 8], which in turn is due to cognitive, social, and physical activities experienced across the lifespan. Therefore, the integrity of functional brain networks, rather than the brain structure, seems to be more important to explain the individual differences in tolerating neurodegeneration. More recently, to explain this issue, the “neural reserve” (NR) hypothesis has been proposed [4, 10]. NR is a concept related to the efficiency of neural circuiting and to the ability to process information. Nevertheless, NR may provide the neural basis of CR, focusing the attention on brain networks functioning.

The human studies that investigate the effects of experience on cognitive functions are paralleled by animal studies mainly based on the effects of environmental enrichment (EE). An enriched environment is defined as a combination of complex inanimate and social stimulations [11]. Classically, for rodents it is realized by rearing the animals in cages wider and in groups larger than those generally used. The cages in which the animals are reared contain running wheels and numerous stimulating objects regularly changed and moved, to encourage exploration and physical activity [12 –14]. The enhanced sensorimotor and cognitive stimulation has a valid control condition in the standard rearing, according to which laboratory rodents are usually housed in small groups (one or two rats; three or four mice) and in standard-size cages provided with bedding and ad libitum food and water [15].

The experimental paradigm of the EE provides a useful and controllable means to investigate the mechanisms underlying structural and functional effects of experience. EE represents a good experimental model for the human lifelong experience that provokes the development of BR, CR, and NR [13, 16]. Anyway, it can be also a model for any experiential treatment provided to patients after the occurrence of damage [17]. Moreover, EE overcomes some limitations of human studies. Studying the effects of the enhanced experience in human individuals entails difficulties in comparing subjects in terms of the outcomes of well-defined factors, since they are basically characterized by different genetic patterns and multifarious life experiences. Moreover, directly investigating a number of brain cellular and molecular indices of reserve in living human subjects is not yet possible. By using animals, EE model ensures comparable background before the exposure to the enhanced stimulation and allows the analysis of a large number of cellular and molecular indexes in the brain [13].

In summary, in the last years several research papers and reviews appeared to address the “reserve question” by separately focusing on humans or animals. The present review specifically aims to combine together the knowledge derived from both clinical and basic research on reserves to obtain a more comprehensive theoretical framework.

Lifestyle Enrichment Indexes

In humans

Clinical research established that reserves can be built during life through enriching experiences [4, 18]. Such enriching experiences regard all the aspects of the individual’s life. In humans, the formal education (expressed as years of schooling), occupational attainment, leisure activities, and cognitive performances have been traditionally considered the most important enrichment factors, and, as a consequence, they have been regarded as proxy measures of CR [4, 19]. An evolution in the CR proxies’ measures is recently emerged. Currently, static and dynamic measures [20 –23] have been postulated (see Table 1A).

Table 1

Cognitive reserve indexes and promoting factors

A) CR Proxy measures
	Static indices	Dynamic indices
	Schooling (years and type of formal education)	Memory
	Occupational attainment (years and type)	Executive functions
	Pre-morbid IQ	General cognitive abilities
B) Reserve-builders
Cognitive leisure activities	Reading books, magazines, newspapers; painting; poetry, sculpture, song writing; attending lectures or organized discussions
Social leisure activities	Playing structured games; participating in hobbies; volunteering
Physical leisure activities	Running, trekking, swimming, biking, skiing, dancing, yoga
Other promoting factors	Bilingualism; continuing education; healthy lifestyles

See text for further details.

The years of schooling, occupational attainment (number of years and type of occupation), pre-morbid IQ and leisure activities (cognitive/social/physical) pursued during childhood-adolescence and early adulthood have been considered static measures. Indeed, these measures cannot directly catch the cognitive changes at individual level.

In humans, schooling is usually considered the most relevant factor impacting on brain resilience [4, 24]. Indeed, schooling may induce an increase of synaptic density in the neocortex promoting neural connectivity [25, 26]. Moreover, schooling appears to be an important environmental factor to drive the engagement in further cognitively stimulating activities during the lifespan [27] and to delay the AD symptom onset [25]. Indeed, it is likely that individuals with higher educational level are interested in more stimulating mental activities in the course of their life, and it may induce structural and functional brain changes and promote the brain resilience to neuronal damage. In addition, schooling can also be considered as a socialization process promoting more efficient learning strategies and responses to cognitive requirements [28]. More recently, by using a multistate survival analysis on six different longitudinal cohorts of patients, Robitaille and co-workers [29] showed a delay in cognitive decline due to schooling. Interestingly, the authors reported a reduced risk of transition from normal level of cognitive functioning to mild level of cognitive decline for the individuals with high education, while no effect was reported in the transition from mild to moderate/severe level of cognitive decline [29].

Interestingly, several studies showed a decreased AD risk in subjects employed in highly mentally demanding occupation [9 , 31] and an increased risk of developing AD in individuals without a lifetime occupation [32]. It is possible to suggest that individuals such as company’s managers, teachers, etc., who are involved in highly demanding works, may develop more flexible/efficient cognitive strategies to be spent when neurodegeneration occurs.

Recently, Soldan and co-workers [33] reviewed a large number of papers focused on the longitudinal effects of CR, considered in terms of educational and occupational attainment. The authors [33] hypothesized that CR may modify the rate of cognitive decline and the risk to dementia progression using four different pathways. In the first one, the CR should act directly without any interaction with other biomarkers (e.g., brain atrophy, brain, or CSF levels of amyloid-β/tau, ApoE genotype, etc.); through the second one, CR should moderate the relationship between biomarkers and clinical outcomes; through the third one, CR should act directly on biomarker levels modifying the amount of accumulation on neuropathology; and finally, through the fourth mechanism, CR should change the relationship between demographical and genetics features and clinical outcomes [33]. From this review of the literature, it becomes clearer that CR considered in terms of static measures implies many complex factors directly or not interconnected with each other. Clarifying the reciprocal relationships among factors seems to be essential.

However, the static measures do not directly assess patients’ cognitive changes [20] and thus they provide an imprecise and scarcely sensitive estimate. To bypass this issue, the concept of dynamic CR has been recently introduced [20 –22]. In particular, some cognitive performances such as memory, executive functions, and general cognitive abilities (see Table 1A) directly reflect the cognitive functioning at a specific time-point, and therefore they can be considered dynamic indexes of CR [20 –23]. More clearly, the patient’s cognitive abilities that are measured in a specific moment can be considered as the residual cognitive abilities after statistically removing the effects of several incidental factors (such as demographic and brain features) [20 –23]. However, the role of the residual cognitive abilities in withstanding dementia remains still unclear. It is possible to suppose that several cognitive abilities may counteract the onset of dementia.

Moreover, several activities have been considered in the literature as “reserve-builders” (Table 1B). In particular, in several studies [4 , 34–41], cognitive, social, and physical activities pursued continuatively and intensely during the entire lifespan have been recognized as factors reducing the risk of AD. More recently, by using a comprehensive questionnaire investigating cognitive, social, physical, and leisure activities, education years and type, and occupation years and type, we showed a higher AD risk in amnesic mild cognitive impairment (aMCI) patients with low CR level [41]. In addition, this study revealed that aMCI patients with high CR and high scores on the Mini-Mental State Examination (MMSE), a test evaluating the level of general cognitive efficiency, remained free of AD symptomatology longer than aMCI patients with high CR but low baseline MMSE scores. Remarkably, Valenzuela and co-workers [38] highlighted that the combination of different CR measures, instead of single CR measure, is more effective in predicting AD risk. Among such cognitive abilities, the role of bilingualism should be better investigated. Indeed, an individual’s lifelong speaking two or more languages may be considered as living in a cognitively enriched environment. Several studies described bilingualism as a protective factor against dementia onset [42 –44]. More recently, the contribution of bilingualism to the enhancement of CR and the relationship between bilingualism and structural brain changes have been described [45]. In particular, in accordance with “reserve paradigm”, it has been reported that bilingual patients with the same level of clinical AD symptoms significantly accumulated more atrophy of the temporal lobe than monolingual patients [46]. Furthermore, bilingual and monolingual healthy elderly showed the same level of cognitive functioning, but bilinguals showed more microstructural damage than monolinguals in the white matter tracts, typically affected in AD patients [47]. In summary, the studies on bilingualism support the role of an “enriched language development” as reserve-promoting factor.

In the literature, there is an increasing interest in the effect of “continuing education” on dementia prevention [48]. Continuing education comprises all structured learning activities provided to adults by formal and non-formal educational institutions [48]. It can be considered as a more organized set of cognitive tasks than cognitive leisure activities. It is possible to suppose that continuing education may act as reserve-promoting factor, delaying the onset of dementia. However, it still remains unclear whether continuing education may prevent dementia more than cognitive leisure activity. Further studies are needed to address this important issue.

Finally, not only cognitive/social/physical activities but also healthy lifestyle (characterized by pursuing a Mediterranean diet, drinking responsibly, limiting caffeine and nicotine, avoiding big-meals, maintaining sleep hygiene, etc.) have been recognized as reserve-promoting factors [49].

As previously affirmed, the assessment of reserves in humans presents some limitations: 1) the proxy measures used are only indirect indexes of BR and CR; 2) the measures considered as indexes cannot be experimentally manipulated by the examiner. Animal models allow us to overcome, at least partially, these limitations.

In animals

A quite different framework has to be described for the reserve indexes considered in animal studies in comparison to the ones reported for humans. Namely, in the animal studies, the lifelong experiences considered “reserve-builders” in humans [41] become factors that may be actively manipulated by using the EE paradigm [13 , 50]. In applying this protocol, the researcher decides what variables to manipulate and to what extent it occurs. Thus, the increased social interactions provided by the rearing in large groups parallel the amplified human social factors. The complex and frequently changing environment the animals are exposed to parallels the enriched human cognitive factors. Finally, the physical activity to explore the large cages and always-novel objects, the motor activity in the running wheels, the controlled diet, etc., parallel the human physical factor (Fig. 1). It is noteworthy that in the EE paradigm the researcher may fix the age at which the animals begin to be enriched and the duration of such an enrichment in order to mimic differently lasting life experiences that in humans can occur in the early, adult, and even old ages [51]. Moreover, the researcher can enhance only one factor or different factors among those above described, given that it is possible to shape the complex environment to stimulate a single sensory channel or more than one [52, 53].

Fig. 1

Lifestyle enrichment indexes in humans and in animals. The panel represents the parallelism between the lifestyle enrichment indexes that are classically considered in humans (on the left) and the environmental enrichment variables that are manipulated in animal models (on the right).

Animal studies mainly utilize two reserve measures, such as the BR and CR [54]. While the BR is investigated by analyzing molecular and supra-molecular biological factors [14], the CR is investigated by analyzing the performance of the animals in a great number of cognitive tasks [13]. Anyway, inferences on the NR can be formulated by analyzing structural changes in the brain, such as neurogenesis, gliogenesis, angiogenesis, synaptogenesis, etc. [14 , 56]. In theory, these measures could be also used as dynamic reserve indexes per se of which to analyze the effects, just like in humans. However, since in the case of animal studies the experiential factors that build the reserves are established and controlled by the researcher, the subsequent identification of dynamic reserve indexes is less required. Thus, in animal designs, the reserve measures are generally analyzed as dependent variables. In any case, the analysis of the correlation between cerebral and cognitive measures of reserve could provide useful information on the biological mechanisms directly associated with successful performance.

Structural and Functional Effects of Reserves

In humans

Behavioral studies investigating different aspects of reserves have shown that the combination of different factors may reduce the risk to develop dementia [38 , 57]. Specifically, Serra et al. [41] showed that a comprehensive CR questionnaire measuring leisure activities (cognitive, social, and physical activities), years and type of formal education and occupational attainment was able to differentiate patients with high or low risk to develop AD. More recently, Cheng [58] highlighted the role of cognitive and physical activity in preventing dementia. In particular, aerobic exercise is associated with reduced age-related grey and white matter loss and the cognitive training for executive functions improves the prefrontal network efficiency [58].

Reserve promoting factors cannot act alone, but they interact with several other biological factors. Significant interactions among promoting factors as levels of CR, CSF-biomarkers (amyloid-β and tau levels), ApoE status, and brain atrophy and risk to develop the clinical symptoms of AD as final outcome [41 , 59–62] have been shown. Some procedures used routinely to diagnose AD in clinical setting are considered sensitive but also highly invasive (e.g., the use of CSF biomarkers). Conversely, quantitative magnetic resonance imaging (MRI) allows the non-invasive investigation of the pathological processes underlying AD in vivo. In particular, metabolic [63 –66], structural, and functional MRI studies have provided in vivo evidence of BR and CR. For the purpose of the present review, we focused only on the structural and functional MRI studies. Structural MRI studies showed a relationship between CR levels and brain resilience. By using un-biased volumetric techniques, Serra and co-workers [60] showed in AD patients with high educational level (and high CR as a consequence) a significant grey matter atrophy in the entorhinal cortex and temporal pole, while AD patients with low CR level showed atrophy in the supramarginal gyrus, posterior cingulate cortex (PCC), and precuneus. In addition, it is also shown in AD patients that education is able to counteract the effects both of hippocampal atrophy and of cerebrovascular diseases. Indeed, several studies [41 , 68] provided evidence that healthy elderly [68] and aMCI and AD patients [41, 67] with high CR suffered from severe white matter damage compared with those with low CR. Globally, in line with the reserve concept, these studies suggested that individuals with high CR need to accumulate more neuropathology to show a cognitive decline. However, the majority of studies focused on the impact of CR assessed by static measures on the brain. Conversely, Serra and co-workers [23] more recently investigated the impact of CR dynamic measures on brain structure. In particular, the authors showed a significant association between regional grey matter volumes and two measures of dynamic CR, one including the general cognitive efficiency scores (named the first latent variable) and one including memory score only (named dynamic CR index) in patients with aMCI. We found volumetric reductions in the anterior cingulate cortex (ACC) and PCC, left hippocampus and parahippocampal gyrus associated with both dynamic CR measures. We hypothesized that these brain areas are likely involved both in memory and in the more general cognitive efficiency processes [23]. On the contrary, the finding that volumetric changes in the precuneus were associated with the first latent variable sustains the precuneus involvement in the general cognitive abilities [23]. Instead, the dynamic CR index was found associated with the atrophy of frontotemporal brain regions (including the ACC). All these regions are considered involved in higher-level cognitive functions (as control and inhibition mechanisms) confirming the importance of the frontal cortices for an efficacious cognitive processing [23].

Several studies have addressed the relationship between CR and brain networks. Brain network and functional connectivity can be considered the most reliable representations of NR in vivo. A modulation of functional connectivity of the PCC has been recently found to be related to the CR levels in aMCI patients [62]. PCC is a critical brain area for AD conversion, and functional disconnection between PCC and temporal lobe structures seems to be essential in AD conversion [62]. Bozzali and co-workers [62] sustained that the CR acts mainly through neural compensation mechanisms.

Very recently, by using the graph theory approach that allows a whole brain network investigation without an a priori hypothesis [69, 70], Serra and co-workers [26] showed functional connectivity changes only in aMCI patients categorized according to their CR level. Only aMCI patients and neither healthy elderly nor AD patients showed modulation (seen as increase or decrease) of functional connectivity in different brain networks, supporting the assumption that NR is sustained by efficient neural networks. In particular, a recent review of 13 functional MRI studies [71] reported that different brain networks are involved in NR and in neural compensation. Specifically, medial temporal regions and ACC and PCC are likely associated with NR, while frontal regions and dorsal attentional network are likely associated with neural compensation. Neural compensation is observed in the advanced stages of disease, while NR is likely observed in the early stages [71].

In animals

Several studies investigated the effects of lifelong exposure to EE in animal models of physiological or pathological aging. On the whole, these studies indicate that the exposure to complex stimulations provides the animals with CR and BR leading to efficient coping with aging [53].

As for CR, evidence indicates that rodents exposed to EE from weaning onwards or during adulthood show better performance in a lot of learning and memory tasks when aged [72, 73]. Mice reared in an enriched environment from weaning to twelve [74] or twenty [75] months of age showed increased learning and memory abilities in spatial and episodic-like memory tasks in comparison to control aged animals. Furthermore, in a couple of studies based on a model of senescence-accelerated prone mice (SAMP8), animals reared in an enriched environment from weaning to three months of age showed superior performance in comparison to controls in tasks requiring spatial and visual recognition memory [76, 77]. Also, mice and rats reared in enriched environment from young adulthood (two or three months of age) showed in aging better performance in spatial and visual recognition memory tasks in comparison to control animals [78, 79].

As for the BR, several studies indicate that the early exposure to EE provokes plastic modifications in the brain (both at molecular and supra-molecular levels) that support a successful coping with aging-related cognitive decline [14]. In rodent models of physiological aging, it has been demonstrated that the exposure to EE from young adulthood (one/three months of age) to aging (twelve/twenty-four months of age) is able to prevent the age-related damage to cholinergic system [79, 80], to increase hippocampal neurogenesis and volume [74, 78], to potentiate brain synaptic plasticity [74, 78], and to increase brain levels of neurotrophic factors [79]. Also, in the SAMP8 model, EE decreases neurodegeneration markers [76] and prevents epigenetic changes that promote oxidative stress and inflammation [77].

Notably, the beneficial effects of EE have been demonstrated also in models of pathological aging, such as AD. Studies on transgenic and non-transgenic models of AD reported that previous exposure to EE is able to diminish the cognitive decline and the brain pathological alterations [81]. Specifically, in transgenic murine models, the exposure to EE before the appearance of symptoms provokes amelioration in spatial learning and memory tasks and in visual recognition memory performance [82, 83]. Similar results were obtained in a rat model of basal forebrain cholinergic damage, which mimics the early brain cholinergic dysfunction present in AD [84]. In this model, the animals exposed to EE from weaning (and lesioned after about three months) showed superior cognitive performance when compared to standard-reared lesioned rats. The improved behavioral performances were associated to normalization of the lesion-induced alteration of the neuronal morphology [84]. In fact, to compensate for the cholinergic deafferentation, the standard-reared lesioned rats showed a rearrangement of dendritic spine distribution in the neocortical pyramidal neurons, which was absent in the enriched-lesioned rats. Moreover, the increased cortical dendritic spine density exhibited by the enriched unlesioned rats was fully maintained in the enriched-lesioned animals. Presumably, the network connectivity potentiated by the exposure to EE was also maintained in the presence of the damage and allowed for a preserved cognitive performance. Accordingly, research on murine transgenic models demonstrated that early exposure to EE is able to reduce AD markers [82], to increase neurogenesis [85 –87] and angiogenesis [88], and to augment neurotrophin levels in the brain [89].

Neurorehabilitation and Reserves

In humans

It is always more evident that healthy lifestyles beneficially impact on brain networks, but it remains to be demonstrated whether interventions later in life can modulate brain resilience.

In fact, an active cognitive stimulation can modulate brain changes, promote learning, and reinforce the neuronal connections, providing alternative cognitive strategies or recruiting different neural networks [4]. Several studies investigated cognitive rehabilitation in aMCI and AD patients [90], but there are no studies directly investigating the relationship with the reserves. Only one study showed an increase in cognitive efficiency after training in aMCI and AD patients with low CR level [91]. This result is in line with the “reserve paradigm”: patients with high CR accumulate more AD neuropathology and therefore can benefit less from cognitive training in comparison to patients with low CR. A recent study on a group of patients at risk of dementia reported that the association between an active lifestyle and cognitive change over time was stronger than the one between short-term specific cognitive interventions and cognitive change over time [92].

While it is well known that physical exercise is able to prevent dementia in healthy subjects [93, 94], currently no studies investigated the efficacy of different levels of physical exercise in improving motor and/or cognitive outcome in patients with AD. As a consequence, the effects of CR on cognitive training, and their impact on the brain remain still under debate. Further studies assessing relationship between different CR levels and non-pharmacological treatments seem to be necessary and desirable in the immediate future.

In animals

The EE paradigm can also be used to model cognitive and physical rehabilitation treatment applied to patients with evident symptoms of cognitive decline. Evidence on therapeutic effects of EE in aging rodents indicates the presence of a beneficial action on brain functionality [17, 72]. In 18-month-old rats, the exposure to six months of EE improved spatial learning performance. The functional amelioration was associated to increased hippocampal neurogenesis and synaptic plasticity, by upregulating acetylation-associated events and epigenetic modulation of the brain-derived neurotrophic factor (Bdnf) gene [95]. Also, even the one-month exposure of 22-month-old rats to EE induced significant amelioration in learning and memory performance, accompanied by enhancement of hippocampal LTP [93]. The exposure to only three weeks of EE was able to facilitate LTP as evaluated in hippocampal slices from 21-month-old rats [96]. Finally, a 2-month exposure to EE in 19-month-old rats potentiated gamma-aminobutyric acid (GABA)-ergic system [97].

Positive effects of the exposure to EE are reported also in models of AD. Arendash et al. [98] reported that the exposure of 16-month-old amyloid-β protein precursor (AβPP)sw transgenic mice to four months of EE resulted in a global improvement of cognitive functions. Paban et al. [99] found that in rats submitted to cholinergic depletion at three months of age, the post-lesional exposure to EE for one year induced amelioration in learning performance and decreased expression of genes involved in apoptosis and glial cell differentiation.

In conclusion, the experimental studies on EE can provide useful suggestions for the managing of potentiation and/or rehabilitation of cognitive functions in old subjects and/or patients. Firstly, it has been demonstrated that the effects of EE are different if the exposure is performed before or after the decline occurs. In 24-month-old rats, the preventive exposure to EE from young adulthood makes the successive (therapeutic) exposure not necessary for spatial memory maintenance. Instead, the therapeutic exposure is required to ameliorate the performance of aged rats previously unexposed to EE [100]. Accordingly, it has been suggested that the effects of preventive (pre-lesional) and therapeutic (post-lesional) EE are mediated by distinct molecular pathways [101]. This evidence supports the idea that the rehabilitation of elderly patients with different levels of BR and CR should be based on tailored strategies. This idea is even strengthened by the evidence that different protocols of EE induce different effects [102]. In 11-month-old AβPP-23 mice, Wolf et al. [103] demonstrated that the cognitive stimulation provided by nine months of EE induced improvement of spatial performance, upregulation of hippocampal growth factors, and increment in hippocampal neurogenesis. Conversely, the only physical stimulation (motor activity on running wheels) did not change spatial performance and hippocampal neurogenesis, although it induced down-regulation of hippocampal and cortical growth factors. Similarly, Cortese et al. [93] demonstrated that while the exposure of 22-month-old rats to a procedure of complex EE induced amelioration in learning and memory and hippocampal LTP, the only social enrichment failed in supporting it.

The Time-Window of Reserve Action

In humans

One of the most intriguing issues about the reserve effect is to address the existence of specific time-windows in which BR, CR, or NR may preferentially act, although in humans the exact time-window of BR/CR action is hardly identifiable because of the variability in the trend of AD neuropathology. A study showed that CR acts mainly in the aMCI stage, succeeding in postponing the decline to dementia of almost two years [41]. In this study, CR was assessed in terms of global enrichment index using a devoted questionnaire, based on formal education, occupational attainment, and leisure activities. The authors showed that the CR level was differently distributed in aMCI, AD, and healthy subjects, and it does not follows the Gaussian distribution reinforcing the Stern’s hypothesis [3, 4]. According to Stern [3, 4], the level of CR is not randomly distributed in a population, but it is related to neuropathological mechanisms. Therefore, it is possible that CR has no influence on cognitive performance of the yet healthy older adults as well as paradoxically during the most advanced stages of AD neuropathology. Instead, CR can play a protective role during the early stages of dementia. By using the graph theory [69, 70], Serra and co-workers [26] showed connectivity differences only in aMCI patients with high or low CR, confirming the hypothesis that CR impacts on neurodegenerative processes only in the early phase of AD. More recently, this hypothesis has also been confirmed by other studies highlighting a specific disease stage for the CR action [29, 104].

In animals

Animal studies emphasized that EE effects are age-dependent and not equally evident at different ages [105 –110], even though some evidence is also present in favor of similar efficacy in young and aged rodents [111]. Studies on transgenic murine models of AD suggest that the beneficial effects of EE are more evident at a milder stage of the pathology [112, 113]. In addition, animal studies suggest that the effects of the exposure to EE are long-lasting [114 –116], even though the exact extent of the efficacy duration has yet to be defined. Longitudinal studies on physiological and pathological aging models could provide insights on the action time-window of reserves and aid in identifying the ideal period to efficiently treat patients with high levels of BR and CR, before the cerebral damage (so far compensated) becomes devastating.

CONCLUSIONS

Since BR and CR might remain indeterminate concepts with limited usefulness in clinical settings, it appears mandatory the identification of sensitive and specific proxy measures of reserves. A differentiation between static and dynamic CR measures has been recently proposed in the literature on human studies. Animal studies may contribute to the evaluation of the effect of well-defined “reserve-builders” providing a high-level experimental control.

The present review of the literature shows the necessity of extensive scientific evidence on the interaction between the different typologies/levels of reserve and the cognitive and/or non-cognitive therapeutic interventions. Animal studies offer the ideal tool to systematically address this issue through the manipulation of the involved variables and the highly controlled experimental designs.

The requirement of identifying the time-window of reserve efficacy arises to propose well-timed rehabilitation/potentiation interventions tailored on the clinical characteristics of patients with different reserve levels. Also in this case, animal models allow designing experiments specifically aimed to analyzed well-defined phases of reserve efficacy.

In conclusion, the integration between clinical and animal investigation offers a unique opportunity to disentangle the multiple features of reserve concept, each with its own specific peculiarities (Fig. 2). Future studies based on such an integrated approach may enlighten the controversial issue of reserve implication in coping with the cognitive decline in the presence of neurodegeneration.

Fig. 2

Summary of the review. The panel represents the main topics that are addressed in the review. On the bottom of the figure (in green) the main aspects arising from the “mash-up” between human (on the left, in red) and animal (on the right, in blue) studies are depicted. BR, brain reserve; CR, cognitive reserve; NR, neural reserve.

Footnotes

Acknowledgments

This work was supported by the Italian Ministry of Health funding 5x1000 2015 to the IRCCS Fondazione Santa Lucia.

The Neuroimaging Laboratory is in part supported by the Italian Ministry of Health.

Authors’ disclosures available online ().

References

Alzheimer’s Disease International (2015) World Alzheimer Report 2015: The Global Impact of Dementia. https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf.

Stern

, Alexander

, Prohovnik

, Mayeux

(1992) Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease, Ann Neurol 32, 371–375.

Stern

(2009) Cognitive reserve, Neuropsychologia 47, 2015–2028.

Stern

(2012) Cognitive reserve in ageing and Alzheimer’s disease, Lancet Neurol 11, 1006–1012.

Katzman

, Terry

, DeTeresa

, Brown

, Davies

, Fuld

, Renbing

, Peck

(1988) Clinical, pathological, and neumical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques, Ann Neurol 23, 138–144.

Mortimer

, Borenstein

, Gosche

, Snowdon

(2005) Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression, J Geriatr Psychiatry Neurol 18, 218–223.

Stern

(2002) What is cognitive reserve? Theory and research application of the reserve concept, J Int Neuropsychol Soc 8, 448–460.

Barulli

, Stern

(2013) Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve, Trends Cogn Sci 17, 502–509.

Stern

, Gurland

, Tatemichi

, Tang

, Wilder

, Mayeux

(1994) Influence of education and occupation on the incidence of Alzheimer’s disease, JAMA 271, 1004–1010.

10.

Steffener

, Stern

(2012) Exploring the neural basis of cognitive reserve in aging, Biochim Biophys Acta 1822, 467–473.

11.

Rosenzweig

, Bennett

, Hebert

, Morimoto

(1978) Social grouping cannot account for cerebral effects of enriched environments, Brain Res 153, 563–576.

12.

Sztainberg

, Chen

(2010) An environmental enrichment model for mice, Nat Protoc 5, 1535–1539.

13.

Petrosini

, De Bartolo

, Foti

, Gelfo

, Cutuli

, Leggio

, Mandolesi

(2009) On whether the environmental enrichment may provide cognitive and brain reserves, Brain Res Rev 61, 221–239.

14.

Gelfo

, Mandolesi

, Serra

, Sorrentino

, Caltagirone

(2018) The neuroprotective effects of experience on cognitive functions: Evidence from animal studies on the neurobiological bases of brain reserve, Neuroscience 370, 218–235.

15.

Nithianantharajah

, Hannan

(2006) Enriched environments, experience-dependent plasticity and disorders of the nervous system, Nat Rev Neurosci 7, 697–709.

16.

Nithianantharajah

, Hannan

(2009) The neurobiology of brain and cognitive reserve: Mental and physical activity as modulators of brain disorders, Prog Neurobiol 89, 369–382.

17.

Hannan

(2014) Environmental enrichment and brain repair: Harnessing the therapeutic effects of cognitive stimulation and physical activity to enhance experience-dependent plasticity, Neuropathol Appl Neurobiol 40, 13–25.

18.

Serra

, Caltagirone

, Bozzali

(2017) Cognitive reserve: The evolution of the conceptual framework, J Syst Integr Neurosci 3, 1–6.

19.

Rentz

, Locascio

, Becker

, Moran

, Eng

, Buckner

, Sperling

, Johnson

(2010) Cognition, reserve, and amyloid deposition in normal aging, Ann Neurol 67, 353–364.

20.

Reed

, Mungas

, Farias

, Harvey

, Beckett

, Widaman

, Hinton

, DeCarli

(2010) Measuring cognitive reserve based on the decomposition of episodic memory variance, Brain 133, 2196–2209.

21.

Zahodne

, Manly

, Brickman

, Siedlecki

, Decarli

, Stern

(2013) Quantifying cognitive reserve in older adults by decomposing episodic memory variance: Replication and extension, J Int Neuropsychol Soc 19, 854–862.

22.

Zahodne

, Manly

, Brickman

, Narkhede

, Griffith

, Guzman

, Schupf

, Stern

(2015) Is residual memory variance a valid method for quantifying cognitive reserve? A longitudinal application, Neuropsychologia 77, 260–266.

23.

Serra

, Bruschini

, Di Domenico

, Bechi Gabrielli

, Marra

, Caltagirone

, Cercignani

, Bozzali

(2017) Memory is not enough: The neurobiological basis of dynamic cognitive reserve, J Alzheimers Dis 58, 171–184.

24.

Dufouil

, Alpérovitch

, Tzourio

(2003) Influence of education on the relationship between white matter lesions and cognition, Neurology 60, 831–836.

25.

Katzman

(1993) Education and the prevalence of dementia and Alzheimer’s disease, Neurology 43, 13–20.

26.

Serra

, Mancini

, Cercignani

, Di Domenico

, Spanò

, Giulietti

, Koch

, Marra

, Bozzali

(2017) Network-based substrate of cognitive reserve in Alzheimer’s disease, J Alzheimers Dis 55, 421–430.

27.

Vance

, Crowe

(2006) A proposed model of neuroplasticity and cognitive reserve in older adults, Act Adapt Aging 30, 61–79.

28.

Gilleard

(1997) Education and Alzheimer’s disease: A review of recent international epidemiological studies, Aging Ment Health 1, 33–46.

29.

Robitaille

, van den Hout

, Machado

RJM

, Bennett

, Cukićv

, Deary

, Hofer

, Hoogendijk

, Huisman

, Johansson

, Koval

, van der Noordt

, Piccinin

, Rijnhart

JJM

, Singh-Manoux

, Skoog

, Starr

, Vermunt

, Clouston

, Muniz Terrera

(2018) Transitions across cognitive states and death among older adults in relation to education: A multistate survival model using data from six longitudinal studies, Alzheimers Dement S1552-5260, 33792–33795.

30.

Evans

, Hebert

, Beckett

, Scherr

, Albert

, Chown

, Pilgrim

, Taylor

(1997) Education and other measures of socioeconomic status and risk of incident Alzheimer disease in a defined population of older persons, Arch Neurol 54, 1399–1405.

31.

Qiu

, Karp

, von Strauss

, Winblad

, Fratiglioni

, Bellander

(2003) Lifetime principal occupation and risk of Alzheimer’s disease in the Kungsholmen project, Am J Ind Med 43, 204–211.

32.

Anttila

, Helkala

, Kivipelto

, Hallikainen

, Alhainen

, Heinonen

, Mannermaa

, Tuomilehto

, Soininen

, Nissinen

(2002) Midlife income, occupation, APOE status, and dementia: A population-based study, Neurology 59, 887–893.

33.

Soldan

, Pettigrew

, Albert

(2018) Evaluating cognitive reserve through the prism of preclinical Alzheimer disease, Psychiatr Clin North Am 41, 65–77.

34.

Friedland

, Fritsch

, Smyth

, Koss

, Lerner

, Chen

, Petot

, Debanne

(2001) Patients with Alzheimer’s disease have reduced activities in midlife compared with healthy control-group members, Proc Natl Acad Sci U S A 98, 3440–3445.

35.

Wilson

, Bennett

, Bienias

, Aggarwal

, De Leon Mendes

, Morris

, Schneider

, Evans

(2002) Cognitive activity and incident AD in a population-based sample of older persons, Neurology 59, 1910–1914.

36.

Crowe

, Andel

, Pedersen

, Johansson

, Gatz

(2003) Does participation in leisure activities lead to reduced risk of Alzheimer’s disease? A prospective study of Swedish twins, J Gerontol B Psychol Sci Soc Sci 58, 249–255.

37.

Verghese

, Lipton

, Katz

, Hall

, Derby

, Kuslansky

, Ambrose

, Sliwinski

, Buschke

(2003) Leisure activities and the risk of dementia in the elderly, N Engl J Med 348, 2508–2516.

38.

Valenzuela

, Brayne

, Sachdev

, Wilcock

, Matthews

(2011) Medical Research Council Cognitive Function and Ageing Study. Cognitive lifestyle and long-term risk of dementia and survival after diagnosis in a multicenter population-based cohort, Am J Epidemiol 173, 1004–1012.

39.

Sattler

, Toro

, Schönknecht

, Schröder

(2012) Cognitive activity, education and socioeconomic status as preventive factors for mild cognitive impairment and Alzheimer’s disease, Psychiatry Res 196, 90–95.

40.

Sumowski

, Leavitt

(2013) Cognitive reserve in multiple sclerosis, Mult Scler 19, 1122–1127.

41.

Serra

, Musicco

, Cercignani

, Torso

, Spanò

, Mastropasqua

, Giulietti

, Marra

, Bruno

, Koch

, Caltagirone

, Bozzali

(2015) Cognitive reserve and the risk for Alzheimer’s disease: A longitudinal study, Neurobiol Aging 36, 592–600.

42.

Graves

, Rajaram

, Bowen

, McCormick

, McCurry

, Larson

(1999) Cognitive decline and Japanese culture in a cohort of older Japanese Americans in King County, WA: The Kame Project, J Gerontol B Psychol Sci Soc Sci 54, 154–161.

43.

Bialystok

, Craik

, Freedman

(2007) Bilingualism as a protection against the onset of symptoms of dementia, Neuropsychologia 45, 459–464.

44.

Chertkow

, Whitehead

, Phillips

, Wolfson

, Atherton

, Bergman

(2010) Multilingualism (but not always bilingualism) delays the onset of Alzheimer disease: Evidence from a bilingual community, Alzheimer Dis Assoc Disord 24, 118–125.

45.

Guzmán-Vélez

, Tranel

(2015) Does bilingualism contribute to cognitive reserve? Cognitive and neural perspectives, Neuropsychology 29, 139–150.

46.

Schweizer

, Ware

, Fischer

, Craik

, Bialystok

(2012) Bilingualism as a contributor to cognitive reserve: Evidence from brain atrophy in Alzheimer’s disease, Cortex 48, 991–996.

47.

Gold

, Johnson

, Powell

(2013) Lifelong bilingualism contributes to cognitive reserve against white matter integrity declines in aging, Neuropsychologia 51, 2841–2846.

48.

Matyas

, Auer

, Gisinger

, Kil

, Keser Aschenberger

, Klerings

, Gartlehner

(2017) Continuing education for the prevention of mild cognitive impairment and Alzheimer’s-type dementia: A systematic review protocol, Syst Rev 6, 157.

49.

Shatenstein

, Barberger-Gateau

, Mecocci

(2015) Prevention of age-related cognitive decline: Which strategies, when, and for whom? J Alzheimers Dis 48, 35–53.

50.

Mandolesi

, Gelfo

, Serra

, Montuori

, Polverino

, Curcio

, Sorrentino

(2017) Environmental factors promoting neural plasticity: Insights from animal and human studies, Neural Plast 2017, 7219461.

51.

Simpson

, Kelly

(2011) The impact of environmental enrichment in laboratory rats–behavioural and neurochemical aspects, Behav Brain Res 222, 246–264.

52.

Hutchinson

, Avery

, Vandewoude

(2005) Environmental enrichment for laboratory rodents, ILAR J 46, 148–161.

53.

Redolat

, Mesa-Gresa

(2012) Potential benefits and limitations of enriched environments and cognitive activity on age-related behavioural decline, Curr Top Behav Neurosci 10, 293–316.

54.

Tucker

, Stern

(2011) Cognitive reserve in aging, Curr Alzheimer Res 8, 354–360.

55.

Gelfo

, De Bartolo

, Giovine

, Petrosini

, Leggio

(2009) Layer and regional effects of environmental enrichment on the pyramidal neuron morphology of the rat, Neurobiol Learn Mem 91, 353–365.

56.

Gelfo

, Florenzano

, Foti

, Burello

, Petrosini

, De Bartolo

(2016) Lesion-induced and activity-dependent structural plasticity of Purkinje cell dendritic spines in cerebellar vermis and hemisphere, Brain Struct Funct 221, 3405–3426.

57.

Wang

, MacDonald

, Dekhtyar

, Fratiglioni

(2017) Association of lifelong exposure to cognitive reserve-enhancing factors with dementia risk: A community-based cohort study. PLoS Med 14, e1002251.

58.

Cheng

(2016) Cognitive reserve and the prevention of dementia: The role of physical and cognitive activities, Curr Psychiatry Rep 18, 85.

59.

Jack

CR Jr

, Knopman

, Jagust

, Petersen

, Weiner

, Aisen

, Shaw

, Vemuri

, Wiste

, Weigand

, Lesnick

, Pankratz

, Donohue

, Trojanowski

(2013) Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers, Lancet Neurol 12, 207–216.

60.

Serra

, Cercignani

, Petrosini

, Basile

, Perri

, Fadda

, Spanò

, Marra

, Giubilei

, Carlesimo

, Caltagirone

, Bozzali

. Neuroanatomical correlates of cognitive reserve in Alzheimer disease, Rejuvenation Res 14, 143–151.

61.

Soldan

, Pettigrew

, Li

, Wang

, Moghekar

, Selnes

, Albert

, O’Brien

(2013) BIOCARD Research Team. Relationship of cognitive reserve and cerebrospinal fluid biomarkers to the emergence of clinical symptoms in preclinical Alzheimer’s disease, Neurobiol Aging 34, 2827–2834.

62.

Bozzali

, Dowling

, Serra

, Spanò

, Torso

, Marra

, Castelli

, Dowell

, Koch

, Caltagirone

, Cercignani

(2015) The impact of cognitive reserve on brain functional connectivity in Alzheimer’s disease, J Alzheimers Dis 44, 243–250.

63.

Garibotto

, Borroni

, Sorbi

, Cappa

, Padovani

, Perani

(2012) Education and occupation provide reserve in both ApoE ɛ4 carrier and noncarrier patients with probable Alzheimer’s disease, Neurol Sci 3, 1037–1042.

64.

Garibotto

, Tettamanti

, Marcone

, Florea

, Panzacchi

, Moresco

, Virta

, Rinne

, Cappa

, Perani

(2013) Cholinergic activity correlates with reserve proxies in Alzheimer’s disease, Neurobiol Aging 34, 2694.

65.

Ewers

, Insel

, Stern

, Weiner

; Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2013) Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease, Neurology 80, 1194–1201.

66.

Carapelle

, Serra

, Modoni

, Falcone

, Caltagirone

, Bozzali

, Specchio

, Avolio

(2017) How the cognitive reserve interacts with β-amyloid deposition in mitigating FDG metabolism: An observational study. Medicine (Baltimore) 96, e5876.

67.

Murray

, Staff

, McNeil

, Salarirad

, Ahearn

, Mustafa

, Whalley

(2011) The balance between cognitive reserve and brain imaging biomarkers of cerebrovascular and Alzheimer’s diseases, Brain 134(Pt 12), 3687–3696.

68.

Brickman

, Siedlecki

, Muraskin

, Manly

, Luchsinger

, Yeung

, Brown

, DeCarli

, Stern

(2011) White matter hyperintensities and cognition: Testing the reserve hypothesis, Neurobiol Aging 32, 1588–1598.

69.

Bullmore

, Sporns

(2009) Complex brain networks: Graph theoretical analysis of structural and functional systems, Nat Rev Neurosci 10, 186–198.

70.

Rubinov

, Sporns

(2010) Complex network measures of brain connectivity: Uses and interpretations, Neuroimage 52, 1059–1069.

71.

Anthony

, Lin

(2017) A systematic review for functional neuroimaging studies of cognitive reserve across the cognitive aging, Arch Clin Neuropsychol. 10.1093/arclin/acx125.

72.

Frick

, Benoit

(2010) Use it or lose it: Environmental enrichment as a means to promote successful cognitive aging, ScientificWorldJournal 10, 1129–1141.

73.

Mora

(2013) Successful brain aging: Plasticity, environmental enrichment, and lifestyle, Dialogues Clin Neurosci 15, 45–52.

74.

Hüttenrauch

, Salinas

, Wirths

(2016) Effects of long-term environmental enrichment on anxiety, memory, hippocampal plasticity and overall brain gene expression in C57BL6 mice, Front Mol Neurosci 9, 62.

75.

Diniz

, Foro

, Rego

, Gloria

, de Oliveira

, Paes

, de Sousa

, Tokuhashi

, Trindade

, Turiel

, Vasconcelos

, Torres

, Cunnigham

, Perry

, Vasconcelos

, Diniz

(2010) Environmental impoverishment and aging alter object recognition, spatial learning, and dentate gyrus astrocytes, Eur J Neurosci 32, 509–519.

76.

Griñan-Ferré

, Pérez-Cáceres

, Gutiérrez-Zetina

, Camins

, Palomera-Avalos

, Ortuño-Sahagún

, Rodrigo

, Pallàs

(2016) Environmental enrichment improves behavior, cognition, and brain functional markers in young senescence-accelerated prone mice (SAMP8), Mol Neurobiol 53, 2435–2450.

77.

Griñan-Ferré

, Puigoriol-Illamola

, Palomera-Ávalos

, Pérez-Cáceres

, Companys-Alemany

, Camins

, Ortuño-Sahagún

, Rodrigo

, Pallàs

(2016) Environmental enrichment modified epigenetic mechanisms in SAMP8 mouse hippocampus by reducing oxidative stress and inflammaging and achieving neuroprotection, Front Aging Neurosci 8, 241.

78.

Leal-Galicia

, Castañeda-Bueno

, Quiroz-Baez

, Arias

(2008) Long-term exposure to environmental enrichment since youth prevents recognition memory decline and increases synaptic plasticity markers in aging, Neurobiol Learn Mem 90, 511–518.

79.

Baraldi

, Schöwe

, Balthazar

, Monteiro-Silva

, Albuquerque

, Buck

, Viel

(2013) Cognitive stimulation during lifetime and in the aged phase improved spatial memory, and altered neuroplasticity and cholinergic markers of mice, Exp Gerontol 48, 831–838.

80.

Harati

, Barbelivien

, Herbeaux

, Muller

, Engeln

, Kelche

, Cassel

, Majchrzak

(2013) Lifelong environmental enrichment in rats: Impact on emotional behavior, spatial memory vividness, and cholinergic neurons over the lifespan, Age 35, 1027–1043.

81.

Nithianantharajah

, Hannan

(2011) Mechanisms mediating brain and cognitive reserve: Experience-dependent neuroprotection and functional compensation in animal models of neurodegenerative diseases, Prog Neuropsychopharmacol Biol Psychiatry 35, 331–339.

82.

Berardi

, Braschi

, Capsoni

, Cattaneo

, Maffei

(2007) Environmental enrichment delays the onset of memory deficits and reduces neuropathological hallmarks in a mouse model of Alzheimer-like neurodegeneration, J Alzheimers Dis 11, 359–370.

83.

Blázquez

, Cañete

, Tobeña

, Giménez-Llort

, Fernández-Teruel

(2014) Cognitive and emotional profiles of aged Alzheimer’s disease (3×TgAD) mice: Effects of environmental enrichment and sexual dimorphism, Behav Brain Res 268, 185–201.

84.

Mandolesi

, De Bartolo

, Foti

, Gelfo

, Federico

, Leggio

, Petrosini

(2008) Environmental enrichment provides a cognitive reserve to be spent in the case of brain lesion, J Alzheimers Dis 15, 11–28.

85.

Catlow

, Rowe

, Clearwater

, Mamcarz

, Arendash

, Sanchez-Ramos

(2009) Effects of environmental enrichment and physical activity on neurogenesis in transgenic PS1/APP mice, Brain Res 1256, 173–179.

86.

Herring

, Ambrée

, Tomm

, Habermann

, Sachser

, Paulus

, Keyvani

(2009) Environmental enrichment enhances cellular plasticity in transgenic mice with Alzheimer-like pathology, Exp Neurol 216, 184–192.

87.

, Xu

, Pigino

, Brady

, Larson

, Lazarov

(2010) Complex environment experience rescues impaired neurogenesis, enhances synaptic plasticity, and attenuates neuropathology in familial Alzheimer’s disease-linked APPswe/PS1DeltaE9 mice, FASEB J 24, 1667–1681.

88.

Herring

, Yasin

, Ambrée

, Sachser

, Paulus

, Keyvani

(2008) Environmental enrichment counteracts Alzheimer’s neurovascular dysfunction in TgCRND8 mice, Brain Pathol 18, 32–39.

89.

, Long

, Pigino

, Brady

, Lazarov

(2013) Molecular mechanisms of environmental enrichment: Impairments in Akt/GSK3β, neurotrophin-3 and CREB signaling, PLoS One 8, e64460.

90.

Sitzer

, Twamley

, Jeste

(2006) Cognitive training in Alzheimer’s disease: A meta-analysis of the literature, Acta Psychiatr Scand 114, 75–90.

91.

Olazarán

, Muñiz

, Reisberg

, Peña-Casanova

, del Ser

, Cruz-Jentoft

, Serrano

, Navarro

, García de la Rocha

, Frank

, Galiano

, Fernández-Bullido

, Serra

, González-Salvador

, Sevilla

(2004) Benefits of cognitive-motor intervention in MCI and mild to moderate Alzheimer disease, Neurology 63, 2348–2353.

92.

Küster

, Fissler

, Laptinskaya

, Thurm

, Scharpf

, Woll

, Kolassa

, Kramer

, Elbert

, von Arnim

, Kolassa

(2016) Cognitive change is more positively associated with an active lifestyle than with training interventions in older adults at risk of dementia: A controlled interventional clinical trial, BMC Psychiatry 16, 315.

93.

Cortese

, Olin

, O’Riordan

, Hullinger

, Burger

(2018) Environmental enrichment improves hippocampal function in aged rats by enhancing learning and memory, LTP, and mGluR5-Homer1c activity, Neurobiol Aging 63, 1–11.

94.

Tolppanen

, Solomon

, Kulmala

, Kåreholt

, Ngandu

, Rusanen

, Laatikainen

, Soininen

, Kivipelto

(2015) Leisure-time physical activity from mid- to late life, body mass index, and risk of dementia, Alzheimers Dement 11, 434–443.

95.

Neidl

, Schneider

, Bousiges

, Majchrzak

, Barbelivien

, de Vasconcelos

, Dorgans

, Doussau

, Loeffler

, Cassel

, Boutillier

(2016) Late-life environmental enrichment induces acetylation events and nuclear factor κB-dependent regulations in the hippocampus of aged rats showing improved plasticity and learning, J Neurosci 36, 4351–4361.

96.

Stein

, O’Dell

, Funatsu

, Zorumski

, Izumi

(2016) Short-term environmental enrichment enhances synaptic plasticity in hippocampal slices from aged rats, Neuroscience 329, 294–305.

97.

Sampedro-Piquero

, Castilla-Ortega

, Zancada-Menendez

, Santín

, Begega

(2016) Environmental enrichment as a therapeutic avenue for anxiety in aged Wistar rats: Effect on cat odor exposition and GABAergic interneurons, Neuroscience 330, 17–25.

98.

Arendash

, Garcia

, Costa

, Cracchiolo

, Wefes

, Potter

(2004) Environmental enrichment improves cognition in aged Alzheimer’s transgenic mice despite stable beta-amyloid deposition, Neuroreport 15, 1751–1754.

99.

Paban

, Chambon

, Manrique

, Touzet

, Alescio-Lautier

(2011) Neurotrophic signaling molecules associated with cholinergic damage in young and aged rats: Environmental enrichment as potential therapeutic agent, Neurobiol Aging 32, 470–485.

100.

Fuchs

, Cosquer

, Penazzi

, Mathis

, Kelche

, Majchrzak

, Barbelivien

(2016) Exposure to an enriched environment up to middle age allows preservation of spatial memory capabilities in old age, Behav Brain Res 299, 1–5.

101.

Herring

, Lewejohann

, Panzer

, Donath

, Kröll

, Sachser

, Paulus

, Keyvani

(2011) Preventive and therapeutic types of environmental enrichment counteract beta amyloid pathology by different molecular mechanisms, Neurobiol Dis 42, 530–538.

102.

Lambert

, Fernandez

, Frick

(2005) Different types of environmental enrichment have discrepant effects on spatial memory and synaptophysin levels in female mice, Neurobiol Learn Mem 83, 206–216.

103.

Wolf

, Kronenberg

, Lehmann

, Blankenship

, Overall

, Staufenbiel

, Kempermann

(2006) Cognitive and physical activity differently modulate disease progression in the amyloid precursor protein (APP)-23 model of Alzheimer’s disease, Biol Psychiatry 60, 1314–1323.

104.

Groot

, van Loenhoud

, Barkhof

, van Berckel

BNM

, Koene

, Teunissen

, Scheltens

, van der Flier

, Ossenkoppele

(2018) Differential effects of cognitive reserve and brain reserve on cognition in Alzheimer disease. Neurology 90, e149–e156.

105.

Kobayashi

, Ohashi

, Ando

(2002) Effects of enriched environments with different durations and starting times on learning capacity during aging in rats assessed by a refined procedure of the Hebb-Williams maze task, J Neurosci Res 70, 340–346.

106.

Gresack

, Kerr

, Frick

(2007) Short-term environmental enrichment decreases the mnemonic response to estrogen in young, but not aged, female mice, Brain Res 1160, 91–101.

107.

Gresack

, Kerr

, Frick

(2007) Life-long environmental enrichment differentially affects the mnemonic response to estrogen in young, middle-aged, and aged female mice, Neurobiol Learn Mem 88, 393–408.

108.

Mirochnic

, Wolf

, Staufenbiel

, Kempermann

(2009) Age effects on the regulation of adult hippocampal neurogenesis by physical activity and environmental enrichment in the APP23 mouse model of Alzheimer disease, Hippocampus 19, 1008–1018.

109.

Sampedro-Piquero

, Begega

, Zancada-Menendez

, Cuesta

, Arias

(2013) Age-dependent effects of environmental enrichment on brain networks and spatial memory in Wistar rats, Neuroscience 248, 43–53.

110.

Mora-Gallegos

, Rojas-Carvajal

, Salas

, Saborío-Arce

, Fornaguera-Trías

, Brenes

(2015) Age-dependent effects of environmental enrichment on spatial memory and neumistry, Neurobiol Learn Mem 118, 96–104.

111.

Goldberg

, Haack

, Meshul

(2011) Enriched environment promotes similar neuronal and behavioral recovery in a young and aged mouse model of Parkinson’s disease, Neuroscience 172, 443–452.

112.

Cotel

, Jawhar

, Christensen

, Bayer

, Wirths

(2012) Environmental enrichment fails to rescue working memory deficits, neuron loss, and neurogenesis in APP/PS1KI mice, Neurobiol Aging 33, 96–107.

113.

Hüttenrauch

, Walter

, Kaufmann

, Weggen

, Wirths

(2017) Limited effects of prolonged environmental enrichment on the pathology of 5XFAD mice, Mol Neurobiol 54, 6542–6555.

114.

Peña

, Prunell

, Rotllant

, Armario

, Escorihuela

(2009) Enduring effects of environmental enrichment from weaning to adulthood on pituitary-adrenal function, pre-pulse inhibition and learning in male and female rats, Psychoneuroendocrinology 34, 1390–404.

115.

Mosaferi

, Babri

, Ebrahimi

, Mohaddes

(2015) Enduring effects of post-weaning rearing condition on depressive- and anxiety-like behaviors and motor activity in male rats, Physiol Behav 142, 131–136.

116.

Dolivo

, Taborsky

(2017) Environmental enrichment of young adult rats (Rattus norvegicus) in different sensory modalities has long-lasting effects on their ability to learn via specific sensory channels, J Comp Psychol 131, 79–88.