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. 2013 Nov 6;35(7):3238–3248. doi: 10.1002/hbm.22398

The APOE ɛ4 allele affects complexity and functional connectivity of resting brain activity in healthy adults

Albert C Yang 1,2,3, Chu‐Chung Huang 4, Mu‐En Liu 5, Yin‐Jay Liou 1,2, Chen‐Jee Hong 1,2,4, Men‐Tzung Lo 3, Norden E Huang 3, Chung‐Kang Peng 6, Ching‐Po Lin 4,✉, Shih‐Jen Tsai 1,2,✉
PMCID: PMC6869578  PMID: 24193893

Abstract

The apolipoprotein E (APOE) gene is associated with structural and functional brain changes. We have used multiscale entropy (MSE) analysis to detect changes in the complexity of resting blood oxygen level‐dependent (BOLD) signals associated with aging and cognitive function. In this study, we further hypothesized that the APOE genotype may affect the complexity of spontaneous BOLD activity in younger and older adults, and such altered complexity may be associated with certain changes in functional connectivity. We conducted a resting‐state functional magnetic resonance imaging experiment in a cohort of 100 younger adults (aged 20–39 years; mean 27.2 ± 4.3 years; male/female: 53/47) and 112 older adults (aged 60–79 years; mean 68.4 ± 6.5 years; male/female: 54/58), and applied voxelwise MSE analysis to assess the main effect of APOE genotype on resting‐state BOLD complexity and connectivity. Although the main effect of APOE genotype on BOLD complexity was not observed in younger group, we observed that older APOE ɛ4 allele carriers had significant reductions in BOLD complexity in precuneus and posterior cingulate regions, relative to noncarriers. We also observed that reduced BOLD complexity in precuneus and posterior cingulate regions was associated with increased functional connectivity to the superior and inferior frontal gyrus in the older group. These results support the compensatory recruitment hypothesis in older APOE ɛ4 carriers, and confer the impact of the APOE genotype on the temporal dynamics of brain activity in older adults. Hum Brain Mapp 35:3238–3248, 2014. © 2013 Wiley Periodicals, Inc.

Keywords: APOE, aging, blood oxygen level dependent, complexity, multiscale entropy

INTRODUCTION

Apolipoprotein E (APOE) is a protein coded by a gene located on chromosome 19, and has three common isoforms, designated ɛ2, ɛ3, and ɛ4 [Strittmatter et al., 1993]. The APOE ɛ4 allele has been studied extensively for its relationship to geriatric cognitive decline [Christensen et al., 2008; Reed et al., 1994] and late‐onset Alzheimer's disease (AD) [Corder et al., 1993; Farrer et al., 1997; Kuusisto et al., 1994; Saunders et al., 1993]. Studies using functional brain imaging have shown that altered brain function associated with APOE ɛ4 allele may occur long before the appearance of AD symptoms [Prvulovic et al., 2011; Small, 1996]. In cognitively normal middle‐aged and older populations, studies with positron emission tomography (PET) found that APOE ɛ4 allele is associated with reduced glucose metabolism [Reiman et al., 1996; Small et al., 1995] but studies with functional magnetic resonance imaging (fMRI) produced mixed results [Trachtenberg et al., 2012; Tuminello and Han, 2011]. Some studies found that APOE ɛ4 allele is associated with reduced functional brain activity in parietal, temporal, and frontal areas [Borghesani et al., 2008; De Blasi et al., 2009; Elgh et al., 2003; Filbey et al., 2010; Lind et al., 2006; Smith et al., 1999; Xu et al., 2009], whereas others found increased activity in the same general regions [Bookheimer et al., 2000; Seidenberg et al., 2009; Smith et al., 2002; Trivedi et al., 2008; Wierenga et al., 2010], and one recent study found both increases and decreases in functional brain activity that varied according to the type of memory function [Kukolja et al., 2010].

Few studies have investigated the impact of APOE genotype on functional brain activities in younger populations. One study with PET found that younger APOE ɛ4 carriers had low rates of glucose metabolism bilaterally in the posterior cingulate, parietal, temporal, and prefrontal cortex, compared with age‐matched noncarriers [Reiman et al., 2004], and a postmortem brain study found that younger APOE ɛ4 carriers had lower posterior cingulate mitochondrial activity than noncarriers [Valla et al., 2010]. fMRI studies found that younger APOE ɛ4 carriers had an increased default mode network (DMN) coactivation, relative to age‐matched noncarriers [Dennis et al., 2010; Filippini et al., 2009a].

Because of mixed fMRI results, an alternative approach is needed for studying the temporal dynamics of functional brain activity, such as the complexity of blood oxygen level‐dependent (BOLD) signals [Fox and Raichle, 2007]. Prior studies have suggested that altered functional connectivity between brain regions is associated with changes in temporal dynamics and the complexity of spontaneous brain activity [Bassett et al., 2012; Fox et al., 2006; Nir et al., 2008]. Because loss of complexity has been suggested as the hallmark of illness and the aging process [Goldberger et al., 2002; Lipsitz and Goldberger, 1992; Vaillancourt and Newell, 2002], investigations of the relationships between complexity and functional connectivity may reveal novel insights into the understanding of the role of aging and genetic predisposition in declining functional brain activity. We have recently applied and validated a complexity measure, multiscale entropy (MSE) analysis [Costa et al., 2002, 2005], to study the complexity of resting BOLD signals in relation to aging and cognitive function [Yang et al., 2011]. We found that reduced complexity of BOLD signals in DMN areas was correlated to increased age and decreased cognitive performance in older adults [Yang et al., 2011]. Another study also showed a regional decrease in complexity of resting‐state BOLD signals among patients with familial AD [Liu et al., 2013].

In this study, we further hypothesized that APOE genotype may affect the complexity of spontaneous BOLD activity in the aging process, and such altered complexity may be associated with certain changes in functional connectivity. Our aims were, therefore, twofold: (1) to assess the effect of APOE genotype on complexity of BOLD signals in the younger and older group, and (2) to assess the functional connectivity associated with altered complexity of BOLD signals, and compare functional connectivity maps across APOE genotypes and age groups. To address these objectives, we conducted a resting fMRI experiment on a large cohort of cognitively normal younger and older adults.

MATERIALS AND METHODS

Participants

This study cohort comprised 212 Han Chinese participants recruited from communities in northern Taiwan. Hundred participants aged 20–39 years comprised the younger group (mean 27.2 ± 4.3 years; male/female: 53/47), and 112 participants aged 60–79 years comprised the older group (mean 68.4 ± 6.5 years; male/female: 54/58). The study was conducted in accordance with the Declaration of Helsinki, receiving approval from the local Institutional Review Board. Each participant was evaluated by a trained research assistant using Mini‐International Neuropsychiatric Interview to exclude the presence of Axis I psychiatric disorders [Sheehan et al., 1998]. All participants were assessed for cognitive function by Mini‐Mental State Examination [Folstein et al., 1975] and Wechsler Digit Span Task [Wechsler, 1997]. Older participants were further assessed by Clinical Dementia Rating Scale (CDR) [Hughes et al., 1982] to exclude dementia (CDR > 0). Overall exclusion criteria for all participants consisted of the following: (1) presence of dementia; (2) presence of Axis I psychiatric disorders, such as schizophrenia, bipolar disorders, or unipolar depression; and (3) a history of neurological conditions, such as head injury, stroke, or Parkinson's disease.

APOE Genotyping

DNA samples for all participants were obtained from blood samples or by buccal swabs. APOE genotypes were determined using PCR‐RFLP [Wenham et al., 1991]. Of 212 participants, there were five genotypes: ɛ2/ɛ2 (n = 1, 0.5%), ɛ2/ɛ3 (n = 30, 14.2%), ɛ3/ɛ3 (n = 148, 69.8%), ɛ2/ɛ4 (n = 5, 2.4%), and ɛ3/ɛ4 (n = 28, 13.2%). The distributions of genotypes did not differ significantly according to the Hardy–Weinberg equilibrium (P = 0.375). When the sample was stratified according to the presence of ɛ4 allele, 33 (15.6%) were ɛ4 carriers and 179 (84.4%) were non‐ɛ4 carriers. The frequency of ɛ4 allele was comparable with frequencies reported in prior studies worldwide (7.9–16.5%) [Hallman et al., 1991; Hong et al., 1996; Myers et al., 1996; Slooter et al., 1998].

fMRI Scanning and Imaging Processing

fMRI scanning was performed at National Yang‐Ming University using a 3.0‐T Siemens MRI Scanner (Siemens Magnetom Tim Trio, Erlangen, Germany) equipped with a 12‐channel head coil. All fMRI experiments were scheduled in the morning. During the experiments, the scanner room was darkened, and the participants were instructed to relax with their eyes closed, without falling asleep. After the resting experiment, the technician routinely asked the participants whether they fell asleep during the resting scan session, and the participants were rescanned if they slept during the resting scan. T2*‐weighted images with BOLD contrast were measured using a gradient echo‐planar imaging (EPI) sequence (repetition time, TR = 2,500 ms, echo time, TE = 27 ms, field of view, FoV = 220 mm, flip angle = 77°, matrix size = 64 × 64, and voxel size = 3.44 mm × 3.44 mm × 3.40 mm). For each run, 200 EPI volume images were acquired along the anterior and posterior commissure (AC–PC) plane. High‐resolution structural T1 images were acquired with three‐dimensional (3D) magnetization‐prepared rapid gradient‐echo sequence (3D‐MPRAGE; TR = 2,530 ms, TE = 3.5 ms, TI = 1,100 ms, FoV = 256 mm, and flip angle = 7°). For each participant, the whole fMRI scanning lasted ∼15 min.

Structural and resting image data were preprocessed and analyzed using SPM8 (Wellcome Department of Imaging Neuroscience, London, UK) implemented in MATLAB (Mathworks, Sherborn, MA). Structural T1 images were segmented and coregistered with resting functional images. Intracranial volume was calculated as the sum of voxel values of gray matter, white matter, and cerebrospinal fluid (CSF) segmented images in native space. Gray matter volumes of left and right hippocampus were also computed using the segmentation and automated anatomical labeling template [Tzourio‐Mazoyer et al., 2002].

Resting images were slice‐timing corrected, realigned, and normalized into the standard stereotaxic space of Montreal Neurological Institute (MNI) EPI template, and resampled to a 3‐mm cubic voxel. Covariates of BOLD time series were regressed out before performing complexity analysis, including the time courses of six head motion, white matter, and CSF. All participants included in this study exhibited a maximum displacement of less than 1.5 mm at each axis and an angular motion of less than 1.5° for each axis. The first five data points (12.5 s) in any BOLD time series were discarded because of the instability of initial MRI scanning, leaving 195 data points in final data. Temporal low‐pass filtering (0.01–0.08 Hz) was performed to reduce the influence of high‐frequency noise from physiologic confounders.

MSE Analysis of BOLD Signals

MSE analysis was developed as a biologically meaningful measure of complexity [Costa et al., 2002, 2005] by quantifying sample entropy [Richman and Moorman, 2000] over multiple timescales inherent in a time series. The procedures involved in MSE calculation can be summarized in the following three steps: (1) construction of coarse‐grained time series according to different scale factors, (2) quantification of the sample entropy of each coarse‐grained time series, and (3) examination of the sample entropy profile over a range of scales. The length of each coarse‐grained time series is equal to the length of original time series divided by the scale factor. For Scale 1, the time series is simply the original time series. Sample entropy is defined by the negative natural logarithm of the conditional probability that a data set of length N, having repeated itself within a tolerance r (similarity factor) for m points (pattern length), will also repeat itself for m + 1 points without allowing self‐matches [Richman and Moorman, 2000]. BOLD time series are usually short (100–200 time points), and the coarse‐grained procedure in MSE with a large‐scale factor may result in short data length and subsequently unreliable sample entropy estimation. To ameliorate this issue, we have estimated the appropriate parameters for MSE calculation from relatively short BOLD signals using parameters of m = 1 and r = 0.35 [Yang et al., 2011].

We averaged the entropy value across all scale factors, and used this averaged entropy as the overall MSE value for a single BOLD time series. We have previously shown that such averaging approach has the advantage of combining information from all scale factors [Yang et al., 2011], and is similar to previous MSE studies with other types of physiological signals [Cheng et al., 2009; Mizuno et al., 2010; Norris et al., 2009; Takahashi et al., 2010; Yang et al., 2013]. For individual resting fMRI data, MSE of BOLD signal was computed at voxelwise levels in all cortical and subcortical voxels to create the whole‐brain MSE parametric map for subsequent group analysis. MSE maps were spatially smoothed (8 mm) in SPM to minimize the differences in the functional anatomy of the brain across subjects [Tomasi and Volkow, 2012]. The MSE algorithm for calculating entropy in functional brain activity is available at (http://www.psynetresearch.org/tools.html).

Seed‐Based Functional Connectivity Analysis

To investigate the relationship between BOLD complexity and functional connectivity, the seed BOLD time series was obtained by averaging the time series of voxels within the brain regions in which the MSE of BOLD signals was affected by APOE genotype. Functional connectivity maps were calculated using Pearson's correlation by correlating the seed BOLD time series against those in every gray matter voxel. Pearson r values were converted to Fisher Z measures using Fisher's r‐to‐z transformation [Lowe et al., 1998].

Statistical Analysis

Statistical analyses of parametric imaging data were conducted using MATLAB. Regional differences in whole‐brain MSE mapping between APOE genotype were examined using the general linear model (GLM), with age, sex, and the interaction of APOE genotype and sex used as regressors to control for nongenetic factors. Because APOE ɛ4 allele is known to affect gray matter structure [Filippini et al., 2009b; Schuff et al., 2009], gray matter probability at the corresponding voxel was also entered in GLM analysis as the covariate of noninterest [Oakes et al., 2007]. However, level of education was not entered in GLM analysis because the assessment of education is often unreliable in older adults.

Significant brain clusters were reported if P value was less than 0.001 for any t test (uncorrected) on a single voxel level with a cluster size greater than 10 voxels, and further corrected by familywise‐error‐rate (FWE) methods at P values of less than 0.05 at the cluster level. The procedures were conducted separately for the younger and older groups to examine the main effect of APOE genotype on the MSE of BOLD signals. The same procedures were also performed for functional connectivity mapping to examine the main effect of APOE genotype on functional connectivity associated with altered BOLD complexity. In addition, GLM was performed on the entire cohort to examine the main effect of age on MSE of BOLD signals, with sex, gray matter probability, and the interaction term of age to sex entered as regressors in the model.

Statistical analyses of demographic, cognitive, and brain morphometric variables were conducted using SPSS for Windows Version 15.0 (SPSS, Chicago, IL). Student's t‐test was used to compare continuous variables, and chi‐squared tests were used to compare categorical variables across APOE genotypes. A P value of less than 0.05 (two‐tailed) was considered statistically significant.

RESULTS

Participants

Within the younger and older groups, significant differences were not observed among the APOE ɛ4 carriers and noncarriers according to age, sex, education, cognitive tests, and brain morphometry measures (Table 1). Furthermore, no differences between APOE ɛ4 carriers and noncarriers were observed in gray matter in both older and younger cohort using a whole‐brain voxel‐based morphometry analysis.

Table 1.

Demographics and clinical characteristics

Younger (20–39) APOE ɛ4 carriers (N = 16) APOE ɛ4 noncarriers (N = 84) t or x P
Age, year 28.8 ± 5.6 26.9 ± 4.0 1.58 0.118
Sex, male 6 (37.5) 47 (56.0) 1.17 0.279
Education, year 16.9 ± 2.3 17.8 ± 2.6 −1.34 0.183
Handedness, right 16 (100) 78 (92.9) 0.28 0.597
Mini‐mental state examination 28.8 ± 1.4 29.2 ± 1.2 −1.10 0.272
Digit forward task 15.2 ± 0.8 15.3 ± 1.6 −0.33 0.739
Digit backward task 11. ± 3.1 10.9 ± 3.0 0.16 0.869
Intracranial brain volume, cm3 1190.8 ± 119.8 1176.1 ± 106.6 0.50 0.621
Gray matter, cm3 669.3 ± 98.5 693.6 ± 70.9 −1.17 0.243
White matter, cm3 521.5 ± 188.2 482.5 ± 89.5 1.29 0.199
CSF volume, cm3 565.5 ± 136.8 528.8 ± 134.8 0.99 0.322
Left hippocampus, cm3 4.1 ± 0.5 4.1 ± 0.4 −0.08 0.935
Right hippocampus, cm3 3.8 ± 0.4 3.9 ± 0.4 −0.66 0.509
Older (60–79) APOE ɛ4 carriers (N = 17) APOE ɛ4 noncarriers (N = 95) t or x P
Age, year 69.4 ± 7.4 68.2 ± 6.4 0.68 0.500
Sex, male 8 (47.1) 46 (48.4) 0.03 0.862
Education, year 11.5 ± 6.5 11.4 ± 5.5 0.06 0.952
Handedness, right 17 (100) 93 (97.9) 0.15 0.698
Mini‐mental state examination 27.7 ± 2.2 27.4 ± 2.4 0.45 0.652
Digit forward task 12.3 ± 2.6 13.3 ± 2.3 −1.66 0.100
Digit backward task 7.3 ± 4.4 6.4 ± 3.3 1.01 0.314
Intracranial brain volume, cm3 1047.4 ± 197.5 1082.4 ± 250.1 −0.55 0.586
Gray matter, cm3 591.0 ± 111.3 633.3 ± 260.4 −0.66 0.512
White matter, cm3 456.4 ± 90.8 449.1 ± 117.0 0.24 0.808
CSF volume, cm3 671.4 ± 124.8 722.5 ± 239.3 −0.86 0.394
Left hippocampus, cm3 3.5 ± 0.4 3.7 ± 0.9 −0.73 0.470
Right hippocampus, cm3 3.3 ± 0.4 3.5 ± 0.9 −0.72 0.475
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Categorical data are given as number (%).

Age and MSE of BOLD Signals

Whole‐brain voxelwise analysis examining the effect of age showed four clusters of brain regions in which the MSE of BOLD signals in the younger group was higher than that in the older group (Table 2). No brain regions showed the opposite effect. The largest cluster was found in the left superior temporal gyrus (K E = 422, t = −6.60, P FWE < 0.001), followed by the right superior temporal gyrus (K E = 307, t = −6.69, P FWE < 0.001) and left posterior cingulate gyrus (K E = 117, t = −5.90, P FWE < 0.001). No significant effect of sex or interaction between age and sex on MSE of BOLD signals was observed.

Table 2.

Regions showing decreased MSE of BOLD signals for the older adult group compared with those of the young adult participants

Brain regiona MNI coordinates (mm) Volume (mm3) b t c PFWE Multiscale entropy (MSE)
x y z Younger (N = 100) Older (N = 112)
Left superior temporal gyrus −45 9 −12 11,394 −6.60 <0.001 1.47 ± 0.02 1.43 ± 0.03
Right superior temporal gyrus 42 0 −12 8,289 −6.69 <0.001 1.47 ± 0.03 1.43 ± 0.04
Left posterior cingulate gyrus −12 −63 −9 3,159 −5.90 <0.001 1.48 ± 0.03 1.45 ± 0.04
Left anterior cingulate cortex 0 30 0 1,323 −5.86 0.003 1.48 ± 0.03 1.45 ± 0.04
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a

No regions showed the opposite effect.

b

Volume was computed from cluster size (3 mm × 3 mm × 3 mm voxel).

c

The main effect of age on MSE of BOLD signals was controlled for sex, gray matter volume, and interactions between age and sex.

APOE and MSE of BOLD Signals

Figure 1 shows the whole‐brain voxelwise analysis examining the main effect of APOE genotype in the younger and older groups (see Table 3 for detailed statistical results). The APOE genotype effect on MSE of BOLD signals was controlled for age, sex, gray matter probability, and APOE and sex interaction in GLM analysis. In the younger group, APOE ɛ4 carriers showed trends of reduced MSE of BOLD signals in the left precuneus and posterior cingulate cortex regions (Fig. 1a), but the cluster was not significant after FWE corrections (K E = 7, P FWE = 0.505). In the older group (Fig. 1b), whole‐brain analysis showed that APOE ɛ4 carriers had significantly reduced MSE of BOLD signals in the same regions at cluster level after FWE corrections (K E = 26, P FWE = 0.036). Neither a significant effect of sex nor an interaction between sex and APOE genotype was detected for MSE of BOLD signals within the younger and older groups.

Figure 1.

Figure 1

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Voxelwise comparison of reduced MSE of BOLD signals in APOE ɛ4 carries, relative to APOE ɛ4 noncarries among (a) the young adult group and (b) the older adult group. Only the older adult group showed significant main effect of APOE genotype after correction for multiple comparisons. No opposite effect of the APOE genotype on MSE of BOLD signals was found. The coordinates represent the location of peak intensity.

Table 3.

Regions showing decreased MSE of BOLD signals for APOE ɛ4 carriers compared with APOE ɛ4 noncarriers, controlling for age, sex, and gray matter volume

Brain regiona BA MNI coordinates (mm) Volume (mm3) b t c PFWE Multiscale entropy (MSE)
x y z APOE ɛ4 carriers APOE ɛ4 noncarriers
Younger
Left posterior cingulate and left precuneus 30 −15 −54 3 189 −3.71 0.505 1.45 ± 0.03 1.47 ± 0.04
Older
Left posterior cingulate and left precuneus 31 −12 −51 27 702 −4.30 0.036 1.32 ± 0.07 1.42 ± 0.06
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BA, Brodmann area.

a

No regions showed the opposite effect.

b

Volume was computed from cluster size (3 mm × 3 mm × 3 mm voxel).

c

The main effect of APOE ɛ4 on MSE of BOLD signals was controlled for age, sex, gray matter volume, and interactions between sex and APOE.

Relationship Between BOLD Complexity and Functional Connectivity

Because only older groups showed the main effect of APOE genotype on the decreased BOLD complexity, we used the cluster identified in the older group (Fig. 1b) as the seed, and computed the voxelwise functional connectivity in all gray matter regions. Table 4 shows the main effect of APOE genotype on the functional connectivity in each age group.

Table 4.

Regions showing altered functional connectivity for APOE ɛ4 carriers and noncarriers using the posterior cingulate seed (x = −12 mm, y = −51 mm, z = 27 mm)

Brain region BA MNI coordinates (mm) Volume (mm3) a t b PFWE
x y z
Younger
None
Older
Right inferior frontal gyrus 46 51 36 9 702 5.03 0.036
Right superior frontal gyrus, medial 6/8 6 30 45 864 4.27 0.017
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BA, Brodmann area.

a

Volume was computed from cluster size (3 mm × 3 mm × 3 mm voxel).

b

The effect of APOE ɛ4 on functional connectivity was controlled for age, sex, and interaction between sex and APOE.

In the younger group, no effect of APOE genotype on functional connectivity was detected. In the older APOE ɛ4 carriers (Fig. 2), increased functional connectivity in two clusters of brain regions was observed, compared with noncarriers (Table 4), including the right inferior frontal gyrus (P FWE = 0.036) and the right medial superior frontal gyrus (P FWE = 0.017). No sex and APOE genotype interaction with functional connectivity was identified in results of GLM analysis.

Figure 2.

Figure 2

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Difference of functional connectivity in older APOE ɛ4 carries and noncarriers between the seed region identified in the older adult group (see Table 3 and Fig. 1b) and all gray matter voxels.

Relationship Between BOLD Complexity and Cognitive Functions

To assess whether reduced BOLD complexity in APOE ɛ4 carriers could be correlated with cognitive functions, we extracted mean MSE values within the cluster identified in the older group (Fig. 1b), and tested the correlation between extracted MSE values and cognitive functions. We did not find significant correlations within younger and older group, or groups stratified by APOE genotype.

DISCUSSION

We applied voxelwise MSE analysis to investigate the effect of APOE genotype on the complexity of resting BOLD signals in younger and older adults. Our key finding is that older participants bearing the APOE ɛ4 allele had significant reductions in BOLD complexity in the precuneus and posterior cingulate regions. We also identified that reduced BOLD complexity in the precuneus and the posterior cingulate regions was associated with increased functional connectivity to superior and inferior frontal areas in older adults. Our study represents a pioneering investigation of genetic effects on the loss of brain complexity in a large, broadly aged cohort.

Effect of APOE Genotype on Resting Brain Complexity

Consistent with loss of brain complexity hypothesis [Yang and Tsai, 2013], our whole‐brain analysis showed significantly reduced BOLD complexity in the precuneus and posterior cingulate only in older APOE ɛ4 carriers, suggesting an effect of APOE genotype on reduced BOLD complexity in older age. This finding also complements prior fMRI research in that older APOE ɛ4 carriers had decreased activation in various brain regions, including cingulate cortex [Filbey et al., 2010; Xu et al., 2009]. Although we did not find the effect of APOE genotype in younger adults, prior PET and DTI studies show that younger APOE ɛ4 carriers exhibited reduced metabolism [Reiman et al., 2004; Valla et al., 2010] and decreased fractional anisotropy in the cingulum regions [Heise et al., 2011], relative to younger noncarriers. The mechanism by which the APOE genotype affects the complexity of brain activity across lifespan remains unclear. Such reduced BOLD complexity may be linked to metabolic dysfunction and disruption of white matter integrity.

Research on the effects of APOE genotype in healthy adults has suggested that the APOE ɛ4 allele may be beneficial at earlier ages, and may only confer risk of cognitive decline later in life, suggesting antagonistic pleiotropy hypothesis [Tuminello and Han, 2011]. However, brain‐imaging studies of the relationships between APOE ɛ4 and cognitive function in early life have yielded inconsistent results [Tuminello and Han, 2011]. In our study, the cognitive test results of the APOE ɛ4 carriers were indistinguishable from their age‐matched noncarrier counterparts. Furthermore, the BOLD complexity findings in our study do not support antagonistic pleiotropy hypothesis because altered complexity of resting brain activity was observed only in older adults.

Effect of APOE Genotype on Functional Connectivity

Prior studies on the relationship between APOE genotype and functional connectivity have produced complex results [Trachtenberg et al., 2012; Tuminello and Han, 2011]. Although the role of APOE genotype on functional connectivity in younger APOE ɛ4 carriers remains uncertain, converging literatures suggest a dysfunction of functional connectivity in asymptomatic older APOE ɛ4 carriers, particularly in DMN area [Seeley, 2011]. Our findings may add to prior functional connectivity research that increased functional connectivity to various frontal areas was seen in older APOE ɛ4 carriers, relative to noncarriers. Because such increased functional connectivity was involved in the DMN area of precuneus and posterior cingulate regions with reduced BOLD complexity, these brain regions are known to be one of the earliest metabolically affected areas among patients with early AD [Huang et al., 2002; Kogure et al., 2000; Minoshima et al., 1997]. Collectively, our findings of disruption in DMN complexity with enhancement of frontal connectivity in older APOE ɛ4 carriers may support the compensatory recruitment hypothesis [Bookheimer et al., 2000; Trachtenberg et al., 2012] that older APOE ɛ4 carriers, although not exhibiting signs and symptoms of declined cognitive function, may exhibit compensatory recruitment in the brain regions that are commonly impacted in AD [Tuminello and Han, 2011]. However, an alternative explanation to our results should also include the disinhibition hypothesis [Seeley, 2011], as a recent study showed that, relative to noncarriers, the older APOE ɛ4 carriers were associated with the decreased functional connectivity in DMN and increased functional connectivity in salience network, which is inversely correlated with DMN in resting state [Machulda et al., 2011]. The disruption of DMN complexity may also lead to disinhibition of salience network, resulting in the enhancement of frontal connectivity.

Effect of Age on Resting Brain Complexity

Our findings of age‐related changes in complexity of BOLD signals are consistent with our previous report that was based on a smaller cohort of men only [Yang et al., 2011]. We advanced our previous findings in terms of voxelwise MSE analysis and the equal representation of the sexes in both age groups. Our complexity approach is parallel to prior investigations of BOLD variability, such as the standard deviation of BOLD signals, which showed that older adults had less variability in BOLD signals than their younger counterparts [Garrett et al., 2011]. However, these previous investigations also showed increased variability in several subcortical regions that were correlated to poorer cognitive performance and increasing age [Garrett et al., 2011; Samanez‐Larkin et al., 2010]; thus, reconciling the findings of complexity and conventional view of variability requires extensive research in the future.

Limitations

We acknowledge certain limitations to the findings of our study. First, the cross‐sectional study design requires longitudinal follow‐up to determine whether reduced BOLD complexity in the precuneus and posterior cingulate is associated with a higher risk of developing later pathologies. Second, similar to other BOLD time‐course studies, we cannot eliminate the effects of physiologic noise entirely because of a relatively low sampling rate (TR = 2.5 s), despite low‐pass filtering [Lowe et al., 1998]. Third, the relatively short BOLD time courses of 195 data points used in our study may contribute to unreliable entropy calculations on a broad scale. However, sample entropy used in MSE is less sensitive to constraint of time series length than other entropy measures, such as approximate entropy [Richman and Moorman, 2000], and we have demonstrated in our previous research that the use of m = 1 and r = 0.35 for computing MSE in short BOLD signals may produce reliable results [Yang et al., 2011]. Indeed, we evaluated the present results using m = 2 and r = 0.35 and did not find significant changes in BOLD complexity related to APOE genotype. Lastly, other genes are also known to contribute to AD risks, such as BACE1 [Cole and Vassar, 2007], S100B [Mrak and Griffinbc, 2001], CALHM1 [Lambert et al., 2010], or CLU/CR1 [Lambert et al., 2009]. Further study is required to investigate the possible influences of these genes on the interactions of the APOE genotype and resting BOLD complexity.

CONCLUSIONS

By using MSE analysis, we evaluated the influence of APOE ɛ4 allele on resting brain complexity in younger and older adults and its relationships to functional connectivity. Our results confer an impact of the APOE genotype on the dynamics of local brain activity in older population. Moreover, because the observed areas of reduced BOLD complexity mirror those seen in early AD, our findings contribute to a better understanding of the increased risk of neurodegenerative diseases in APOE ɛ4 carriers.

Conflicts of interest: Nothing to report.

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