Distinct Aspects of Frontal Lobe Structure Mediate Age-Related Differences in Fluid Intelligence and Multitasking

ARTICLE

Received 26 Aug 2014 | Accepted 24 Oct 2014 | Published 18 Dec 2014 DOI: 10.1038/ncomms6658 OPEN Distinct aspects of frontal lobe structure mediate age-related differences in ﬂuid intelligence and multitasking

Rogier A. Kievit1, Simon W. Davis2, Daniel J. Mitchell1, Jason R. Taylor1,3, John Duncan1, Cam-CAN* & Richard N.A. Henson1

Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N ¼ 567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate ﬂuid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and ﬂuid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing.

1 MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK. 2 Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK. 3 School of Psychological Sciences, The University of Manchester, Brunswick Street, Manchester M13 9PL, UK. Correspondence and requests for materials should be addressed to R.A.K. (email: [email protected]). *A full list of the Cam-CAN Research team can be found at the end of the paper.

NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications 1 & 2014 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658

ge-related changes are widespread, but not homogeneous More specifically, our goals are to examine whether age-related in terms of cognitive function or brain substrates. differences in these executive functions are unitary or multi- AExecutive functions and frontal brain regions, for factorial; whether the dependence on frontal lobe differences is example, show disproportionately strong age-related declines1,2 global or specific and to what extent neural differences explain compared to other cognitive functions. These declines have far- age-related differences in these cognitive factors. In a large reaching consequences for successful day-to-day functioning in (N ¼ 567) age-heterogeneous population-based sample from the old age3. This is concerning, given that a rapidly ageing Cambridge Centre for Ageing and Neuroscience (Cam-CAN), we population in western countries (a 50% increase in use (confirmatory) structural equation modelling (SEM), a over 65’s expected by 2035 (ref. 4)) engenders an increasing powerful multivariate technique that fits observed covariances demand for high-functioning older adults. Together, these issues between variables15, and allows for the joint testing of several a present an urgent challenge for the cognitive neuroscience of priori predictions. Structural equation modelling has been used to ageing: how exactly do higher cognitive functions differ across the model age-related differences in cognition, and is increasingly lifespan, and how are they related to concurrent neural changes? applied to capture cognitive and neural differences across Prominent theories of age-related cognitive decline include lifetime16–22. Using these techniques, we show how age-related models that focus on a single cause or factor that is posited to differences in fluid intelligence and multitasking are related to influence a wide range of cognitive functions. Many one-factor distinct measures of frontal lobe integrity. models of aging suggest that declines in cognition represent a The challenge in any neuroscientific study of ageing is how continuum, where the pathological neurocognitive extremes (for to best assess and interpret age-related differences23. As extant example, Alzheimer’s disease) represent the ends of that models of aging rely largely on age-heterogeneous cross-sectional continuum5. This unitary view has often focused on a set of data, it is important to keep in mind that such studies complex cognitive operations grouped under the label of likely reflect a combination of intra-individual decline and executive functions. Although a precise definition has proved inter-individual cohort effects (for example, year of birth). elusive, executive functions are commonly considered to include Methodological approaches have focused on the challenges for abilities such as ‘choosing and initiating goal-directed age-heterogeneous cross-sectional designs23–25. Others have behaviours’, ‘set shifting’ and ‘cognitive flexibility’2, all of which defended cross-sectional studies26,27, arguing that longitudinal show steep age-related declines relative to other cognitive studies also suffer from inferential challenges, including under- functions. Concurrent decline in grey matter (GM)1 and white estimation of true ageing effects due to practice effects28 and matter integrity (WMI)6 of the frontal lobe has been observed in selective attrition27,29. longitudinal samples, leading to an influential theory of ageing Madden et al.30 proposed three heuristic criteria for known as the frontal lobe hypothesis7,8, in which declines in interpreting brain–behaviour relationships in cross-sectional executive functions are attributed to age-related changes in the neurocognitive ageing studies. First, the relationship between frontal lobe. However, the precise nature of age-related changes brain and behaviour should be significant when age is taken into within the frontal lobe, such as the connection between GM and account, to ensure that the cross-sectional correlation is not WM changes, remains unclear. This issue is key, as improved merely due to co-occurrence of decline31. Second, the age-to- understanding of the effects of frontal atrophy on cognitive cognition relationship should be attenuated when age-associated performance is necessary to develop age-related interventions neural differences are taken into account19. Finally, to establish if that may preserve executive function into old age. there is a difference in the brain–behaviour relationship for young In this paper, we estimate the unique contributions of age- and old, this difference should be formally tested, as opposed to related differences in frontal GM and WM structures for two comparing significant versus non-significant relationships in the executive functions: fluid intelligence and multitasking. Fluid young and old. We will show that the data reported here satisfy intelligence has been defined as the ability to think logically and all three criteria. solve problems in the absence of task-specific knowledge or We focus on four neural properties selected based on the experience9. Fluid intelligence lies at the core of psychometric current literature—(see Methods), two involving grey matter analyses of intelligence9 and predicts real world outcomes volume (GMV; Brodmann Area 10 (BA10) and the Multiple including life expectancy, expected income and work Demand (MD) System), and two involving WMI (the Forceps performance10. Moreover, fluid intelligence correlates highly Minor (FM) and the Anterior Thalamic Radiations (ATR)). with tests that assess successful day-to-day functioning in Fitting a series of structural equation models, we show that society such as the Basic Skills Test11, even across intervals of multitasking and fluid intelligence are distinct cognitive abilities several years3. Multitasking, on the other hand, is the general that show diverging age-related differences, and are mediated by ability to perform several tasks simultaneously without adverse distinct neural subsystems and show differential brain–behaviour effects on performance. Lower multitasking ability is associated patterns in older versus younger individuals. These results show with a history of other age-related challenges such as recurrent that the relation between age-related differences in executive falls12 and possibly underlies age-related problems on other function and lifetime changes in neural structure is multi- cognitive abilities such as working memory13. dimensional. Principled application of formal statistical models More generally, behavioural tests of executive functions are will further increase understanding of these processes. likely to share certain cognitive components, and are generally positively correlated14. They therefore represent a valuable avenue of insight into models of age-related changes in Results executive function: They allow for comparison of single versus Competing factor models of brain and behavioural differences. multifactorial models, and an examination of distinct Following best practice in SEM32, we first report our measurement contributions of different neural properties of the frontal lobe. model on the full sample of N ¼ 567 (for further details on the Our goal here is not to exhaustively cover the domain of executive sample and model fitting, see Methods). For this model, we functions, but instead to focus on two specific tests that are likely hypothesize that two latent variables (fluid intelligence and to measure dissociable executive functions (fluid intelligence and multitasking; tasks shown in Fig. 1, see Methods for more detail) multi-tasking)—to put to test the prevalent unidimensional capture the covariance between the six behavioural variables theories of executive function in ageing. described in the Methods section, freely estimating every factor

2 NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications & 2014 Macmillan Publishers Limited. All rights reserved. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658 ARTICLE

Example item 2

Possible responses

0 Ability Fluid intelligence Multitasking –2 Standardized score Looking up phone Compiling individual numbers bills based on till rolls –4

20 40 60 80 Age (years)

Figure 2 | Linear fit of fluid intelligence and multitasking with age. Sorting conference Sorting the charity Proof-reading the Fluid intelligence shows a significantly stronger age-related difference delegate labels into collection new hotel leaflet (r 0.67) than multitasking (r 0.29) (William’s test for dependent alphabetical order ¼À ¼À (r ¼ 0.38) correlations t(564) ¼ 10.66, Po0.00001). Figure 1 | Fluid intelligence and multitasking tasks. (a) Shows an example of a fluid reasoning item (trial). The Cattell test yields scores on four subtests used for further modelling. (b) Shows the multitasking task, which Table 1 | Robust regression of age-related behavioural is a simulation of a hotel environment, in which participants were asked to differences. perform each of the five tasks for equal amounts of time within a 10-min period. Variables-of-interest are the number of different tasks people Variable Estimate s.e. Test statistic P-value performed and total time misallocated. Fluid intelligence and age Age 0.034 0.002 t-value 2e 16 w2 À o À loading. This model fits the data well, ¼ 15.40, degrees of À 21.070 freedom (df) ¼ 8, P ¼ 0.052, root mean square error of Age (robust sandwich) À 0.037 0.002 z-value o2e À 16 approximation (RMSEA) ¼ 0.04 (0.00–0.070), comparative fit À 20.831 index (CFI) ¼ 0.993, standardized root mean square residual (SRMR) ¼ 0.023, Satorra–Bentler scaling factor ¼ 1.009. As the Multitasking and age two latent factors are positively correlated (standardized Age À 0.014 0.002 t-value o1.66e À 11 estimate ¼ 0.325, Z ¼ 6.17, Po0.001), we can ask whether a À 6.874 more parsimonious model with only a single cognitive factor (for Age (robust sandwich) À 0.018 0.002 z-value o4.46e À 11 example, ‘executive function’) shows better fit. Such a model would À 6.588 be compatible with a unitary perspective on the age-related decline Robust regression using heteroscedasticity consistent sandwich estimator to regress cognitive of higher cognitive function. However, this one-factor model fits factor scores on age. Results show highly similar results for robust and regular regression, 2 suggesting that the increase in behavioural variance with age does not affect our estimate of very poorly: w ¼ 334.149, df ¼ 9, Po0.00001, RMSEA ¼ 0.252 age-related differences in the two cognitive factors. (0.231–0.275), CFI ¼ 0.685, SRMR ¼ 0.121, Satorra–Bentler scaling P values for the robust regression are computed using a heteroscedasticity-consistent robust sandwich estimator. factor ¼ 1.109, significantly worse than the two-factor model 2 (w diff ¼ 46.224, dfdiff ¼ 1, Po0.00001). One additional alternative, a hierarchical model with a third, higher-order latent variable, fits estimates and s.e. are virtually identical, suggesting no adverse the data equivalently to the single-factor model above, but has one effects of the heteroscedastic residuals. extra parameter, so the more parsimonious model is preferred Having established the proper fit of our measurement model, (AICdiff ¼ 2). we then relate these factors to the four neural variables based Together these findings suggest that multitasking and fluid on the literature (see Methods for more details). These four intelligence are separable cognitive factors. We subsequently neural variables are GMV in the frontopolar cortex (BA10), GMV estimate the impact of age on both cognitive factors. Figure 2 in the MD system, WMI of the FM and WMI of the ATR, as shows that although both factors differ significantly with age, shown in Fig. 3. scores on the latent variable of fluid intelligence decline Next, we fit the full model relating behavioural variables to the significantly more quickly (r(565) ¼À0.67, Po0.0001, 95% brain variables (for the full covariance matrix, see Supplementary confidence interval (CI): ( À 0.71, À 0.61)) than scores on multi- Table 1) using a type of SEM called a Multiple Indicators, tasking (r(565) ¼À0.29, Po0.0001, 95% CI: ( À 0.35, À 0.20)): Multiple Causes model (MIMIC model33,34, see Methods for William’s test for dependent correlations sharing a variable: further details). This model captures the hypothesis that t(564) ¼ 10.66, Po0.00001. individual differences in the two cognitive factors are causally There is a small but significant increase in inter-individual dependent on the combination of neural properties in the variability with age on both latent variables (age and fluid prefrontal cortex (PFC). The full model, shown in Fig. 4, fits the intelligence: Breusch–Pagan test w2 ¼ 15.4876, df ¼ 1, Po0.0001; data well: w2 ¼ 52.912, df ¼ 24, P ¼ 0.001, RMSEA ¼ 0.046 age and multitasking: Breusch–Pagan test w2 ¼ 17.0027, df ¼ 1, (0.029–0.063), CFI ¼ 0.979, SRMR ¼ 0.029, Satorra–Bentler Po0.0001). To ensure that the significant increase in residuals scaling factor ¼ 1.028. The good fit of the full model allows us does not affect our estimates of age-related decline in fluid to further investigate the relations between the cognitive factors intelligence and multitasking, we estimate both a traditional and the neural variables. parametric regression and a heteroscedasticity-consistent robust First and foremost, the model shows clear diverging predic- sandwich estimator. As can be seen in Table 1, the parameter tions within PFC: GMV in BA10 and WMI of the FM strongly

NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications 3 & 2014 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658 predict individual differences in fluid intelligence but not in hypothesized to not predict either fluid intelligence or multi- multitasking, whereas WMI of the ATR predicts performance on tasking. In line with this hypothesis, these control measures did multitasking, but not fluid intelligence. Contrary to our expecta- not have significant influences on the latent variables and, unlike tions, GMV in the MD system does not predict fluid intelligence the neural variables-of-interest described above, fixing their above and beyond GMV of BA10. We formally test the equality of parameters to zero does not adversely affect model fit. the neural parameters using a w2-test between the fit of the full The above analyses demonstrate clear differential predictions model and that of another model where the brain–behaviour of the GM and WM variables-of-interest. However, we can also parameters are constrained to be equal for fluid intelligence and formally test whether a simpler, one-factor model representing multitasking. This comparison shows that the model in which the ‘general PFC integrity’ can capture the covariance between these brain parameters are freely estimated fits the data significantly frontal lobe measures. This ‘frontal lobe’ model, shown in Fig. 5, better than the model in which they are constrained to be can be seen as representing the frontal lobe hypothesis, where the 2 equal (w diff ¼ 79.914, df ¼ 4, Po0.0001), suggesting that overall health of the PFC is a global property of a person, which specific prefrontal brain properties have different impacts on determines their level of (preserved) executive function. However, the cognitive factors. To further quantify these relationships, we regress the behavioural factor scores on the respective neural variables. This C1 shows that BA10 and FM together strongly predict individual 2 FM 0.764*** differences in fluid intelligence: adjusted R ¼ 0.471, F(2, C2 0.574*** Fluid 0.715*** 564) ¼ 253.4, Po0.00001. The relationship between multitasking intelligence 0.813*** 2 and ATR is less strong but also pronounced, adjusted R ¼ 0.074, 0.710*** C3 F(1, 546) ¼ 46.21, Po0.00001. To test the specificity of the four 0.697 ATR 0.131 C4 neural variables shown in the model in Fig. 4, we fit an additional 0.167* model shown in Supplementary Materials (Supplementary Fig. 1). 0.240*** 0.138 0.196 BA10 For this analysis, we include two additional control measures 0.183* (GMV in the temporal pole and WMI in the forceps major) 0.171

MD TTM 0.791 0.821*** Multitasking 0.865*** #tasks

Figure 4 | Full MIMIC model relating four frontal brain variables to fluid intelligence and multitasking. Significant brain–behaviour parameters are shown in green solid lines. All parameter estimates shown are fully standardized. This model fits the data well, w2 ¼ 52.912, df ¼ 24, P ¼ 0.001, RMSEA ¼ 0.046 (0.029–0.063), CFI ¼ 0.979, SRMR ¼ 0.029, Satorra– Bentler scaling factor ¼ 1.028. C1–C4 refer to the four sub-scores on the Cattell test of fluid intelligence; TTM (total time misallocated) and #tasks (number of tasks attempted) are two indices of performance on Hotel test of multitasking. *Po0.05, ***Po0.001. These P-values are based on a z-test statistic derived from a Maximum Likelihood Structural Equation Model using robust standard errors and a Satorra–Bentler scaled test statistic. See ref. 67 for further details.

FM Fluid C2 intelligence C3 ATR C4

Prefrontal BA10 integrity

TTM MD Multitasking #tasks

Figure 5 | Frontal lobe model. This model represents the hypothesis Figure 3 | Four frontal regions-of-interest. The four neural structures-of- that age-related individual differences in frontal lobe structure can be interest: (a) Brodmann Area 10 (BA10), (b) the Multiple Demand (MD) captured by a single factor, representing overall PFC integrity. This model system, (c) the Forceps Minor (FM), passing through the genu of the ﬁts the data poorly, w2 ¼ 670.11, df ¼ 32, Po0.00001, RMSEA ¼ 0.188 corpus callosum, (d) the Anterior Thalamic Radiations (ATR) and (e) all (0.176–0.200), CFI ¼ 0.728, SRMR ¼ 0.158, Satorra–Bentler scaling four structures superimposed. factor ¼ 1.05).

4 NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications & 2014 Macmillan Publishers Limited. All rights reserved. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658 ARTICLE this global PFC model shows poor fit, w2 ¼ 670.11, df ¼ 32, ( À 0.57, À 0.44)), William’s test for dependent correlations Po0.00001, RMSEA ¼ 0.188 (0.176–0.200), CFI ¼ 0.728, sharing a variable: (t(564) ¼ 13.42, Po0.00001). Together, these SRMR ¼ 0.158, Satorra–Bentler scaling factor ¼ 1.050, suggesting results show that there is clear differential ageing within PFC, and that structural brain properties of the PFC cannot be captured by that this has different consequences for specific cognitive factors. a single factor. We fit a second control model, where the two types of neural Mediation of age-related differences in cognition. Having property—grey matter (GMV) and white matter (WMI)—are shown that individual differences in fluid intelligence and mul- separate latent variables, each of which affects fluid intelligence titasking can be predicted by specific brain measures, we next and multitasking. This ‘neural latent variable’ model, shown in examine whether age-related differences in these measures might Supplementary Fig. 2, fits the data better than the frontal lobe explain concurrent age-related differences in the two cognitive model described above, but considerably worse than the MIMIC factors. To this end, we use mediation analysis, commonly used to 19 model shown in Fig. 4 (AICdiff ¼ 41.915). Taken together, these study neurocognitive ageing . Mediation models hypothesize comparisons show that although global factors like GMI or WMI three paths, a, b and c, between three variables-of-interest, and are known to predict important general aspects of aging16, they that taking into account the indirect paths (a and b) should may miss more fine-grained details in the specific brain regions/ attenuate the key relationship (c, in this case the relationship tracts involved. This differentiation within the PFC is further between age and cognition). We fit mediation models within a exemplified by closer inspection of age-related differences in each SEM framework that simultaneously models all paths, giving of the neural variables, as can be seen in Fig. 6. The GM variables more powerful, accurate and robust estimation of mediation MD and BA10 show similar age-related differences (age and effects35 than more traditional tests based on sequential BA10: r(565) ¼À0.20, Po0.0001, 95% CI: ( À 0.28, À 0.12), MD regressions. As noted above, we here model age-related and age: r(565) ¼À0.18, Po0.0001, 95% CI: ( À 0.26, À 0.10)), differences in brain structure as a partial cause of the William’s test for dependent correlations sharing a variable: concurrent age-related differences in the two cognitive t(564) ¼ 0.7, P40.4). However, WMI averaged across ATR and dimensions, which is not identical to observing concurrent age- FM shows significantly steeper decline than GMV averaged across related decline (rates of change). To ensure that possible BA10 and MD (t(564) ¼ 11.84, Po0.00001), and WMI in FM in mediation effects are not solely due to concurrent age-related turn shows significantly steeper decline than WMI in ATR decline of brain and behaviour (which may create artificial (r(565) ¼À0.77, Po0.00001, 95% CI: ( À 0.80, À 0.73)) than patterns of mediation31,36), we first examine the partial WMI in ATR (r(565) ¼À0.51, Po0.00001, 95% CI: correlations between brain and behaviour after controlling for

MD BA10 4 4

2 2

0 0

Standardized GMV –2 Standardized GMV –2

–4 –4 20 40 60 80 20 40 60 80 Age (years) Age (years)

r(565) = –0.18, P < 0.0001, r(565) = –0.20, P < 0.0001, 95% CI: (–0.26, –0.10) 95% CI: (-0.28, –0.12)

ATR FM 4 4

2 2

0 0 Standardized GMV Standardized WMI –2 –2

–4 –4 20 40 60 80 20 40 60 80 Age (years) Age (years) r(565) = –0.51, P < 0.00001, r(565) = –0.77, P <0.00001, 95% CI: (–0.570, –0.44) 95% CI: (–0.80, –0.73) Figure 6 | Differential prefrontal ageing. Plots show differential ageing patterns of the four PFC structures-of-interest. Forceps minor shows greatest age-related differences, followed by the anterior thalamic radiations. The two GMV regions-of-interest show equal age-related differences. This pattern further supports differentiation within the PFC.

NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications 5 & 2014 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658 age. This shows that the correlations between fluid intelligence the attenuation of the age-related differences in multi- and BA10 (r(564) ¼ 0.251, Po0.0001), between fluid intelligence tasking when taking into account WM in the ATR, is shown in and FM (r(564) ¼ 0.221, Po0.0001) and between multitasking Fig. 7b. It too fits the data well (w2 ¼ 0.515, df ¼ 1, P40.47, and ATR (r(564) ¼ 0.161, Po0.001) are all significant when RMSEA ¼ 0.0 (0.0–0.0), CFI ¼ 1, SRMR ¼ 0.003, Satorra–Bentler controlling for age. scaling factor ¼ 0.94) demonstrating that age-related differences We first fit a mediation model where WMI in the FM in multitasking ability (cage4multitasking ¼À0.189, Z ¼À3.49, and GMV in BA10 simultaneously mediate age-related Po0.001) are mediated by WMI of the ATR (aage4ATR ¼À0.51, differences in fluid intelligence. This model, shown in Fig. 7a, Z ¼À13.00, Po0.0001, bATR4multitasking ¼ 0.155, Z ¼ 2.73, 2 fits the data well (w ¼ 23.410, df ¼ 12, P ¼ 0.024, Po0.01, abmediation ¼À0.079, Z ¼À2.63, Po0.01), with an RMSEA ¼ 0.041 (0.015–0.065), CFI ¼ 0.993, SRMR ¼ 0.021, effect size of 29.4% variance explained, corresponding to a large Satorra–Bentler scaling factor ¼ 1.055), and shows that the age- mediation effect. Again, the significant b-path in this mediation related differences in fluid intelligence (cage4fluidint ¼À0.588, model indicates an age-independent role for the ATR in Z ¼À9.14, Po0.00001) are multiply mediated by both supporting multitasking performance. For both fluid BA10 GMV (a1age4BA10 ¼À0.199, Z ¼À4.97, Po0.0001, intelligence and multitasking, the brain measures-of-interest b1BA104fluidint ¼ 0.156, Z ¼ 4.49, Po0.0001, a1b1mediation ¼ mediate the age-related differences in cognition. However, the À 0.031, Z ¼À3.53, Po0.001) and FM WMI (a2age4FM ¼ mediation effects are partial, as is the case in almost all well- 35 À 0.77, Z ¼À27.36, Po0.0001, b2FM4fluidint ¼ 0.131, Z ¼ 2.29, powered mediation analyses . The residual age-related Po0.05, a2b2mediation ¼À0.10, Z ¼À2.29, Po0.05). Comput- differences in the two factors may be explained by additional ing the effect size (see Methods) yields a mediation effect of 18.2% brain measures, or other types of (neural, genetic or variance explained, corresponding to a medium to large effect environmental) variables. size. This finding is striking, since it shows that GMV and WM connectivity of the PFC make partially independent contributions in explaining age-related differences in fluid intelligence. Notably, Multigroup neural models. Finally, we explore whether the the b1 and b2 paths (between BA10/FM and fluid intelligence, relationship between brain and behavioural factors is identical respectively) are significant in the model even though the among the youngest and oldest in our sample. To do this, we relationship between age and fluid intelligence is already create a ‘young’ and ‘old’ subset, balancing the conflicting goals of included. This shows that the associations reported here cannot large samples for accurate estimation with maximal age-differ- be fully explained by mere co-occurrence of age-related ences across groups. We implement a common guideline of differences in the neural and cognitive variables. having 10 participants per free parameter estimated in the As described above, cross-sectional mediation models should model15. This yields 210 young (M ¼ 34.76, s.d. ¼ 7.35) and 210 be interpreted with caution36. However, our findings are old (M ¼ 74.16, s.d. ¼ 6.48) participants with an equal age-range consistent with, and extend, prior research using longitudinal (Levene’s test for homogeneity of variance, P40.05) for the analyses37 and lesion studies38 to relate frontal regions to changes model shown in Fig. 4. First, we fit the full model without in cognition. The second mediation model, which estimates any constraints, showing it fit this data well (w2 ¼ 65.222,

BA10 0.156*** b1 C1 C2 C3 C4 –0.199*** a1

–0.77*** FM 0.131* a2 b2 c Fluid Age intelligence –0.588*** (a1 × b1) + (a2 × b2) Mediation effect size= = 18.2% (a1 × b1) + (a2 × b2) + c

ATR 0.155** TTM #tasks –0.509*** a b

c Age Multitasking –0.189***

a×b Mediation effect size= = 29.4% (a × b) + c

Figure 7 | Mediation models. (a) Mediation model for ﬂuid intelligence. (b) Mediation model for multitasking. In both models, the brain structures- of-interest signiﬁcantly mediate the age-related differences in cognitive abilities. *Po0.05, **Po0.01, ***Po0.001. These P-values are based on a z-test statistic derived from a Maximum Likelihood Structural Equation Model using robust standard errors and a Satorra–Bentler scaled test statistic. See ref. 67 for further details.

6 NATURE COMMUNICATIONS | 5:5658 | DOI: 10.1038/ncomms6658 | www.nature.com/naturecommunications & 2014 Macmillan Publishers Limited. All rights reserved. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6658 ARTICLE df ¼ 48, P ¼ 0.05, RMSEA ¼ 0.041 (0.003–0.065), CFI ¼ 0.974, mediated by two PFC properties, GMV of BA10 and integrity of SRMR ¼ 0.038, Satorra–Bentler scaling factor ¼ 1.009). We then FM WM, that together explain 47.1% of individual differences in fit the model for both young and old by separately, imposing fluid intelligence scores. This provides evidence for models of equality constraints on both (i) the behavioural factor loadings to PFC function that suggest key roles in integrating information for ensure measurement invariance and (ii) the brain–behaviour BA10 (ref. 42) and cross-hemispheric communication for FM43. relationships (but allowing the means to differ). Next, we use Recent studies in patients with focal brain lesions have linked modification indices to ask the following question: which brain– impaired fluid intelligence to damage in both MD regions38 and behaviour relationships, if any, would significantly improve anterior frontal cortex44, suggesting an important role for both. In model fit if we were to allow them to differ in young and old? our data, despite strong correlation of GMVs in the two regions, it This comparison showed that two fixed parameters in the was BA10 that was the stronger predictor. Our multimodal constrained model would significantly improve model fit if approach thus suggests that preserved fluid intelligence in old age estimated freely. Specifically, the influence of both ATR depends both upon the complex information processing in BA10 2 2 (w diff ¼ 6.044, df ¼ 1, Po0.05) and FM (w diff ¼ 5.84, df ¼ 1, and on the successful interhemispheric integration of those Po0.05) on multitasking is more positive in the older group processes by means of an intact FM. Moreover, the partially when estimated freely. Notably, no such difference was observed independent contributions of GMV and WMI suggest a for GMV. However, we note that although the age variance in multifactorial role in individual differences, such that people both groups is equal, the variance in behaviour (Levene’s test for with relatively poor WMI might have comparable fluid homogeneity of variance Po0.0001 for both fluid intelligence and intelligence if complemented by having greater GMV, and vice multitasking) and WMI (Levene’s test for homogeneity of versa. In addition, we observe an age-related increase in inter- variance o0.0001 for both ATR and FM), but not in GMV, individual variability in the behavioural scores for both cognitive also increases significantly in the elderly (see Raz and dimensions, which is compatible with prior empirical and Lindenberger25, for a discussion of the importance of increasing theoretical work25. Importantly, our brain data suggest that one variance with age). This leaves open the possibility that the mechanism contributing to this behavioural phenomenon is likely greater importance of ATR is due to greater variance to be to be the differential rate of ageing of frontal lobe systems. explained, reorganization or a combination of both. In contrast to fluid intelligence, age-related differences in multitasking ability are significantly mediated by a distinct WM structure, the ATRs, a structure also known to be important for Discussion performance on complex, timed tasks45. Though older adults A key question in the cognitive neuroscience of aging is whether show consistent impairments in multitasking, these effects are less aging represents a single- or multi-factorial process2,18. Our pronounced than differences in fluid intelligence. It is known findings show that even for two relatively similar executive that ageing increases the dependency on external cues (or functions and four exclusively frontal brain measures, unifactorial environmental support) compared to internally generated cues. models of prefrontal aging, such as the frontal lobe hypothesis7 or Moreover, performance of impaired adults on multitasking has the disconnection hypothesis39, are oversimplifications. A single- been shown to improve by the addition of trivial external cues46, factor model of the four PFC variables fits poorly, and these four suggesting that a failure to internally cue the key cognitive steps neural variables show distinct age-related differences and specific, (switching to other subtasks at the appropriate time) might be a differential, predictions of two cognitive factors. Second, we show possible mechanism of age-related differences in multitasking. that GMV in BA10 predicts individual differences in fluid However, recent work47 suggests that increased age-related intelligence beyond prefrontal WMI, showing that disconnection dependence on external cues is not deterministic, but instead alone is unlikely to explain age-related differences in fluid varies across tasks and likely across individuals, such that intelligence. We further extend the role of WM connectivity in successfully aging individuals can, under certain circumstances, mediating age-related cognitive decline, by showing that, for switch to increased reliance on internal cues, diminishing age- multitasking, WMI becomes increasingly important for preserved related performance differences. This suggests that our finding of cognitive function with advancing age. A related single-factor a more positive influence of frontal WMI in older adults, most model of ageing that we do not explicitly address here is the notably the ATR, may represent a possible structural correlate ‘processing speed’ hypothesis40, which suggests that age-related that underlies this ability to shift from external to internal cues47, slowing in mental processing underlies decline on a diversity of (page 6). This could be interpreted as a form of neurocognitive cognitive tasks. However, we note that, if an age-related decline in scaffolding48, although testing this hypothesis would require processing speed was the only underlying cause of cognitive further structural and functional investigation. Together, these ageing in executive functions, we would not expect to observe findings suggest that ATR may be fundamental in the automated the complex multifactorial pattern of age-related differences generation of time-related internal cues relevant for high described here. multitasking performance by facilitating communication Although we show considerable age-related differences in both between the thalamus and PFC. executive functions, we also note that the specificity of the In summary, we have examined the relationship between four findings here offers avenues for potential interventions. Recent prefrontal structures and age-related differences in two related yet work has increasingly shown evidence for plasticity induced by distinct executive functions in a large and unique new cohort of various interventions extending into old age, which affect WM, population-based individuals across the adult lifespan. Our results GM and the interplay between brain structure, brain activity and show that cognitive tasks within the domain of executive function complex cognitive functions41. The present findings raise the can be differentiated, and that their decline across lifespan is possibility that targeted interventions might offer more selective multiply-determined by specific patterns of GMV and WMI, improvement in cognitive skills. within a number of sub-regions of PFC. These results provide Fluid intelligence lies at the heart of psychometric analyses of evidence against the dominant, unitary accounts of age-related cognitive abilities9, with strong statistical relationships with other prefrontal brain atrophy and cognitive decline, revealing instead a higher cognitive abilities. Here we replicate the common finding new perspective, where distinct prefrontal systems make of a strong age-related decline in fluid intelligence, but independent contributions to executive functioning. Multimodal furthermore show that these age-related differences are multiply neuroimaging combined with psychometric models will allow

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processing speed and general intelligence20 and performance on a complex, timed us to investigate the most promising targets for ameliorating 45 age-related differences in executive functions. task . Figure 3 shows all four regions-of-interest (ROI). Imaging parameters and image preprocessing. The MRI data were collected Methods from a Siemens 3 T TIM TRIO (Siemens, Erlangen, Germany). GM was estimated Sample. A healthy, population-based sample (N ¼ 610) was collected as part of the from a 1-mm-isotropic T1-weighted 3D Magnetization Prepared RApid Gradient Cam-CAN. Exclusion criteria included low Mini Mental State Examination Echo (MPRAGE) sequence (repetition time (TR) 2250 ms, echo time (TE) 2.98 ms, (MMSE) (r24), poor hearing (failing to hear 35 dB at 1,000 Hz in either ear), poor inversion time (TI) 900 ms, 190 Hz per pixel; flip angle 9°; field of view ¼ 256 vision (o20/50 on Snellen test), poor English knowledge (non-native or non- Â 240 Â 192 mm; GRAPPA acceleration factor ¼ 2), using the SPM8 software bilingual English speakers), self-reported substance abuse and current serious (Wellcome Department of Imaging Neuroscience, London, UK), release 4537, health conditions (for example, self-reported major psychiatric conditions, current implemented in the AA 4.0 pipeline (https://github.com/rhodricusack/auto- chemo/radiotherapy or a history of stroke). We also excluded people who were not maticanalysis). A unified segmentation-normalization approach (‘New segment’) appropriate for magnetic resonance imaging (MRI) or magnetoencephalography employed tissue class prior probability maps for GM, WM, cerebral-spinal fluid, scanning, which included those with safety- and health-related contraindications skull, soft tissue and remaining non-brain voxels58. A first-pass bias correction was (for example, disallowed implants, pacemakers, recent surgery or any previous implemented by an initial run of the segmentation routine. The resulting bias- brain surgery, current pregnancy, facial or very recent tattoos or a history of corrected image was then re-segmented using a sampling distance of 1 mm and a multiple seizures or fits) as well as those with comfort-related issues (for example, segmented voxel size of 1.5 mm. This produced a posterior probability map for each claustrophobia or self-reported inability to lay supine for an hour). Only partici- tissue class in the native image space, as well as a posterior probability map for GM pants who underwent all structural scans and performed all behavioural tests in a standard Montreal Neurological Institute (MNI) space. The voxel intensities in without equipment failure were included, resulting in N ¼ 571 in the final sample this MNI GM image were multiplied by the determinant of the Jacobian (mean age 54.5, s.d. ¼ 18.14, range 18.46–88.9 at the time of first contact, 287 transformation matrix that maps from the native to MNI space, to convert from female). Outliers were defined as region-specific GM or WM values with z-scores GM probability to GMV. The GMV for each ROI was then obtained simply by exceeding 4 or À 4 and were removed (N ¼ 4) prior to further analysis, leaving averaging over all voxels within that ROI. BA10 was derived from a standard MNI N ¼ 567, 286 female participants. All participants took part in a range of psy- atlas59 and the temporal pole was derived from the Harvard–Oxford probabilistic chological tests (no other cognitive domains were analyzed in the context of this atlas60. The MD mask was created by averaging contrasts that isolated cognitive analysis). Ethical approval for the study was obtained from the Cambridgeshire 2 demand across 7 diverse tasks and 40 individuals (data from ref. 54). To create a (now East of England—Cambridge Central) Research Ethics Committee. Partici- symmetrical volume, data from left and right hemispheres were averaged then pants gave full informed consent. P values for specific tests reported are corrected projected back to both hemispheres; the volume was smoothed with a 4-mm full for multiple comparisons by means of False Discovery Rate at 0.05. width at half maximum Gaussian kernel and thresholded at a value of t41.5. We then defined the final MD mask for the purpose of studying PFC structure by Behavioural tasks. We assess fluid intelligence using the Cattell Culture Fair averaging GMV across a (frontal) subset of regions comprising presupplementary Test49, consisting of pencil-and-paper subtests that yield four summary scores motor area, bilateral middle frontal gyri, bilateral frontal operculi and bilateral (series completions, odd-one-out, matrices and topology) used in further dorsolateral PFC. This mask is shown in Fig. 2. modelling. We administered Scale 2, Form A, according to the standard protocol. To estimate WMI, diffusion-weighted images were acquired in 64 directions This consisted of four subtests yielding a sum score each. In contrast to the Cattell with 2 averages, with TR ¼ 6.5 s, TE ¼ 93 ms, b ¼ 1,000 s mm À 3 and GRAPPA test of fluid intelligence, the Hotel test46 simulates a hotel work environment and parallel reconstruction (acceleration factor ¼ 2). Each volume consisted of 48 slices measures the ability to distribute performance across multiple tasks, which we will in the intercommissural plane, 2.5 mm thick with 0.5 mm gap, with an in-plane refer to from here on as multitasking. Figure 1 shows both tasks. For the Hotel task, resolution of 1.8 mm and field of view ¼ 230 Â 230 mm. Diffusion data was participants were asked to spend a total of 10 min performing five different tasks in acquired on the same scanner with a twice-refocused-spin-echo sequence, with 30 a simulated hotel administration environment, where each individual task would diffusion gradient directions each for b-values 1,000 and 2,000 s mm À 2, and three take at least 10 min to complete. Participants were instructed to spend as much images acquired using a b-value of 0 (TE ¼ 104 ms, TR ¼ 9.1 s, voxel time as possible on each of the tasks. This task requires goal maintenance, cognitive size ¼ 2 Â 2 Â 2mm3, field of view (FOV) ¼ 192 Â 192 mm2, 66 axial slices, control and task shifting abilities in the sense that participants who became too GRAPPA acceleration factor ¼ 2). All preprocessing was completed using a absorbed in one task at the expense of others would score lower (2 min per task combination of functions from FSL version 4.1.8 and custom MATLAB scripts. represents optimal performance). Variables-of-interest are how many of the tasks The diffusion data were pre-processed for eddy currents and subject motion using people performed in the 10-min interval (with a range between 1 and 5, with 5 an affine registration model. After removal of non-brain tissue, a non-linear being optimal) and the total time misallocated (defined as the summed deviation of diffusion tensor model was fit to the DWI volumes. Non-linear fitting of the the optimal performance of 120 s per task, ranging from 0 to 960). Although other diffusion tensor provides a more accurate noise modelling than standard linear tasks of multitasking are available13, recent work suggests that this more model fitting and enables various constraints on the diffusion tensor, such as naturalistic setting of multitasking has greater ecological validity50, and provides positive definiteness61. greater sensitivity in detecting real-life changes in such abilities in clinical The tensor’s eigensystem was used to compute the fractional anisotropy (FA) at conditions such as dementia, bipolar disorder attention deficit hyperactivity each voxel; FA maps were spatially normalized into a standard stereotactic space disorder50,51 than more traditional, lab-based cognitive tests of multitasking. using tract-based spatial statistics62, with a standard WM template (the JHU FA atlas; http://cmrm.med.jhmi.edu/) as the target. Images were then smoothed with a Neural measures. The first neural variable is GMV in frontopolar BA10. BA10 6 mm full width at half maximum Gaussian kernel to address possible residual has been found to be active during a wide range of cognitively demanding tasks42, errors and inter-individual variability and to ensure the normality requirements of particularly those that require retaining multiple rules of a task52. Lesions in BA10 parametric statistics were met, and then masked with a binarized version of each have been associated with declining performance both in multitasking14,53 and participants FA map, such that voxels below an FA threshold of 0.35 were not general intelligence44. The second (non-overlapping) neural variable is mean GMV considered for further analysis. Next, to verify how different tracts contribute to within the frontal section of the MD system. This system comprises a set of different cognitive functioning, we extracted the mean FA values from the two distributed frontoparietal regions that include the superior and middle frontal gyri, ROIs, FM and ATRs, using the JHU white matter atlas (http://cmrm.med.jhmi.edu/). and is known to be active across a wide range of cognitively demanding tasks54, and whose damage is selectively associated with reduced fluid intelligence38.We Structural equation modelling. There are two common empirical approaches in here focus on the frontal subset of MD regions (see Methods section for further research on the structural brain bases of individual differences in cognitive skills. details), comprising the presupplementary motor area, bilateral frontal operculi The mass univariate approach examines the relationship between cognitive per- and bilateral dorsolateral PFC. formance and a measure at each voxel or region in a structural MR image of the Recently, a growing number of papers have begun to view WM differences in whole brain. For example, a cognitive variable representing a measure of executive the frontal cortex as the main cause of age-related differences in executive function might be used to predict grey matter density63 or WMI64. This general functions17. Two WM tracts form dense interconnections between frontal regions approach has provided a wealth of findings and conjectures of brain and behaviour and to the rest of the brain, and likely play an important role in coordinating the relationships that are then related to age-related declines in executive functions. function of these regions. The FM represents the interhemispheric pathway However, the exhaustiveness of whole-brain analysis and potential for false connecting left and right BA10 through the genu of the corpus callosum. WMI of positives may simultaneously be a weakness, leading to heterogeneous findings. the FM has been associated with better performance in higher cognitive faculties Most importantly however, a mass univariate approach does not consider the relevant to fluid intelligence and multitasking, such as problem solving55, set interrelationships among the voxel estimates of the local brain properties. For these shifting43 and cognitive control56. Conversely, the ATR (also known as the reasons, structural equation modelling is often a preferable alternative. frontostriatal tract) connects the thalamus to anterior prefrontal cortices including Using structural equation modelling, recent studies have found various the anterior superior frontal gyrus, the anterior middle frontal gyri and BA10. Thus relationships between global brain variables and individual differences in cognitive the frontal terminus of this classically defined WM tract has considerable measures, including global WM and information processing speed and connectivity with the prefrontal component of the MD network. Recent work57 in intelligence16,17,21, total brain volume and memory, executive function and verbal ageing populations has shown that WMI in the ATR is strongly related to scores on fluency21, global brain shrinkage, fluid intelligence and information processing22 an executive function factor. Similarly, WMI of the ATR has been related to both and total cerebral blood flow and intelligence21. However, as powerful as these

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global factors have proved to be, an emerging debate has focused on the possibility 6. Barrick, T. R., Charlton, R. A., Clark, C. A. & Markus, H. S. White matter that the covariance of neural properties in the (aging) brain cannot be fully structural decline in normal ageing: a prospective longitudinal study using explained by single global factors18,34. This suggests that modelling of more specific tract-based spatial statistics. Neuroimage 51, 565–577 (2010). neural properties may be necessary to inform neurocognitive theories of ageing. 7. West, R. L. In defense of the frontal lobe hypothesis of cognitive aging. J. Int. Some studies measure multiple cognitive tests per cognitive factor. Given the nature of our study this was not feasible, so we use model-specific subtests within Neuropsychol. Soc. 6, 727–729 (2000). well-validated tests instead. Thus, the latent variables here reflect what is common 8. Greenwood, P. M. The frontal aging hypothesis evaluated. J. Int. Neuropsychol. across the subtasks of the Cattell (series completions, odd-one-out, matrices and Soc. 6, 705–726 (2000). topology) and Hotel (total time misallocated and number of tasks attempted), an 9. Carpenter, P. A., Just, M. A. & Shell, P. What one intelligence test measures: A approach common in other types of latent variable models (for example, Item- theoretical account of the processing in the Raven Progressive Matrices Test. Response theory, where individual items are modelled to estimate the latent trait). Psychol. Rev. 97, 404–431 (1990). SEM can test whether observed patterns in the data are compatible with proposed 10. Gottfredson, L. S. & Deary, I. J. Intelligence predicts health and longevity, but causal hypotheses concerning the relationships between measured and latent why? Curr. Dir. Psychol. Sci. 13, 1–4 (2004). variables. To best relate neural and behavioural measurements we fit a multifactor 11. Willis, S. L. & Schaie, K. W. Practical Intelligence: Nature and Origins of MIMIC model33. The MIMIC model is conceptually similar to partial least squares, Competence in the Everyday World 236–268 (Cambridge University Press, 1986) another popular method in neurocognitive ageing research. However, the MIMIC ohttp://books.google.com/books?hl=nl&lr=&id=-Cw7AAAAIAAJ&pgis=14. approach has the benefits of explicit model fit, model comparisons and full 12. Faulkner, K. A. et al. Multitasking: association between poorer performance information estimation methods (for example, Maximum Likelihood). For this and a history of recurrent falls. J. Am. Geriatr. Soc. 55, 570–576 (2007). reason, partial least squares is often considered a pragmatic alternative to the more 13.Clapp,W.C.,Rubens,M.T.,Sabharwal,J.&Gazzaley,A.Deficitinswitching principled, theory-driven SEM approach, which is why we here prefer the latter to between functional brain networks underlies the impact of multitasking on 65 test our a priori conceptualization . In this (MIMIC) model, the cognitive working memory in older adults. Proc. Natl Acad. Sci. USA 108, 7212–7217 (2011). domains are assumed to be causally dependent on structural brain properties (for a 14. Roca, M. et al. Executive function and fluid intelligence after frontal lobe theoretical and empirical foundation of the MIMIC model for neuroimaging, lesions. Brain 133, 234–247 (2010). see refs 34,66). Moreover, all influence of the neural variables on behavioural 15. Bollen, K. A. Structural Equations with Latent variables (Wiley, 1989). performance goes via these latent variables (that is, direct paths between neural 16. Penke, L. et al. A general factor of brain white matter integrity predicts indicators and observed behavioural variables are not allowed). This model can be information processing speed in healthy older people. J. Neurosci. 30, captured by two equations: The measurement model relates the ith behavioural 7569–7574 (2010). measure, y , to the jth latent variable, Z , with factor loadings l as shown in i j ij 17. Penke, L. et al. Brain white matter tract integrity as a neural foundation for equation (1) general intelligence. Mol. Psychiatry 17, 1026–1030 (2012). 18. Lo¨vdeń, M. et al. The dimensionality of between-person differences in white yi ¼ kijgj þ ei ð1Þ matter microstructure in old age. Hum. Brain Mapp. 34, 1386–1398 (2013). where ei refers to random measurement error in the behavioural measures. 19. Salthouse, T. A. Neuroanatomical substrates of age-related cognitive decline. The latent variables Zj in turn are assumed to be causally dependent on a Psychol. Bull. 137, 753–784 (2011). weighted summation of the neural variables xk weighted by gjk as shown in 20. Booth, T. et al. Brain white matter tract integrity and cognitive abilities in equation (2) community-dwelling older people: the Lothian Birth Cohort, 1936. X Neuropsychology 27, 595–607 (2013). g ¼ c x þ z ð2Þ j jk k j 21. Rabbitt, P. M. et al. Effects of global atrophy, white matter lesions, and cerebral k blood flow on age-related changes in speed, memory, intelligence, vocabulary, The residual term zj captures both measurement error of the neural measurements and frontal function. Neuropsychology 21, 684–695 (2007). and all residual variance in the latent variables not predicted by our neural 22. Rabbitt, P. M. et al. Age-associated losses of brain volume predict longitudinal variables. 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Cam-CAN Research team Lorraine K. Tyler4, Carol Brayne4, Ed Bullmore4, Andrew Calder4, Rhodri Cusack4, Tim Dalgleish4, Fiona Matthews4, William Marslen-Wilson4,JamesRowe4, Meredith Shafto4; Research Associates: Karen Campbell4, Teresa Cheung4, Linda Geerligs4, Anna McCarrey4, Kamen Tsvetanov4, Nitin Williams4; Research Assistants: Lauren Bates4,Tina Emery4,SharonErzinc¸lioglu4,AndrewGadie4, Soﬁa Gerbase4, Stanimira Georgieva4, Claire Hanley4,BethParkin4, David Troy4; Research Interviewers: Jodie Allen4, Gillian Amery4, Liana Amunts4, Anne Barcroft4, Amanda Castle4, Cheryl Dias4, Jonathan Dowrick4,MelissaFair4, Hayley Fisher4, Anna Goulding4,AdarshGrewal4, Geoff Hale4, Andrew Hilton4, Frances Johnson4, Patricia Johnston4, Thea Kavanagh-Williamson4, Magdalena Kwasniewska4, Alison McMinn4,KimNorman4, Jessica Penrose4, Fiona Roby4,DianeRowland4, John Sargeant4,MaggieSquire4, Beth Stevens4, Aldabra Stoddart4, Cheryl Stone4, Tracy Thompson4, Ozlem Yazlik4; and Administrative Staff: Dan Barnes4,MarieDixon4, Jaya Hillman4, Joanne Mitchell4, Laura Villis4

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