Differential effects of the APOE genotype on brain function across the lifespan

NeuroImage 54 (2011) 602–610

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Differential effects of the APOE genotype on brain function across the lifespan N. Filippini a,b,c, K.P. Ebmeier a, B.J. MacIntosh b, A.J. Trachtenberg b, G.B. Frisoni c, G.K. Wilcock d, C.F. Beckmann b,e, S.M. Smith b, P.M. Matthews e,f, C.E. Mackay a,b,⁎ a

University Department of Psychiatry, University of Oxford, Oxford, UK FMRIB Centre, University of Oxford, Oxford, UK LENITEM, Laboratory of Epidemiology, Neuroimaging and Telemedicine - IRCCS S. Giovanni di Dio-FBF, Brescia, Italy d University of Oxford, Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, UK e Department of Clinical Neuroscience, Imperial College, Hammersmith Campus London, UK f GSK Clinical Imaging Centre, Hammersmith Hospital, London, UK b c

a r t i c l e

i n f o

Article history: Received 23 July 2010 Accepted 4 August 2010 Available online 10 August 2010 Keywords: APOE Neuroimaging fMRI Memory Aging

a b s t r a c t Increasing age and carrying an APOE ε4 allele are well established risk factors for Alzheimer's disease (AD). The earlier age of onset of AD observed in ε4-carriers may reflect an accelerated aging process. We recently reported that APOE genotype modulates brain function decades before the appearance of any cognitive or clinical symptoms. Here we test the hypothesis that APOE influences brain aging by comparing healthy ε4carriers and non-carriers, using the same imaging protocol in distinct groups of younger and older healthy volunteers. A cross-sectional factorial design was used to examine the effects of age and APOE genotype, and their interaction, on fMRI activation during an encoding memory task. The younger (N = 36; age range 20–35; 18 ε4-carriers) and older (35 middle-age/elderly; age range 50–78 years; 15 ε4-carriers) healthy volunteers taking part in the study were cognitively normal. We found a significant interaction between age and ε4status in the hippocampi, frontal pole, subcortical nuclei, middle temporal gyri and cerebellum, such that aging was associated with decreased activity in e4-carriers and increased activity in non-carriers. Reduced cerebral blood flow was found in the older ε4-carriers relative to older non-carriers despite preserved grey matter volume. Overactivity of brain function in young ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment. The APOE genotype determines age-related changes in brain function that may reflect the increased vulnerability of ε4-carriers to late-life pathology or cognitive decline. © 2010 Elsevier Inc. All rights reserved.

Introduction The APOE ε4 allele is the best-known genetic risk factor for Alzheimer's disease (AD), increasing the risk of developing AD (Okuizumi et al., 1994; Strittmatter et al., 1993) and lowering the age of onset (Corder et al., 1993) in a gene-dose dependent manner. Healthy older ε4-carriers demonstrate both cognitive and brain structural and functional changes relative to non-carriers. Memory is the first cognitive domain to be affected by AD (Morris, and Kopelman, 1986), and impairments have been found in ε4-carriers relative to non-carriers (Caselli et al., 2009; Deary et al., 2002). Moreover, both regional brain volume and resting glucose metabolism [measured with Positron Emission Tomography (PET)] reductions have been observed in ε4-carriers relative to non-carriers in ⁎ Corresponding author. University Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK. Fax: +44 1865 222717. E-mail address: [email protected] (C.E. Mackay). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.08.009

areas affected by AD pathology, such as the posterior cingulate and medial temporal lobe (MTL) (Reiman et al., 1996; Small et al., 2000; Wishart et al., 2006). Functional imaging studies using task-based fMRI paradigms have consistently shown differential effects of the APOE genotype on the blood-oxygenation-level-dependent (BOLD) response, with the direction of change depending both on the specifics of the task used and on other AD risk factors (e.g. age and family history) (Trachtenberg et al., 2010). Increased BOLD signal in APOE ε4-carriers has been interpreted as a putative compensatory mechanism to maintain normal cognitive performance (Bondi et al., 2005), whereas decreased BOLD signal in ε4-carriers has been attributed to effects of early pathology in people at genetic risk of developing AD (Borghesani et al., 2008). We recently reported functional brain changes in young healthy APOE ε4 carriers, suggesting that the ε4 allele modulates neuronal activity decades before any expression of disease (Filippini et al., 2009). This finding has been replicated independently (Dennis et al., 2010).

N. Filippini et al. / NeuroImage 54 (2011) 602–610

Age is the greatest risk factor for AD and is associated with changes in brain morphology and function, but little is known about the differential effects of the APOE genotype on brain aging. Here, we extend our previous study to investigate the effect of age on brain structure and function in APOE ε4-carriers and non-carriers in distinct groups of younger (20–35 years) and older (50–78 years) healthy subjects. An identical imaging protocol comprising structural MRI and fMRI using an encoding memory paradigm was adopted for each group. In addition, a new arterial spin labelling (ASL) sequence was used to measure cerebral blood flow (CBF) in the older group. To our knowledge, this represents the first analysis of combined brain structure and function in APOE ε4carriers and non-carriers across distinct age groups using a common protocol, thus providing the opportunity to assess whether the brain ages differently according to genetic risk of developing AD.

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having an ε4 allele (ε4-carrier) or being matched for gender and age with an ε4-carrier (non-carrier). APOE ε2-carriers were excluded because the ε2 allele has been reported to have protective effects against AD (Benjamin et al., 1994) and cardiovascular diseases (Wilson et al., 1994). Genotyping was done at the Wellcome Trust Centre for Human Genetics in Oxford and was successful for 94 subjects out of 98. The genotypic distribution was ε2ε2 (0/94—0%), ε2ε3 (9/94—9.57%), ε2ε4 (3/94—3.19%), ε3ε3 (61/94—64.84%), ε3ε4 (19/94—20.12%), and ε4ε4 (2/94—2.12%) and reflected the distribution expected in the normal population (X2 = 2.515, d.f. = 5, p = 0.95) (Menzel et al., 1983). Of these, 17 ε4-carriers (including 2 ε4ε4) and 22 non-carriers (all ε3ε3) completed the imaging protocol. The younger group comprised 18 ε4-carriers (including 1 ε4ε4) and 18 non-carriers. Genotype breakdown for the young group can be found in Filippini et al. (2009).

Methods and materials Neuroimaging protocol Participant recruitment The older participants comprised 17 APOE ε4-carriers and 22 noncarriers, and were selected from 98 right-handed subjects aged 50 to 78 years old. A priori exclusion criteria were current or past history of neurological or psychiatric disorders, memory complaints, head injury, substance abuse (including alcohol), corticosteroid therapy and hypertension. Participants suffering from hypercholesterolemia and diabetes were included if their medical conditions were under stable pharmacological control for at least 6 months. A posteriori (based on analysis of MRI data) exclusion criteria included presence of brain vascular insults and white matter (WM) lesions. In order to minimise possible confounds due to cognitive complaints, subjects underwent a pre-screening cognitive test [Addenbrooke's Cognitive Examination-revised version (ACE-r) (Mioshi et al., 2006)]. A detailed description of the younger sample can be found in Filippini et al. (2009). In brief, this sample comprised 18 ε4-carriers (11 males) and 18 matched non-carriers (10 males) identified among 98 healthy adults aged 20 to 35 years old, living in the Oxfordshire, and who underwent the same imaging protocol. The study was approved by the local Ethics Committee, and written informed consent was signed by all participants. Demographic information for the younger and older groups are shown in Table 1.

Scanning was carried out at the University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR) using a 3 T Siemens Trio scanner with a 12-channel head coil. The neuroimaging protocol comprised functional, perfusion and structural MRIs. Functional MRI Encoding memory was assessed from a single run using a gradient echo planar imaging (EPI) sequence covering the whole brain [repetition time (TR) = 3000 ms, echo time (TE) = 28 ms, flip angle = 89°, field of view (FOV) = 192 mm, voxel = 3 × 3 × 3 mm, acquisition time= 9min and 6 s]. For a detailed description of the task see Filippini et al. (2009). Briefly, a set of colour images representing animals and landscapes were shown to participants. Before scanning, subjects were presented 8 times with 8 images (“familiar” images) and tested to ensure images had been encoded. During the scanning session, images were presented in a pseudorandom order in a “blocked” design alternating blocks of “familiar” and “novel” (never seen before) images. Subjects were asked to indicate whether each image contained an animal or not (to ensure compliance), and were instructed to try to remember the images. Outside the scanner, approximately 50 min after initial presentation, “novel” and “familiar” images and distractors were presented, and subjects were asked to judge whether the images had been seen inside the scanner or not.

APOE genotyping Older participants were screened for APOE genotype using a cheek swab sample, and then selected for the study on the basis of either

Perfusion MRI Whole-brain perfusion imaging was carried out using pulsed ASL with a two-shot 3D gradient and spin echo readout (GRASE). ASL data

Table 1 Socio-demographic and task-based fMRI memory features of the four study groups. The younger group's data (shaded grey) have previously been reported and are included for ease of reference (Filippini et al., 2009). Values denote mean (± standard deviation) or numbers of subjects. NA (not applicable). Older group

Younger group

APOE ε4 non-carriers

APOE ε4-carriers

N = 20

N = 15

Socio-demographics Age, years 64.4 (± 6.0) Education, years 16.7 (± 3.6) Sex (male/female) 7/13 Diabetes 1 Hypercholesterolemia 1 Family history of dementia 6 fMRI Memory test (% of corrected responses) Global performance 83.0% “Familiar” images 98.1% “Novel” images 76.1% Distractions 90.7% Reaction time (expressed in seconds) “Familiar” blocks 0.72 (± 0.11) “Novel” Blocks 0.96 (± 0.16)

p

APOE ε4 non-carriers

APOE ε4-carriers

N = 18

N = 18

p

64.4 (± 8.3) 15.7 (± 3.9) 6/9 1 4 2

0.98 0.43 1 1 0.18 0.45

28.6 (± 3.9) 19.5 (± 1.5) 10/8 NA NA 2

28.4 (± 4.9) 19.6 (± 2.0) 11/7 NA NA 2

0.91 0.93 1

82.2% 99.1% 74.3% 91.1%

0.80 0.46 0.73 0.89

83.8% 97.8% 75.5% 94.6%

84.4% 97.8% 75.8% 95.4%

0.86 1 0.96 0.58

0.74 (± 0.09) 0.99 (± 0.15)

0.45 0.55

0.75 (± 0.14) 0.99 (± 0.26)

0.80 (± 0.22) 1.09 (± 0.42)

0.44 0.39

1

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were collected at multiple inflow periods, starting at 400 ms and ending at 2200 ms in increments of 170 ms (TR/TE = 3136 ms/23 ms, FOV = 200 × 200 × 130 mm, 22 slices, voxel = 3.125 × 3.125 × 5mm, acquisition time = 11 min). A series of calibration 3D GRASE readout images in which no tagging was performed, required for CBF quantification, was also collected. The calibration scan was performed with a long TR (6000 ms) and TI (5000 ms) and in the absence of background suppression, but otherwise identical imaging parameters. From the calibration scan, a mask of cerebrospinal (CSF) fluid voxels was generated by manual segmentation. Relative CBF estimates were then converted to absolute values using a known proton density ratio between CSF and blood (Wu and Wong, 2006) and following correction for the TR and TI values used in the calibration scans. Subjects were instructed to keep their eyes open. Structural MRI High-resolution 3D T1-weighted MRI scans were acquired using a magnetization-prepared rapid gradient echo sequence (MPRAGE: TR/ TE/flip = 2040 ms/4.7 ms/8°, FOV = 192 mm, voxel = 1 × 1 × 1mm, acquisition time = 12 min). Whole-brain T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging was performed using a spin echo sequence (TR/TE = 9000 ms/89 ms, FOV = 220 mm, voxel dimension = 1.1 × 0.9 × 3 mm, acquisition time = 5 min and 8 s). Image analysis Data analysis was performed using FSL tools (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl) (Smith et al., 2004). Functional MRI fMRI analysis was carried out using FEAT (FMRI Expert Analysis Tool v. 5.98, http://www.fmrib.ox.ac.uk/fsl/feat5/). Details of the analysis can be found elsewhere (Filippini et al., 2009). In brief, preprocessing consisted of head motion correction, brain extraction, spatial smoothing using a Gaussian kernel of FWHM (full width at half maximum) 5 mm, and high pass temporal filtering equivalent to a period of 130 s. To reduce MR distortion due to magnetic field inhomogeneities, field-map images were used (Jenkinson, 2003). Functional data was aligned to structural images (within-subject) initially using linear registration (FMRIB's Linear Image Registration Tool, FLIRT), then optimized using Boundary-Based Registration approach (Greve and Fischl, 2009). Structural images were transformed to standard space using a non-linear registration tool (FNIRT), and the resulting warp fields applied to the functional statistical summary images. Group level analysis was carried out using FMRIB's Local Analysis of Mixed Effects (FLAME) (Woolrich et al., 2004). The main contrast of interest for the encoding memory paradigm was “novel versus familiar”. The general linear model (GLM) included two main factors AGE (younger or older) and GENE (ε4-carriers or noncarriers) and their interaction (AGE by GENE). The main task-related contrast of interest was the “novel versus familiar” contrast. Z (Gaussianised T/F) statistic images were thresholded using clusters determined by Z N 2.3 and a family-wise-error (FWE) corrected cluster significance threshold of p b 0.05 was applied to the suprathreshold clusters. Because age-related effects have been reported to influence the BOLD signal (Aizenstein et al., 2004; D'Esposito et al., 2003), each individual fMRI image was normalised on a voxel-by-voxel basis by each individual whole-brain mean after pre-processing.

using threshold-free cluster enhancement (TFCE) (Smith and Nichols, 2009) and an FWE corrected cluster significance threshold of p b 0.05. The ASL protocol used here was an enhancement of the one used in the younger sample (Filippini et al., 2009; Macintosh et al., 2010), thus perfusion data were not directly comparable between the younger and older groups. All other imaging sequences included in the protocol were identical for the older and younger groups. Structural MRI Whole-brain analysis was done using a voxel-based morphometry analysis (FSL-VBM) (Douaud et al., 2007), using default settings as described at www.fmrib.ox.ac.uk/fsl/fslvbm/. In brief, brain extraction and tissue-type segmentation were performed and resulting grey matter (GM) partial volume images were aligned to standard space using first linear (FLIRT) and then non-linear (FNIRT) registration tools. The resulting images were averaged, modulated and smoothed with an isotropic sigma Gaussian kernel of 4 mm (~ 9 mm FWHM) to create a study-specific template, and the GM images re-registered to this, including modulation by the warp field Jacobian. Voxel-wise GLM was applied to test for effects of GENE, AGE and AGE by GENE interaction using permutation-based non-parametric testing (5000 permutations), correcting for multiple comparisons across space. Volumetric analysis of WM lesions and periventricular hyperintensities in FLAIR images was performed manually by a trained neuroscientist blind to the APOE genotype using the Jim 4.0 software (Xinapse Medical Systems, Thorpe Waterville, UK). Total volume of hyperintensities and periventricular lesions was calculated for each subject. Subjects were excluded if they had hyperintense lesions equal to or larger than 10 mm in diameter, or more than eight hyperintense lesions with a diameter from 5 to 9 mm (Bozzali et al., 2006). Region of Interest (ROI) analysis To aid interpretation of group differences, masks of the significant differences during the fMRI task were created for each subject. In addition, based on previous results showing an effect played by the ε4 allele on hippocampal regions (Filippini et al., 2009), masks of the left and right hippocampi were created. Individual hippocampal ROIs were obtained using FMRIB's Integrated Registration and Segmentation Tool (FIRST, www.fmrib.ox.ac.uk/fsl/ first/). ROIs were visually inspected in the coronal plane to ensure accuracy. ROIs were registered to functional coordinate space and used to extract BOLD percentage signal changes from fMRI data using Featquery (part of FSL) for the main contrast of interest (“novel versus familiar”). Values for the other two contrasts (“novel versus rest” and “familiar versus rest”) were included for completeness. Covariates Structural images were used as additional covariates on a voxelby-voxel basis to interrogate fMRI data (Oakes et al., 2007). GM images of each subject were registered to standard space, smoothed to match the intrinsic smoothness of the fMRI data, demeaned within each group and added as confound regressors (nuisance) to the GLM design matrix. Similarly, perfusion maps were added to the GLM model as nuisance covariates for the older group comparison. Because of the difference in the ASL sequence between the two samples (younger and older), only GM maps were added to the AGE by GENE analysis. Statistics

Perfusion MRI Whole-brain analysis was carried out using FSL tools and Matlab (Mathworks, Natick, MA) software that performed the voxel-wise fitting of a two-parameter single compartment model to the ASL data producing estimates of CBF (Buxton et al., 1998). Group comparison between older ε4-carriers and non-carriers was tested using permutation-based non-parametric testing (5000 permutations),

Statistical analyses of non-imaging variables were carried out using SPSS software (SPSS, Inc., Chicago IL). T-tests were used for socio-demographic variables, brain structure volumes, memory performance, and reaction times. Exact Fisher's test and the Yates continuity correction were used for categorical variables (sex, family history of dementia, hypercholesterolemia and diabetes). Multivariate

N. Filippini et al. / NeuroImage 54 (2011) 602–610 Table 2 Neuropsychological performance and brain features of the older APOE ε4-carriers and non-carriers. Values denote mean (± standard deviation) and [range of values]. §Values are expressed as percentage of whole-brain volume. APOE ε4 non-carriers APOE ε4-carriers Neuropsychological performance Global performance 99.2 (± 1.3) [95–100] Attention and orientation 17.9 (± 0.2) [17–18] Memory 25.6 (± 0.7) [24–26] Fluency 13.9 (± 0.2) [13–14] Language 25.9 (± 0.2) [25–26] Visuo-spatial 15.8 (± 0.5) [14–16] Mini-Mental State Examination 29.9 (± 0.2) [29–30] Brain features Whole-brain volume, cc 1873.4 (± 189.2) § Grey matter 41.8 (± 0.6) § White matter 33.1 (± 1.1) § CSF 25.2 (± 1.2) § Left Hippocampus 0.20 (± 0.03) § Right Hippocampus 0.22 (± 0.03) White matter lesions, mm3 1284.80 (± 1768.80)

p

98.9 (±0.9) [97–100] 17.9 (± 1.3) [17–18] 25.4 (± 0.7) [24–26] 13.8 (± 0.7) [11–14] 25.9 (± 0.3) [25–26] 15.9 (± 0.3) [15–16] 29.8 (± 0.6) [28–30]

0.35 0.40 0.30 0.41 0.40 0.55 0.34

1912.3 (± 172.0) 41.5 (± 1.1) 33.5 (± 1.2) 24.9 (± 1.5) 0.20 (± 0.03) 0.21 (± 0.04) 2133.32 (± 2836.53)

0.54 0.47 0.29 0.68 0.51 0.37 0.32

ANOVA with AGE and GROUP as factors of interest was used to analyse percentage signal change measurements for ROIs. ROI analyses were thresholded at p b 0.01 to account for multiple comparisons.

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between AGE and GENE was observed in all ROIs: (temporal: F = 13.6, p b 0.001; frontal: F = 15.5, p b 0.001; cerebellum: F = 19.9, p b 0.001; left hippocampus: F = 10.8, p = 0.002; right hippocampus: F = 9.4, p = 0.003) (Table S1). Plotting the percentage signal change for each region revealed that aging was associated with increased BOLD in noncarriers and decreased BOLD in ε4-carriers (Fig. 1). Post-hoc tests of the effect of AGE within genotype groups revealed significant increases in BOLD signal with age in the non-carriers in the cerebellar, middle temporal and frontal ROIs, but not the hippocampi (temporal: p = 0.007; frontal: p = 0.001; cerebellum: p = 0.001), and significant decreases in BOLD signal with age in ε4-carriers in the cerebellum and hippocampus bilaterally (temporal: p = 0.023; frontal: p = 0.056; cerebellum: p = 0.006; left hippocampus: p = 0.007; right hippocampus: p = 0.006). Age-related changes in brain functional activation associated with the APOE genotype variation are not explained by differential brain atrophy No significant interactions between AGE and GENE and no main effect of GENE on GM volume were found using either a whole-brain analysis or an ROI approach. A main effect of AGE was detected in brain regions previously associated with age-related reduction in brain volume (Raz et al., 1998, 2005). GM maps and ‘years of education’ were added nuisances in the fMRI analysis model and BOLD-related group differences survived.

Results Participants Of the older group, two subjects (1 per genotype group) could not complete the fMRI task because of visual acuity problems and two (1 per group) were excluded because of extensive WM lesion volume (3 standard deviations greater than the group mean). Older APOE ε4carriers (N = 15) and non-carriers (N = 20) did not differ in age, family history of dementia, or incidence of hypercholesterolemia or diabetes (Table 1). Older carriers and non-carriers were also matched on cognitive performance as measured using the memory recognition task (Table 1) and the ACE-r (Table 2). There were no differences between younger and older groups in memory recognition performance or reaction times on the fMRI task (Table 1). Educational level was different between younger and older subjects (p b 0.001), but not between ε4-carriers and non-carriers. Differential effects of APOE genotype on age-related change of brain function For all subjects, the “novel versus familiar” contrast produced increases in BOLD signal in the hippocampus, temporal fusiform cortex and parahippocampal gyrus bilaterally and in associative areas in the frontal, parietal and occipital lobes, as described in previous studies (Filippini et al., 2009; Fleisher et al., 2005; Golby et al., 2005). With all subjects together there was no main effect of AGE or GENE on brain activation. However, significant AGE GENE interactions in the BOLD signal for the “novel versus familiar” contrast (Fig. 1a) were found in three brain regions: i) the cerebellum, extending to both lateral and cortical portions [reporting here and below, maximum z score, cluster size in voxels, (peak x, y, and z coordinates in standard space): 4.49, 2895, (10 −80 −48)], ii) the right middle temporal gyrus extending into the inferior temporal gyrus [5.78, 485, (58 −36 −4)] and iii) the left thalamus, the frontal pole and the caudate bilaterally [4.1, 862, (20, 56, −10)]. There were no significant group differences in the “familiar versus novel” or “rest versus novel” contrasts, used to investigate brain deactivations. Percentage BOLD signal changes for ROIs derived from significant functional group differences and from the left and right hippocampi are reported in Table S1 (Supplementary material). A significant interaction

Reduced brain activation for memory in older APOE ε4-carriers Analysis confined to the older group revealed significantly decreased BOLD signal in ε4-carriers relative to non-carriers in two clusters: temporal, including the right inferior, middle and superior temporal gyri [4.07, 420, (54 − 36 −6)]; and frontal, including the right superior frontal gyrus and the frontal pole bilaterally [4.08, 508, (8 42 38)] (Fig. 2a). Using ROI analysis, decreased BOLD signal was found in ε4-carriers relative to non-carriers in the left hippocampus (p = 0.01) and a trend towards significance in the right hippocampus (p = 0.08) (Fig. 2b). BOLD signal values for hippocampal regions are reported in Table S1. There were no brain regions where non-carriers showed reduced BOLD signal relative to ε4-carriers. There were no significant group differences in the “familiar versus novel” or “rest versus novel” contrasts. Decreased regional cerebral blood flow in older APOE ε4-carriers despite preserved GM volume Reduced resting CBF was observed in older ε4-carriers relative to non-carriers in three separate clusters located in the left anterior and posterior cingulate cortex and in the cerebellum (Fig. 2c). There were no brain regions in which non-carriers had decreased resting CBF values relative to ε4-carriers. Resting brain perfusion maps were added as nuisances in the fMRI analysis of the older group. BOLD group contrast differences observed between APOE ε4-carriers and non-carriers were not affected by adding these covariates, and group differences in temporal and frontal brain regions were unchanged. For the older group, no differences were observed between ε4carriers and non-carriers in GM (total brain volume or voxel-wise), WM, CSF or hippocampal volumes (Table 2). Moreover, there were no genotype differences in age-related WM lesion volumes (Table 2). Addition of the GM maps as nuisances in the fMRI analysis did not alter the results. Discussion A recent review of fMRI studies comparing APOE ε4-carriers and non-carriers suggested that age could contribute to the reported

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a)

1.4

ε4

1.2

NC

% signal change

1.6

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6

younger

older

younger

older

0.4

% signal change

0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4

1

% signal change

0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6

2.3

-0.8

5.7

younger

older

1

b) % signal change

0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6

younger

older

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a)

b)

c)

% signal change

4 NC

left

right p<0.001 uncorrected

p<0.05 TFCE corrected

Fig. 2. fMRI results for the “novel versus familiar” contrast of the encoding task in the older group. a) Regions of significantly reduced BOLD signal for ε4-carriers relative to noncarriers (p b 0.05, corrected for multiple comparisons). b) Box plots showing percentage signal change (*p b 0.05) for the left and right hippocampal ROI. ε4 (orange) defines ε4carriers, and NC (green) defines non-carriers. c) Regions of significantly reduced resting CBF values for ε4-carriers relative to non-carriers (p b 0.05, corrected for multiple comparisons).

inconsistencies in the direction and magnitude of activation differences (Trachtenberg et al., 2010). Here, we tested this by using the same protocol in two distinct age groups, and found differential patterns of functional brain aging in carriers and non-carriers of the ε4 allele. We previously reported increased activation (BOLD signal) to an encoding memory paradigm in young APOE ε4-carriers relative to non-carriers (Filippini et al., 2009). Here we show that this pattern is reversed in an older group of subjects. Developing an understanding of the basis of agerelated brain functional changes associated with APOE may shed light on the increased risk of old-age pathology in ε4-carriers. Effect of APOE genotype and age on brain activation We found differential activation differences in ε4-carriers and non-carriers in distinct groups of healthy volunteers with mean ages in mid-twenties and mid-sixties. Significant interactions between age and APOE genotype were found in the frontal and temporal lobes, the cerebellum, and in both hippocampi (using whole-brain and ROI analyses). Brain regions showing a significant interaction effect reflected task-related activation in older non-carriers and younger ε4-carriers, whereas they were largely deactivated in older ε4carriers. Older non-carriers of the ε4-allele exhibit significantly increased activation relative to younger non-carriers in all three regions in which there was a significant age-by-genotype interaction.

Age-related increases in activation in the frontal and temporal regions during a memory task (Dennis et al., 2007; Nielson et al., 2006; Wang et al., 2010) have been interpreted as evidence for compensatory mechanisms employed during normal aging [see also Park and Reuter-Lorenz (2009) for a review]. For example, activation of brain regions in the temporal lobes has been observed during semantic processing (Kable et al., 2002), suggesting that older people may be using different strategies during the encoding stage. Moreover, cerebellar involvement has been observed in higher cognitive tasks, including memory processes (Andreasen et al., 1995; Fliessbach et al., 2007); particularly long-term memory encoding and information storage (Andreasen et al., 1995). Increased BOLD signal in cerebellar regions has been recently reported with normal aging (Cerf-Ducastel and Murphy, 2009). Our AGE by APOE interaction is consistent with a previous study investigating a genetic mutation (presenilin1) responsible for familial AD, that reported increased activation in younger carriers relative to non-carriers and reduced activation in older carriers relative to non-carriers (Mondadori et al., 2006). Taken together these results suggest that AD risk genes are associated with a failure of normal age-related compensatory processes which can be observed using fMRI before the onset of cognitive decline. Consistent with most other fMRI studies of APOE we found no differences in cognitive performance between our ε4-carriers and non-carriers, and thus it is not possible to determine whether the

Fig. 1. AGE by GENE interactions in the “novel versus familiar” contrast of the encoding task. a) Regions showing significant interaction between AGE and GENE factors (p b 0.05, corrected for multiple comparisons) with plots of percentage signal change in brain regions showing group-related differences where ε4 (orange) defines ε4-carriers and NC (green) defines non-carriers. b) ROIs for the left and right hippocampi overlaid on a structural image (left) with associated plot of average hippocampal percentage signal change showing significant age-by-gene interaction (left hippocampus: p = 0.002, right hippocampus: p = 0.003).

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changes that we observe in brain function are beneficial or deleterious. However, animal studies using knock-in human APOE genes show that age-related changes associated with the ε4-allele lead to neuronal, dendritic and synaptic losses, which suggests a deleterious role played by the ε4 allele on the normal aging process (Buttini et al., 1999). Decreased resting brain perfusion in older APOE ε4-carriers Reduced resting CBF was observed in ε4-carriers relative to noncarriers using ASL contrast in brain regions located within the anterior and posterior cingulate cortices and the cerebellum, suggesting that the APOE polymorphism modulates neurovascular function in healthy older subjects. This is consistent with previous studies reporting altered metabolism associated with the APOE ε4 allele (Reiman et al., 1996; Small et al., 2000) in the anterior and posterior cingulate regions. Moreover, in the same brain regions pathophysiological changes of MCI and AD are found (Alsop et al., 2000; Johnson et al., 2005). However, in contrast to our finding of reduced CBF, a recent study reported increased CBF in subjects carrying the APOE ε4 allele relative to non-carriers (Fleisher et al., 2009). Methodological differences may explain this discrepancy. In particular, Fleisher et al. acquired perfusion measurement simultaneously with fMRI task-data, whereas our perfusion acquisition was performed when subjects were at rest. This difference may be important given the recent report of an increase in CBF from rest to task (Pfefferbaum et al., 2010). Moreover, Fleisher et al.'s (Fleisher et al., 2009) high risk group has two AD risk factors (APOE ε4 allele and family history of dementia) that are known to interact (Trivedi et al., 2008b; Xu et al., 2009), whereas their lowrisk group had neither risk factor. We controlled for family history in order to investigate a ‘pure’ effect of the APOE genotype. Finally, in contrast to our whole-brain approach, Fleisher et al. limited their analysis and acquisition to the temporal lobe, where we found no differences between groups. Two interpretations for altered CBF in healthy APOE ε4-carriers have been suggested. Firstly, the APOE ε4 allele is associated with a reduction in the normal clearance process of amyloid plaques (Aβ) (Holtzman et al., 2000; Kim et al., 2009; Mahley et al., 1996) which may influence CBF subclinically through direct vascular effects (Iadecola, 2004). Secondly, the APOE ε4 allele may affect neurovascular control through modulation of the toxic effects of Aβ1–42 on endothelial cells (Folin et al., 2006). Greater insight into the effects of the APOE ε4 allele on neurophysiological processes is needed. The use of recently developed imaging techniques to measure amyloid plaque accumulation (i.e. PET with PIB compounds) (Klunk et al., 2004) in healthy ε4-carriers and non-carriers may help to distinguish any effects of APOE genotype on amyloid clearance from the central nervous system. Importantly, because the magnitude of the BOLD signal is determined by the interaction between increase in CBF, rate of oxygen consumption and cerebral blood volume, and is affected by differences in resting CBF, resting perfusion maps should be routinely used as covariates of no interest in fMRI analysis as done here. BOLDrelated differences surviving “over and above” perfusion-related differences can be more carefully interpreted as suggestive of activationrelated neurophysiological differences. Potential mechanisms of action of APOE in the brain The role played by the APOE ε4 allele through the lifespan is still not well understood. Reduced neuronal growth, survival, branching and extension in in-vitro and in-vivo studies (Bellosta et al., 1995; Nathan et al., 1994; Nathan et al., 2002; Sun et al., 1998) suggest that the ε4 allele influences normal development. However, other hypotheses cannot be excluded. Indeed, APOE may be an example of a gene that has positive effects during youth, at the expense of negative effect in later life (antagonistic pleiotropic) (Becher et al.,

2006; Marchant et al., 2010; Ravaja et al., 1997; Sozou and Seymour, 2004; Wright et al., 2003; Zetterberg et al., 2002). Fig. 1 shows that the distribution of relative signal change in younger ε4-carriers resembles that observed in older non-carriers. One interpretation is that functional aging is accelerated in ε4carriers. Specifically, increased activity in younger ε4-carriers may cause neurophysiological changes that lead to earlier age-related decline in brain function. Indeed, increased neuronal and synaptic activity is associated with higher hippocampal Aβ deposition (Cirrito et al., 2005), and APOE is directly involved in amyloid processing (Bu, 2009). Indeed, amyloid aggregation load is associated with reduced functional connectivity in healthy older individuals (Cohen et al., 2009; Hedden et al., 2009). Thus, increased activity in younger ε4carriers may lead to greater Aβ deposition, which in turn may modulate neuronal activity later in life (Wei et al., 2010). Limitations and considerations We found no differences in brain morphology or memory performance between APOE ε4-carriers and non-carriers, which is in contrast to some (Caselli et al., 2009; Deary et al., 2002; den Heijer et al., 2002; Wishart et al., 2006), but not all (Alexander et al., 2007; Cherbuin et al., 2008; Schmidt et al., 1996) previous studies. Sample size may have contributed to the lack of significant difference in morphological features between ε4-carriers and non-carriers. Indeed studies showing cognitive decline in ε4-carriers have tended to recruit much larger groups (Caselli et al., 2009; Deary et al., 2002). Our task may not have been sensitive to performance differences with this relatively small group of subjects, despite being suitable for eliciting BOLD fMRI differences between the groups. Any sub-threshold differences in GM were accounted for in our fMRI analyses by adding GM maps as covariates. All participants recruited in this study were highly-educated and are therefore not representative of the entire population. It is possible that the interaction we observe between age and genotype is particular to higher-functioning individuals. Future studies should specifically address this point by including subjects with a wider range of educational level. A previous study investigating functional age changes associated with APOE found increased activation with age in a sample including ε4carriers in their mid-fifties and mid-seventies who had a family history of AD (Trivedi et al., 2008a). This is somewhat at odds with our result. The discrepancy could be explained by different age ranges and by controlling for family history of AD, which independently influences the BOLD signal (Bassett et al., 2006; Xu et al., 2009). The same group previously reported reduced activation in older ε4-carriers relative to non-carriers (Trivedi et al., 2006), which is consistent with our results. Our observations need to be replicated by studies testing for age by gene interactions using a longitudinal sample covering the entire span of life, from young adulthood to late adulthood. Finally, other more complex interpretations cannot be excluded. For example other gene(s) with an effect on the BOLD response may be in linkage disequilibrium with the ε4 allele variant (Roses et al., 2009), or conversely, with the ε3 allele, and could be driving what appears to be an effect of APOE genotype. The preferential inheritance of other genes alongside the different APOE alleles, and any related effects on brain function, need further elucidation. Conclusion APOE genotype has different consequences for brain function depending on age. Our data demonstrate the importance of covering the entire life span when characterising the effects of gene variants on brain structure and function, or focusing on a limited range of age when very few subjects per group are available. Understanding the mechanisms of brain functional changes in the healthy human brain

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that are associated with APOE may shed light on the increased risk of old-age pathology in ε4-carriers. Supplementary materials related to this article can be found online at doi:10.1016/j.neuroimage.2010.08.009. Acknowledgments We thank Prof. Jonathan Flint and Amarjit Bhorma, Wellcome Trust Centre for Human Genetics, for genotyping APOE data; Dr. Natalie Voets for contributing to fMRI task development; Prof. Peter Jezzard and Dr. David Feinburg for the ASL pulse sequence; and Dr. Doug Greve (MGH) for helping us use the BBR tool. The authors acknowledge academic collaborative contribution from GlaxoSmithKline to fund data acquisition. P.M.M. is a full-time employee of GlaxoSmithKline. N.F. is funded by the Gordon Edward Small's Charitable Trust (Scottish Charity Register: SC008962). GKW is partly funded by the NIHR Biomedical Research Centre Programme, Oxford. AJT is supported by a scholarship from the Rhodes Trust. 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