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The role of cognitive reserve in cognitive aging:Results from the neurocognitive study on agingErasmia Giogkaraki a, Michalis P. Michaelides a & Fofi Constantinidou ba Department of Psychology, University of Cyprus, Nicosia, Cyprusb Center for Applied Neuroscience and Department of Psychology, University of Cyprus, Nicosia, CyprusPublished online: 18 Oct 2013.

To cite this article: Erasmia Giogkaraki, Michalis P. Michaelides & Fofi Constantinidou (2013) The role of cognitivereserve in cognitive aging: Results from the neurocognitive study on aging, Journal of Clinical and ExperimentalNeuropsychology, 35:10, 1024-1035, DOI: 10.1080/13803395.2013.847906

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Journal of Clinical and Experimental Neuropsychology , 2013Vol. 35, No. 10, 1024–1035, http://dx.doi.org/10.1080/13803395.2013.847906

The role of cognitive reserve in cognitive aging: Resultsfrom the neurocognitive study on aging

Erasmia Giogkaraki 1 , Michalis P. Michaelides 1 , and Fo Constantinidou 2

1Department of Psychology, University of Cyprus, Nicosia, Cyprus2Center for Applied Neuroscience and Department of Psychology, University of Cyprus, Nicosia,Cyprus

(Received 10 April 2013; accepted 19 September 2013 )

The study tested the cognitive reserve hypothesis by quantifying cognitive reserve (CR) and subsequently deter-mining its role in executive function and verbal episodic memory performance. A neuropsychological battery was

administered to 383 Greek-Cypriot older adults. A multiple indicators multiple causes (MIMIC) latent constructwas utilized to dene CR incorporating three indicators: years of education, vocabulary, and reading performance.Findings from two structural equation models supported the moderating role of CR in reducing the direct negativeeffect of age on verbal episodic memory and on executive function. The study illustrates a parsimonious way of dening CR and provides empirical support for the CR hypothesis.

Keywords: Aging; Cognitive reserve; Education; Episodic memory; Executive function.

Aging is associated with cognitive decline andincreased risk of dementia. Cognitive decline is acentral feature for understanding the aging pro-cess. Individual trajectories of cognitive change arehighly heterogeneous, with some declining rapidlyand others declining slowly or even improving(Reedet al., 2010 ; Wilson et al., 2002 ). Additionally,dissociation between brain pathology and clini-cal expression is observed (Satz, Cole, Hardy, &Rassovsky, 2011 ). The concept of reserve providesa framework for explaining the above individualdifferences (Stern, 2011 ).

Reserve has two forms: brain reserve and cog-nitive reserve (CR). Brain reserve and CR areconsidered as independent, as well as interactive in

The authors would like to thank the many volunteers and their families for participating in this project. We are indebted to the staff from the many adult community centers who assisted our research team in the recruitment of study participants and facilitated ourextensive testing processes, especially Kallia Sophocleous and Anna Filippou from the Strovolos Municipality, Sophia Metti from theLacadamia Municipality, Panagiotis and Christina Karamani from Nicosia Municipality, Elena Theodoulidou Polidorou from AyiosDometios Municipality, and the staff from EOKA Veterans Association and the Cypriot Retiree Union (EKYSY). Additionally, we arevery thankful to the many researchers in the Neurocognitive Research Laboratory at the University of Cyprus who participated in thedata collection and data management, and especially Juliana Prokopiou who manages the Neurocognitive Study on Aging.

This work was funded by the Cyprus Research Promotion Foundation through grant awarded to Fo ConstantinidouAN P I TIKE / KOIN / 0308(BE) / 07 and NEA Y O OMH / TPATH / 0309 / 37.

Address correspondence to: Erasmia Giogkaraki, Department of Psychology, University of Cyprus, 65 Kallipoleos St., P.O.Box 20537, 1678, Nicosia, Cyprus (E-mail: erasmia. [email protected] ).

explaining individual differences in cognitive andfunctional resilience of brain pathology (Tucker &Stern, 2011 ). Brain reserve is dened as the brain’scapacity to sustain a certain amount of pathologi-cal change before the emergence of the associatedclinical symptoms (Brickman et al., 2011 ; Satz,1993 ; Stern, 2002). According to the brain reservehypothesis, the quality of development in early life(Little, Busschang, Pena Reyes, Tan, & Malina,2006; Wadsworth, Hardy, Paul, Marshall, & Cole,2002 ) may provide resistance against the develop-ment of brain pathology (Brickman et al., 2011 ).CR can be dened as the ability to use alternate cog-nitive strategies, in order to optimize or maximizeperformance on cognitive tasks (Baldivia, Andrade,

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COGNITIVE RESERVE IN AGING 1025

& Bueno, 2008). The CR hypothesis explains whythose with “higher IQ, education, occupationalattainment, or participation in leisure activitiesevidence less severe clinical or cognitive changesin the presence of age-related or even AlzheimerDisease (AD) pathology” (Tucker & Stern, 2011 ,p. 354). According to the CR hypothesis, individ-uals with higher reserve are able to cope with brainpathology through some form of active compen-satory strategy better than those with lower reserve.Therefore CR is hypothesized to moderate the asso-ciation between brain pathology and the expres-sion of the pathology rather than protecting thebrain against the development of brain pathology(Brickman et al., 2011 ; Singh-Manoux et al., 2011 ).In other words, greater CR allows individuals tocope better with the cognitive changes associatedwith aging, by promoting more exible usage of

cognitive processes, such as new strategies. Thepresent study is part of the rst longitudinal projecton cognitive aging in Cyprus, the NeurocognitiveStudy on Aging (Constantinidou, Christodoulou,& Prokopiou, 2012). Although brain reserve is animportant theoretical construct in its own right,the purpose of the current study was to propose adenition of the CR construct, in order to exam-ine the cognitive reserve hypothesis on a largecohort of healthy aging adults in association withage and cognitive functions. We refer to CR as adistinct construct that represents a meaningful dis-

tinct dimension of individual differences in aging(Siedlecki et al., 2009).Cognitive reserve is often conceptualized as a

capacity that is progressively built through cogni-tively demanding and stimulating experiences, suchas education (Reed et al., 2010). In fact, yearsof formal education seems to be the most widelystudied variable, and it is commonly used as thesingle proxy for the concept of CR (Valenzuela &Sachdev, 2006a , 2006b ). The theoretical rationalefor using formal education as a proxy measure of cognitive reserve is based on the assumption that

it generates new cognitive strategies (Stern, 2002).Other single proxy measures could be occupationalstatus or other socioeconomic status variables, pre-morbid IQ, and mental activities (Valenzuela &Sachdev, 2006b ). An important limitation of thesingle proxy approach is that most single measuresof CR may be linked to neuropsychological test per-formance through many alternative paths and notonly through the hypothesized reserve mechanisms(Jones et al., 2011 ).

Alternatively, CR could be conceived as a hypo-thetical factor, not directly measured by a single

measure, but as a latent construct (Jones et al.,2011 ; Stern, 2006; Whalley, Deary, Appleton, &

Starr, 2004). Latent variable data analysis approachmight help to test theories regarding the role of CR,at least until a direct measure is identied. Jonesand colleagues (2011) support a multiple indicatormodel, inferring that the latent variable “reserve”may have several advantages. First, it may be a moreprecise measure of reserve than those obtained withany single indicator. Moreover, it may avoid some of the limitations of the nonreserve pathways of inter-action with cognitive measures. Finally, it couldhelp summarize the relationship between reserveand a cognitive function with a single coefcient.

In the present study, we propose a latent con-struct of CR, drawing on information from edu-cational attainment and estimates of crystallizedability. Each of these variables has been used inthe literature as a single proxy of CR becausethey reect life experiences that could potentially

protect against clinical manifestation of brain dis-ease (Siedlecki et al., 2009). Receptive vocabularyappears to be a good indication of crystallizedknowledge and is typically tested in passive vocab-ulary tasks. Constantinidou, Christodoulou, et al.(2012 ) demonstrated that while education can affectreceptive vocabulary performance, this ability isresistant to age as measured in two groups of matched older adults ages 65–75 and 76 + years.The present study included receptive vocabularyperformance in the development of the proposedCR latent construct. In contrast, the study incor-

porated cognitive measures that are sensitive to thechanges of biological aging processes as outcomes.Research indicates that changes in cognitive per-

formance associated with aging are probably aresult of the dynamic neurobiological processesthat occur during the brain development acrossthe lifespan. These processes seem to be selective,affecting different areas of the brain at differ-ent rates. Prefrontal cortical areas, inferior tempo-ral lobe areas, and the hippocampus sustain thegreatest diminution of blood ow, neuronal loss,and shrinkage (Kramer, Mungas, & Reed, 2007).

Furthermore, the availability or reuptake efciencyof certain neurotransmitters seems to be affectedby age, which in turn can result in a decrease inthe speed of synaptic signal transmission (Wu, Oh,& Distenhoft, 2002). The above neuropathologicalchanges provide a framework for understandingthe cognitive changes observed with aging becausethe aforementioned areas and associated networksare critical for organizing, categorizing, learn-ing, and retrieving information (Constantinidou &Baker, 2002; Constantinidou, Christodoulou, et al.,2012 ; Glisky, Polster, & Routhieaux, 1995; Lee,

Yuen, Chu, & Chi, 2004; Salthouse & Ferrer-Caja,2003 ).

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1026 GIOGKARAKI, MICHAELIDES, CONSTANTINIDOU

The present work focuses on two neuro-psychological domains—episodic memory andexecutive function—that are associated with theabove neurobiological changes in aging. Episodicmemory can be affected by normal aging andby multiple brain disorders; hence, it is a sensi-tive marker of pathological aging (Brickman &Stern, 2009; Christensen, 2001; Reed et al., 2010).Executive function as a neuropsychological con-struct is commonly associated with the frontal-lobehypothesis of aging. The frontal-lobe hypothesispredicts that age-related changes should be moreobservable in tasks involving executive control thanin tasks with lesser control demands. Cortical con-trol is supported largely by circuits in the prefrontallobes and particularly dorsolateral prefrontal lobenetworks, which are vulnerable to the aging process(Constantinidou, Wertheimer, Tsanadis, Evans, &

Paul, 2012; Span, Ridderinkhof, & van der Molen,2004).Additionally, studies reveal executive function

and episodic memory as distinct factors account-ing for age-related changes in cognition (Gliskyet al., 1995 ; Salthouse & Ferrer-Caja, 2003). Thesedifferential effects may arise from separate agingprocesses on frontal-striatal circuits and on themedial temporal lobe memory system (Buckner,2004 ). Moreover, distinct white matter hyperinten-sity loci are associated with lower executive func-tion scores and episodic memory scores (Smith

et al., 2011 ). However, these two cognitive domainsare not completely independent, as there is evi-dence that they are correlated. Executive function isthought to interfere with the successful engagementin the acquisition and the retrieval of informa-tion (Stuss & Alexander, 2000). Studies in normalaging showed that decits in executive functionhave an indirect impact on memory performance(Crawford, Bryan, Luszcz, Obonsawin, & Stewart,2000; Salthouse & Ferrer-Caja, 2003). Given theeffects of aging on executive function and episodicmemory performance, both of these abilities were

incorporated as outcome variables in our proposedmodel.The purpose of this study was to propose

a denition of the CR construct, in order toexamine the CR hypothesis on a large cohortof healthy aging adults in association with ageand cognitive functioning. We tested the CRhypothesis on two main cognitive domains: ver-bal episodic memory and executive function.A latent variable model was implemented to testthe hypothesis stating that CR plays a moderat-ing role in the relationship between age and both

episodic memory and executive function in healthyaging.

METHOD

Participants

Participants for this study were recruited fromthe Neurocognitive Study for the Aging, a lon-

gitudinal project exploring neuropsychological– neurocognitive performance, health indices, andbiological markers, as well as quality of life issuesin elderly Greek Cypriot community volunteers.The study was conducted in compliance withthe Helsinki Declaration and was approved bythe National Bioethics Committee, Republic of Cyprus. Volunteers were recruited from major dis-tricts in Cyprus that include Nicosia, Limassol,and Paphos. Out of the 483 participants in thedatabase, 383 Greek-Cypriot adults (182 malesand 201 females, ranging in age from 60 to

92 years) met the inclusion / exclusion criteria forthis study. The demographic distribution (age, gen-der, and education) of the study is in line with theCyprus Government census data (Cyprus StatisticalService, 2009). Table 1 presents the group demo-graphics.

The inclusion criteria for all participants were thefollowing: (a) native Greek speakers, (b) males andfemales of age 60 and above, (c) good general healthwith no previous history of neurological disordersuch as head trauma, stroke, or neurodegenera-tive disorder, (d) no history of severe psychiatric

or emotional disorder requiring hospitalization, (e)Mini-Mental State Examination (MMSE) score of 23 or higher, and (f) Geriatric Depression Score(GDS) of 9 or lower. Moreover, we included in theanalysis the participants that we were assured werefunctional in their daily activities.

Procedure

Participants were administered a battery of neurocognitive and language tests (translated

and adapted into Greek and previously used inother research studies) to assess certain aspectsof cognitive and language functioning. Researchdata established that these tests are sensitive tocognitive decline (Constantinidou, Christodoulou,et al., 2012; Greenlief, Margolis, & Erker, 1985 ;Lezak, Howieson, & Loring, 2004; Margolin, Pate,Friedrich, & Elia, 1990 ; Pfeffer et al., 1981). Beloware the measures included in the study:

General cognitive screening

Mini Mental Status Examination (MMSE;Fountoulakis, Tsolaki, Chantzi, & Kazis, 2000).

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TABLE 1Participants’ demographic characteristics and scores on

neuropsychological measures

Measure Mean SD Range

Age (years) 73.328 6.363 60–92Education (years) 7.898 4.176 0–21

Vocabulary 18.678 7.173 0–31Reading pseudo 59.590 12.062 0–110HVLT1 4.324 1.673 0–9HVLTimmediate 17.214 5.085 4–30HVLTdelayed 4.317 2.880 0–12LMimmediate 10.000 3.925 0–19LMdelayed 6.611 3.891 0–19TMTA (s) 88.643 40.226 30–369TMTB (s) 213.932 105.174 53–755SDMT 21.455 10.060 0–51Fluency category 10.448 3.066 2–28Phonemic uency 8.133 3.432 1–25

Note. Vocabulary: number of correct responses in the Greek ver-sion of the Peabody Picture Vocabulary Test (PPVT–III; Simoset al., 2011 ); Reading pseudo: the number of total pseudowordsread correctly in 45 s (Simos et al., 2013 ); HVLT: Greek ver-sion of the Hopkins Verbal Learning Test–Revised (Benedictet al., 1998; adapted in Greek by Constantinidou upon permis-sion from the publisher); HVLT1: the number of recalled wordsin the rst trial; HVLTimmediate: the total number of wordsimmediately recalled in the three learning trials; HVLTdelayed:the number of words recalled in the delayed recall trial; LM:Story A of Logical Memory subtest from Wechsler MemoryScale–Revised (Wechsler, 1997); LMimmediate: the score in theimmediate recall; LMdelayed: the score in the delayed recall;TMT: Trail Making Test; TMTA: time in seconds for the TMTPart A; TMTB: time in seconds for the TMT Part B; SDMT:the correct responses in 90 s in the Symbol Digit Modalities

Test (Smith, 1982); Fluency category: the number of the cor-rect words generated in 60 s for the category animals; Phonemicuency: the number of correct words starting with the letter Fgenerated in 60 s.

Depression screening

Geriatric Depression Scale (GDS; Fountoulakiset al., 1999).

Executive function tests

Trail Making Tests (TMT) A and B (Cons-tantinidou, Papacostas, Nicou, & Themistocleous,2008 ; Zalonis et al., 2008 ): TheTMT provides infor-mation on visual search, scanning, speed of pro-cessing, mental exibility, and executive functions.Originally, it was part of the Army Individual TestBattery (1994 ) and subsequently was incorporatedinto the Halstead–Reitan Battery.

Symbol Digits Modalities Test (SDMT; Smith,1982 ): It primarily assesses complex scanning andvisual tracking.

Verbal Fluency: Two verbal uency tasks:

Animal Recall and Words from the letter Fwere implemented, modied from the Controlled

Oral Word Association Test (COWAT; Kosmidis,Vlahou, Panagiotaki, & Kiosseoglou, 2004).

Verbal episodic memory tests

Greek version of the Logical Memory Story

A from the Wechsler Memory Scale–Revised:Immediate and delayed recall of a short story mate-rial (Constantinidou & Ioannou, 2008; Wechsler,1997 ).

Greek version of the Hopkins Verbal LearningTest–Revised (HVLT: Benedict, Schretlen,Groninger, & Brandt, 1998 ; adapted in Greekby Constantinidou upon permission fromthe publisher); Learning trials (rst trial:HVLT1 and the total score of the three learn-ing trials: HVLTimmediate), delayed recall(HVLTdelayed), to assess list learning and delayed

recall performance.

Vocabulary

The total number of correct responses in theGreek Version of the Peabody Picture VocabularyTest (PPVT–III; Simos, Kasselimis, & Mouzaki,2011) to assess receptive or passive vocabulary.

Reading measure

The total number of pseudowords read correctly

in 45 s as measured by a test of pseudowords inGreek (Simos, Sideridis, Kasselimis, & Mouzaki,2013 ).

Denition of the latent constructs

Episodic memory was dened as a latent fac-tor with ve reective observed-score indicators:Hopkins Verbal Learning Test Trial 1 (HVLT1),the total score of the HVLT three learning tri-als (HVLTimmediate), and the delayed recall score

(HVLTdelayed), Logical Memory immediate, andLogical Memory delayed. These measures are com-monly used for measuring verbal episodic memoryin adults in clinical settings, but also in researchstudies and are considered to represent encodingand retrieval capacities.

Executive function was also dened as a latentfactor with ve reective indicators: Trail Making A(TMTA), Trail Making B (TMTB), Symbol DigitsModalities Test, Verbal Fluency for semantic cate-gory, and phonemic verbal uency. The above tasksare speeded, mainly measuring attentional control

and set-shifting and verbal uency ability (Fisk &Sharp, 2004; Jurado & Rosselli, 2007).

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Dening a CR latent construct is a task withimportant challenges. Latent variable models allowthe formulation of a CR factor from a combina-tion of observed variables. Years of education is ademographic variable used as an indicator of priorexperience contributing to CR. We added two moreobserved measures: receptive vocabulary and read-ing performance. Both measures introduce impor-tant information about the quality of education(Manly, Jacobs, Touradji, Small, & Stern, 2002) andcould be used as estimates of “premorbid” cognitiveability (Crawford, Deary, Starr, & Whalley, 2001).Moreover, theseobserved variables do not representan important overlap between the cognitive reserveconstruct and the studied cognitive domains.

The specication of CR as a reective latentconstruct faces some conceptual difculties. Yearsof education, for example, is a characteristic that

occurs early in life and captures the extent of prior experience. Evidently, years of education can-not be conceptualized as an effect of CR; rather,it could be thought of as a contributor of theCR. Instead, specifying CR as a formative latentcomposite—that is, that it is “caused” by the threeobserved measures—is also problematic becauseformative measurement models have identicationproblems (Kline, 2006) and provide no informationfor the validity of the hypothesized latent factor(Borsboom, 2005; Jones et al., 2011 ). Our approachwas to consider years of education as a formative

indicator for CR and the vocabulary and readingperformance as effect indicators that “reect” thelevel of CR. CR is thus specied as a multipleindicators multiple causes (MIMIC) factor.

Structural equation modeling

Two structural equation models (SEMs) wereevaluated:

1. To test the direct relationships between ageand the two latent variables of verbal episodicmemory and executive function ( Figure 1 );

2. To test the moderating effect of CR betweenage and verbal episodic memory and executivefunction ( Figure 2 ).

Conrmatory factor analysis and structuralequation modeling analyses were conducted withAMOS 20 (Arbuckle, 2011 ), and maximum like-lihood estimation was applied on the covariancematrix of the data. Model t was evaluated withthe chi-square test, as well as the following approx-

imate t indices: Bentler’s ( 1990 ) Comparative FitIndex (CFI) an incremental t index that measures

the relative improvement in the t of the hypoth-esized model over a baseline model that assumesindependence among factors. The root mean squareerror of approximation (RMSEA; Hu & Bentler,1998 ; Steiger, 1990) is a parsimony-adjusted indexthat quanties badness of t. The standardized rootmean square residual (SRMR; Hu & Bentler, 1999)represents a measure of the difference betweenobserved and predicted correlation, which shouldbe close to zero for an adequate model t.

RESULTS

The correlation coefcients between all the vari-ables appear on Table 2 . As expected, perfor-mance on cognitive tasks is inversely related toage and positively related to years of education.

Intercorrelations among the variables measuringexecutive function were medium to high and signif-icant; the same was true for the variables measuringverbal episodic memory.

As a rst step, a conrmatory factor analysismodel was run with the two outcome latent vari-ables. Executive function and episodic memory hadve reective indicators each, and the two latentvariables were allowed to correlate as is demon-strated by several studies that we have alreadymentioned (Crawford et al., 2000 ; Salthouse &Ferrer-Caja, 2003 ; Stuss & Alexander, 2000). Fitindices were adequate, χ 2(30) = 69.089, p < .001,CFI = .979, RMSEA = .059, SRMR = .053 ,1 andall indicators load signicantly on their respectivelatent factors. As expected, the two latent con-structs of episodic memory and executive functionwere signicantly correlated in the conrmatoryfactor analysis model ( r = .620, p < .001).

The rst model of the relationship between age,episodic memory, and executive function had anadequate model t, χ 2(38) = 85.643, p < .001,CFI = .976, RMSEA = .058, SRMR = .052.The estimated coefcients reveal signicant nega-tive effects of age on both latent cognitive factors(see Figure 1 ). The unstandardized coefcient of age on episodic memory is –0.084 ( SE = 0.011,

1Two error covariances were added in the verbal episodicmemory indicators between (a) Logical Memory immediate andLogical Memory delayed,whichcan be explained by the fact thatthe two scores are based on the same test, and (b) HVLT1 andLogical Memory delayed possibly indicating a common under-lying mechanism of encoding. Two error covariances were addedin the executive function indicators between (a) the TimeA andTimeB observed measurements probably because both are forms

of the same test and (b) uency category and phonemic uency,as they are both measures of verbal uency ability.

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Figure 1. Schematic representation of the rst structural equation model. The rst model illustrates the relationship of age with episodic

memory and executive function. HVLT = Hopkins Verbal Learning Test; HVLT1: the number of recalled words in the rst trial;HVLTimmediate: the total number of words immediately recalled in the three learning trials; HVLTdelayed: the number of wordsrecalled in the delayed recall trial; TMTA = Trail Making Test Part A; TMTB = Trail Making Test Part B; SDMT = Symbol DigitModalities Test.

p < .001) and on executive function is –0.557(SE = 0.071, p < .001). The standardized esti-mates, –0.380 and –0.425, respectively, show thatthese effects are roughly of the same magnitude andmore than a third of a standard deviation. Squaredmultiple correlations are .180 for executive functionand .145 for episodic memory and suggest that a

small portion of their variance is explained by age.The second model (Figure 2 ), which included CRas moderator, had acceptable t indices, χ 2(68) =204.959, p < .001, CFI = .945, RMSEA = .073,SRMR = .075. The regression coefcients onTable 3 indicate that age has signicant negativedirect effects on the outcome variables; however,unstandardized estimates are much smaller in thesecond than in the rst model, but still signicantat level .05. Moreover, with the introduction of CR,the effects in the indirect paths are in the expecteddirection: Age has a signicant negative effect on

CR, while the CR has a large positive signicant

effect on verbal episodic memory and a very largepositive effect on executive function. Squared mul-tiple correlations are much larger in the secondmodel: .861 for executive function and .248 for ver-bal episodic memory, indicating that the additionof cognitive reserve in the model has contributed inexplaining much more variance in the two outcome

variables.Finally, after removing the direct paths of ageon the two cognitive latent variables, the t issignicantly worse, χ 2(70) = 227.088, p < .001,CFI = .937, RMSEA = .077, SRMR = .078,and squared multiple correlations are very high:.888 for executive function and .255 for verbalepisodic memory. Overall, these results demon-strate a signicant moderating role of the CR con-struct as dened using information about educationand observed verbal measures in the relationshipbetween age and executive function and episodic

memory.

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Figure 2. Schematic representation of the second structural equation model. The second model introduces the multiple indicatorsmultiple causes (MIMIC) cognitive reserve latent variable as a moderator between age and the two cognitive outcomes. Schematicrepresentation of the rst structural equation model. The rst model illustrates the relationship of age with episodic memory and exec-utive function. HVLT = Hopkins Verbal Learning Test; HVLT1: the number of recalled words in the rst trial; HVLTimmediate: thetotal number of words immediately recalled in the three learning trials; HVLTdelayed: the number of words recalled in the delayed recalltrial; TMTA = Trail Making Test Part A; TMTB = Trail Making Test Part B; SDMT = Symbol Digit Modalities Test.

DISCUSSION

The contribution of this study is twofold: method-ological and theoretical. First , even if similar CRconstructs have been proposed in the literature (e.g.,Siedlecki et al., 2009), our proposed MIMIC modelcould be considered as a possible solution to themethodological problem of using formative versusreective models in the denition of CR. Second ,the proposed CR construct has a moderating rolein reducing the direct effect of age in executive

functions and episodic memory, thus contributingto the growing body of literature exploring the

potential role of CR on cognitive performance inolder adults.

We developed a MIMIC latent construct of CRusing three variables: years of education, recep-tive vocabulary, and reading ability. We proposea simple construct of cognitive reserve based ontheoretical evidence and practical for large-scalestudies using variables that can be included as partof a general population screening protocol. Thevariable years of education is the most commonlyused measure for cognitive reserve and functions

as a formative indicator for CR. Years of educa-tion is considered to represent prior experience,

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TABLE 2Pearson correlation coefcients among the study variables

2 3 4 5 6 7 8 9 10 11 12 13 14

1. Age (years) − .210∗∗ − .225 ∗∗ − .092 − .323∗∗ − .349 ∗∗ − .336∗∗ − .145∗∗ − .147∗∗ .316∗∗ .321∗∗ − .372∗∗ − .146∗∗ − .177∗∗

2. Education(years)

.535 ∗∗ .312 ∗∗ .357∗∗ .330∗∗ .297∗∗ .333∗∗ .329∗∗ − .352∗∗ − .431∗∗ .661∗∗ .356∗∗ .360∗∗

3. Vocabulary .310∗∗

.256∗∗

.207∗∗

.316∗∗

.302∗∗

.299∗∗

− .423∗∗

− .492∗∗

.566∗∗

.429∗∗

.299∗∗

4. Reading .190 ∗∗ .189∗∗ .184∗∗ .137∗ .117 − .224∗∗ − .321∗∗ .271∗∗ .091 .178 ∗∗

5. HVLT1 .799 ∗∗ .561∗∗ .436∗∗ .413∗∗ − .362∗∗ − .429∗∗ .381∗∗ .273∗∗ .314∗∗

6. HVLTimmediate .666 ∗∗ .488∗∗ .483∗∗ − .402∗∗ − .440∗∗ .423∗∗ .336∗∗ .382∗∗

7. HVLTdelayed .433 ∗∗ .531∗∗ − .286∗∗ − .405∗∗ .416∗∗ .320∗∗ .313∗∗

8. LMimmediate .723 ∗∗ − .242∗∗ − .307∗∗ .379∗∗ .318∗∗ .256∗∗

9. LMdelayed − .260∗∗ − .311 ∗∗ .373∗∗ .339∗∗ .264∗∗

10. TMTA .730 ∗∗ − .587∗∗ − .314∗∗ − .285∗∗

11. TMT B − .648∗∗ − .391∗∗ − .353∗∗

12. SDMT .424 ∗∗ .417∗∗

13. Fluencycategory

.409∗∗

14. Phonemicuency

Note. Vocabulary: number of correct responses in the Greek version of the Peabody Picture Vocabulary Test (PPVT–III; Simos et al.,2011 ); Reading: the number of total pseudowords read correctly in 45 s (Simos et al., 2013); HVLT: Greek version of the Hopkins VerbalLearning Test–Revised (Benedict et al., 1998; adapted in Greek by Constantinidou upon permission from the publisher); HVLT1: thenumber of recalled words in the rst trial; HVLTimmediate: the total number of words immediately recalled in the three learning trials;HVLTdelayed: the number of words recalled in the delayed recall trial; LM: Story A of Logical Memory subtest from Wechsler MemoryScale–Revised (Wechsler, 1997); LMimmediate: the score in the immediate recall; LMdelayed: the score in the delayed recall; TMT: TrailMaking Test; TMTA: time in seconds for the TMT Part A; TMTB: time in seconds for the TMT Part B; SDMT: the correct responsesin 90 s in the Symbol Digit Modalities Test (Smith, 1982); Fluency category: the number of the correct words generated in 60 s for thecategory animals; Phonemic uency: the number of correct words starting with the letter F generated in 60 s.∗ p < .05. ∗ ∗ p < .01.

TABLE 3Estimates of the unstandardized and standardized estimates of regression weights of the SEM models

ModelsUnstandardized

estimatesStandard

errorStandardized

estimates p

Structural Equation Model 1 (effect of age on cognitive outcomes)

Age → VEM − 0.084 0.011 − 0.380 < .001Age → EF − 0.557 0.071 − 0.425 < .001

Structural Equation Model 2 (cognitivereserve as moderator)

Age → VEM − 0.059 0.011 − 0.270 < .001Age → EF − 0.215 0.071 − 0.162 .002Age → CR − 0.121 0.046 − 0.153 .008CR → VEM 0.105 0.018 0.379 < .001CR → EF 1.498 0.124 0.889 < .001

Note. SEM = structural equation modeling; VEM: the latent variable reecting verbal episodic memory; EF: the latent variable reectingexecutive function; CR: the latent variable reecting cognitive reserve.

which is contributing to CR. In this study, we havebeen able to capture the last generation of Cypriotswho have attended very little formal schooling andtherefore study the effects of education in respectto aging and cognition. We stress that this is thelast generation of individuals with very low edu-cation, because since the independence of Cyprusand the establishment of the Republic of Cyprus in1960, public education has been free and manda-tory through Grade 9 (Cyprus Statistical Service,2009 ). Prior to 1960, it was standard practice toattend a few years of elementary school educationand then enter the work force. It is possible that

years of education might reect cognitive potentialto some extent; however, in pre-1960 Cyprus, var-ious socioeconomic factors such as poverty, socialstatus, gender, and place of birth dictated educationchoices. We also added two measures as reectiveindicators (receptive vocabulary and reading abil-ity), which introduce important information aboutthe quality of education (Manly et al., 2002) andcould be used as estimates of “premorbid” cognitiveability (Crawford et al., 2001). Additionally, there isnot an important overlap between the outcome cog-nitive domains and the cognitive reserve construct.This study reinforces the repeated observation that

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educational experience has a strong inuence oncognitive processing, and, therefore, educationalattainment, receptive vocabulary, and reading levelare good proxies for cognitive reserve (Stern, 2011 ).

Furthermore, we tested two SEM models.We used SEM to test our hypothesis as it is con-sidered a powerful statistical tool for analyzingmultivariate data in studies of neurobiology andaging. We developed a parsimonious model, con-taining only the most relevant associations, whichare more likely to be generalizable (Penke & Deary,2010 ).

The rst model shows an expected negative effectof age on both verbal episodic memory and exec-utive function. The effect seems to be of the samemagnitude, showing that both of these cognitivedomains account for age-related changes in cog-nition. The second model assessed the moderating

role of CR. Findings reveal that CR moderates thedirect negative effect of aging in cognitive aging andespecially on executive function, in accordance withthe CR hypothesis. Both models have adequate t.

Additionally, executive function and verbalepisodic memory are correlated. Therefore, wecould suppose a common underlying mechanismthat could interfere with CR. Several aspects of executive function have indirect impact on mem-ory: the organization and elaboration of materialof encoding, strategic retrieval of information, andthe ability to avoid the effects of interference (Stuss

& Alexander, 2000). If we take into account thetheoretical framework proposing that CR gener-ates new cognitive strategies (Stern, 2002), strategythinking could be considered as the commonunder-lying mechanism between executive function andepisodic memory. Our ndings are in agreementwith a recent study that demonstrated that theassociation between markers of reserve and cog-nition was weakest for the measure of memoryand strongest for the measure of reasoning (Singh-Manoux et al., 2011 ). In other words, adults withhigher levels of CR apply more elaborate learn-

ing and cognitive strategies, which may be morerelevant for reasoning and executive function andmore indirectly related to memory performance,and this could probably explain why in the secondmodel we observe a stronger indirect effect of cog-nitive reserve on executive function than in verbalepisodic memory.

However, the present study has several limi-tations that should be noted. First, we did notinclude brain measures (e.g., imaging). Therefore,we could not evaluate the relationship of CR tobrain reserve and how these two constructs inter-

act. Additionally, we included in our study a fewparticipants with low or even zero years of formal

education and consequently very low scores insome neuropsychological measures. However, theymet our inclusion / exclusion criteria as their per-formance was within the expected ranges (for theireducation levels), and they had no subjective com-plaints of memory or other cognitive impairments.Future studies may decide to exclude participantswith the aforementioned prole.

Several latent variable models of reserve haveappeared in the scientic literature. Satz et al.(2011 ) used the executive function construct as acandidate measure of cognitive reserve. A robustoverlap seems to exist between a construct of executive function and a construct of cognitivereserve as “lifetime experiences” (Siedlecki et al.,2009 ). However, certain aspects of executive func-tion are linked to the prefrontal cortex, an areathat develops last during normal development and

at the same time is very vulnerable to age-relateddegeneration (Raz, 2000). This degeneration of theprefrontal cortex may lead to early decline in severalexecutive abilities (West, 1996). Thus, in accordancewith the gain / loss hypothesis, one should carefullyselect aspects of executive function that are robustand resistant to aging in order to include themin future CR theoretical frameworks. Otherwise,the executive function construct as dened in thepresent study may not be an appropriate measurefor cognitive reserve since it is not stable across thelifespan, and it is vulnerable to the changes asso-

ciated with aging. Hence it could not have a dualrole, as a dependent measure of the impact of aging(or pathology) and as a factor that modies thisrelationship.

Another construct, general intelligence (g), hasbeen reported as an important construct of cog-nitive reserve. Unlike crystallized or uid intelli-gence (when adjusted for g), the construct g isstrongly linked to intracranial and brain volume,especially prefrontal volume (Christensen, Anstey,Leach, & Mackinnon, 2008). General intelligence,unlike uid ability, is also stable across the lifes-

pan and is associated with cognition, education,occupation, mental activities, and survival (Staff,Murray, Deary, & Whalley, 2004; Sternberg, 2008).However, there is also an overlap between the mea-sures of cognitive performance and the estimationof general intelligence.

A recent alternative approach to the measureof cognitive reserve consists of decomposing thevariance in cognitive function scores (Reed et al.,2010 ) and dening CR as a prediction score thatcan be modied across time. Specically, Reed andcolleagues described CR as a measure of episodic

memory adjusted for the effect of magnetic res-onance imaging (MRI) and demographics (Reed

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et al., 2010). This approach, while interesting andinnovative, it is not practical for large-scale stud-ies since MRI ndings are not routinely includedas part of a general population screening protocol.Interestingly, Reed et al. ( 2011 ) showed that engag-ing in leisure cognitive activities at 40 and in latelife was the strongest predictor of cognitive reserve,compared to socioeconomic status and education.We plan to include cognitive leisure activities in ourfuture studies aiming at dening CR.

Future studies should also explore the poten-tial effect of neurocognitive training in improvingor expanding CR. Recent clinical trials seem toindicate that cognitive training can have a pos-itive protective effect on longitudinal cognitivechange in both normative and preclinical groups(Derwinger, Stitgsdotter Neely, & Backman, 2005;Oswald, Gunzelamann, Rupprecht, & Hagen, 2006;

Valenzuela, 2008). The benets are apparent notonly for the trained cognitive domains, but also forthe functionality of older adults in daily activities(Valenzuela, 2008; Willis et al., 2006). It remains aquestion whether people with higher reserve maybenet more from cognitive training or cognitivetraining may offer an opportunity for those withlow cognitive reserve to “build” one.

Cognitive aging is a heterogeneous and highlyindividualized and complex process. The presentndings support the CR hypothesis and its mod-erating role in the relationships between chrono-

logical age and verbal episodic memory and execu-tive function. The proposed MIMIC latent variableoffers a parsimonious and practical model of CR,which can be easily implemented in large-scale stud-ies on cognitive aging. Additionally, our proposedmodel allows room for further development andfuture expansion in order to eventually develop anaccurate and representation of reality. Specically,we plan to explore the contribution of biologicalmarkers, such as cardiovascular health and geneticfactors (apolipoprotein E, ApoE) and indexes of daily activities (cognitive, physical, and social) in

our proposed model on aging. We believe that thisapproach will contribute to our understanding of CR on healthy aging with important implicationson cognitive decline associated with the biologicalaging.

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