White matter hyperintensities and cognitive reserve during a working memory task: a functional magnetic resonance imaging study in cognitively normal older adults

Accepted Manuscript White matter hyperintensities and cognitive reserve during a working memory task: a functional magnetic resonance imaging study in cognitively normal older adults Sara Fernández-Cabello, Cinta Valls-Pedret, Matthias Schurz, Dídac Vidal-Piñeiro, Roser Sala-Llonch, Nuria Bargallo, Emilio Ros, David Bartrés-Faz PII:

S0197-4580(16)30183-X

DOI:

10.1016/j.neurobiolaging.2016.08.008

Reference:

NBA 9694

To appear in:

Neurobiology of Aging

Received Date: 17 February 2016 Revised Date:

8 July 2016

Accepted Date: 9 August 2016

Please cite this article as: Fernández-Cabello, S., Valls-Pedret, C., Schurz, M., Vidal-Piñeiro, D., SalaLlonch, R., Bargallo, N., Ros, E., Bartrés-Faz, D., White matter hyperintensities and cognitive reserve during a working memory task: a functional magnetic resonance imaging study in cognitively normal older adults, Neurobiology of Aging (2016), doi: 10.1016/j.neurobiolaging.2016.08.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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White matter hyperintensities and cognitive reserve during a working memory task: a functional

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Magnetic Resonance Imaging study in cognitively normal older adults

3 Sara Fernández-Cabelloa,b,c, CintaValls-Pedretd,e,f, Matthias Schurza,b, Dídac Vidal-Piñeirog, Roser Sala-

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Llonchg, Nuria Bargalloe,h, Emilio Ros,d,e,f David Bartrés-Fazc,e*

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Department of Psychology, University of Salzburg, Hellbrunnerstr. 34, 5020 Salzburg, Austria

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Center for Neurocognitive Research, University of Salzburg, Hellbrunnerstr. 34, 5020 Salzburg, Austria

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Department of Medicine, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona,

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Spain.

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d

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e

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f

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g

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Oslo, Oslo, Norway.

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Barcelona, Spain.

Lipid Clinic, Endocrinology and Nutrition Service, Hospital Clínic, Barcelona, Spain

Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Barcelona, Spain.

Ciber Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Spain.

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Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of

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Neuroradiology Section, Radiology Service, Centre de Diagnòstic per la Imatge, Hospital Clínic de

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*Corresponding author

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David Bartrés-Faz

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Department of Psychiatry and Clinical Psychobiology, Faculty of Medicine, University of

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Barcelona, Barcelona, Spain.

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C/ Casanova 143, (08036) Barcelona, Spain.

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Tel: +34 93 403 72 63 // E-mail: [email protected]

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Abstract

27 Cognitive reserve (CR) models posit that lifestyle factors such as education modulate the relationship

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between brain damage and cognition. However, the functional correlates of CR in healthy aging are still

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under investigation. White matter hyperintensities (WMH) are a common age-associated finding that

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impacts cognition. In this study, we used fMRI to characterize the patterns of brain activation during a

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working memory task in older participants with high and low levels of education (as a proxy of CR) and

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high and low WMH volumes. Ninety older volunteers (aged 63-76) and sixteen young adults (aged 21-27)

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completed the study. We found that older adults with higher education had better working memory

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performance than their less educated peers. Among the highly educated participants, those with WMH over-

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recruited areas engaged by young volunteers and showed activation in additional cortical and subcortical

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structures. However, those with low WMH differed little with respect to their younger counterparts. Our

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findings demonstrate that the functional mechanisms subtending the effects of education, as a proxy of CR,

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are modulated according to the WMH burden.

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Keywords: aging, cognitive reserve, fMRI, compensation, neural efficiency, white matter hyperintensities,

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education, working memory

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ACCEPTED MANUSCRIPT 1. Introduction

The concepts of brain and cognitive reserve (CR) emerged to account for the observation that individuals with the same extent of brain damage may differ in the expression of their clinical or

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cognitive status (Stern, 2009). Brain reserve models posit that individuals exhibiting greater measures of brain size (Guo et al., 2013), or neural counts or synaptic density (Perez-Nievas et al., 2013) can sustain higher amounts of brain damage until functional decline becomes evident. In these passive models, once injury reduces the brain reserve below a critical threshold, clinical

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deficits become apparent. In contrast, CR reserve models are considered as active models; they propose that the threshold for functional decline is not fixed and can be modified based on

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experience. Several clinical and epidemiological studies have confirmed that education level and cognitively stimulating or leisure activities protect against the manifestation of clinical conditions, including the expression of dementia (Valenzuela and Sachdev, 2006), cognitive and functional decline in multiple sclerosis (Sumowski et al., 2013a; Sumowski et al., 2009),

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traumatic brain injury (Sumowski et al., 2013b), and stroke (Nunnari et al., 2014). Moreover, interventional studies have shown that training in cognitive stimulating activities may have an impact on other cognitive abilities (Chan et al., 2014; Park et al., 2014; Anguera et al., 2013;

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Carlson et al., 2008; E. Dahlin et al., 2008), brain morphology (Lövdén et al., 2012) and function (Anguera et al., 2013; McDonough et al., 2015). In this regard, McDonough et al.

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(2015) reported decreases in brain activity during the easy condition of a semantic task after engaging in high challenge learning activities.

The functional brain mechanisms through which CR operates are still under debate. Two concepts have been proposed: neural reserve, and neural compensation (Stern, 2009). Neural reserve is related to networks that have developed over the lifespan; it addresses differences in the network efficiency, capacity, or flexibility that healthy older individuals deploy in order to perform tasks and to cope with increasing task difficulty. In this regard, neural reserve may be evident when aged individuals invoke the same brain networks as young participants and may 3

ACCEPTED MANUSCRIPT help explain the susceptibility to brain injury and age-related brain changes. On the other hand, neural compensation refers to the recruitment of alternative brain networks to accomplish cognitive tasks when the brain has been injured or affected by age; thus, individuals with higher CR would be able to better recruit additional resources that help them maintain their

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performance (Barulli and Stern, 2013). Both neural reserve and neural compensation have been studied using functional magnetic resonance imaging (fMRI). Neural efficiency has been previously observed in healthy older adults. As an example, in a previous study by our group we observed that healthy older adults with higher estimated levels of CR showed reduced activation

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in frontal regions during a working memory task when matched for cognitive performance with those with lower CR (Bartrés-Faz et al., 2009). In terms of neural compensation, Steffener et al.

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(2011) showed that the increased expression of compensatory networks has a less negative impact on older adults with higher CR than in those with lower CR. Thus, higher CR decreased the impact of the expression of the compensatory network on task performance.

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A growing number of studies use both structural and functional neuroimaging techniques to investigate CR in aging and dementia (Bartrés-Faz and Arenaza-Urquijo, 2011). In particular, the study of the core mechanisms of CR, including neural efficiency or neural compensation, is

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particularly well suited for investigations in which participants are faced with demanding cognitive tasks, because compensation networks may not emerge until task demands exceed a

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threshold (Steffener and Stern, 2012). Yet, to our knowledge, there has been little research specifically addressing how cognitive reserve estimates modulate the expression of brain activity patterns during cognitively challenging tasks in individuals suffering from different degrees of common age-related brain structural white matter damage. White matter hyperintensities (WMH), hyperintense areas that appear in T2-weighted images and are presumed to have a vascular origin (Wardlaw et al., 2013), tend to accumulate with age (de Leeuw, 2001) and are particularly detrimental for executive functioning (Birdsill et al., 2014; Tullberg, 2004). Besides, WMH may also underlie cerebral dysfunction and cognitive decline (Prins and Scheltens, 2015; Tuladhar et al., 2015) and predict an increased risk of developing 4

ACCEPTED MANUSCRIPT dementia (Debette and Markus, 2010). Further, WMH appear to have an impact on functional brain activation patterns, and have been associated with both higher (Lockhart et al., 2015) and lower functional brain activation (Nordahl et al., 2006; Venkatraman et al., 2010). As CR is supposed to mediate the relation between brain pathology and cognition, WMH can serve as a

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model of neural injury to study the reorganization of brain networks in the presence of brain damage among cognitively normal older adults with high and low levels of CR. In support of this view, both Nebes et al. (2007) and Dufouil et al. (2003) found that the relationship between WMH and cognition was attenuated in individuals with a high level of education. Other studies

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also suggest that CR moderates the impact of WMH on cognition (Brickman et al., 2011; Vemuri et al., 2015). However, there is scarce evidence of the functional mechanisms of CR in

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the context of WMH. In terms of neural compensation, Venkatraman et al. (2010) found that higher WMH were associated with lower brain activity during an executive control task. However, the recruitment of task-related and non-task-related areas in the presence of WMH was associated with higher accuracy, indicating possible compensatory mechanisms. Recently,

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Griebe et al. (2014) found higher activation in all levels of a working memory task in the group with greater WMH, suggesting the presence of compensatory mechanisms already at low task demands. However, performance was not addressed in that study. While these reports

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investigated the impact of WMH on brain activation, to our knowledge none of the ones published to date have explored the interaction between measures of WMH and CR estimates

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during a working memory task.

The aim of this study was to investigate the concepts of neural reserve and neural compensation in a sample of healthy older adults during a working memory task. Participants were grouped according to their WMH burden and years of education (used as a proxy of CR) in order to explore the effect of the interactions between WMH burden and CR on cognitive performance and on brain activity associated with increasing loads of an n-back working memory task (3 > 2 > 1 > 0, see below). Subsequently, we aimed to compare the pattern of differences in brain

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ACCEPTED MANUSCRIPT activation with those of a group of young participants in order to characterize areas of taskrelated activation. We hypothesized that the effect of WMH on brain activation would differ as a function of education, in so far as individuals with high WMH and high education would show putative compensatory mechanisms in order to maintain performance. We further expected

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participants with low education and high WMH rates to present the poorest performance, due to compromised neural reserve or compensation mechanisms. Finally, we predicted that older adults with high education and low WMH burden would exhibit high WM performance and

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would tend to make a greater use of the networks recruited by young individuals.

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2. Methods

2. 1. Participants

One hundred and sixteen cognitively normal individuals (79 women, mean age = 68 y; range: 63-76 y) participated in this study. Candidates were recruited from retirement homes and centers

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registered with the Institut Català de l’Envelliment (ICN), a non-profit organization caring for older adults, in the area of Barcelona. All participants underwent a neuropsychological assessment covering major cognitive domains (memory, language, attention, processing speed)

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and had a normal cognitive profile with scores > -1.5 SD according to normative data, and Mini-Mental State Examination test (MMSE) scores > 25 (Vidal-Piñeiro et al., 2014). Informed

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consent was obtained from all participants. The study was approved by the Hospital Clinic de Barcelona ethical committee, which follows the guidelines of the Declaration of Helsinki. High and low education groups were divided according to median years of formal education (YoE; median score = 12). Likewise, median WMH burden (4.34) was used to classify participants into high and low WMH groups (see section 2.4). Participants in these groups did not differ in terms of the presence of vascular risk factors such as hypertension, type 2 diabetes or dyslipidemia (see Table 1 and Supplementary Table 1). For comparison purposes, an additional sample of 16 young participants (mean age: 22 (1.9) range: 21-27) who underwent the same

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ACCEPTED MANUSCRIPT scanning procedure were included in the study (Sala-Llonch et al., 2012). The main characteristics of the study participants according to age and education/WMH are displayed in Table 1.

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2.2. MRI Protocol Acquisition Imaging data were acquired using a 3T Siemens Magnetom Trio Tim MRI scanner (Siemens Medical Systems, Germany) with a 32-channel coil. The structural scan was a high resolution T1-weighted 3D MPRAGE, sagittal plane acquisition, TR = 2300 ms, TE = 2.98 ms, 240 slices,

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slice thickness = 1 mm, FOV = 256 mm and matrix size = 256 × 256. In addition, we acquired FLAIR (Axial; TR = 9000 ms, TE = 96 ms, 40 slices, slice thickness = 3 mm, FOV = 240 mm,

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matrix size = 228 × 256) and functional images (T2*weighted, TR = 2000 ms, TE = 16 ms, 336 volumes, 40 slices, slice thickness = 3 mm, distance factor 25%, FOV = 220 mm, matrix size = 128 ×128).

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2.3. N-Back Task

We used a letter n-back working memory task with different loads (0, 1, 2 and 3) as described previously (Sala-Llonch et al., 2012). Briefly, a sequence of white capital letters was presented

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on a black screen and participants were instructed to press a button when the letter shown matched the one seen one time (1 back), two times (2 back) or three times (3 back) before or

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when the letter “X” appeared (0 back). The task consisted of a total of 16 blocks (four blocks per condition) presented in a pseudo-randomized order with a duration of 26s each one and with inter-block fixation periods of 13s (a white cross over a black screen). For each condition, 12 letters appeared on the center of the screen during 500 ms with an inter-stimulus interval of 1500 ms. Prior to each block, an instruction screen appeared to inform the participants of the nature of the subsequent block. All participants were trained before entering the scanner.

Our main behavioral variables of interest in the 3 back condition were: hits, d’ (Z hit rate − Z false alarm rate) and reaction times. Individuals exhibiting performance (hits at 3 back) below 7

ACCEPTED MANUSCRIPT chance (50%) were excluded; 26 participants from an initial sample of 116 were excluded for this reason, leaving a final sample size of 90. Due to a failure in the saving procedure of the false alarm responses, these data were lost for the young group. Therefore, only hits performance in the young group is reported, while the complete behavioral variables are

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described for the older group, as data storage was unaffected by this problem in the older sample.

2.4. WMH preprocessing

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MRI scans were analyzed with the Lesion Segmentation Toolbox version 1.2.3 (http://www.neuro.uni-jena.de/software/lst/; Schmidt et al., 2012), an extension for the SPM

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software (http://www.fil.ion.ucl.ac.uk/spm). This toolbox allows the detection of T2 hyperintensities based on T1 and FLAIR images. First, T1 images were segmented into the three main tissue classes: gray matter, white matter and cerebrospinal fluid. This information was then combined with the co-registered FLAIR intensities in order to calculate lesion belief

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maps. By thresholding these maps with a pre-chosen initial threshold of 0.3, as recommended by default, an initial binary lesion map was obtained which was subsequently grown along voxels that appear hyperintense in the FLAIR image. The result was a lesion probability map. The total volume of subcortical WMH was calculated and further divided by a scaling factor of intracranial

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the

with

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FSL

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(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA; Smith, 2002) to control for differences in brain size. Then, the median of WMH burden (4.34) was used to assign participants to the high or low WMH burden group. The median split was used due to the skewed distribution of the data.

In order to test differences in WMH location between subgroups of YoE and WMH burden, probability lesion maps were normalized to MNI space and smoothed with a Gaussian kernel of 7 mm at full width at half maximum (FWHM). Voxel-wise nonparametric permutation testing was performed to test a 2 * 2 ANOVA with WMH and YoE as factors, and age, gender and head size as nuisance variables. The resulting maps were set to a threshold of p<0.05 and 8

ACCEPTED MANUSCRIPT corrected for multiple comparisons with family-wise error (FWE). An experienced senior neuroradiologist reviewed all the patients’ images in order to rule out ischemic events or other brain pathologies. Only cases with a isolated foci of high signal abnormalities in the white

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matterwere considered as age-related changes and were included in the sample.

2.5. N-back Analysis.

Task-related data were analyzed with the FEAT-FSL software (FMRIB’s Software Library version 5.0.6; http://fsl.fmrib.ox.ac.uk/fsl/; Smith et al., 2004). Prior to the first level analysis,

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individual images were preprocessed, which included non-brain tissue removal, motion correction, spatial smoothing with a Gaussian kernel of 7 mm of FWHM, temporal filtering

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with a high pass filter of 150 s, and a two-step linear registration to each participants’ high resolution anatomical image and to a standard template. Then, data were fit to a block design with a gamma convolution of the hemodynamic response function and four regressors, and their first temporal derivatives were modeled: 0 back, 1 back, 2 back and 3 back. The contrast of

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interest was cognitive load (3 > 2> 1 > 0) which reflects increasing activity with greater loads. For the group analysis (higher-level analysis), a mixed effects model was performed using FLAME (FMRIB's Local Analysis of Mixed Effects). A pre-threshold gray matter mask was

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applied to all analyses and statistical significance was set at p < 0.05 and z > 2.3 (cluster-wise corrected). Gender and age were introduced as covariates in all the n-back analyses. First, to

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obtain the activity map of the cognitive load network, we tested the mean group-activation against 0. Then, to investigate the effect of the interaction between WMH burden and education on cognitive load in the sample of old participants, a two-way ANOVA with WMH burden (high and low) and YoE (high and low) was performed. Finally, we used a region of interest (ROI) approach to investigate regional differences in brain activity in the areas that showed a significant interaction. 8 mm radius sphere ROIs were delineated in the peak coordinates of the aforementioned contrast within cortical and subcortical regions and were masked by a gray matter mask in order to exclude signals of no interest. ROIs were selected following two principles: highest statistical value of the local maxima, and non-overlapping. The mean signal 9

ACCEPTED MANUSCRIPT change of these ROIs from the individual contrast images of cognitive load was extracted and compared between WMH and YoE subgroups. Finally, to determine whether the areas of the interaction between groups of older participants were areas of activation (increasing activations with loads) or deactivation (decreasing activation with increasing loads; reverse contrast of

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cognitive load: 0 > 1 > 2 > 3) in the young group, the mean signal change of these ROIs was also extracted for the sample of young participants. Images are displayed using Caret software version 5.65 (Van Essen et al., 2001).

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2.6. Statistical Analyses.

Statistical analyses were performed using IBM SPSS Statistics version 20.0.0 (Chicago, IL,

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http://www-01.ibm.com/software/analytics/spss/) and R version 3.3.0. Differences between sample characteristics were tested with a one-way ANOVA followed by post-hoc comparisons and chi square tests. A two-way ANOVA was used to explore the differences in the behavior at 3 back, with WMH burden and YoE as the factors and age and gender as covariates. To test the

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results of the fMRI interaction, a one-way ANOVA was performed for each ROI signal change. All post-hoc pairwise comparisons were Tukey corrected for multiple comparisons. One sample

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3. Results

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t-tests were performed to test the activation and deactivation within each ROI and each group.

3.1. Behavioral results

Univariate ANOVA revealed a main effect of YoE on the hits (F(1,84)=6.290; p=0.014) and d’ (F(1,84)=8.357; p=0.005; ηp2 = 0.070), such that the highest educated participants had a greater proportion of hits and d’ values at 3 back than the least educated (Table 1). The main effect of WMH burden (Hits: F(1,84)=1.31; p=0.256; ηp2 = 0.015; d’: F(1,84)=1.229; p=0.271; ηp2 = 0.014) and the interaction between WMH and YoE were not significant (Hits: F(1,84)=0.119; p=0.731; ηp2 = 0.001; d’: F(1,84)=1.016; p=0.316; ηp2 = 0.012). There was no significant

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ACCEPTED MANUSCRIPT interaction (F(1,84)=1.40; p=0.240; ηp2 = 0.016), main effect of YoE (F(1,84)=0.238; p=0.627; ηp2 = 0.003) or main effect of WMH (F(1,84)=2.342; p=0.130; ηp2 = 0.027) on the reaction times at 3 back.

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One-way ANOVA showed that the four groups of older adults and the young participants differed in their hits performance (F= 4.082; p = 0.004; ηp2 = 0.139). Post-hoc tests revealed that hits performance was significantly lower in the low YoE/high WMH volume group than in

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the young participants (p=0.004; d = 1.222) and also in the low YoE/low WMH volume group compared to the young group (p=0.017; d = 0.877; see Supplementary Table 1). Similarly,

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reaction times were slower in the two groups with high WMH volumes, high YoE (p=0.04; d = 0.913) and low YoE (p<0.01; d = 1.157), see Figure 1.

3. 1.1. Bayesian Null Hypothesis Testing

Given that one of our main hypotheses was that there would be no behavioral differences

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between the two groups with high education, and since classical null hypothesis testing does not allow acceptance of a null hypothesis, we used a t-test Bayesian approach. First, we used a model comparison procedure to calculate the Bayes Factor (BF), that is, the likelihood of

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differences in performance given the null versus the alternative hypothesis (Rouder et al., 2009). The resulting BF was 0.344 for hits at 3 back and 0.284 for d’. These differences support the

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acceptance of the null hypothesis, as by convention the alternative hypothesis is accepted with a BF > = 3 (Kruschke, 2011). In addition, we used a Bayesian Parameter Estimation approach, which is usually more informative (Kruschke, 2012). Parameter values that fall within the 95% high density interval (HDI) have higher probability values. The results of this approach converged with the previous one, as the value of 0 falls within the 95% HDI (see Supplementary Figure S1), leading to the conclusion that there is a null effect; there were no differences in performance at 3 back (hits and d’) between the two high educated groups (with low and high WMH).

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3.2. Cognitive load map The cognitive load network obtained from fMRI data of old participants included typical taskrelated activation areas such as the bilateral middle frontal gyrus, precuneus cortex, bilateral

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supramarginal gyrus/intraparietal sulcus area, paracingulate gyrus, insular cortex and lingual gyrus and areas that showed more activation in the reverse contrast (0 > 1 > 2 > 3) as the anterior cingulate and ventral precuneus among others (Figure 2). Descriptively, compared to the young participants, we observed that the network of old volunteers was more widely

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distributed. The high education and high WMH volume group showed the greatest activation in the cognitive load network, and the low education with high burden group showed the lowest,

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although this is not a statistical comparison (for a detailed description of the activations and deactivations see Supplementary Table 2). An additional exploratory analysis comparing each group of older participants to young ones (see Supplementary Data, Supplementary Figure 2) revealed that the group with high YoE and low WMH differed little from the young group in the

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cognitive load network.

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3.3. Interaction between WMH and YoE

There was a significant main effect of YoE in areas of the left anterior temporal lobe, revealing

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that the high educated participants showed greater activation of these areas during the cognitive load contrast (Figure 3, middle row; Table 2). There was no main effect of the WMH burden (Figure 3, lower row).

Four ROIs were created on the local maxima coordinates from the main effect of YoE map and the mean signal changes were computed (Supplementary Figure 3; Table 2). Left parahippocampal gyrus, left hippocampus, left temporal pole and left fronto-orbital cortex were mainly deactivated; thus, the differences found were mainly due to the lower level of deactivation in the groups with high education. 12

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We further tested the effect of the interaction between YoE and WMH on brain activation. Areas such as the left precentral gyrus, left postcentral gyrus, left anterior supramarginal gyrus/intraparietal sulcus, thalamus and right and left putamen showed a significant interaction

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(Figure 3, upper row; Table 2). To determine whether older participants extended the areas recruited by their young counterparts, we tested whether the interaction areas were part of the young participants’ network by performing a conjunction analysis in which the young participants’ network masked the map of the interaction. Areas of the left superior parietal lobe,

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the left precentral gyrus and the left insula proved to be part of the young cognitive load network (Figure 3, upper row). Given that the factorial procedure used here may lead to a loss

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of power, we additionally found an interaction when using the two independent variables as continuous values, thus reinforcing the robustness of our results (see Supplementary materials and Supplementary Figure S4).

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Mean signal changes of eight ROIs were extracted for every group and also for the sample of young adults from the interaction map, which revealed differences between groups of old participants according to their YoE and WMH (Figure 4; Table 2). By observing the patterns of

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signal change in the young group, we can summarize the behavior of the ROIs of this group as significant activation (increasing activations with loads), deactivation (decreasing activations

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with increasing loads) and no significant activation and deactivation. First, left precentral gyrus and left supramarginal gyrus/intraparietal sulcus were activated in the young group. In these areas, the high YoE and high WMH and low YoE and low WMH burden groups consistently showed activation, while the other old groups did not. Second, the dorsal left postcentral gyrus and left putamen/insular are areas that were deactivated in young participants. In the dorsal left postcentral gyrus, the groups with high YoE/high WMH and low YoE/low WMH showed more activation in comparison to the young group. Third, the young participants did not show significant activation or deactivation in the ventral left postcentral gyrus, bilateral putamen and right thalamus. In these regions, except for the thalamus, both the high YoE/high WMH and low 13

ACCEPTED MANUSCRIPT YoE/low WMH volume groups showed consistent activation, and in addition the high YoE/high WMH group showed significantly greater activation than the high YoE/low WMH group. The groups with high YoE and low lesions and low YoE and high WMH did not differ from the young group in any of the ROIs. In addition, testing the same model with the hits performance

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and reaction times as nuisance variables we found similar results (data not shown).

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3.4. Differences in WMH location

Voxel-wise analysis did not reveal any significant effect of the interaction between WMH and

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YoE in lesion location or main effect of YoE. A main effect of WMH was found on lesion location. WMH were mainly located in periventricular and occipital bilateral areas (Supplementary Figure 5).

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4. Discussion

The main goal of this study was to assess the functional brain mechanisms engaged by healthy

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older adults during a cognitively challenging task as a function of educational status, a proxy measure of CR, and WMH burden. We observed that high level of education was related to

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better cognitive performance, regardless of WMH status. Functionally, higher education was globally associated with broader patterns of brain activity, but the specific brain mechanisms subtending the cognitive advantage provided by education differed depending on the status of white matter integrity. In particular, older adults with higher estimates of CR and high WMH burden recruited regions engaged by young individuals to a greater extent, and also recruited additional areas; slight differences between young individuals and the high education/low WMH older group were also observed. Finally, lesser educated groups, showed lower behavioral performance. Particularly those individuals with high WMH loads, showed the least deactivations activations when considering task loads at high and low levels, respectively (See 14

ACCEPTED MANUSCRIPT Supplementary figure 6).

In accordance with previous reports, the working memory task elicited a fronto-parietal network activation in both older adults and young adults, including areas such as the bilateral

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dorsolateral prefrontal cortex, bilateral and medial superior parietal cortex and the paracingulate gyrus (Owen et al., 2005). In general, old participants performed worse and more slowly than young ones (Cappell et al., 2010; Holtzer et al., 2009; Nagel et al., 2011). When assessed according to WMH volume and education, old participants with high education and high WMH

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burden showed broader activation in the working memory network, while the group with low education and high burden showed less activation which was constrained to the left prefrontal

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cortex. Behaviorally, the groups with low CR showed poorer performance than the high CR groups and young participants and, independently of CR, the two groups with the highest amount of WMH pathology were slower than the young group. These results corroborate previous reports that have associated WMH with speed of processing (Birdsill et al., 2014; Prins

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4.1 Neural Reserve

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and Scheltens, 2015).

In this study we aimed to test differences in neural reserve and neural compensation. Neural

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reserve refers to the utilization of similar networks by young and old participants and may be implemented in the form of differences in efficiency or capacity of the same network as a function of age (Stern, 2009). We found that some of the regions that showed differences between groups of old participants also pertained to the young participants’ working memory network, although the mean activation in the old groups was heterogeneous. The left precentral gyrus was engaged by the young and old groups except for the low YoE and high WMH burden group. A similar pattern was found in the left supramarginal gyrus/intraparietal sulcus which was also activated in the young group but not in the groups with high education/low WMH burden and low YoE/high WMH burden. For the group with high CR and no white matter 15

ACCEPTED MANUSCRIPT damage one possible explanation could be that these participants had developed a high efficiency network during their lifespan and did not need to activate it to a higher degree, as they showed no differences in performance with respect to the young participants. In this regard, Bartrés-Faz et al. (2009) found that old individuals with higher CR showed less activation in the

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right inferior frontal cortex during a working memory task than those with lower CR; that study, however, did not consider the burden of white matter pathology and lacked a young control group for comparison. For the low YoE/high WMH group, this pattern may reflect the capacity limitations of the network (Holtzer et al., 2009). Similarly, previous research has shown lower

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activation in old groups than in young groups in high task demands and lower efficiency at low burdens (Reuter-Lorenz and Cappell, 2008; Stern, 2009). In contrast, the group with high YoE

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but high WMH showed higher activation, possibly indicating a greater capacity of these areas to respond to increasing cognitive demands or increasing neural inefficiency.

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4.2 Neural Compensation

Previous studies have reported more widespread activation in old groups than in younger ones, suggesting that older adults need more neural resources to perform equivalent tasks (Cabeza,

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2002). The term “neural compensation” is proposed to explain the alteration of brain circuits and the recruitment of secondary networks due to brain pathology or the effects of aging (Stern,

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2005, 2009). In our study, several of the areas that revealed differences between old groups also differed from the areas involved in the working memory network of young participants. These are cortical areas such as the ventral and dorsal left postcentral gyrus, and subcortical structures such as the right thalamus and bilateral putamen. The general pattern is that while young participants do not activate (or may even deactivate) these regions, some of the old groups show increased activation. Specifically, the ventral left postcentral gyrus is engaged in both the high CR/high WMH and the low CR/low WMH groups but is not recruited in the other groups, and the dorsal part is deactivated in young participants but is recruited in the low education/low WMH group. Beyond the well-known fronto-parietal substrates of verbal working memory, 16

ACCEPTED MANUSCRIPT some studies have reported subcortical activation (Kim et al., 2010; McNab and Klingberg, 2008; Moore et al., 2013; Owen et al., 2005). We found that the rostral right thalamus was active in the high education/high WMH burden group but was unengaged in the rest of the groups. Further, the high education/high WMH group and the low YoE/low WMH burden

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group recruited the bilateral putamen. The thalamus (Owen et al., 2005) and basal ganglia (McNab and Klingberg, 2008; Moore et al., 2013) have also been previously related to working memory. The fronto-striatal loop between the dorsolateral prefrontal cortex and the striatum is involved in executive functions (Frank et al., 2001; Tekin and Cummings, 2002). Furthermore,

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basal ganglia have a high density of dopaminergic receptors (Seeman, 1987) and dopamine plays an important role in working memory (D’Esposito and Postle, 2014). McNab and

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Klingberg (2008) claimed that putamen and globus pallidus activation served to filter irrelevant and distracting information during a spatial working memory task. Moreover, Dahlin et al. (2008) found robust striatal activation for young participants in a letter-memory task, while no activation was observed in the old group. Our results reflect subcortical activation in old adults

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with high education and high WMH burden as well as in older adults with low education and low burden, and the recruitment of the left dorsal postcentral gyrus and the left putamen and insular cortex. Individuals in these two groups may need additional resources to cope either with

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pathology or with the effects of aging. However, older individuals with high YoE and low WMH volumes did not show activation of these areas. In addition, this group appeared to differ

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little from the young group, especially in areas belonging to the default mode network. This observation may be consistent with a maintenance theory of brain aging (Nyberg et al., 2012). However, this issue is not directly addressed in this study. As a note of caution, it should be mentioned that the interpretation of the fMRI patterns observed in terms of efficiency and compensation provides only limited information about the putative underlying biological mechanisms (i.e., the use of distinct energetic pathways, different biophysical synaptic dynamics) or psychological mechanisms (i.e., what different cognitive processes or strategies are being used) accounting for the observed effects (Poldrack, 2014).

17

ACCEPTED MANUSCRIPT Furthermore, the use of the term “efficiency” (and that of “functional capacity”), can also be accommodated to other complementary conceptual frames related to the CR theory. For example, the pattern of more widespread brain activation observed in high YoE high WMH older adults, which according to the CR theory and as interpreted in our manuscript, may reflect

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neural compensation, could also fit with an interpretation of evidence of de-differentiation described as the loss of functional specificity in the brain regions engaged during a performance of a task. Further, given that this group exhibited equivalent performance it would also corroborate evidence favoring the scaffolding theory of aging and cognition (STAC) which

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assumes the engagement of neural resources to preserve function in the face of structural and functional decline (recently reviewed in Sala-Llonch et al., 2015). The fact that we explicitly

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designed our study to investigate the impact of education, a proxy of reserve originally proposed to allow more operative research around the CR construct (Stern, 2003), favors the interpretation of our results within the frame of the CR theory rather than according to other

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models.

Our study has several limitations. First, we used the median calculation of the WMH volume to divide participants into high and low WMH groups. Although there were no significant

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differences between the homologous WMH burden groups, the highly educated participants with a high burden showed the highest mean volume. This result is in line with other studies of

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CR which have reported more brain lesions in highly educated groups (Brickman et al., 2011). Thus the activity-related differences in this group may be partially due to the greater interruption of the main structural pathways of the task-related network. However, as no differences in WMH location were found in this group, it could not be argued that there was a greater interruption of different pathways. Another limitation is that we did not split the sample of young participants according to CR levels. The presence of low CR or low performers may also influence the mean activation of the young group. Other studies have found differences between high and low performing young participants in the mean activation of task-positive regions with high loads on a working memory task (Nagel et al., 2009). Stern et al. (2005) found 18

ACCEPTED MANUSCRIPT differences in network expression between young participants with high and low estimated CR, and so our results may be affected by this phenomenon. We used YoE as a proxy of CR, but factors such as leisure activities and social networks may also explain the relationship between neuropathology and cognition (Bennett et al., 2014). Another limitation is the small sample size

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in some of the groups. In addition, cross-sectional comparisons between young and old adults may be biased by the presence of high or low performing participants in the cohort. Although we added cross-sectional evidence of the different brain mechanisms of CR, the only way to ultimately demonstrate that the impact of the WMH affects individuals with high and low

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education in different ways would be to show that the decline in performance over time is lower for those individuals with higher education. Therefore, further longitudinal studies are needed to

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confirm the present results. In addition, to address the question of the true adaptive value of the networks involved in these tasks, within-subject event-related designs would be needed to confirm the relationship between performance and activation.

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In conclusion, we evidenced that the neural correlates of working memory differed between older individuals with different levels of education and white matter lesions. We found that individuals with high YoE in the presence of WMH showed reduced functional efficiency of

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task-positive regions compared with their high educated/low WMH burden peers;, together with their additional recruitment of other cortical and subcortical structures, this resulted in

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maintained performance. In contrast, older adults with high education and no white matter lesions showed no compensatory mechanisms and, in addition, their pattern of activation appeared similar to that in the young group.

Aging is a heterogeneous process that encompasses changes in structural and functional brain measures, which, in interaction with genetic aspects and lifetime experiential variables, results in the relative preservation or impairment of cognitive function. In this regard, our findings with regard to a common age-related brain change (i.e., WMH) in interplay with a proxy measure of CR shed new light on how cognitive performance during a challenging cognitive task may be 19

ACCEPTED MANUSCRIPT maintained in high educated older individuals through the implementation of distinct functional

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brain mechanisms.

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ACCEPTED MANUSCRIPT Table 1. Characteristics of the study groups. Mean and standard deviation (SD). Abbreviations: YoE = Years of education; MMSE = Mini Mental State Examination; WMH = White matter hyperintensities; ICV = Intracranial volume. YOUNG n = 16

High YoE High WMH n = 30

Age, y

67 (2.90)

22 (1.93)

68 (2.82)

YoE

11 (4.02)

-

14 (1.54)

MMSE

29 (0.84)

-

29 (0.72)

Sex (male)

31

7

20

% Hits 3 back

64.16 (10.26)

73.40 (13.51)

65.55 (9.21)

d’ 3 back

2.05 (0.48)

-

2.19 (0.43)

RT 3 back

0.66 (0.15)

0.55 (0.11)

0.67 (0.16)

WMH/ICV

6.35 (6.21)

-

Hypertension No. (%)

45 (50.6%)

Dyslipidemia no. (%)

48 (53.9%)

-

-

Low YoE High WMH n = 15

Low YoE Low WMH n = 24

69 (3.52)

67 (2.88)

14 (2.22)

7 (1.92)

7 (2.55)

29 (0.40)

29 (0.83)

29 (1.12)

6

4

1

67.46 (10.50)

59.44 (8.83)

62.49 (11.26)

1.74 (0.34)

1.95 (0.57)

0.64 (0.11)

0.73 (0.19)

0.62 (0.13)

11.52 (7.74)

2.66 (0.94)

7.84 (2.80)

2.17 (1.06)

17 (56.7%)

11 (52.4%)

8 (57.1%)

9 (37.5%)

3 (10.0%)

1 (4.8%)

1 (7.1%)

0

19 (63.3%)

11 (52.4%)

5 (35.7%)

13 (54.2%)

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66 (1.81)

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Type 2 Diabetes no. (%)

-

High YoE Low WMH n = 21

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OLD n = 90

2.19 (0.42)

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ACCEPTED MANUSCRIPT Table 2. Peak whole brain activations from the cognitive load contrast. (A) Coordinates of the local maxima peaks that showed a significant interaction effect and (B) a main effect of YoE. In bold, peak coordinates of the cluster. Abbreviations: BA = Brodmann area; L = Left; R = Right

Area

Hemisphere

x

y

z

L

-36

-4

56

Precentral gyrus

4.19

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Postcentral

z max.

Cluster size

ROI

(voxels)

index

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MNI Coordinates

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A. Interaction YoE * WMH

4771

1

-

2

gyrus(ventral)

L

-32

-36

48

3.97

Precentral gyrus

L

-34

-4

52

3.96

-

Precentral gyrus

L

-34

-10

60

3.82

-

40

3.66

-

3

-

4

sulcus Postcentral gyrus (dorsal)

L

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gyrus/intraparietal

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Supramarginal

-42

-36

L

-46

-30

64

3.66

R

4

2

0

4.42

3759

5

Putamen

R

22

16

-4

4.2

-

6

Putamen

L

-22

18

2

4.09

-

7

Putamen

L

-22

12

-10

4.06

-

Putamen

L

-22

12

-4

3.96

-

L

-28

6

-10

3.94

-

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Thalamus

Putamen/Insular 8

cortex

22

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B. Main effect of YoE (High education > Low education) Parahippocampal L

-24

-2

-28

4.75

1419

9

Hippocampus

L

-24

-20

-10

4.1

-

10

Amygdala

L

-18

-6

-22

4.04

-

Temporal Pole

L

-54

16

-28

3.63

-

L

-30

12

-26

L

-18

SC

Frontal Orbital

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gyrus

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Cortex

3.34

Parahippocampal gyrus

-20

-16

3.25

11 12

-

-

cognitive load network)

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C. Conjunction Analysis (Areas that showed an interaction of YoE * WMH and were active in the young

Area

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Centre of gravity Cluster size

Hemisphere

x

y

z

(voxels)

L

-42

-36

40

606

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Superior Parietal lobe Middle Frontal gyrus

L

-28.5 -3.18

56.9

469

Insular Cortex

L

-30.8

3.26

146

Superior Parietal lobe

L

-18.8 -57.6

64.4

4

24

23

ACCEPTED MANUSCRIPT Figure 1. Behavioral performance during the working memory task. Bars showing the mean hits and reaction times for the 3 back load. Error bars represent ± 1 Standard Error (SE). WMH= white matter hyperintensities; YoE= years of education.

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Figure 2. Cognitive Load Network. In yellow, areas that showed activation under the cognitive load contrast (3 >2 > 1 > 0) for the whole sample of old participants, young participants and each old group. In blue, areas that revealed significant for the reverse contrast (0 > 1 > 2 > 3). Color maps represent thresholded z statistics (2.3) at p<0.05 cluster-wise

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corrected for multiple comparisons. WMH= white matter hyperintensities; YoE= years of

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education.

Figure 3. Effect of YoE and WMH on brain activation. In yellow, interaction between YoE and WMH (upper row), main effect of YoE (middle row) and main effect of WMH (lower row) on brain activation from the cognitive load contrast. In green, areas that overlap with the young

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cognitive load activation network. Z scores are presented at p>0.05 cluster-wise corrected. WMH= white matter hyperintensities; YoE= years of education.

Figure 4. ROIs from the interaction map. Spatial location of the ROIs from the significant

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interaction areas (Figure 3, upper row), bar plots showing mean signal change ± 1 SE. Lines with an asterisk represent statistical significant differences between pair groups at p>0.05. +

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represents significant activations or deactivations following a one sample t-test against 0, ++ are Bonferroni corrected. Abbreviations: SE, Standard Error. WMH= white matter hyperintensities; YoE= years of education.

Acknowledgements SF-C was supported by the Doctoral College "Imaging the Mind" (FWF-W1233) of the Austrian Science Foundation (FWF-W1233). Partially funded by a Spanish Ministry of Economy and Competitiveness (MINECO) grant to D-BF (PSI2015-64227-R) and the Walnuts

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and Healthy Aging (WAHA) study (http://www.clinicaltrials.gov NCT01634841) funded by the California Walnut Commission, Sacramento, California, USA.

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ACCEPTED MANUSCRIPT Higher years of education is related to better performance in a working memory task, irrespectively of the amount of white matter hyperintensities

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Groups of old participants differ in their brain activations depending on their white matter hyperintensities burden and years of education

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Highly educated participants with broad amounts of white matter lesions show reduced efficiency of typical task regions and the use of compensatory networks

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Participants with high education and low WMH burden show an efficient task network without compensation, and resemble the younger counterparts

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