Skip to main content
NCBI home page
Cerebral Cortex (New York, NY) logoLink to Cerebral Cortex (New York, NY)
. 2013 Mar 28;24(8):2210–2218. doi: 10.1093/cercor/bht076

Is Verbal Episodic Memory in Elderly with Amyloid Deposits Preserved Through Altered Neuronal Function?

Rik Ossenkoppele 1,2,3,✉, Cindee Madison 1, Hwamee Oh 1, Miranka Wirth 1, Bart N M van Berckel 2, William J Jagust 1,4
PMCID: PMC4089385  PMID: 23537530

Abstract

A potential mechanism that enables intellectual preservation in cognitively normal elderly that harbor beta-amyloid (Aβ) pathology is heightened cerebral glucose metabolism. To investigate cross-sectional inter-relationships between Aβ, glucose metabolism, and cognition, 81 subjects (mean age: 75 ± 7 years) underwent [11C]Pittsburgh Compound-B and [18F]fluorodeoxyglucose positron emission tomography scans and neuropsychological testing. They were divided into low-Aβ (n = 53), intermediate-Aβ (n = 13) and high-Aβ (n = 15) groups as defined by their global cortical [11C]PIB retention. Glucose metabolism was assessed using a MetaROI mask that covers metabolically critical regions in Alzheimer's disease (AD) (i.e., posterior cingulate and bilateral angular and inferior temporal gyri). Previously validated factor scores for verbal and visual episodic memory, semantic memory, working memory, and executive functioning were used to evaluate cognitive performances. Greater Aβ deposition in the precuneus was associated with higher metabolic activity (at trend level) and lower visual episodic memory scores. Glucose metabolism did not correlate with cognition across all subjects. However, heightened metabolic activity was associated with better verbal episodic memory performance in subjects with elevated amyloid levels. This preliminary study suggests that neural compensation, as a manifestation of brain reserve, enables elderly supposedly on the path to AD, at least temporarily, to preserve cognitive function.

Keywords: aging, [11C]PIB, beta-amyloid, cognition, [18F]FDG, glucose metabolism, PET

Introduction

Deposition of amyloid-beta (Aβ) is a neuropathological hallmark of Alzheimer's disease (AD), but significant plaque burden is frequently present in cognitively normal elderly at autopsy as well (Price and Morris 1999; Wolf et al. 1999; Bennett et al. 2006). In vivo positron emission tomography (PET) studies employing [11C]Pittsburgh compound-B (PIB, Klunk et al. 2004) confirmed that ∼30% of persons over 70 years display cerebral amyloidosis (Morris et al. 2010; Villemagne et al. 2011; Jack et al. 2012). According to new research criteria, these individuals are in a preclinical stage of AD (Sperling et al. 2011). In a recently proposed biomarker model (Jack et al. 2010), it was hypothesized that Aβ deposition precedes tau-mediated neuronal dysfunction, brain atrophy, and cognitive deterioration that eventually lead to the onset of dementia. The estimated time between initial Aβ accumulation and manifestation of symptoms is 15 years (Rowe et al. 2010; Bateman et al. 2012). Following this model, it may be expected that some individuals with high Aβ burden already show subtle signs of neurodegeneration. Indeed, morphological reductions in AD-specific regions were observed in elderly with amyloid deposits on structural magnetic resonance imaging (MRI) (Dickerson et al. 2009; Mormino et al. 2009; Chételat et al. 2010; Becker et al. 2011; Oh et al. 2011). Functional MRI studies, paradoxically, demonstrated increased brain activation in aging (Cabeza et al. 2002; Rosen et al. 2002; Park and Reuter-Lorenz 2009; Mormino et al. 2012a) and genetic risk populations (Bookheimer et al. 2000; Filippini et al. 2009) that potentially capture the earliest stage of AD development. Similarly in patients with mild cognitive impairment, increased cerebral glucose metabolism as measured using [18F]fluorodeoxyglucose (FDG) PET was associated with greater Aβ load (Cohen et al. 2009). Heightened synaptic activity could be a manifestation of cognitive brain reserve that reflects a compensatory response to counteract neurotoxic effects of Aβ. Increased neuronal activity may enable the brain to suppress pathological insult and maintain normal cognitive function for a longer period. In keeping with this idea, the cross-sectional effect of Aβ on cognition, if any, is small. Some studies showed a negative correlation between the presence of amyloid and cognitive performance in cognitively normal elderly (Pike et al. 2007; Rentz et al. 2010; Rodrigue et al. 2012), whereas others could not replicate this (Aizenstein et al. 2008; Rowe et al. 2010). There is more robust evidence for long-term effects of amyloid pathology as individuals with elevated Aβ are at higher risk for cognitive decline over time (Morris et al. 2009; Storandt et al. 2009; Resnick et al. 2010; Villemagne et al. 2011; Doraiswamy et al. 2012; Lim et al. 2012; Wirth et al. 2013).

[18F]FDG-PET is an eligible marker of resting-state synaptic activity and is capable of capturing up- and downregulation of brain function (Attwell and Laughlin 2001; Rocher et al. 2003). Neuronal activity as measured with [18F]FDG-PET may provide more insight in the complex relationship between Aβ deposition and cognitive performance. The objectives of this cross-sectional study were to investigate relationships between 1) Aβ load and glucose metabolism, 2) Aβ load and cognition, 3) glucose metabolism and cognition, and 4) glucose metabolism and cognition as a function of amyloid status in a group of cognitively normal elderly.

Materials and Methods

Subjects

Eighty-one cognitively normal elderly subjects with available [11C]PIB and [18F]FDG-PET data were included in the present study. All volunteers were recruited through the Berkeley Aging Cohort study (BACs); a community-dwelling cohort that is a convenience sample of healthy individuals who are older than 60 years and reside in the San Francisco Bay Area of California. BACs participants were recruited through advertisements in senior centers and in local newspapers. Inclusion criteria were independent daily living, absence of any neurological or psychiatric condition that potentially affects brain structure and function, absent cognitive complaints normal performance on cognitive tests (maximally 1.5 SD's below age-, education-, and sex-adjusted means), no use of psychoactive drugs and absence of sensory impairment that might interfere with cognitive testing. Subjects completed genotyping for apolipoprotein E (APOE) ε4 carrier status. The local ethics committee approved the study and subjects gave written informed consent.

Neuropsychological Testing

Seventy-four subjects underwent an extensive neuropsychological test battery, thus cognitive data were missing for 7 subjects. Previously, a principal component analysis was conducted on 189 cognitively normal elderly (mean age: 73 ± 7 years; 63 males; mean MMSE: 29 ± 1) and 108 young adults (mean age: 25 ± 4 years; 62 males; mean MMSE: 29 ± 1) in order to obtain concise and reliable cognitive component scores (Oh et al. 2012). This revealed 5 major cognitive components: 1) verbal episodic memory (Free recall trials 1–5, Short-delay free recall, Short-delay cued recall, Long-delay free recall, and Long-delay cued recall of the California Verbal Learning Test) (Delis et al. 2000), 2) visual episodic memory (Immediate recall, Delayed recall, Retention and Recognition from the Visual Reproduction Test in the Wechsler Memory Scale) (Wechsler 1997), 3) semantic memory (Category fluency “vegetables” and “animals”) (Benton et al. 1983), 4) working memory (Digit span forward and backward) (Wechsler 1997), and 5) executive functioning (“Trail B minus A” score from Trail Making Test [Reitan 1958], Symbol Digit Modalities Test [Smith 1982], and Stroop Test [Golden 1978]). Factor loadings for individual neuropsychological tests were used to calculate factor scores for participants in this study on these cognitive component scales. Factor score = 0 represents the mean of the derivation sample, with a standard deviation of 1. Negative component scores indicate worse cognitive performance and positive values mean better test scores compared to the derivation sample.

Imaging Data Acquisition

[11C]PIB-PET

All PET scans were performed at Lawrence Berkeley National Laboratory (LBNL) using a Siemens ECAT EXACT HR scanner (Siemens, Inc., Iselin, NJ, USA) in 3D acquisition mode. The dynamic [11C]PIB scans consisted of 34 frames increasing progressively in duration (4 × 15, 8 × 30, 9 × 60, 2 × 180, 8 × 300, and 3 × 600 s) for a total scanning time of 90 min. More details on the [11C]PIB-PET imaging data acquisition and data analysis can be found in a previously published study (Oh et al. 2011).

[18F]FDG-PET

[18F]FDG-PET imaging was performed ∼2 h after [11C]PIB injection. Following an injection of 6–10 mCi of the tracer, 6 × 5 min frames of emission data were collected starting 30-min postinjection. All PET data were reconstructed using an ordered subset expectation maximization algorithm with weighted attenuation. Images were smoothed with a 4 × 4 × 4-mm Gaussian kernel with scatter correction.

Structural MRI

High-resolution structural MRI scans were performed at LBNL on a 1.5-T Magnetom Avanto system (Siemens, Inc.) with a 12-channel head coil run in triple mode. Three T1-weighted magnetization-prepared rapid gradient echo scans were collected axially for each subject (repetition time: 2110 ms; echo time: 3.58 ms; flip angle: 15°; field of view: 256 × 256 mm; matrix size: 256 × 256; slices: 160; voxel size: 1 × 1 × 1 mm3). Reference regions and regions of interest (ROI) for [11C]PIB and [18F]FDG were generated using the FreeSurfer Version 4.4 software package (surfer.nmr.mgh.harvard.edu). In addition, total cortical gray matter volumes were obtained using the automated segmentation of the Freesurfer software (Version 5.1) and entered as covariate to correct for potential confounding by brain atrophy in models including both [11C]PIB and [18F]FDG.

Imaging Data Analysis

[11C]PIB

PIB-PET data analysis methods are described elsewhere (Oh et al. 2011). In summary, all PET images were preprocessed using Statistical Parametric Mapping 8 (SPM8; www.fil.ion.ucl.ac.uk/spm). Frames 6–34 collected over 90 min were realigned to the middle frame (17th frame) and co-registered to the subject's structural MRI image. Global index (encompassing frontal, temporal, parietal and anterior, and posterior cingulate cortices) and precuneus distribution volume ratio's (DVR) of [11C]PIB were calculated using Logan graphical analysis with cerebellar gray matter as reference region (Logan et al. 1996; Price et al. 2005). In a previous study, elderly BACs subjects were contrasted against 11 young control subjects (25 ± 3 years) and classified as low-PIB (index DVR: <1.08), intermediate-PIB (1.08–1.16), or high-PIB (>1.16) (Mormino et al. 2012b). We adopted this classification in the present study. In previous reports, we have characterized intermediate- and high-PIB subjects as PIB-positive (PIB+) (Mormino et al. 2012b; Oh et al. 2012).

[18F]FDG-PET

The 6 [18F]FDG-PET frames were aligned to the first frame and averaged. Then, each [18F]FDG frame was realigned to the resultant mean image. The native space realigned images were summed to create one [18F]FDG image that was then intensity normalized to the pons, as pontine glucose metabolism is known to be preserved in AD patients (Minoshima et al. 1995).

In the present study, [18F]FDG data were analyzed using the MetaROI approach (Landau et al. 2011). This method comprises 5 ROIs (posterior cingulate cortex (PCC), bilateral angular gyri, and bilateral inferior temporal gyri) that were most strongly associated with metabolic and cognitive decline indicative of AD dementia in a literature review of published reports. To generate [18F]FDG SUVr within the MetaROI, at first, structural MRI scans were co-registered to corresponding [18F]FDG scans in native space. Subsequently, all co-registered MRI scans were registered to MNI space to generate a study specific template using the DARTEL tool in SPM8. Next, [18F]FDG scans were warped to MNI space using the flowfields obtained with the transformation of the MRI scans. Finally, a MetaROI mask was applied to extract mean [18F]FDG uptake values for the composite ROI and the individual regions comprising this ROI.

Statistics

Differences between groups for baseline characteristics were assessed using ANOVA and χ2 tests, where appropriate. We adjusted the analyses for age, education, and APOE genotype given their effects on [11C]PIB retention, [18F]FDG uptake, and cognitive scores in the present (see Table 2) and in previous studies (Jagust and Landau 2012; Kantarci et al. 2012; Stern 2012; Vemuri et al. 2012). Pearson correlations were used to assess associations between age, education, APOE genotype, precuneus [11C]PIB, composite [18F]FDG MetaROI, and cognitive factor scores. Linear regression analyses, adjusted for age, education, and APOE, were used to test the relationships between precuneus [11C]PIB retention (as a continuous variable) and composite and regional [18F]FDG MetaROI uptake. In an additional model, we adjusted for partial volume effects by entering cortical gray matter volumes as covariate. We also performed a voxelwise analysis in SPM8 using a MetaROI [18F]FDG mask to define the search region as dependent variable, precuneus [11C]PIB as regressor, and age, education, APOE, and cortical gray matter volume as nuisance variables. This analysis was performed in MNI space (see subsection “[18F]FDG-PET” for transformation), and results were displayed at P < 0.05 (2-tailed, uncorrected) with a cluster size of 10 voxels. Next, linear regression analyses were performed to assess the relationships between precuneus [11C]PIB/[18F]FDG MetaROI and the cognitive factor scores. In the first model, we entered no covariates while in the second model, we adjusted for age, education, and APOE. Finally, interactions between [11C]PIB status (low, intermediate, or high) and composite [18F]FDG MetaROI on the cognitive factor scores were assessed using multivariate ANCOVA with adjustment for age, education, and APOE status. Post hoc linear regression analyses were used to further explore these relationships within [11C]PIB groups.

Table 2.

Correlations between age, education, APOE genotype, [11C]PIB, [18F]FDG, and cognition

Age Education APOE [11C]PIB [18F]FDG Verbal EM Visual EM WM SM EXE
Age X
Education −0.05 X
APOE ε4 status −0.03 0.17 X
Precuneus [11C]PIB 0.23* −0.18 0.28* X
Composite [18F]FDG MetaROI −0.25* 0.10 0.02 0.11 X
Verbal episodic memory −0.15 −0.03 0.01 0.10 0.12 X
Visual episodic memory −0.39** 0.01 −0.10 −0.30* 0.03 −0.18 X
Working memory 0.15 0.02 0.03 −0.07 0.10 −0.15 −0.13 x
Semantic memory −0.15 0.09 0.10 0.01 0.13 0.05 0 −0.03 X
Executive functions −0.07 0.25* 0.13 0.04 0.01 0.12 −0.19 −0.16 −0.29* X
Open in a new tab

Associations between age, education, APOE genotype, [11C]PIB, [18F]FDG, and cognition were assessed using Pearson correlations.

*P < 0.05.

**P < 0.01.

Results

Subjects

Demographics, cognitive scores, index [11C]PIB, and composite [18F]FDG are presented in Table 1. Fifty-three subjects were completely amyloid negative while the balance had evidence of Aβ deposition ranging from mild to extensive. There were no group differences in terms of age, gender, level of education, APOE genotype, MMSE, any of the cognitive factor scores, or composite [18F]FDG MetaROI. By design, the high-PIB group showed increased global [11C]PIB retention compared to both intermediate-PIB and low-PIB groups (P < 0.001), and intermediate-PIB subjects had higher index compared to the low-PIB group (P < 0.01).

Table 1.

Demographics, cognition, [11C]PIB index, and composite [18F]FDG

All (n = 81) Low-PIB (n = 53) Intermediate-PIB (n = 13) High-PIB (n = 15)
Age 75.0 ± 6.6 74.8 ± 6.2 72.4 ± 6.9 78.1 ± 7.0
Gender (male/female) 29/52 19/34 5/8 5/10
Education 17.0 ± 1.8 17.2 ± 1.8 16.9 ± 1.6 16.5 ± 1.8
APOE ε4 carriers (%) 27 21 25 47
MMSE 29.0 ± 1.2 29.1 ± 1.2 29.5 ± 0.7 28.5 ± 1.2
Verbal episodic memory −0.04 ± 1.04 −0.05 ± 1.1 −0.21 ± 0.85 0.10 ± 1.07
Visual episodic memory −0.15 ± 0.89 −0.05 ± 0.89 0.04 ± 0.93 −0.61 ± 0.73
Working memory −0.04 ± 1.05 −0.08 ± 1.08 0.17 ± 1.20 −0.10 ± 0.83
Semantic memory 0.03 ± 1.18 −0.10 ± 1.09 0.41 ± 1.52 0.15 ± 1.15
Executive functions −0.28 ± 1.03 −0.27 ± 1.00 −0.36 ± 0.54 −0.26 ± 1.42
[11C]PIB index 1.11 ± 0.18 1.02 ± 0.04 1.11 ± 0.02* 1.43 ± 0.18**
Composite [18F]FDG 1.65 ± 0.18 1.65 ± 0.18 1.61 ± 0.17 1.67 ± 0.18
Open in a new tab

Note: Data are presented as mean ± standard deviation unless indicated otherwise.

Differences between groups were assessed using ANOVA with post hoc Bonferroni tests (age, education, MMSE, cognitive factor scores, index PIB, and composite FDG) and χ2 (gender, APOE genotype).

*Intermediate-PIB > low-PIB: P < 0.01.

**High-PIB > low-PIB and intermediate-PIB: P < 0.001.

Across groups, Pearson correlations revealed significant associations between age and precuneus [11C]PIB (r = 0.23), age and composite [18F]FDG MetaROI (r = −0.25), education and executive functions (r = 0.25), APOE genotype and precuneus [11C]PIB (r = 0.28), precuneus [11C]PIB, and visual episodic memory (r = −0.30), executive functions and semantic memory (r = −0.29, all P < 0.05), and between age and visual episodic memory (r = −0.39, P < 0.01). No other significant correlations were found (Table 2).

Correlations Between Precuneus PIB and FDG MetaROI

Linear regression analysis with adjustment for age, education, and APOE showed an association at trend level between precuneus [11C]PIB retention and composite [18F]FDG MetaROI (standardized β: 0.21, P = 0.10, Fig. 1A). Additional adjustment for partial volume effects using cortical gray matter volumes revealed an even stronger association (standardized β: 0.33, P < 0.05). Precuneus [11C]PIB retention was significantly correlated with PCC (standardized β: 0.36, P < 0.05) and right angular (standardized β: 0.28, P < 0.05) [18F]FDG uptake, which was confirmed in a voxelwise analysis in SPM8 within MetaROIs (Fig. 1B). Precuneus [11C]PIB retention did not correlate significantly with left angular gyrus (standardized β: 0.16, P = 0.25) and left (standardized β: 0.19, P = 0.15) and right (standardized β: 0.17, P = 0.18) inferior temporal gyri uptake.

Figure 1.

Figure 1.

Open in a new tab

Linear regression analyses, with adjustment for age, education, and APOE, showed an association at trend level between precuneus [11C]PIB DVR and composite [18F]FDG MetaROI (A) (standardized β: −0.21, P = 0.10). Additionally, we performed a voxelwise regression analysis within MetaROI regions (in white), thresholded at an uncorrected P < 0.05 (k > 10 voxels) (B). Color maps represent mean T-values. Effects in the right angular gyrus and posterior cingulate mainly drive the relationship between precuneus [11C]PIB and composite [18F]FDG MetaROI.

Precuneus PIB and Cognitive Factor Scores

Linear regression analysis showed a negative association between precuneus [11C]PIB retention and performance on visual episodic memory tasks across groups (standardized β: −0.30, P < 0.05, Fig. 2A). This result was no longer significant after adjustment for age, education, and APOE (standardized β: −0.19, P = 0.12). Post hoc analysis revealed a borderline significant correlation between precuneus [11C]PIB and visual episodic memory factor scores in the high-PIB group (adjusted: standardized β: −0.55, P = 0.08; unadjusted: standardized β: −0.64, P < 0.05), whereas this association was absent in intermediate-PIB (standardized β: −0.17, P = 0.69) and low-PIB (standardized β: 0.12, P = 0.43) groups. Across and within groups, there were no associations between precuneus [11C]PIB retention and verbal episodic memory, working memory, semantic memory, and executive functions (all P > 0.05).

Figure 2.

Figure 2.

Open in a new tab

Linear regression analysis showed an association between increased [11C]PIB retention in the precuneus and lower visual episodic memory factor scores (A) (standardized β: −0.30, P < 0.05). Across groups, there was no effect of [18F]FDG on cognition. In subjects with intermediate-PIB levels, verbal episodic memory performance was positively associated with (B) composite (standardized β: 0.68, P < 0.01), (C) left (standardized β: 0.80, P < 0.01), and (D) right (standardized β: 0.68, P = 0.01) angular gyri [18F]FDG SUVr.

MetaROI FDG and Cognitive Factor Scores

Linear regression analyses, irrespective of whether or not adjusted for age, education, and APOE, showed no association between the composite and regional [18F]FDG MetaROI and any of the cognitive factor scores across groups (all P > 0.05).

Relationships Between FDG and Cognition According to PIB Status

Multivariate ANOVA with adjustment for age, education, and APOE status showed a significant interaction between [11C]PIB status (low, intermediate, or high) and composite [18F]FDG MetaROI on verbal episodic memory (P < 0.05), but not on other cognitive factor scores (all P > 0.05). Post hoc linear regression analyses with adjustment for age, education, and APOE, revealed strong associations between the composite ROI (standardized β: 0.68, P < 0.01, Fig. 2B) and left (standardized β: 0.80, P < 0.01, Fig. 2C) and right (standardized β: 0.68, P = 0.01, Fig. 2D) angular gyrus [18F]FDG uptake and verbal episodic memory in subjects with intermediate [11C]PIB retention. Moderate, albeit nonsignificant, correlations with verbal episodic memory were found in the left (standardized β: 0.52, P = 0.11) and right (standardized β: 0.59, P = 0.17) inferior temporal gyri and in the PCC (standardized β: 0.28, P = 0.38) of intermediate-PIB subjects (Table 3). In addition, we found an association between left angular gyrus [18F]FDG uptake and working memory scores (standardized β: 0.76, P < 0.05). In the high-PIB group, there was an association between left inferior medial temporal [18F]FDG uptake and verbal episodic memory (standardized β: 0.57, P < 0.05). No other significant correlations were found in any of the groups between composite or regional [18F]FDG uptake and cognitive factor scores (see Supplementary Material).

Table 3.

Relationships between [18F]FDG uptake and verbal episodic memory scores according to [11C]PIB status

Low-PIB Intermediate-PIB High-PIB
Verbal episodic memory versus
 Composite [18F]FDG MetaROI −0.14 0.68** 0.33
 Right angular gyrus −0.10 0.80** 0.31
 Left angular gyrus −0.10 0.68* 0.44
 PCC −0.16 0.28 −0.10
 Right inferior temporal gyrus −0.11 0.59 0.57*
 Left inferior temporal gyrus −0.01 0.52 0.36
Open in a new tab

Associations between composite and regional MetaROI [18F]FDG with verbal episodic memory according to [11C]PIB status were assessed using linear regression analyses. Estimates are presented as standardized β-values, to allow comparison of effect sizes. All analyses were adjusted for age, education, and APOE genotype.

*P < 0.05

**P < 0.01.

Discussion

In the present study, cognitively normal subjects underwent [11C]PIB and [18F]FDG-PET and extensive neuropsychological testing, allowing exploration of the inter-relationships between amyloid burden, glucose metabolism, and cognition. We found that greater Aβ pathology was associated with heightened metabolic activity in AD-specific regions (at trend level) and worse performance on visual episodic memory tasks. Across all subjects, cerebral glucose metabolism was not associated with cognitive performance. In individuals with intermediate or high amyloid burden, however, metabolic activity in several AD-specific regions was positively correlated to verbal episodic memory scores. This potential mechanism of cognitive brain reserve may reflect neural compensation that suppresses neurotoxic effects of Aβ pathology. These findings suggest that asymptomatic elderly with cerebral amyloidosis are, at least temporarily, able to preserve cognitive function through increased brain activity.

Aβ Burden and Glucose Metabolism

In the present study, we used highly sensitive PET measures to detect biological processes related to AD. [11C]PIB retention was used to quantify fibrillar amyloid plaque deposition in the precuneus, one of the regions earliest affected in AD (Mormino et al. 2012b). In addition, we used a meta-analysis based approach (MetaROI) for [18F]FDG to measure cerebral glucose metabolism in AD-specific regions (Landau et al. 2011). The association between heightened metabolic activity and increased amyloid deposition is consistent with other functional imaging studies showing elevated brain activation in aging, mild cognitive impairment, and genetic risk populations (Bookheimer et al. 2000; Cabeza et al. 2002; Rosen et al. 2002; Cohen et al. 2009; Filippini et al. 2009; Park and Reuter-Lorenz 2009; Mormino et al. 2012a). There are at least 2 mechanisms that may account for this phenomenon. The first is that the brain starts to recruit neuronal resources more intensively as a response to neurotoxicity of Aβ. This neural compensation may be an appearance of cognitive brain reserve, a concept that is often used to explain why some individuals can tolerate substantial pathological burden longer before showing cognitive loss, whereas others have less resilient brain capacity and decline earlier (Stern 2006, 2012). A second interpretation of our data is that increased neural activity leads to Aβ accumulation in the brain (Jagust and Mormino 2011). Animal studies have shown that a state of wakefulness and long-term unilateral vibrissal stimulation in transgenic mice enhance Aβ release and the formation and growth of amyloid plaques (Kang et al. 2009; Bero et al. 2011). In addition, Aβ plaques preferentially accumulate in metabolically highly active regions found in multimodal association cortices and the default mode network in the human brain (Buckner et al. 2005). The link between increased neural activity and amyloid pathology could not be attributed separately to low-, intermediate-, or high-PIB groups. In a previous study (Drzezga et al. 2011), amyloid-positive cognitively normal elderly had minor metabolic reductions compared to their amyloid-negative counterparts. Discrepancies with the present study may be explained by the fact that we used [11C]PIB as a continuous variable and both studies selected different target regions to assess glucose metabolism. In addition, we included only subjects with cerebral amyloidosis who had glucose metabolism (and cognitive function) in the normal range. Potentially, the relationships between Aβ deposition and metabolic activity change along the spectrum of preclinical AD.

Aβ Burden and Cognition

Primarily driven by subjects with high [11C]PIB retention, greater precuneus Aβ burden was associated with worse visual episodic memory performance. This result was no longer significant after adjustment for age, education, and APOE genotype, and there were no associations with other cognitive functions, including verbal episodic memory performance. It could be speculated that the discrepancy between modalities is a consequence of a higher degree of complexity of our visual episodic memory task or, alternatively, it may be harder to process visual information rather than verbal stimuli. This would be consistent with a recent study (Rentz et al. 2011) showing that highly demanding cognitive tasks increase the sensitivity to detect subtle Aβ-related impairment. Previous reports on the effect of amyloid burden on cognition have been equivocal; some have shown significantly lower memory scores in subjects with Aβ (Pike et al. 2007; Rentz et al. 2010; Rodrigue et al. 2012), whereas others did not (Aizenstein et al. 2008; Rowe et al. 2010). Assuming that Aβ deposition eventually leads to clinical symptoms, mediating factors are needed to explain the weak cross-sectional association between amyloid plaques and cognition. These mediators could boost the effects of Aβ pathology (e.g. neuroinflammation, tau pathology, or vascular damage) (Desikan et al. 2012) or, conversely, suppress them by means of cognitive brain reserve factors.

Glucose Metabolism and Cognition

Across all subjects, metabolic activity was not associated with cognitive factor scores. The majority (53 of 81 subjects) of subjects in the present study showed no in vivo evidence of cerebral amyloidosis. These subjects with low [11C]PIB retention, however, displayed comparable basal cerebral glucose metabolism as subjects with intermediate and high levels of Aβ (Fig. 1A), and there was a wide dynamic range. Relatively, low metabolic activity in these subjects is most likely independent of AD pathology and could be due to developmental factors or life-long experiences. Stronger correlates with cognition may be expected in prodromal AD or AD dementia patients who have crossed the threshold for abnormal [18F]FDG uptake and display clinical symptoms (Chételat et al. 2003; Landau et al. 2012; Ossenkoppele et al. 2012).

Glucose Metabolism and Cognition as a Function of Aβ

Looking specifically at subjects harboring Aβ pathology in the brain, there was a positive correlation between metabolic activity and verbal episodic memory scores. This was most prominent in the intermediate-PIB group, that comprised individuals with [11C]PIB retention 2–4 standard deviations above the mean distribution volume ratio of young healthy controls. In a previous study, it was shown that this elevation of [11C]PIB occurred in a pathologically confirmed AD like pattern and is therefore of likely biological relevance (Mormino et al. 2012b). In this group, composite (weighted average of the 5 MetaROIs) and bilateral angular gyri [18F]FDG uptake related strongly to one of the earliest cognitive functions affected in AD, namely, verbal episodic memory (Salmon 2000). In other words, among elderly supposedly on the path to AD, those who display heightened cerebral glucose metabolism have better preserved cognitive function than those with lower metabolic activity. It is unclear if increased neuronal activity is an adaptive response of the brain to the presence of Aβ pathology or that individuals just start out differently and show distinct cognitive trajectories when Aβ comes into play.

In high-PIB subjects, [18F]FDG uptake in the left inferior temporal lobe correlated with verbal episodic memory performance. In addition, moderate correlations were appreciated for the metabolic composite, bilateral angular gyri, and right inferior temporal cortex, but due to small sample size, the statistical threshold was not reached. The pattern of better cognitive performance in individuals with higher metabolic activity was thus similar to that seen in the intermediate-PIB group, only to a smaller extent. Longitudinal imaging studies have shown amyloid plaque growth in normal elderly, particularly in those that already have substantial Aβ load (Sojkova et al. 2011; Villemagne et al. 2011; Vlassenko et al. 2011). Individuals with strongly elevated [11C]PIB retention are potentially more advanced in the amyloid cascade model and thus closer toward entering the clinical stage of AD (Jack et al. 2010). This could imply that neural compensation is temporarily beneficial but long-term exposure of Aβ will eventually tip over glucose metabolism and subsequent cognitive performance. This model fits well with previous studies that report only a modest effect of Aβ on cross-sectional cognitive performance (Pike et al. 2007; Aizenstein et al. 2008; Rentz et al. 2010; Rodrigue et al. 2012; Rowe et al. 2010), whereas subjects harboring Aβ are consistently more prone to longitudinal cognitive deterioration (Morris et al. 2009; Storandt et al. 2009; Resnick et al. 2010; Villemagne et al. 2011; Doraiswamy et al. 2012; Lim et al. 2012; Wirth et al. 2013). An alternative explanation is that these elderly with cerebral amyloidosis—but normal cognitive function—are “survivors” and are protected against cognitive deterioration not only via neuronal mechanisms but also by interactions of currently unknown genetic and environmental factors.

Limitations

The main limitation of the present study is the relatively small sample size of the intermediate- (n = 13) and high-PIB (n = 15) groups. The results, however, seem not to be driven by outliers and effects are often appreciated in multiple brain regions, indicating a certain robustness of the findings. Also, the cross-sectional design of this study does only allow speculation that individuals with Aβ pathology who are no longer capable of compensation through neural circuits are the most likely to decline cognitively. Longitudinal studies that include more subjects could help to test this hypothesis. Finally, the present study was not designed to assess the impact of cognitive reserve variables on preservation of cognitive function by means of increased metabolic function.

Conclusions

We found relationships between presence of Aβ pathology and higher metabolic activity (at trend level) and lower visual episodic memory scores. Glucose metabolism did not correlate with cognition across all subjects, but heightened metabolic activity was associated with better verbal episodic memory performance in subjects with moderately elevated amyloid levels. This preliminary study indicates that neural compensation, as a manifestation of cognitive brain reserve reflected in measures of glucose metabolism, is a mechanism that enables elderly with amyloid deposits to preserve cognitive function.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/

Funding

This work was supported by NIH grant AG034570.

Supplementary Material

Supplementary Data
supp_24_8_2210__index.html (960B, html)

Notes

We thank Alzheimer Nederland and Internationale Stichting Alzheimer Onderzoek (ISAO) for providing fellowships to R.O. for conducting this research at Helen Wills Neuroscience Institute, University of California, Berkeley. Conflict of Interest. None declared.

References

  1. Aizenstein HJ, Nebes RD, Saxton JA, Price JC, Mathis CA, Tsopelas ND, et al. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol. 2008;65:1509–1517. doi: 10.1001/archneur.65.11.1509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Attwell D, Laughlin SB. An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab. 2001;21:1133–1145. doi: 10.1097/00004647-200110000-00001. [DOI] [PubMed] [Google Scholar]
  3. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med. 2012;367:795–804. doi: 10.1056/NEJMoa1202753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Becker JA, Hedden T, Carmasin J, Maye J, Rentz DM, Putcha D, et al. Amyloid-β associated cortical thinning in clinically normal elderly. Ann Neurol. 2011;69:1032–1042. doi: 10.1002/ana.22333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bennett DA, Schneider JA, Arvanitakis Z, Kelly JF, Aggarwal NT, Shah RC, et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology. 2006;66:1837–1844. doi: 10.1212/01.wnl.0000219668.47116.e6. [DOI] [PubMed] [Google Scholar]
  6. Benton A, Hamsher K, Sivan A. Multilingual aphasia examination. Iowa City (IA): AJA Associates; 1983. [Google Scholar]
  7. Bero AW, Yan P, Roh JH, Cirrito JR, Stewart FR, Raichle ME, et al. Neuronal activity regulates the regional vulnerability to amyloid-β deposition. Nat Neurosci. 2011;14:750–756. doi: 10.1038/nn.2801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bookheimer SY, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance MA, Mazziotta JC, et al. Patterns of brain activation in people at risk for Alzheimer's disease. New Engl J Med. 2000;343:450–456. doi: 10.1056/NEJM200008173430701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–7717. doi: 10.1523/JNEUROSCI.2177-05.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cabeza R, Anderson ND, Locantore JK, McIntosh AR. Aging gracefully: compensatory brain activity in high-performing older adults. NeuroImage. 2002;17:1394–1402. doi: 10.1006/nimg.2002.1280. [DOI] [PubMed] [Google Scholar]
  11. Chételat G, Desgranges B, de la Sayette V, Viader F, Eustache F. Mild cognitive impairment: can FDG-PET predict who is to rapidly convert to Alzheimer's disease? Neurology. 2003;22:1374–1377. doi: 10.1212/01.WNL.0000055847.17752.E6. [DOI] [PubMed] [Google Scholar]
  12. Chételat G, Villemagne VL, Bourgeat P, Pike KE, Jones G, Ames D, et al. Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol. 2010;67:317–324. doi: 10.1002/ana.21955. [DOI] [PubMed] [Google Scholar]
  13. Cohen AD, Price JC, Weissfeld LA, James J, Rosario BL, Bi W, et al. Basal cerebral metabolism may modulate the cognitive effects of Abeta in mild cognitive impairment: an example of brain reserve. J Neurosci. 2009;29:14770–14778. doi: 10.1523/JNEUROSCI.3669-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Delis D, Kramer J, Kaplan E, Ober B. California verbal learning test. San Antonio (TX): Psychological Corporation; 2000. [Google Scholar]
  15. Desikan RS, McEvoy LK, Thompson WK, Holland D, Brewer JB, Aisen PS, et al. Amyloid-β associated clinical decline occurs only in the presence of elevated P-tau. Arch Neurol. 2012;69:709–713. doi: 10.1001/archneurol.2011.3354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dickerson BC, Bakkour A, Salat DH, Feczko E, Pacheco J, Greve DN, et al. The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex. 2009;19:497–510. doi: 10.1093/cercor/bhn113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Doraiswamy PM, Sperling RA, Coleman RE, Johnson KA, Reiman EM, Davis MD, et al. Amyloid-β assessed by florbetapir F18 PET and 18-month cognitive decline: a multicenter study. Neurology. 2012;79:1636–1644. doi: 10.1212/WNL.0b013e3182661f74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Drzezga A, Becker JA, Van Dijk KR, Sreenivasan A, Talukdar T, Sullivan C, et al. Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain. 2011;134:1635–1646. doi: 10.1093/brain/awr066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, et al. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci USA. 2009;106:7209–7214. doi: 10.1073/pnas.0811879106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Golden C. Stroop color and word test: a manual for clinical and experimental uses. Chicago (IL): Stoelting; 1978. [Google Scholar]
  21. Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9:119–128. doi: 10.1016/S1474-4422(09)70299-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jack CR, Knopman DS, Weigand SD, Wiste HJ, Vemuri P, Lowe V, et al. An operational approach to National Institute on Aging-Alzheimer's Association criteria for preclinical Alzheimer disease. Ann Neurol. 2012;71:765–775. doi: 10.1002/ana.22628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jagust WJ, Landau SM. Apolipoprotein E, not fibrillar β-amyloid, reduces cerebral glucose metabolism in normal aging. J Neurosci. 2012;32:18227–18233. doi: 10.1523/JNEUROSCI.3266-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jagust WJ, Mormino EC. Lifespan brain activity, β-amyloid, and Alzheimer's disease. Trends Cog Sci. 2011;15:520–526. doi: 10.1016/j.tics.2011.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kang JE, Lim MM, Bateman RJ, Lee JJ, Smyth LP, Cirrito JR, et al. Amyloid-beta dynamics are regulated by orexin and the sleep-wake cycle. Science. 2009;326:1005–1007. doi: 10.1126/science.1180962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kantarci K, Lowe V, Przybelski SA, Weigand SD, Senjem ML, Ivnik RJ, et al. APOE modifies the association between Aβ load and cognition in cognitively normal adults. Neurology. 2012;78:232–240. doi: 10.1212/WNL.0b013e31824365ab. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–319. doi: 10.1002/ana.20009. [DOI] [PubMed] [Google Scholar]
  28. Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32:1207–1218. doi: 10.1016/j.neurobiolaging.2009.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Landau SM, Mintun MA, Joshi AD, Koeppe RA, Petersen RC, Aisen PS, et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol. 2012;72:578–586. doi: 10.1002/ana.23650. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lim YY, Ellis KA, Pietrzak RH, Ames D, Darby D, Harrington K, et al. Stronger effect of amyloid load than APOE genotype on cognitive decline in healthy older adults. Neurology. 2012;79:1645–1652. doi: 10.1212/WNL.0b013e31826e9ae6. [DOI] [PubMed] [Google Scholar]
  31. Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab. 1996;16:834–840. doi: 10.1097/00004647-199609000-00008. [DOI] [PubMed] [Google Scholar]
  32. Minoshima S, Frey KA, Foster NL, Kuhl DE. Preserved pontine glucose metabolism in Alzheimer disease: a reference region for functional brain image (PET) analysis. J Comp Assist Tomogr. 1995;19:541–547. doi: 10.1097/00004728-199507000-00006. [DOI] [PubMed] [Google Scholar]
  33. Mormino EC, Brandel MG, Madison CM, Marks S, Baker SL, Jagust WJ. Aβ deposition in aging is associated with increases in brain activation during successful memory encoding. Cereb Cortex. 2012a;22:1813–1823. doi: 10.1093/cercor/bhr255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mormino EC, Brandel MG, Madison CM, Rabinovici GD, Marks S, Baker SL, et al. Not quite PIB-positive, not quite PIB-negative: slight PIB elevations in elderly normal control subjects are biologically relevant. NeuroImage. 2012b;59:1152–1160. doi: 10.1016/j.neuroimage.2011.07.098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, et al. Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain. 2009;132:1310–1323. doi: 10.1093/brain/awn320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Morris JC, Roe CM, Grant EA, Head D, Storandt M, Goate AM, et al. Pittsburgh compound B imaging and prediction of progression from cognitive normality to symptomatic Alzheimer disease. Arch Neurol. 2009;66:1469–1475. doi: 10.1001/archneurol.2009.269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Morris JC, Roe CM, Xiong C, Fagan AM, Goate AM, Holtzman DM, et al. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol. 2010;67:122–131. doi: 10.1002/ana.21843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Oh H, Madison C, Haight TJ, Markley C, Jagust WJ. Effects of age and β-amyloid on cognitive changes in normal elderly people. Neurobiol Aging. 2012;33:2746–2755. doi: 10.1016/j.neurobiolaging.2012.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Oh H, Mormino EC, Madison C, Hayenga A, Smiljic A, Jagust WJ. β-Amyloid affects frontal and posterior brain networks in normal aging. NeuroImage. 2011;54:1887–1895. doi: 10.1016/j.neuroimage.2010.10.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ossenkoppele R, Tolboom N, Foster-Dingley JC, Adriaanse SF, Boellaard R, Yaqub M, et al. Longitudinal imaging of Alzheimer pathology using [11C]PIB, [18F]FDDNP and [18F]FDG PET. 2012;39:990–1000. doi: 10.1007/s00259-012-2102-3. [DOI] [PubMed] [Google Scholar]
  41. Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol. 2009;60:173–196. doi: 10.1146/annurev.psych.59.103006.093656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Pike KE, Savage G, Villemagne VL, Ng S, Moss SA, Maruff P, et al. Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease. Brain. 2007;130:2837–2844. doi: 10.1093/brain/awm238. [DOI] [PubMed] [Google Scholar]
  43. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;25:1528–1547. doi: 10.1038/sj.jcbfm.9600146. [DOI] [PubMed] [Google Scholar]
  44. Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol. 1999;45:358–368. doi: 10.1002/1531-8249(199903)45:3&#x00026;lt;358::AID-ANA12&#x00026;gt;3.0.CO;2-X. [DOI] [PubMed] [Google Scholar]
  45. Reitan R. Validity of the Trail Making Test as an indicator of organic brain damage. Percept Mot Skills. 1958;8:271–276. [Google Scholar]
  46. Rentz DM, Amariglio RE, Becker JA, Frey M, Olsen LE, Frishe K, et al. Face-name associative memory performance is related to amyloid burden in normal elderly. Neuropsychologia. 2011;49:2776–2783. doi: 10.1016/j.neuropsychologia.2011.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Rentz DM, Locascio JJ, Becker JA, Moran EK, Eng E, Buckner RL, et al. Cognition, reserve, and amyloid deposition in normal aging. Ann Neurol. 2010;67:353–364. doi: 10.1001/archneurol.2010.19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Resnick SM, Sojkova J, Zhou Y, An Y, Ye W, Holt DP, et al. Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB. Neurology. 2010;74:807–815. doi: 10.1212/WNL.0b013e3181d3e3e9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rocher AB, Chapon F, Blaizot X, Baron JC, Chavoix C. Resting-state brain glucose utilization as measured by PET is directly related to regional synaptophysin levels: a study in baboons. NeuroImage. 2003;20:1894–1898. doi: 10.1016/j.neuroimage.2003.07.002. [DOI] [PubMed] [Google Scholar]
  50. Rodrigue KM, Kennedy KM, Devous MD, Rieck JR, Hebrank AC, Diaz-Arrastia R, et al. β-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology. 2012;78:387–395. doi: 10.1212/WNL.0b013e318245d295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rosen AC, Prull MW, O'Hara R, Race EA, Desmond JE, Glover GH, et al. Variable effects of aging on frontal lobe contributions to memory. Neuroreport. 2002;13:2425–2428. doi: 10.1097/00001756-200212200-00010. [DOI] [PubMed] [Google Scholar]
  52. Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31:1275–1283. doi: 10.1016/j.neurobiolaging.2010.04.007. [DOI] [PubMed] [Google Scholar]
  53. Salmon D. Disorders of memory in Alzheimer's disease. Handbook of neuropsychology vol. 2: Memory and its disorders. Amsterdam (NL): Elsevier; 2000. [Google Scholar]
  54. Smith A. Symbol digit modalities test. Los Angeles (CA): Western Psychological Services; 1982. [Google Scholar]
  55. Sojkova J, Zhou Y, An Y, Kraut MA, Ferrucci L, Wong DF, et al. Longitudinal patterns of β-amyloid deposition in nondemented older adults. Arch Neurol. 2011;68:644–649. doi: 10.1001/archneurol.2011.77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:280–292. doi: 10.1016/j.jalz.2011.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20:112–117. doi: 10.1097/01.wad.0000213815.20177.19. [DOI] [PubMed] [Google Scholar]
  58. Stern Y. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol. 2012;11:1006–1012. doi: 10.1016/S1474-4422(12)70191-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Storandt M, Mintun MA, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta deposition identified with Pittburgh compound B: cognitive decline associated with Abeta deposition. Arch Neurol. 2009;66:1476–1481. doi: 10.1001/archneurol.2009.272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Vemuri P, Lesnick TG, Przybelski SA, Knopman DS, Roberts RO, Lowe VJ, et al. Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Ann Neurol. 2012;72:730–738. doi: 10.1002/ana.23665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Villemagne VL, Pike KE, Chételat G, Ellis KA, Mulligan RS, Bourgeat P,, et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol. 2011;69:181–192. doi: 10.1002/ana.22248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Vlassenko AG, Mintun MA, Xiong C, Sheline YI, Goate AM, Benzinger TL, et al. Amyloid-beta plaque growth in cognitively normal adults: longitudinal [11C]Pittsburgh compound B data. Ann Neurol. 2011;70:857–861. doi: 10.1002/ana.22608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Wechsler D. Wechsler Adult Intelligence Scale-III (WAIS-III) manual. New York (NY): The Psychological Coorporation; 1997. [Google Scholar]
  64. Wirth M, Oh H, Mormino EC, Markley C, Landau SM, Jagust WJ. The effect of beta-amyloid on longitudinal cognitive decline is modulated by neural integrity in cognitively normal elderly. Alzheimer Dement. 2013 doi: 10.1016/j.jalz.2012.10.012. Forthcoming. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wolf DS, Gearing M, Snowdon DA, Mori H, Markesbery WR, Mirra SS. Progression of regional neuropathology in Alzheimer disease and normal elderly: findings from the Nun study. Alzheimer Dis Assoc Disord. 1999;13:226–231. doi: 10.1097/00002093-199910000-00009. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data
supp_24_8_2210__index.html (960B, html)
supp_bht076_bht076supp.doc (64KB, doc)

Articles from Cerebral Cortex (New York, NY) are provided here courtesy of Oxford University Press

RESOURCES