Topographical APOE ɛ 4 Genotype Influence on Cerebral Metabolism in the Continuum of Alzheimer’s Disease: Amyloid Burden Adjusted Analysis

Abstract

Background: APOE ɛ4 contributes to Alzheimer’s disease (AD) pathogenesis by amyloid-beta (Aβ)-dependent and Aβ-independent processes.

Objective: We investigated the APOE ɛ4 influence on regional cerebral glucose metabolism (rCMglc) in the continuum of AD after Aβ adjustment.

Methods: We included 318 cognitively normal (CN) elderly, 498 mild cognitive impairment (MCI), and 178 AD from the Alzheimer’s Disease Neuroimaging Initiative database. They had [¹⁸F] florbetapir positron emission tomography (PET) and [¹⁸F] fluorodeoxyglucose (FDG)-PET conducted within 3 months of a clinical and cognitive assessment visit and APOE genotype. At first, the rCMglc differences between APOE ɛ4 carriers (ɛ4+) and non-carriers (ɛ4–) were estimated on a voxel-based analysis using a ‘two-sample t-test’ design. In the second analysis, Aβ was added as covariate.

Results: In CN, ɛ4+ showed reduced rCMglc compared to ɛ4–in the bilateral frontal, temporal, and the left parietal regions. In MCI, ɛ4+ showed reduced rCMglc compared to ɛ4– in the bilateral posterior parietal, temporal, and left frontal regions. In AD, ɛ4+ showed reduced rCMglc in the left hippocampus, right insular, and right temporal gyrus. However, after Aβ adjustment, the significant differences in the temporal regions were absent in CN and MCI, and none of the areas detected as significant in the first analysis were statistically significant in AD.

Conclusions: Our study demonstrated that Aβ-independent APOE ɛ4 influence on rCMglc is limited to the parietal and frontal, but not temporal lobes. These results suggest that APOE ɛ4 may predispose for regional vulnerability according to Aβ-independent and Aβ-dependent processes.

Keywords

Aβ burden APOE cerebral glucose metabolism mild cognitive impairment

INTRODUCTION

The apolipoprotein E (APOE) ɛ4 allele is a major genetic risk factor for the development of late-onset Alzheimer’s disease (AD) dementia [1, 2]. Mountains of evidence suggest that APOE ɛ4 contributes to AD pathogenesis by amyloid-beta (Aβ)-dependent and Aβ-independent processes [3 –5]. It has also been shown that APOE ɛ4 has a detrimental effect on synaptic plasticity, dendritic spine integrity, and that it may also promote neurotoxicity [6].

Regional cerebral glucose metabolism (rCMglc), measured by [¹⁸F] fluorodeoxyglucose positron emission tomography (FDG-PET), displays a typical pattern of hypometabolism in the temporo-parietal cortex in AD [7 –10]. Many studies have confirmed that FDG-PET imaging is highly sensitive in detecting early AD pathology as well as performing an AD prognosis [10 –14].

A relationship between APOE ɛ4 and rCMglc has been described mainly in cognitively normal (CN) elderly population. For example, previous studies suggest that APOE ɛ4 carriers in CN elderly [15 –17], CN with memory complaints, and CN with a family history of AD [18, 19] show a decrease of rCMglc in the regions typically affected by AD. APOE ɛ4 influence on rCMglc in AD is far less clear. Some studies reported more pronounced hypometabolism in APOE ɛ4 carriers [20 –22], whereas others reported no APOE ɛ4 effects in AD [23, 24]. Few studies focused on individuals with mild cognitive impairment (MCI). These studies reported APOE ɛ4 -related hypometabolism in the temporo-parietal and frontal regions in MCI [25, 26], which is similar to patterns in the alterations observed in CN.

The important role that APOE ɛ4 plays in Aβ binding and clearance during AD pathogenesis has been well-characterized [4, 27]. In line with these roles, the effect of APOE ɛ4 on rCMglc is more robust in MCI with high Aβ burden than in MCI with low Aβ burden [25]. On the other hand, there seems to be Aβ-independent APOE ɛ4 effect on brain function via other neuropathological changes including tau, neuroinflammation, and neuronal plasticity [3, 5]. However, surprisingly few studies have investigated the influence of APOE ɛ4 on rCMglc after adjusting for the effect of Aβ burden level. Only two studies [15, 17] considered the Aβ burden influence when investigating the effects of APOE ɛ4 on rCMglc. Although both these studies found an Aβ burden-independent effect of APOE ɛ4 on rCMglc, the result pattern was different between the studies: Jagust et al. [15] detected an hypometabolism, while a significant hypermetabolism emerged in Yi et al. [17]. These two studies included only CN elderly. However, the distribution of APOE ɛ4 and Aβ burden levels are different in the AD continuum. Recent comprehensive meta-analysis revealed that small numbers of CN are Aβ-positive (24.4%) [28], whereas most AD dementia participants are Aβ-positive (88%) [29]. Similarly, prevalence of APOE ɛ4 is low in CN (29.5%) [28], whereas high in AD dementia (61.1%) [29]. On the contrary, MCI group showed a relatively even distribution for both factors (52.9% for Aβ-positive; 47.1% for APOE ɛ4 carrier) [28]. This differential distribution of APOE ɛ4 status and Aβ burden level across groups could potentially contribute to the statistical results. For example, when Aβ-independent APOE ɛ4 effects on rCMglc was investigated, controlling for Aβ burden levels may be far less effective for individuals with CN because of disproportionally low Aβ burden. On the other hand, differential effects of APOE ɛ4 on rCMglc according to Aβ-dependent and Aβ-independent process can be large in MCI. Nevertheless, no studies have investigated the influence of APOE ɛ4 on rCMglc after controlling for Aβ burden levels in MCI. The biological underlying mechanism for the association between APOE ɛ4 and brain metabolism could be more clearly understood in the context of a continuum of AD, particularly in MCI.

Therefore, this study aims to investigate the effects of APOE ɛ4 on rCMglc in the continuum of AD. We further examined these effects after controlling for Aβ burden levels.

METHODS

Participants

Participants were selected from the ADNI database (http://adni.loni.usc.edu). For a detailed explanation and up-to-date information on ADNI, please see http://www.adni-info.org. We included participants from all phases of ADNI only if [¹⁸F] florbetapir PET and FDG-PET had been conducted within 3 months from a clinical and cognitive assessment visit, and their APOE genotype was available. Subjects with the APOE 2/4 genotype were excluded due to the unclear effects of these alleles. The final analysis included 318 CN elderly participants, 498 individuals with MCI, and 178 patients with AD dementia who underwent clinical evaluations and florbetapir PET scans (Table 1). Detailed eligibility criteria for each study group has been described elsewhere [30]. Briefly, CN subjects had a Clinical Dementia Rating (CDR) of 0 and Mini-Mental State Examination (MMSE) scores between 24 and 30. These subjects were non-depressed, non-demented, and had not been diagnosed with MCI. Subjects with MCI had a CDR of 0.5 and MMSE scores between 24 and 30, they complained of objective memory loss but showed no impairment in other cognitive domains, demonstrated preserved activities of daily living, and were non-demented. AD dementia subjects had a CDR of 0.5 or 1.0 and MMSE scores between 20 and 26 and met the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD [31].

Clinical and neuropsychological assessment

We included the CDR sum of boxes (CDR SOB) as a clinical severity measure. This measure covers six domains of cognitive and daily functioning with a score ranging from 0 to18. This is a tool commonly used for staging clinical severity. For everyday functioning, we included the functional assessment questionnaire (FAQ). This questionnaire assesses the instrumental activities of daily living with a score ranging from 0 to 30 [32]. We included MMSE score for assessing the global cognitivefunction.

APOE genotyping

APOE genotyping was performed at the time of participant enrollment in the ADNI study. APOE genotypes were determined using standard polymerase chain reaction methods, which have been described previously [33]. Individuals with one or two copies of ɛ4 allele were designated as APOE ɛ4 carriers (ɛ4+); individuals with no ɛ4 allele were designated as APOE ɛ4 non-carriers (ɛ4–).

Florbetapir PET

We collected the mean florbetapir standard uptake value ratio (SUVR) for each participant. A detailed description of florbetapir PET acquisition and processing can be found on the ADNI website (https://adni.loni.usc.edu/wp-content/uploads/2010/05/ADNI2_PET_Tech_Manual_0142011.pdf) or in previously published reports [34]. Briefly, the subject’s first florbetapir image was coregistered to their magnetic resonance image and segmented into cortical regions (frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal) defined using Freesurfer (version 4.5.0). The mean florbetapir uptake from those gray matter regions was extracted relative to uptake in the whole cerebellum. The SUVR cutoff of 1.11 was applied to determine amyloid positivity [34].

FDG-PET and image preprocessing

To investigate APOE effects, we collected the most preprocessed form of FDG-PET data from the ADNI. The ADNI preprocessing steps of FDG-PET data were previously described [35]. A quality control process was applied to all scans. To reduce inter-scanner differences (17 different scanner models from three vendors), images were smoothed with a scanner-specific filter derived from each site’s Hoffman phantom [36], and then provided a common isotropic resolution of 8 mm full width at half of the maximum resolution [35]. We applied a further preprocessing for the group-level analysis. These scans were adjusted for their origin and spatially normalized to the Montreal Neurological Institute (MNI, McGill University, Montreal, Que., Canada) space using Statistical Parametric Mapping 8 (SPM8) (Institute of Neurology, University College of London, UK) implemented on Matlab. Then the scans were smoothed with a Gaussian kernel of 8 mm full width at half of the maximum resolution. Since we investigated the APOE effects separately within each diagnostic group, intensity normalization to pons or cerebellum was not performed. Instead, global normalization using proportional scaling was performed because it shows a higher signal-to-noise ratio compared to the cerebellar count normalization [37]. For the global FDG index, we downloaded the values from ADNI. The mean FDG uptake was determined by the mean uptake of in the bilateral inferior temporal and lateral parietal regions and the bilateral posterior cingulate cortex region. The details were previously described[34].

Statistical analysis

Demographic and clinical data were compared between groups using separate one-way analysis of variance (ANOVA) and χ² test for continuous and categorical variables, respectively. These analyses were performed using SPSS version 21.0 for Windows (SPSS Inc., Chicago, IL); p-values less than 0.05 were considered significant.

The difference of rCMglc between ɛ4+ and ɛ4–were estimated on a voxel-by-voxel basis using a ‘two-sample t-test’ design with age, gender, and education as covariates. To control for Aβ burden level, florbetapir SUVR was further added as a covariate. We applied p < 0.001 (two-tailed, uncorrected for multiple comparisons) as a significance height threshold at the voxel-level across the whole brain, with an extent threshold of greater than 20 contiguous voxels. These analyses were performed using SPM8.

Ethics statement

Study procedures were approved by the institutional review boards of 55 research centers in the United States and Canada participating in ADNI. Written informed consent to share data for scientific research purposes was obtained from each participant.

RESULTS

Participant characteristics

The demographic and clinical characteristics of the 994 individuals are presented in Table 1. No group differences in education were detected. Participants with MCI were younger than CN and AD dementia participants (p < 0.001). The CN group included significantly more women than the other two study groups. The frequencies of ɛ4+ and Aβ positive were lowest in CN group and highest in AD dementia group. MCI group showed relatively an even distribution in the positivity for both ɛ4+ and Aβ. As expected, subsequent comparisons of Aβ, CDR SOB, FAQ, and MMSE revealed significant differences between groups (Table 1). Global Aβ levels were significantly higher in ɛ4+ than in ɛ4–within each group (all p < 0.001).

Effect of APOE ɛ4 on rCMglc

In the CN group, ɛ4+ showed reduced rCMglc in the bilateral frontal and temporal regions, and the left parietal regions compared to ɛ4–. When Aβ was adjusted, significant reductions in the bilateral frontal and parietal regions were remained, whereas the difference in the bilateral temporal regions were no longer statistically significant. In the MCI group, the pattern was largely similar to the result of the CN group but were found to be more robust. ɛ4+ showed reduced rCMglc mainly in the bilateral parietal regions, bilateral temporal regions and left frontal gyrus compared to ɛ4–. After Aβ was adjusted, significant rCMglc reductions in the bilateral precuneus and left frontal gyrus were remained, whereas the bilateral temporal regions were no longer statistically significant (Fig. 1 and Table 2). In the AD group, ɛ4+ showed reduced rCMglc in the left hippocampus, right insular, and right temporal regions. However, these difference were no longer statistically significant after Aβ adjustment (Fig. 1 and Table 2). No increased regions were emerged from the analysis in ɛ4+ compared with ɛ4–for all groups.

DISCUSSION

We investigated the effects of APOE ɛ4 on rCMglc after adjusting for Aβ burden in a large group of CN elderly, individuals with MCI, and AD. Our findings suggest that Aβ-independent APOE ɛ4-related hypometabolism was limited in the parietal and frontal lobe in the CN and MCI groups, whereas no significant differences between ɛ4+ and ɛ4–were observed in the AD group. The APOE ɛ4-related temporal lobe dysfunctions, commonly reported in previous studies might be mediated by Aβ-dependent pathway.

In agreement with previous reports [15 , 26], our analysis highlighted an AD-related regional hypometabolism in ɛ4+ than ɛ4– in the CN and MCI groups. Regional areas of APOE ɛ4– related reductions in the MCI group were much larger than the areas observed in the CN group. This may be partly due to the reported difference in ɛ4+ frequency in the two groups. Frequency of MCI with ɛ4+ were approximately twice than that of CN with ɛ4+ (e.g., 45.6% in MCI versus 25.8% in CN). This difference in ɛ4+ distribution may lead to statistically more robust differences at the regional level. The AD group displayed APOE ɛ4-related reductions mainly in temporal lobe.

When we statistically adjusted the Aβ burden effect, the significant effect previously detected in the temporal region hypometabolism was no longer present in all three groups. This result suggests that the temporal region function in the AD continuum is mediated by the Aβ burden rather than by a direct APOE ɛ4 effect. On the other hand, hypometabolism in the bilateral precuneus and middle frontal gyrus in ɛ4+ groups in CN and MCI remained significant even after adjusting for Aβ burden, indicating that those regions are affected by Aβ-independent APOE ɛ4 process. These results were largely supported by the additional analysis performed separately for those participants with low and high Aβ burden. Especially for MCI group, ɛ4-related bilateral precuneus and right temporal hypometabolism was observed in Aβ burden positive group. However, the significant effect previously detected in the temporal region hypometabolism was no longer present but bilateral precuneus hypometabolism remained in Aβ burden negative group (Supplementary Figure 1). It has been suggested that APOE ɛ4 may contribute to the AD pathogenesis through two distinct pathways:Aβ-dependent and -independent processes [3 –6]. The Aβ-dependent process includes production, aggregation, and clearance of Aβ, while the Aβ-independent process includes tau pathology, synaptic dysfunction, brain metabolic alterations, and mitochondrial dysfunction. Our results also support the idea that APOE ɛ4 associated brain functions are mediated by those two pathophysiological processes.

One possible interpretation of the present results is that APOE ɛ4 may predispose for regional vulnerability differently according to Aβ-independent and Aβ-dependent processes, although further studies are needed to confirm this interpretation. In other words, APOE ɛ4 may predispose for a preferential vulnerability of the posterior parietal and frontal lobe that is independent of Aβ burden. On the contrary, APOE ɛ4 influence on temporal lobe vulnerability may be mediated by the Aβ burden. The posterior parietal lobe, particularly the precuneus/posterior cingulate cortex is the most commonly affected area in the very early course of AD. One postmortem brain study reported that ɛ4+ young adults with no evidence of Aβ pathology showed lower mitochondrial activity in posterior cingulate regions than ɛ4–individuals [38]. Accordingly, one study performed on a large sample of cognitively healthy young individuals reported that APOE ɛ4-related effects on the posterior parietal regions were the most prominent [16]. Another study also reported that CN elderly with ɛ4+ had significant reductions in functional brain complexity in the precuneus and posterior cingulate regions, and abnormal frontal-parietal connectivity compared to ɛ4- individuals [39]. The precuneus and the middle frontal region largely overlap with the default mode network which has been consistently reported as altered in ɛ4+ individuals [40 –42]. The frontal lobe and precuneus both seem to play a critical role in a wide spectrum of highly integrated cognitive tasks such as those measured in executive function tests. In close agreement with our results, APOE ɛ4 predominantly influenced the performance on frontal executive function tasks that was not explained by Aβ status[43].

On the other hand, the absence of hypometabolism in the temporal region after Aβ adjustment suggests a close association between the Aβ burden and the temporal lobe dysfunction. Our result is consistent with previous studies reporting a significant correlation between Aβ magnitude and medial temporal lobe (MTL) hypometabolism [44] or disrupted MTL connectivity [45]. A convincing hypothesis on the mechanism underlying spatial separation of the site of Aβ and tau pathology postulates that MTL tauopathy is a downstream event of Aβ deposition, the so-called hypothesis of Aβ-facilitated tauopathy in MTL [45, 46]. Based on our analysis, APOE ɛ4 influence on temporal lobe dysfunction seems to be mediated by Aβ burden.

There are some limitations and future directions to be discussed. First, this study is based on a cross-sectional design. To better understand the predictive value of cerebral metabolic changes related to APOE ɛ4, longitudinal follow-up studies are needed, particularly for the CN and MCI groups. Second, partial volume correction was not performed during FDG-PET image processing. Therefore, we cannot exclude the possibility that the hypometabolism we have observed may have been biased by the presence of brain atrophy. Future studies controlling partial volume effect are needed to replicate ourfindings.

In conclusion, we have shown that the Aβ-independent APOE ɛ4 influence on cerebral metabolism is limited to the parietal and frontal lobe, while it has no effect on the temporal lobe. The present results suggest that APOE ɛ4 may differentially predispose for regional vulnerability according to Aβ-independent and Aβ-dependent processes. APOE ɛ4 itself may play a role in the pathogenesis of AD in the precuneus and the middle frontal regions, and it may also modulate Aβ-related pathophysiological processes in the temporal regions.

Footnotes

ACKNOWLEDGMENTS

This research was supported by a Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A1A2054062).

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; AraclonBiotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research &Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.

Authors’ disclosures available online (.

Appendix

References

Bertram

, Lill

, Tanzi

(2010) The genetics of Alzheimer disease: Back to the future. Neuron 68, 270–281.

Poirier

, Davignon

, Bouthillier

, Kogan

, Bertrand

, Gauthier

(1993) Apolipoprotein E polymorphism and Alzheimer’s disease. Lancet 342, 697–699.

Huang

(2010) Abeta-independent roles of apolipoprotein E4 in the pathogenesis of Alzheimer’s disease. Trends Mol Med 16, 287–294.

Kanekiyo

, Xu

, Bu

(2014) ApoE and Abeta in Alzheimer’s disease: Accidental encounters or partners? Neuron 81, 740–754.

Kim

, Basak

, Holtzman

(2009) The role of apolipoprotein E in Alzheimer’s disease. Neuron 63, 287–303.

, Tan

, Hardy

(2014) Apolipoprotein E in Alzheimer’s disease: An update. Annu Rev Neurosci 37, 79–100.

Della Rosa

, Cerami

, Gallivanone

, Prestia

, Caroli

, Castiglioni

, Gilardi

, Frisoni

, Friston

, Ashburner

, Perani

, Consortium

E-P

(2014) A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 12, 575–593.

Lucignani

, Nobili

(2010) FDG-PET for early assessment of Alzheimer’s disease: Isn’t the evidence base large enough? Eur J Nucl Med Mol Imaging 37, 1604–1609.

Minoshima

, Frey

, Koeppe

, Foster

, Kuhl

(1995) A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med 36, 1238–1248.

10.

Minoshima

, Giordani

, Berent

, Frey

, Foster

, Kuhl

(1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 42, 85–94.

11.

Cohen

, Klunk

(2014) Early detection of Alzheimer’s disease using PiB and FDG PET. Neurobiol Dis 72 Pt A, 117–122.

12.

Durand-Martel

, Tremblay

, Brodeur

, Paquet

(2010) Autopsy as gold standard in FDG-PET studies in dementia. Can J Neurol Sci 37, 336–342.

13.

Mosconi

, Tsui

, De Santi

, Li

, Rusinek

, Convit

, Li

, Boppana

, de Leon

(2005) Reduced hippocampal metabolism in MCI and AD: Automated FDG-PET image analysis. Neurology 64, 1860–1867.

14.

Pagani

, De Carli

, Morbelli

, Oberg

, Chincarini

, Frisoni

, Galluzzi

, Perneczky

, Drzezga

, van Berckel

, Ossenkoppele

, Didic

, Guedj

, Brugnolo

, Picco

, Arnaldi

, Ferrara

, Buschiazzo

, Sambuceti

, Nobili

(2015) Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s Disease Consortium (EADC) study. Neuroimage Clin 7, 34–42.

15.

Jagust

, Landau

, Alzheimer’s Disease Neuroimaging Initiative (2012) Apolipoprotein E, not fibrillar beta-amyloid, reduces cerebral glucose metabolism in normal aging. J Neurosci 32, 18227–18233.

16.

Knopman

, Jack

Jr , Wiste

, Lundt

, Weigand

, Vemuri

, Lowe

, Kantarci

, Gunter

, Senjem

, Mielke

, Roberts

, Boeve

, Petersen

(2014) 18F-fluorodeoxyglucose positron emission tomography, aging, and apolipoprotein E genotype in cognitively normal persons. Neurobiol Aging 35, 2096–2106.

17.

, Lee

, Sohn

, Choe

, Seo

, Byun

, Woo

(2014) Beta-amyloid associated differential effects of APOE epsilon4 on brain metabolism in cognitively normal elderly. Am J Geriatr Psychiatry 22, 961–970.

18.

Reiman

, Chen

, Alexander

, Caselli

, Bandy

, Osborne

, Saunders

, Hardy

(2005) Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. Proc Natl Acad Sci U S A 102, 8299–8302.

19.

Small

, Mazziotta

, Collins

, Baxter

, Phelps

, Mandelkern

, Kaplan

, La Rue

, Adamson

, Chang

, Guze

, Corder

, Saunders

, Haines

, Pericak-Vance

, Roses

(1995) Apolipoprotein E type 4 allele and cerebral glucose metabolism in relatives at risk for familial Alzheimer disease. JAMA 273, 942–947.

20.

Drzezga

, Riemenschneider

, Strassner

, Grimmer

, Peller

, Knoll

, Wagenpfeil

, Minoshima

, Schwaiger

, Kurz

(2005) Cerebral glucose metabolism in patients with AD and different APOE genotypes. Neurology 64, 102–107.

21.

Mosconi

, Sorbi

, Nacmias

, De Cristofaro

, Fayyaz

, Bracco

, Herholz

, Pupi

(2004) Age and ApoE genotype interaction in Alzheimer’s disease: An FDG-PET study. Psychiatry Res 130, 141–151.

22.

Ossenkoppele

, van der Flier

, Zwan

, Adriaanse

, Boellaard

, Windhorst

, Barkhof

, Lammertsma

, Scheltens

, van Berckel

(2013) Differential effect of APOE genotype on amyloid load and glucose metabolism in AD dementia. Neurology 80, 359–365.

23.

Corder

, Jelic

, Basun

, Lannfelt

, Valind

, Winblad

, Nordberg

(1997) No difference in cerebral glucose metabolism in patients with Alzheimer disease and differing apolipoprotein E genotypes. Arch Neurol 54, 273–277.

24.

Hirono

, Mori

, Yasuda

, Imamura

, Shimomura

, Hashimoto

, Tanimukai

, Kazui

, Yamashita

(1999) Lack of effect of apolipoprotein E E4 allele on neuropsychiatric manifestations in Alzheimer’s disease. J Neuropsychiatry Clin Neurosci 11, 66–70.

25.

Carbonell

, Charil

, Zijdenbos

, Evans

, Bedell

(2014) beta-Amyloid is associated with aberrant metabolic connectivity in subjects with mild cognitive impairment. J Cereb Blood Flow Metab 34, 1169–1179.

26.

Mosconi

, Perani

, Sorbi

, Herholz

, Nacmias

, Holthoff

, Salmon

, Baron

, De Cristofaro

, Padovani

, Borroni

, Franceschi

, Bracco

, Pupi

(2004) MCI conversion to dementia and the APOE genotype: A prediction study with FDG-PET. Neurology 63, 2332–2340.

27.

Dorey

, Chang

, Liu

, Yang

, Zhang

(2014) Apolipoprotein E, amyloid-beta, and neuroinflammation in Alzheimer’s disease. Neurosci Bull 30, 317–330.

28.

Jansen

, Ossenkoppele

, Knol

, Tijms

, Scheltens

, Verhey

, Visser

, Amyloid Biomarker Study Group, Aalten

, Aarsland

, Alcolea

, Alexander

, Almdahl

, Arnold

, Baldeiras

, Barthel

, van Berckel

, Bibeau

, Blennow

, Brooks

, van Buchem

, Camus

, Cavedo

, Chen

, Chetelat

, Cohen

, Drzezga

, Engelborghs

, Fagan

, Fladby

, Fleisher

, van der Flier

, Ford

, Forster

, Fortea

, Foskett

, Frederiksen

, Freund-Levi

, Frisoni

, Froelich

, Gabryelewicz

, Gill

, Gkatzima

, Gomez-Tortosa

, Gordon

, Grimmer

, Hampel

, Hausner

, Hellwig

, Herukka

, Hildebrandt

, Ishihara

, Ivanoiu

, Jagust

, Johannsen

, Kandimalla

, Kapaki

, Klimkowicz-Mrowiec

, Klunk

, Kohler

, Koglin

, Kornhuber

, Kramberger

, Van Laere

, Landau

, Lee

, de Leon

, Lisetti

, Lleo

, Madsen

, Maier

, Marcusson

, Mattsson

, de Mendonca

, Meulenbroek

, Meyer

, Mintun

, Mok

, Molinuevo

, Mollergard

, Morris

, Mroczko

, Van der Mussele

, Na

, Newberg

, Nordberg

, Nordlund

, Novak

, Paraskevas

, Parnetti

, Perera

, Peters

, Popp

, Prabhakar

, Rabinovici

, Ramakers

, Rami

, Resende de Oliveira

, Rinne

, Rodrigue

, Rodriguez-Rodriguez

, Roe

, Rot

, Rowe

, Ruther

, Sabri

, Sanchez-Juan

, Santana

, Sarazin

, Schroder

, Schutte

, Seo

, Soetewey

, Soininen

, Spiru

, Struyfs

, Teunissen

, Tsolaki

, Vandenberghe

, Verbeek

, Villemagne

, Vos

, van Waalwijk van Doorn

, Waldemar

, Wallin

, Wiltfang

, Wolk

, Zboch

, Zetterberg

(2015) Prevalence of cerebral amyloid pathology in persons without dementia: A meta-analysis. JAMA 313, 1924–1938.

29.

Ossenkoppele

, Jansen

, Rabinovici

, Knol

, van der Flier

, van Berckel

, Scheltens

, Visser

, Amyloid

PETSG

, Verfaillie

, Zwan

, Adriaanse

, Lammertsma

, Barkhof

, Jagust

, Miller

, Rosen

, Landau

, Villemagne

, Rowe

, Lee

, Na

, Seo

, Sarazin

, Roe

, Sabri

, Barthel

, Koglin

, Hodges

, Leyton

, Vandenberghe

, van Laere

, Drzezga

, Forster

, Grimmer

, Sanchez-Juan

, Carril

, Mok

, Camus

, Klunk

, Cohen

, Meyer

, Hellwig

, Newberg

, Frederiksen

, Fleisher

, Mintun

, Wolk

, Nordberg

, Rinne

, Chetelat

, Lleo

, Blesa

, Fortea

, Madsen

, Rodrigue

, Brooks

(2015) Prevalence of amyloid PET positivity in dementia syndromes: A meta-analysis. JAMA 313, 1939–1949.

30.

Petersen

, Aisen

, Beckett

, Donohue

, Gamst

, Harvey

, Jack

Jr , Jagust

, Shaw

, Toga

, Trojanowski

, Weiner

(2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74, 201–209.

31.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944.

32.

Pfeffer

, Kurosaki

, Harrah

Jr , Chance

, Filos

(1982) Measurement of functional activities in older adults in the community. J Gerontol 37, 323–329.

33.

Saykin

, Shen

, Foroud

, Potkin

, Swaminathan

, Kim

, Risacher

, Nho

, Huentelman

, Craig

, Thompson

, Stein

, Moore

, Farrer

, Green

, Bertram

, Jack

Jr , Weiner

, Alzheimer’s Disease Neuroimaging Initiative (2010) Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement 6, 265–273.

34.

Landau

, Mintun

, Joshi

, Koeppe

, Petersen

, Aisen

, Weiner

, Jagust

, Alzheimer’s Disease Neuroimaging Initiative (2012) Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 72, 578–586.

35.

Jagust

, Bandy

, Chen

, Foster

, Landau

, Mathis

, Price

, Reiman

, Skovronsky

, Koeppe

, Alzheimer’s Disease Neuroimaging Initiative (2010) The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimers Dement 6, 221–229.

36.

Joshi

, Koeppe

, Fessler

(2009) Reducing between scanner differences in multi-center PET studies. Neuroimage 46, 154–159.

37.

Dukart

, Mueller

, Horstmann

, Vogt

, Frisch

, Barthel

, Becker

, Moller

, Villringer

, Sabri

, Schroeter

(2010) Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies. Neuroimage 49, 1490–1495.

38.

Valla

, Yaari

, Wolf

, Kusne

, Beach

, Roher

, Corneveaux

, Huentelman

, Caselli

, Reiman

(2010) Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE epsilon4 allele, the major late-onset Alzheimer’s susceptibility gene. J Alzheimers Dis 22, 307–313.

39.

Yang

, Huang

, Liu

, Liou

, Hong

, Lo

, Huang

, Peng

, Lin

, Tsai

(2014) The APOE varepsilon4 allele affects complexity and functional connectivity of resting brain activity in healthy adults. Hum Brain Mapp 35, 3238–3248.

40.

Filippini

, MacIntosh

, Hough

, Goodwin

, Frisoni

, Smith

, Matthews

, Beckmann

, Mackay

(2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 106, 7209–7214.

41.

Machulda

, Jones

, Vemuri

, McDade

, Avula

, Przybelski

, Boeve

, Knopman

, Petersen

, Jack

Jr (2011) Effect of APOE epsilon4 status on intrinsic network connectivity in cognitively normal elderly subjects. Arch Neurol 68, 1131–1136.

42.

Sheline

, Morris

, Snyder

, Price

, Yan

, D’Angelo

, Liu

, Dixit

, Benzinger

, Fagan

, Goate

, Mintun

(2010) APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Abeta42. J Neurosci 30, 17035–17040.

43.

Seo

, Kim

, Park

, Kang

, Choo

, Alzheimer’s Disease Neuroimaging Initiative (2016) Independent and interactive influences of the apoe genotype and beta-amyloid burden on cognitive function in mild cognitive impairment. J Korean Med Sci 31, 286–295.

44.

Frings

, Spehl

, Weber

, Hull

, Meyer

(2013) Amyloid-beta load predicts medial temporal lobe dysfunction in Alzheimer dementia. J Nucl Med 54, 1909–1914.

45.

Song

, Insel

, Buckley

, Yohannes

, Mezher

, Simonson

, Wilkins

, Tosun

, Mueller

, Kramer

, Miller

, Weiner

(2015) Brain amyloid-beta burden is associated with disruption of intrinsic functional connectivity within the medial temporal lobe in cognitively normal elderly. J Neurosci 35, 3240–3247.

46.

Blurton-Jones

, Laferla

(2006) Pathways by which Abeta facilitates tau pathology. Curr Alzheimer Res 3, 437–448.