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The apolipoprotein E (APOE) ϵ4 allele is a confirmed genetic risk factor and the APOE ϵ2 allele is a protective factor related to late-onset Alzheimer's disease (AD). Intriguingly, recent studies demonstrated similar brain function alterations between APOE ϵ2 and ϵ4 alleles, despite their opposite susceptibilities to AD. To address this apparent discrepancy, we recruited 129 cognitively normal elderly subjects, including 36 ϵ2 carriers, 44 ϵ3 homozygotes, and 49 ϵ4 carriers. All subjects underwent resting-state functional MRI scans. We hypothesized that aging could influence the APOE ϵ2 and ϵ4 allele effects that contribute to their appropriate AD risks differently. Using the stepwise regression analysis, we demonstrated that although both ϵ2 and ϵ4 carriers showed decreased functional connectivity (FC) compared with ϵ3 homozygotes, they have opposite aging trajectories in the default mode network—primarily in the bilateral anterior cingulate cortex. As age increased, ϵ2 carriers showed elevated FC, whereas ϵ4 carriers exhibited decreased FC. Behaviorally, the altered DMN FC positively correlated with information processing speed in both ϵ2 and ϵ4 carriers. It is suggested that the opposite aging trajectories between APOE ϵ2 and ϵ4 alleles in the DMN may reflect the antagonistic pleiotropic properties and associate with their different AD risks.
Aging remains the primary risk factor for Alzheimer's disease (AD). The apolipoprotein E (APOE) gene serves as the established genetic factor that influences AD onset in an isoform-dependent manner (ϵ4 > ϵ3 > ϵ2) (Corder et al. 1994; Seripa et al. 2009). A more complete understanding of the functional link between the 2 factors could provide insights into the APOE polymorphism's role in insidious AD progression. During the last decade, researchers introduced an imaging genetics strategy to study the APOE polymorphism's influence on human cognition at a neural system level (Bigos and Weinberger 2010). Specifically, most studies are dedicated to uncovering the neural basis of the APOE ϵ4 allele. The effect of APOE ϵ4 allele on brain function is directly linked to heightened AD risk. The resulting pathogenic signs have a potential clinical application in terms of their role as an AD biomarker.
However, several recent studies belie the existence of such a linkage. Trachtenberg, Filippini, Cheeseman, et al. (2012) reported that cognitively normal adult APOE ϵ4 and ϵ2 carriers showed nearly identical brain activation changes during memory and nonmemory tasks, relative to ϵ3 homozygotes . Further study using the resting-state functional connectivity (FC) MRI approach revealed overwhelming intrinsic neural network patterns that were similar between ϵ4 and ϵ2 carriers, compared with ϵ3 homozygotes (Trachtenberg, Filippini, Ebmeier, et al. 2012). These new results are intriguing. It was demonstrated that, in cognitively normal subjects, the ϵ4 allele was linked to accelerated cognitive decline (Caselli et al. 2009), whereas ϵ2 carriers exhibited relatively preserved cognitive function through time (Wilson et al. 2002; Blair et al. 2005). This raises the question of why ϵ4 and ϵ2 carriers exhibit similar brain function alterations relative to ϵ3 homozygotes rather than showing opposite changes in respect to their different AD risks. One answer lies in understanding the APOE neural effects beyond AD (Trachtenberg, Filippini, Cheeseman, et al. 2012). However, such an interpretation seems arbitrary in that it neglected the consistent association between the APOE polymorphism and AD, as well as the controversy about the APOE's role in other neurological disorders (Biffi et al. 2011).
To reconcile these differing views, an alternative interpretation could be that age contributes to the reported inconsistencies in the direction and magnitude of brain function changes (Trachtenberg, Filippini, Mackay, 2012). We hypothesized that the link between the different AD risks and associated brain function changes related to the APOE polymorphism was not merely dependent on the absolute brain activity detected at a specific age range. Rather, the link was associated with the trajectory of brain function changes across the life span. That is, brain function changes related to the APOE polymorphism proved to be age dependent. The ϵ4 allele had a negative trajectory from high to low with aging, whereas the ϵ2 allele had a positive trajectory from low to high. These opposite aging trajectories between the APOE ϵ2 and ϵ4 alleles contributed to their different AD risks. In this study, we tested these hypotheses by addressing the following questions. Did the APOE ϵ4 and ϵ2 alleles have similar effects on brain FC compared with the ϵ3 allele in cognitively normal elderly subjects? Did the APOE ϵ4 and ϵ2 alleles have the same or opposite aging trajectories in FC changes? At what specific ages would these APOE polymorphism trajectories intersect? Did the APOE polymorphism modulate the relationship between FC changes and the education years? If so, how?
We selected the default mode network (DMN) and its association with the APOE polymorphism to address these questions for 3 reasons. (1) It is suggested that the DMN, which consists of a set of brain hubs, including the posterior cingulate cortex (PCC)/precuneus (Pcu), inferior parietal lobule, medial prefrontal cortex, and lateral temporal cortex, is essential for interconnecting distinct networks. The DMN facilitates functional integration of the whole brain (Deshpande et al. 2011; Li et al. 2011). (2) The DMN is implicated in the AD development, as its disruption is consistently identified in AD-spectrum patients and cognitively normal elderly subjects with the APOE ϵ4 allele (Greicius et al. 2004; Sorg et al. 2007; Sheline et al. 2010). (3) The cortical distribution of the β-amyloid (Aβ) deposition in AD patients resembles the DMN pattern (Buckner et al. 2009). Therefore, in this study, we focused on the DMN to detect the APOE polymorphism's neural effects in cognitively normal elderly subjects between the ages of 54 and 80. Then, partial correlation analysis examined the cognitive significance of the altered DMN FC by APOE polymorphism. Finally, stepwise regression analyses examined how the altered DMN FC strengths were differently modulated by aging and the education years among the 3 APOE alleles.
Subjects were recruited using media advertisements and community health screening events. The Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee approved the study. Each participant provided a written informed consent. In this study, we initially enrolled 135 cognitively normal elderly subjects, including 37 APOE ϵ2ϵ3 subjects, 46 ϵ3 homozygotes, and 52 ϵ4 carriers. Specifically, 1 APOE ϵ2 carrier, 2 ϵ3 homozygotes, and 3 ϵ4 carriers were excluded for excessive motion artifacts (i.e., during the resting-state fMRI scan, the head motion exceeded 2 mm of maximum displacement in any direction or 2° of angular motion relative to the first volume) or incomplete image scans. Finally, the remaining 129 subjects, including 36 APOE ϵ2ϵ3 subjects (abbreviated as ϵ2 carriers), 44 ϵ3 homozygotes, and 49 ϵ4 carriers (47 subjects with the APOE ϵ3ϵ4 genotype and 2 ϵ4 homozygote subjects) entered further analysis.
All subjects underwent a standardized clinical interview, including demographic inventory, medical history, and neurological and mental status examination. General cognitive function was assessed by the Mini-Mental State Examination (MMSE) and Mattis Dementia Rating Scale-2 (MDRS-2). Moreover, a neuropsychological battery test covering episodic memory, visuospatial function, information processing speed, and executive domains was utilized. It incorporated an Auditory Verbal Learning Test-20-min delayed recall (AVLT-20-min DR), Logical Memory Test-20-min delayed recall (LMT-20-min DR), Rey–Osterrieth Complex Figure Test (CFT) with its 20-min delayed recall (CFT-20-min DR), Clock Drawing Test (CDT), Digital Symbol Substitution Test (DSST), Trail Making Test-A and B (TMT-A and TMT-B), Stroop Color and Word Test A, B, and C, Verbal Fluency Test (VFT), Digital Span Test (DST), and Semantic Similarity Test (Similarity).
For all study subjects, the following criteria were met: (1) Age was between 54 and 80; (2) education level was above junior middle school; (3) right-handed; (4) generally in good health with adequate visual and auditory acuity allowing cognitive testing; (5) normal neurological examination and no complaints of cognitive impairment; and (6) MMSE score of >24, MDRS-2 score of >120, and the neuropsychological battery performance was in normal range. The exclusion criteria included: (1) Existence or history of neurological or psychiatric diseases; (2) ferrous or electronic implants; and (3) gross structural abnormalities revealed by MRI images.
Each subject's genomic DNA was extracted from 250-μL EDTA-anticoagulated blood, using a DNA direct kit (Tiangen, China). A polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP) assay detected the alleles of rs7412 and rs429358, respectively. Ultimately, the haplotype of rs7412 and rs429358 determined the APOE genotype. The specific process is outlined in Supplementary Material.
MRI images were acquired in a Siemens Verio 3.0-T scanner (Siemens, Erlangen, Germany) with a 12-channel head coil at the Affiliated ZhongDa Hospital of Southeast University. The subjects were instructed to relax and close their eyes during the scan. Their ears were occluded with earplugs. A pair of stabilizers immobilized their heads. Resting-state functional images, including 240 volumes, were obtained by gradient-recalled echo-planar imaging (GRE-EPI) sequence: repetition time (TR) = 2000 ms; echo time (TE) = 25 ms; flip angle (FA) = 90°; acquisition matrix = 64 × 64; field of view (FOV) = 240 × 240 mm; thickness = 4.0 mm; gap = 0 mm; number of slices = 36. High-resolution T1-weighted anatomical images covering the whole brain were acquired by a 3D-magnetization prepared rapid gradient-echo sequence: TR = 1900 ms; TE = 2.48 ms; FA = 9°; acquisition matrix = 256 × 256; FOV = 250 × 250 mm; thickness = 1.0 mm; gap = 0 mm, number of slices = 176. Additionally, routine axial T2-weighted images were obtained to exclude subjects with major white matter (WM) changes, cerebral infarction, or other lesions.
The resting-state functional imaging data were preprocessed, using the Analysis of Functional NeuroImages (AFNI) software (http://afni.nimh.nih.gov/afni) and MATLAB programs (The MathWorks, Inc., Natick, MA, USA). The first 5 volumes of the data were discarded to allow for T1 equilibration. Then, the raw data spikes were removed (3dDespike). Correction was performed for the intravolume acquisition time differences among slices and the intervolume motion effects during the scan (3dvolreg). Detrending was conducted to remove Legendre polynomials (3dDetrend). The obtained image was spatially normalized into a standard stereotaxic space with a 12-parameter affine approach and an EPI template image, which was resampled to 4 × 4 × 4 mm3 voxels. Possible confounding signals of WM, cerebrospinal fluid, and six-motion vectors were regressed out from the voxel-wise time series. No significant difference of head motion was observed among the 3 groups. We calculated the Global Negative Index, the ratio of the number of voxels negatively correlated with the global signal to the total number of voxels, for each subject. All were greater than 0.03, suggesting that our data's global signal was irrelevant to nonneural noise and should not be regressed out (Chen et al. 2012). Finally, a band-pass filter was applied to ensure that only low-frequency fluctuations between 0.015 and 0.1 Hz could be analyzed further.
The PCC cortical hub [coordinate in the Montreal Neurological Institute (MNI) space: −2, −45, 34] was selected to generate a 6-mm radius spherical seed region (Buckner et al. 2009), followed by coregistration to the functional data (3dfractionize). Individual time courses were extracted based on the coregistered seed region (3dmaskave). Furthermore, voxel-wise cross-correlation (CC) values between the seed region and the whole brain were calculated (3dfim+). A Fisher's Z-transformation was then applied to improve the normality of the CC values [m = 0.5ln(1 + CC)/(1 − CC)] (3dcalc). Finally, the voxel-wise values were smoothed with a 6-mm Gaussian kernel, and the individual DMN patterns were obtained; they were represented by the PCC FC network.
The voxel-wise gray matter (GM) volumes were included as covariates in the FC analysis to avoid the bias of FC strength due to anatomical variation (Bai, Watson, et al. 2011; Bai, Xie, et al. 2011). First, individual GM volume maps were obtained and then normalized to the MNI space, using the toolbox of voxel-based morphometry 8 (VBM8; http://dbm.neuro.uni-jena.de/vbm/). Second, the normalized GM volume maps were resampled to the same voxel size as the functional data, and further subjected to a logit transformation [logit(a) = 0.5ln(a/1 − a)] to improve the normality. Third, the voxel-wise values were smoothed as the functional image. Finally, the resulting GM values were voxelwisely regressed out as the nuisance regressor from the FC values to control the GM volume influence on the FC strength. The voxel-wise GM volume correction was performed for each subject. Additionally, we voxelwisely investigated the APOE genotype and aging effects, and their interaction, on the GM volume. The detailed method and results are provided in Supplementary Material, Supplementary Figures 4 and 5, as well as in Supplementary Tables 3 and 4.
One-way analysis of variance (ANOVA) and χ2 tests (only applied in the comparisons of gender and family history, FH) were used to compare the demographic data and neuropsychological performances among the 3 groups. The statistical significance was set at P < 0.05.
Individual DMN maps were grouped together for each group, respectively. Then, each group's DMN pattern was determined by a random-effects one-sample t-test, according to the AlphaSim program based on the Monte Carlo simulation algorithm (α = 0.05, voxel-wise P < 0.05, cluster sizes >5504 mm3; http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf).
A one-way ANOVA test compared the DMN pattern among the 3 groups (3dANOVA program, corrected by AlphaSim, α = 0.05, voxel-wise P < 0.05, cluster sizes >5504 mm3). Then, post hoc tests by the 3dttest++ program were separately applied in the comparison between groups of ϵ4 carriers and ϵ3 homozygotes, as well as between ϵ2 carriers and ϵ3 homozygotes. We also detected the difference between ϵ2 and ϵ4 carriers. All post hoc tests, controlling the effects of age, gender, FH, and education years, were corrected by the AlphaSim program. The α-value was set at 0.01, as 3 post hoc tests were involved and the significance level was no greater than 0.05/3 = 0.0167, according to the Bonferroni correction principle (α = 0.01, voxel-wise P < 0.05, cluster sizes >7104 mm3).
The FC strengths of the regions showing altered DMN FC in ϵ2 and ϵ4 carriers, relative to ϵ3 homozygotes, were, respectively, extracted. Partial correlation analyses were applied between the extracted FC strengths and the neuropsychological performances within each group, controlling the effects of age, gender, education years, and FH. To increase statistical power by reducing random variability, as previously described (Xie et al. 2012), we grouped the neuropsychological tests into 4 cognitive domains and transformed the raw scores into 4 composite Z scores. First, for each neuropsychological test, the individual raw scores were transformed to Z scores, according to the mean and standard deviation of the scores for all subjects, as shown in following equation:
where is the Z score of the ith subject; is the raw score of the ith subject; is the average raw score of the neuropsychological test for all subjects; S is the standard deviation of the scores. Notably, for tests measured by timing, including TMT-A, TMT-B, Stroop A, Stroop B, and Stroop C, the raw scores were defined as the reciprocal of the time for the tests. Then, each cognitive domain's composite Z score was determined by averaging the Z scores of relevant tests, according to the following divisions: episodic memory (3 tests, including AVLT-20-min DR, LMT-20-min DR, and CFT-20-min DR), visuospatial function (2 tests, including CFT and CDT), information processing speed (4 tests, including DSST, TMT-A, Stroop A, and Stroop B), and executive function (5 tests, including VFT, DST-backward, TMT-B, Stroop C, and Similarity). All statistical procedures utilized the SPSS 20.0 software (SPSS, Inc., Chicago, IL, USA). Bonferroni correction for multiple comparisons was used with the significance level considered at P < 0.0125 (P = 0.05/4 composite scores).
To investigate whether aging may differently influence the similar effects of APOE ϵ2 and ϵ4 alleles on the DMN FC, we focused on the intersection region disconnecting to the PCC in both ϵ2 and ϵ4 carriers compared with ϵ3 homozygotes (see the Results section). A stepwise regression analysis, including the APOE status and age interaction, was employed. The initial model is shown below:
where m is the Fisher's Z-transformed CC value of the intersection region for each subject across the 3 groups; is the intercept of the fitting line; are the effects of the APOE ϵ2 and ϵ4 alleles relative to the ϵ3 allele; are the effects of age, education years, gender, and family history, respectively; represent the interaction between APOE status and age separately for the ϵ2 and ϵ4 alleles, using the APOE ϵ3ϵ3 as the reference group; denotes the random errors. Finally, the different aging trajectories of the m values among groups were plotted, if the interaction between APOE status and age was found in the final model best predicting the m values.
Furthermore, as the education years yielded a positive correlation with the m values in the final model (see the Results section), we performed a post hoc stepwise regression analysis. This was done to detect if education years differently modulated the FC strength among groups, in addition to the effects of APOE status and age. The initial model is illustrated below:
where represents the adjusted m values that subtract the contribution of age and APOE status from the original m values in the intersection region, according to the final model stem from equation (2); is the effect of education years; represent the interaction between APOE status and education years separately for ϵ2 and ϵ4 alleles, using the APOE ϵ3ϵ3 as the reference group; the implications of refer to those in equation (2). Similarly, the discrepant trajectories of the adjusted m values with education years among groups were depicted, if the interaction between APOE status and education years was observed in the final model.
As Table Table11 and Supplementary Table 1 illustrate, no significant difference was observed in the demographic information and neuropsychological performance among the 3 groups, except for education years (F = 3.43, P = 0.04).
The DMN FC patterns of the 3 APOE genotype groups were obtained by using PCC seed-based FC analysis (Supplementary Fig. 1). Voxel-wise one-way ANOVA detected the APOE polymorphism effects on the DMN FC strength among groups. As shown in Supplementary Figure 2, the APOE polymorphism had a significant effect on the DMN FC in the bilateral Pcu and anterior cingulate cortex (ACC) regions.
Further post hoc analysis between the ϵ2 carrier and ϵ3 homozygote groups revealed that the DMN FC reduction occurred in the bilateral Pcu and ACC regions for ϵ2 carriers compared with ϵ3 homozygotes (Fig. (Fig.11A and Supplementary Table 2). For numeric presentation, we averaged the DMN FC values within the 6-mm radius spherical regions centered at the peak coordinate in the ACC and Pcu areas, respectively. As shown in Figure Figure11B, the DMN FC in ϵ2 carriers was reduced 69.4% in the ACC region and 57.6% in the Pcu region, compared with ϵ3 homozygotes. With partial correlation analysis, the averaged DMN FC strength of these 2 regions was significantly correlated with the information processing speed in APOE ϵ2 carriers (indexed as the Zspeed score) (P = 0.002, R2 = 0.26). The lower the FC was, the worse the information processing speed, as shown in the left panel of Figure Figure11C. However, such a relationship was not found for ϵ3 homozygotes (right panel of Fig. Fig.11C).
Using a similar post hoc analysis, as described above, it was found that the ϵ4 carriers also had reduced DMN FC in the bilateral ACC/right dorsolateral prefrontal cortex (DLPFC) region. The ϵ4 carriers' mean FC value of the 6-mm radius spherical region centered at the peak coordinate was 62.7% below than ϵ3 homozygotes (Fig. (Fig.22A,B and Supplementary Table 2). The DMN FC of individual ϵ4 carriers had a strong tendency to positively correlate with the information processing speed in ϵ4 carriers (P = 0.02, R2 = 0.11, left panel of Fig. Fig.22C), as it did not reach the statistical significance level after performing the Bonferroni correction. For ϵ3 homozygotes, no such a relationship was observed (right panel of Fig. Fig.22C). In addition, we compared the DMN FC between ϵ2 and ϵ4 carrier groups, and found that ϵ2 carriers exhibited decreased DMN FC in the left Pcu relative to ϵ4 carriers, as illustrated in Supplementary Figure 3.
To specifically investigate if aging influences similar effects differently between the APOE ϵ2 and ϵ4 alleles, the intersection region in the bilateral ACC (between Figs Figs11A and and22A) was obtained (Fig. (Fig.33A). In this region, both ϵ2 and ϵ4 carriers showed comparably reduced DMN FC relative to ϵ3 homozygotes, and the mean FC values were 39.0 and 37.8% lower than ϵ3 homozygotes for ϵ2 and ϵ4 carriers, respectively, as shown in Figure Figure33B. With the stepwise regression analysis using APOE ϵ3ϵ3 as the reference group, the model with APOE2, APOE4, APOE2 × age, APOE4 × age, and edu variables provided the best prediction of the DMN FC value in this region (adjusted R2 = 0.185, P < 0.0001, Table Table2).2). Of note, the ϵ2 and ϵ4 carriers showed opposite neural trajectories with aging (Fig. (Fig.33C). In the case of ϵ2 carriers, the aging trajectory of DMN FC changes was positive. The older the ϵ2 carriers were, the stronger their FC values. In the case of ϵ4 carriers, however, the aging trajectory was negative. That is, the older the ϵ4 carriers were, the weaker their FC values. Interestingly, the modeled trajectories of ϵ3 homozygotes and ϵ4 carriers intersected at around age 55. After this age, the ϵ4 carriers started to show lower FC values than the ϵ3 homozygotes, despite the fact we estimated that they would exhibit higher FC at a younger age. Additionally, the modeled trajectories of ϵ2 and ϵ4 carriers intersected at around age 70. The FC of ϵ2 carriers was always lower than that of ϵ4 carriers. However, it gradually increased with age, and was estimated to surpass the FC of ϵ4 carriers at approximately age 70.
Also, we observed that the factor of education years yielded a regression coefficient of 0.006 in the final model (Table (Table2).2). This indicates that the greater the education years, the stronger the DMN FC values were for the whole cohort. Moreover, the post hoc stepwise regression analysis, including the interaction between the APOE status and education years, revealed the degree to which the effect of education years on the DMN FC values changed as a function of the APOE status (adjusted R2 = 0.084, P = 0.003). Particularly, relative to the ϵ3 homozygotes exhibiting the regression coefficient of 0.006, ϵ4 carriers showed a significantly increased slope, whereas ϵ2 carriers showed a decreased slope. The absolute slopes for the ϵ2, ϵ3, and ϵ4 groups were 0.004, 0.006, and 0.008, respectively, with the highest slope for the ϵ4 carriers and the lowest for the ϵ2 carries, as shown in Figure Figure44 and Table Table33.
This study's major novel finding is that APOE ϵ2 and ϵ4 carriers have opposite aging trajectories of DMN FC changes, primarily in the bilateral ACC regions, although they both showed similar decreased FC compared with ϵ3 homozygotes. Our findings corroborate prior results indicating that ϵ4 carriers exhibit significantly lower DMN FC than noncarriers in an elderly cohort (Sheline et al. 2010; Brown et al. 2011). Furthermore, we expand these results in 4 important ways. First, previous imaging genetic studies established a conceptual framework of APOE neuroimaging characteristics. The brain functional differences due to APOE polymorphism are associated with different AD risks in an ϵ4 allele dose-dependent manner for cognitively normal subjects (Bookheimer et al. 2000; Reiman et al. 2005; Sheline et al. 2010; Schraml et al. 2013). However, most of these studies only compared ϵ4 carriers and noncarriers. They were limited in terms of the separation of the ϵ4 noncarriers group into subgroups of ϵ2 carriers and ϵ3 homozygotes (Suri et al. 2013). We expanded previous studies by comparing functional differences among the 3 APOE alleles. We demonstrated that, in cognitively normal elderly subjects, both APOE ϵ2 and ϵ4 alleles similarly decreased DMN FC, rather than showing opposite effects according to their different AD risks. This result, combined with recent findings in a middle-aged cohort (Trachtenberg, Filippini, Ebmeier, et al. 2012), indicates that the absolute functional differences due to APOE polymorphism are not directly linked to their discrepant predispositions to AD.
Second, to explore the paradox between the comparable brain function changes and the opposite AD risks for APOE ϵ2 and ϵ4 alleles, we hypothesized that aging may interact with the functional differences of APOE polymorphism, as AD is indeed a multifactor neurodegenerative disease. We extended our study by detecting aging trajectories of the brain function change. This revealed that the APOE ϵ4 and ϵ2 alleles had different trajectories. The ϵ4 carriers showed a negative (high-to-low) aging trajectory, whereas the ϵ2 carriers exhibited a positive (low-to-high) aging trajectory. We believe that these opposite aging trajectories between APOE ϵ2 and ϵ4 alleles contribute to their different AD risks. This notion corresponds with the hypothesis that the APOE ϵ4 allele possesses the antagonistic pleiotropic property, which benefits individuals during their early life span; yet, it advances the disease in the later life (Wright et al. 2003; Han and Bondi 2008; Tuminello and Han 2011; Jochemsen et al. 2012). For example, converging evidence indicated that young ϵ4 carriers had better memory and neural efficiency (Mondadori et al. 2007), as well as higher brain activity than noncarriers (Dennis et al. 2010; Filippini et al. 2011). In contrast, older ϵ4 carriers showed accelerated cognitive decline (Caselli et al. 2009) and greater neural recruitment during tasks than noncarriers; this was presumably associated with disease encroachment (Bookheimer et al. 2000; Bondi et al. 2005; Wishart et al. 2006; Han and Bondi 2008; Bakker et al. 2012). In regard to the APOE ϵ2 allele, the observed positive aging trajectory corroborates Morris et al.'s finding that ϵ2 carriers exhibited decreased Aβ deposition with aging (Morris et al. 2010). In all, our study demonstrated that APOE ϵ2 and ϵ4 alleles have opposite aging trajectories of FC alteration. We propose that AD-related imaging genetic studies should take this effect into account, in addition to the exclusive comparison of the absolute functional differences among APOE alleles.
Third, our results regarding the functional link between APOE polymorphism and aging reveal the network dysfunction's role in insidious AD progression. The AD network dysfunction hypothesis assumes that the aberrant network activity causally contributes to the cognitive deficits in AD progression, probably through its mutual regulation with the Aβ (Palop and Mucke 2010). Specifically, it is demonstrated that the Aβ production could be regulated by both stimulated and intrinsic neural activity fluctuations. Elevated neural activity would remarkably enhance the Aβ production (Kamenetz et al. 2003; Bero et al. 2011). Consequently, the abnormally elevated Aβ deposition would further disrupt the DMN (Mormino et al. 2011; Adriaanse et al. 2014), probably by its synaptotoxic effect on the excitatory transmission (Mucke and Selkoe 2012). Also, it is suggested that tau shares a common pathway from abnormal Aβ level to aberrant neural activity, and it is a critical downstream mediator in contributing to network dysfunction and Aβ-induced dementia (Liao et al. 2014). Supporting the above hypothesis, young ϵ4 carriers were observed exhibiting increased resting-state DMN coactivation compared with noncarriers (Filippini et al. 2009). Using this study's elderly cohort, we estimated that the cross-point between ϵ4 carriers and ϵ3 homozygotes was at around age 55. This is consistent with a series of findings, indicating that ϵ4 carriers in their 50s start to exhibit increased Aβ deposition, disrupted structural connectivity, and inferior memory performance compared with noncarriers, despite the fact that they benefit before that age (Morris et al. 2010; Brown et al. 2011; Jochemsen et al. 2012). We speculate that, based on the antagonistic pleiotropic effect introduced above, the enhanced basal DMN activity in early life would increase the vulnerability of these regions to Aβ deposition that contributes to FC disruption in late life. In addition, ϵ2 carriers' DMN hypoactivity in early life may slow the Aβ deposition progression, thereby facilitating gradually stronger FC with aging, and hence, could be recognized as an AD protective factor. In all, our results show that the APOE polymorphism has potential to influence AD risk decades before cognitive symptoms emerge, which may be supported by the association between the network activity alteration and established AD hallmarks, including Aβ and tau.
Fourth, our study investigated the education effects on the DMN among the 3 APOE alleles. Education level is considered the primary proxy of cognitive reserve in that a high education level would increase the reserve capability and allow the brain to resist AD-related pathogenesis (Valenzuela and Sachdev 2006). In this study, we found that the factor of increased education years could enhance the DMN FC in the ACC region across groups, in addition to the effects of age and APOE status. This result corroborates a recent study identifying the ACC region as the neural basis of education-related cognitive reserve in a cognitively normal elderly cohort (Arenaza-Urquijo et al. 2013). Moreover, after regressing out the influences of age and APOE status, we observed that the contribution of education years differed by APOE status. Education years had the strongest effect on ϵ4 carriers, followed by ϵ3 homozygotes and, finally, ϵ2 carriers. This APOE isoform-dependent effect of education years indicates that a high education level would reverse the negative aging trajectory for ϵ4 carriers. The more education obtained, the stronger the FC at older ages. These inspiring findings indicate that there is cognitive reserve modulation on the aging trajectory of FC, showing the potential of cognitive intervention for subjects with a high AD risk.
Additionally, we found that the disrupted DMN was associated with inferior information processing speed in both ϵ2 and ϵ4 carriers. Recently, it was indicated that the DMN and its anticorrelated task control network are mutually influenced, and such a reciprocal relationship may regulate individual performances for goal-directed tasks (Uddin et al. 2009; Wen et al. 2013). Importantly, for this study, the brain region associating with inferior information processing speed for the ϵ2 and ϵ4 carriers was primarily located in the bilateral ACC extending to the right DLPFC. This area is the principal region subserving cognitive control (Dosenbach et al. 2007), and its greater connectivity to the DMN is believed to facilitate better cognitive control performance (Hampson et al. 2006). In contrast, in attention deficit/hyperactivity disorder patients characterized as having a cognitive control deficit, their ACC connectivity to the DMN was diminished (Castellanos et al. 2008). In addition, similar to our findings, it was demonstrated that the anterior DMN, which primarily involves the ACC and prefrontal regions, exhibited reduced integrity that associated with poorer information processing speed in the context of aging (Damoiseaux et al. 2008). These studies convergently indicate the roles of the ACC and prefrontal region in information processing efficiency. Accordingly, our findings suggest that the reduced DMN integrity in the ACC and prefrontal region may contribute to less effective information processing for ϵ2 and ϵ4 carriers.
It should be noted that our study has several limitations. First, the relatively narrow age range between 54 and 80 in our sample challenges our expectation of different functional trajectories across the lifespan among the 3 APOE alleles. Although the modeled trajectories were estimated to intersect at around age 55 between ϵ4 carriers and ϵ3 homozygotes, we were not able to verify the extrapolation of the trajectories because subjects younger than 54 were not available for this study. Second, this study's cross-sectional design limits our understanding regarding the dynamic characteristics of the DMN integrity with aging for different APOE alleles. In view of the fact that there are large variations of brain functions within each APOE genotype group with the cross-sectional data, we are following up with these subjects in order to statistically confirm the modeled trajectories at an individual level in the future. Ultimately, by overcoming these limitations, we will be able to discern accurately the APOE genotype influence on the neural network across different age groups.
In this study, we reported that APOE ϵ2 and ϵ4 carriers have opposite aging trajectories of DMN FC changes, although both of them showed decreased FC compared with ϵ3 homozygotes. These findings revise previous APOE neuroimaging conceptual framework and indicate that the aging trajectories of brain function change, in addition to the absolute change, may influence susceptibility to AD in regard to different APOE alleles. Our data support the antagonistic pleiotropic effects of both ϵ2 and ϵ4 alleles on the neural network activity in an opposite way to link their different AD risks. Furthermore, we proposed that age should be included as an important regulator when investigating the role of APOE polymorphism in AD development at a neural system level.
This work was supported by the National Natural Science Foundation of China (81171021, 91132727, and 91232000); the China Scholarship Council (201206090088); the USA National Institutes of Health grant (R01AG020279); and the Key Program for Clinical Medicine and Science and Technology: Jiangsu Province Clinical Medical Research Center (BL2013025).
We sincerely thank Ms Carrie M. O'Connor, M.A., for editorial assistance; Dr Piero G. Antuono, M.D., for neurocognitive assessment instruction; Youming Zheng, Haixia Feng, Hong Zhu, and Xiaofa Huang for subject recruitment; Min Wang and Xiaohui Chen for technical support on MRI scan. Conflict of Interest: None declared.