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Original Contributions |

Using Positron Emission Tomography and Florbetapir F 18 to Image Cortical Amyloid in Patients With Mild Cognitive Impairment or Dementia Due to Alzheimer Disease FREE

Adam S. Fleisher, MD; Kewei Chen, PhD; Xiaofen Liu, MS; Auttawut Roontiva, MS; Pradeep Thiyyagura, MS; Napatkamon Ayutyanont, PhD; Abhinay D. Joshi, MS; Christopher M. Clark, MD; Mark A. Mintun, MD; Michael J. Pontecorvo, PhD; P. Murali Doraiswamy, MBBS, FRCP; Keith A. Johnson, MD; Daniel M. Skovronsky, MD, PhD; Eric M. Reiman, MD
[+] Author Affiliations

Author Affiliations: Banner Alzheimer's Institute (Drs Fleisher, Chen, Ayutyanont, and Reiman, Ms Liu, and Messrs Roontiva and Thiyyagura), Arizona Alzheimer's Consortium (Drs Fleisher, Chen, and Reiman), Department of Psychiatry, University of Arizona College of Medicine (Dr Reiman), Neurogenomics Division, Translational Genomics Research Institute, Phoenix (Dr Reiman), and Department of Mathematics, Arizona State University, Tempe (Dr Chen); Department of Neurosciences, University of California, San Diego (Dr Fleisher); Avid Radiopharmaceuticals (Drs Clark, Mintun, Pontecorvo, and Skovronsky and Mr Joshi) and University of Pennsylvania School of Medicine, Philadelphia (Drs Clark and Skovronsky); Washington University School of Medicine, St Louis, Missouri (Dr Mintun); Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, and Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (Dr Johnson); and Departments of Psychiatry, Duke University Medical Center, Durham, North Carolina (Dr Doraiswamy).


Arch Neurol. 2011;68(11):1404-1411. doi:10.1001/archneurol.2011.150.
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Objectives To characterize quantitative florbetapir F 18 (hereafter referred to as simply florbetapir) positron emission tomographic (PET) measurements of fibrillar β-amyloid (Aβ) burden in a large clinical cohort of participants with probable Alzheimer disease (AD) or mild cognitive impairment (MCI) and older healthy controls (OHCs).

Design Cerebral–to–whole-cerebellar florbetapir standard uptake value ratios (SUVRs) were computed. Mean cortical SUVRs were compared. A threshold of SUVRs greater than or equal to 1.17 was used to reflect pathological levels of amyloid associated with AD based on separate antemortem PET and postmortem neuropathology data from 19 end-of-life patients. Similarly, a threshold of SUVRs greater than 1.08 was used to signify the presence of any identifiable Aβ because this was the upper limit from a separate set of 46 individuals 18 to 40 years of age who did not carry apolipoprotein E (APOE) ε4.

Setting Multiple research imaging centers.

Participants A total of 68 participants with probable AD, 60 participants with MCI, and 82 OHCs who were 55 years of age or older.

Main Outcome Measure Florbetapir-PET activity.

Results All of the participants (ie, those with probable AD or MCI and those who were OHCs) differed significantly in mean (SD) cortical florbetapir SUVRs (1.39 [0.24], 1.17 [0.27], and 1.05 [0.16], respectively; P < 1.0 × 10−7), in percentage meeting levels of amyloid associated with AD by SUVR criteria (80.9%, 40.0%, and 20.7%, respectively; P < 1.0 × 10−7), and in percentage meeting SUVR criteria for the presence of any identifiable Aβ (85.3%, 46.6%, and 28.1%, respectively; P < 1.0 × 10−7). Among OHCs, the percentage of florbetapir positivity increased linearly by age decile (P = .05). For the 54 OHCs with available APOE genotypes, APOE ε4 carriers had a higher mean (SD) cortical SUVR than did noncarriers (1.14 [0.2] vs 1.03 [0.16]; P = .048).

Conclusions The findings of our analysis confirm the ability of florbetapir-PET SUVRs to characterize amyloid levels in clinically probable AD, MCI, and OHC groups using continuous and binary measures of fibrillar Aβ burden. It introduces criteria to determine whether an image is associated with an intermediate-to-high likelihood of pathologic AD or with having any identifiable cortical amyloid level above that seen in low-risk young controls.

Figures in this Article

Using positron emission tomography (PET) to image fibrillar amyloid has begun to have transformational effects on the scientific study, early detection, and tracking of Alzheimer disease (AD) and on the evaluation of amyloid-modifying treatments. Amyloid imaging offers great promise to facilitate the evaluation of patients in a clinical setting. Because of their longer radioactive half-lives, 18F-labeled ligands are needed to make this technique widely available through commercial PET radiotracer distribution sites for use in research and clinical settings. Florbetapir F 18 [(E)-4-(2-(6-(2-(2-(2-18F-fluoroethoxy)ethoxy)ethoxy)pyridin-3-yl)vinyl)- N-methyl benzenamine; previously 18F-AV-45 and hereafter referred to as florbetapir F 18 when the tracer is named formally as a compound] is a PET ligand that has been shown in in vitro, ex vivo, and in vivo studies to measure cortical fibrillar β-amyloid (Aβ).15

Florbetapir F 18 has been studied in 6 human clinical studies registered with the US Food and Drug Administration. A total of 269 subjects have received florbetapir in 2 phase I studies and 3 phase II studies. A recent phase III study of 35 terminally ill participants compared florbetapir imaging and postmortem amyloid immunohistochemistry. Florbetapir-PET images were also obtained from 74 cognitively normal young adults at variable genetic risk of late-onset AD based on apolipoprotein E (APOE) genotype.5 In this study by Clark et al,5 35 end-of life patients with and without dementia demonstrated highly significant correlations between florbetapir-PET and subsequent immunohistochemistry measurements of fibrillar Aβ, using either blind visual ratings (ρ = 0.78) or automatically characterized cerebral–to–whole-cerebellar standard uptake value ratios (SUVRs) (ρ = 0.75). Florbetapir-PET visual ratings, using the median rating from 3 blinded readers, were found to have 96% accuracy for characterizing whether or not the research participants had fibrillar Aβ levels consistent with intermediate or high likelihood of pathologic AD (National Institute on Aging–Reagan Institute criteria6).

In our study, we pooled data from the 4 registered phase I and II trials of florbetapir-PET imaging that used standard dosing of florbetapir F 18 and nondynamic PET acquisition, permitting us to assess a large combined cohort of patients with probable AD, mild cognitive impairment (MCI), and age-matched older healthy controls (OHCs). We evaluated both continuous and binary measures of florbetapir-PET activity to assess global differences between clinical diagnostic groups, confirm expected patterns of regional distributions of fibrillar Aβ, and determine proportions of positive scans using cutoff thresholds for global cortical florbetapir F 18 activity. In doing so, we introduce empirically predetermined SUVR thresholds for defining florbetapir-PET positivity based on Avid Radiopharmaceutical's previously reported study of expired end-of-life patients and a specificity cohort of young APOE ε4 (APOE4) noncarriers.

PARTICIPANTS

A total of 210 participants, including 82 cognitively normal volunteers (ie, OHCs), 60 individuals with MCI, and 68 individuals with probable AD, were assessed at 31 US research sites. Florbetapir-PET scans were taken of all participants. We evaluated cognitively normal individuals who were 55 years of age or older. They were required to have no subjective cognitive complaints as corroborated by an informant report, to have a Mini-Mental State Examination (MMSE) score of 29 or greater, and to be cognitively normal based on psychometric testing. Participants with probable AD met National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria7 for probable AD and had an MMSE score at screening in the range of 10 to 24. Participants with MCI had complaints of memory or cognitive decline corroborated by an informant, objective cognitive impairment or marginally normal performance with a documented history of high cognitive performance, generally preserved functional abilities and activities of daily living, a Clinical Dementia Rating scale global score of 0.5, and an MMSE score of greater than 24 and presented for initial diagnosis of cognitive impairment not more than 1 year from the day of the screening visit. APOE genotyping was performed for 155 participants as an optional procedure, in a double-blind fashion. Participants were excluded if they had other current clinically relevant neurologic or psychiatric illnesses, were receiving any investigational medications, or ever received an anti-amyloid experimental therapy. Trials were conducted in accordance with good clinical practice guidelines after approval from local institutional review boards. Study procedures were performed after written informed consent was obtained from study participants, authorized representatives, or both, according to local guidelines and degree of cognitive impairment.

FLORBETAPIR-PET IMAGING ACQUISITION

Across the study sites, there were 14 different PET scanner models from 3 manufactures (Siemens, Washington, DC; General Electric, Little Chalfont, Buckinghamshire, England; and Phillips, Andover, Massachusetts). Native-slice thickness ranged from 2 to 4.25 mm, with field of views from 550/153 to 700/153. All volunteers underwent a florbetapir-PET session that consisted of intravenous injection of approximately 10 mCi of florbetapir F 18 while resting quietly outside the PET scanner. After approximately 45 minutes, the participant was positioned in the scanner such that the entire brain (including the cerebellum) was in the field of view and a computed tomographic (for PET/computed tomographic scanners) or a PET attenuation scan (for PET-only scanners) was performed to allow estimation of attenuation factors. Starting at 50 minutes after injection, a 10-minute emission acquisition was performed (as a dynamic scan with two 5-minute frames). Images were reconstructed with an iterative reconstruction algorithm (4 iterations, 16 subsets), using a Gaussian filter of 5-mm full-width at half-maximum, and were saved as a series of 128 × 128 matrices with a voxel size of 2 × 2 × 2 mm. Between-scanner variability in attenuation, scatter, and uniformity was corrected on the basis of Hoffman brain phantom scans acquired on each scanner.

IMAGE ANALYSIS

First, a region of interest (ROI) analysis was performed on individual PET images, spatially normalized into Montreal Neurological Institute (MNI) atlas space using statistical parametric mapping (SPM) software. For this analysis, no spatial smoothing was performed. Previously defined mean cortical and whole-cerebellar ROI templates were applied to all PET scans to calculate mean regional cerebral-to-cerebellar SUVRs.5 The ROIs were defined from 11 patients with AD and 15 age-matched healthy controls participating in an early phase I study, all of whom underwent 90-minute dynamic florbetapir-PET acquisitions (images excluded from our analysis) and structural magnetic resonance imaging. The whole-cerebellar reference ROI was hand drawn from mean group magnetic resonance imaging scans after they were spatially normalized to MNI atlas space. Flow maps from the first 10 minutes of PET data acquisition and a voxelwise comparison of between-group activity in patients with AD vs controls were used to identify key cortical regions of increased PET signal in MNI brain atlas space. After gray or white matter and cerebrospinal fluid space segmentation, by use of participants' MRI scans registered in MNI space, 6 cortical gray matter ROIs were defined from the Automated Anatomic Labeling Atlas8 or were manually delineated in gray matter regions that had prominent PET activity in patients with AD compared with controls: medial orbital frontal (Automated Anatomic Labeling), temporal, anterior, and posterior cingulate; parietal lobe; and precuneus (Figure 1).5 The average of these 6 regions was evaluated as a measure of global mean cortical florbetapir F 18 binding and was used as the primary outcome measure for ROI analyses. Between-group t tests and analyses of variance were used to assess mean cortical florbetapir differences. Linear regression was used to assess the interaction of mean cortical SUVRs and age. Differences in mean cortical SUVRs based on APOE4 gene carrier status was compared among OHCs to asses this risk factor's influence on amyloid PET measures in cognitively normal older adults.

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Figure 1. Cortical region of interest template, which includes the orbital frontal cingulate (A), the temporal cingulate (B), the anterior cingulate (C), the posterior cingulate (D), the parietal lobe (E), and the precuneus (F).

Cerebral–to–whole-cerebellar florbetapir SUVRs were compared on a voxel-by-voxel basis between groups. An automated brain mapping algorithm (SPM5, http://www.fil.ion.ucl.ac.uk/spm/) was used to deform each person's PET image into the coordinates of the MNI brain atlas. For voxelwise comparisons, each individual image was smoothed with a 12-mm full-width at half-maximum Gaussian kernel, with intensity normalization applied using the whole cerebellum as a reference region. Between-group t tests and analyses of variance were used to assess voxelwise florbetapir SUVR group differences. Statistical parametric maps of between-group differences in cerebral–to–whole-cerebellar SUVRs were computed before and after controlling for each person's APOE4 carrier status.

QUANTITATIVE THRESHOLDS FOR CEREBRAL AMYLOIDOSIS IN FLORBETIPIR-PET SCANS

In our study, we introduce 2 complementary thresholding procedures to determine whether a person's mean cortical–to–whole-cerebellar florbetapir SUVR is consistent with (1) florbetapir F 18 levels associated with intermediate or high likelihood neuropathological AD (hereafter referred to as PATHAMY levels) or (2) florbetapir F 18 levels associated with the presence of any identifiable cortical Aβ (hereafter referred to as any-amyloid levels), such that it exceeds that in young adult APOE4 noncarriers who are highly unlikely to have cortical Aβ plaques.9 First, to determine an empirical cutoff for the minimal mean cortical florbetapir F 18 levels typically associated with neuropathological amyloidosis in AD (PATHAMY levels), mean cortical SUVRs were calculated from 19 participants with clinical dementia and neuropathologically confirmed AD who were in Avid Radiopharmaceutical's phase III study.5 Autopsy diagnosis of AD was determined using standardized criteria described by Braak and Braak10 and the Consortium to Establish a Registry for Alzheimer Disease11 (modified to exclude age and clinical information) and described in the National Institute on Aging–Reagan Institute criteria.12 Given the small sample size, we used an SUVR cutoff for florbetapir positivity defined as the minimal mean cortical SUVR seen in this convenience sample of individuals with pathological AD. Notably, these mean global cortical SUVRs were previously reported to be highly correlated with amyloid burden measured by immunohistochemistry (ρ = 0.75, P < .001) and neuritic plaque counts (ρ = 0.74, P < .001).5

Next, individuals most likely to be free of cortical fibrillar amyloid were defined using the specificity cohort of 46 young (aged 18-40 years), cognitively normal, APOE4 noncarriers (10 who carried APOE ε2/3 and 36 who carried APOE ε3/3) from the same phase III trial.5 Previous studies suggest that this group is highly unlikely to have cortical amyloidosis.5,9,13,14 As a confirmatory step, all young adult APOE4 noncarriers' raw PET scans were determined to be negative for florbetapir-PET cortical binding on median visual semiquantitative ratings and were previously reported as such.5 These same visual global ratings were found to correlate with cortical amyloid load in the 35 subjects who underwent autopsy in the phase III trial (ρ = 0.78, P < .001) and were reported to be 100% accurate in detecting negative scans in young controls.5

ASSESSMENT OF FLORBETAPIR POSITIVITY DIFFERENCES

Percentage of florbetapir positivity based on SUVR cutoff thresholds consistent with PATHAMY levels and any-amyloid levels of cortical amyloid were determined within each diagnostic group. Differences in proportions of positivity in a subgroup of OHCs (n = 54) who had APOE genotyping available was assessed between APOE4 carriers and noncarriers. To determine whether age was associated with binary florbetapir-PET positivity, the percentage of cognitively normal elderly participants with either any-amyloid or PATHAMY levels of Aβ was evaluated within age deciles using a χ2 linear-by-linear association (not corrected for cell sample size owing to meeting minimum cell size requirements). Differences in percentage of florbetapir positivity between diagnostic groups and between the 2 cutoff thresholds were compared using the nonparametric McNemar test, accounting for comparison of data from the same set of participants (ie, samples are not independent).

The OHC, MCI, and probable AD groups did not differ significantly in their age or sex distribution (Table). As expected, the patients with probable AD had significantly greater cognitive impairment on the MMSE, the AD Assessment Scale–cognitive subscale, and the Wechsler Memory Scale immediate recall test than did the patients with MCI or the OHCs (P < .001) (Table). There were significant differences among the probable AD, MCI, and OHC groups in their percentage of APOE4 carriers (51%, 39%, and 24%, respectively; P = .002). Fifty-four OHCs had APOE4 genotyping performed: 13 were found to be carriers, and 41 noncarriers; 28 participants opted out of genotyping.

Table Graphic Jump LocationTable. Characteristics of Participants in Study Designed to Characterize Quantitative Florbetapir F 18 Positron Emission Tomographic Measurements of Fibrillar Aβ Burden
MEAN CORTICAL SUVR MEASUREMENTS

Mean (SD) cortical–to–whole-cerebellar SUVRs were significantly different among the 3 groups and in the expected direction: 1.39 (0.24) for the probable AD group, 1.17 (0.27) for the MCI group, and 1.05 (0.16) for the OHC group (P = 2.9 × 10−14) (Figure 2). There were no significant linear relationships between mean cortical SUVR and age in the AD group (P = .17) or in the MCI group (P = .73), but there was an age-related increase in mean cortical SUVR in the OHC group (P = .002, with a regression slope of 0.005). Among OHCs, APOE4 carriers had a higher mean (SD) cortical SUVR than did noncarriers (1.14 [0.2] vs 1.03 [0.16]; P = .048).

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Figure 2. Scatterplots of mean cortical standard uptake value ratios (SUVRs) for persons having florbetapir F 18 levels associated with intermediate or high likelihood neuropathological Alzheimer disease (AD) (hereafter referred to as the PATHAMY levels) and young adult APOE ε4 noncarriers (YNC), and for the following clinical diagnostic groups: probable AD (PAD), mild cognitive impairment (MCI), and older healthy control (OHC) group. The black line represents a florbetapir threshold associated with minimal SUVRs seen in the PATHAMY group (≥1.17). The red line represents the minimal SUVR associated with the presence of any identifiable Aβ (hereafter referred to as the any-amyloid levels; >1.08). Mean cortical SUVRs differed significantly among diagnostic groups (P = 2.9 × 10−14).

STATISTICAL PARAMETRIC MAPPING

Statistical brain maps revealed significantly greater regional–to–whole-cerebellar florbetapir SUVRs among the 3 groups (with the probable AD group having the highest SUVRs and the OHC group having the lowest, with voxelwise P values ranging from .05 to 4 × 10−15, uncorrected for multiple comparisons) (Figure 3). The pattern of SUVR increases in the AD and MCI groups was consistent with that previously reported in amyloid PET studies, with preferential uptake in the precuneus, the posterior cingulate, the parietal lobe, and the temporal and frontal cortex (Figure 3).5,1517 Group differences remained significant when controlling for the effects of APOE4, but with attenuated statistical power (voxelwise P values ranging from .05 to 4 × 10−9) with similar distribution patterns.

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Figure 3. Mean between-group differences in cerebral-to-cerebellar florbetapir standard uptake value ratios (SUVRs). Representative horizontal, coronal, and sagittal sections show mean between-group increases. The images are displayed as mean differences (P ≤ .05). Typical patterns of amyloid distribution in Alzheimer disease (AD) are demonstrated with preferential uptake in the parietal, frontal, and temporal lobes. MCI indicates mild cognitive impairment group of patients; OHC, older healthy control group; PAD, probable AD group.

PERCENTAGE OF FLORBETAPIR POSITIVITY ASSOCIATED WITH PATHOLOGIC LEVELS OF AMYLOID IN AD AND ANY-AMYLOID LEVELS ABOVE NORMAL

Evaluation of mean cortical florbetapir levels in the PATHAMY group and in the group of young adult APOE4 noncarriers established predetermined florbetapir-PET criteria associated with a neuropathological diagnosis of (intermediate-to-high likelihood) AD (SUVR ≥ 1.17 for the PATHAMY group) or having an any-amyloid level above that typically seen in young adult APOE4 noncarriers (SUVR > 1.08) (Figure 4). The percentage of patients in each clinical diagnostic group who were at or above criteria for PATHAMY SUVR levels was significantly different for each group (80.9% of patients with probable AD, 40.0% of patients with MCI, and 20.7% of OHCs; P < 1.0 × 10−7). Similarly, those individuals meeting florbetapir positivity criteria above any-amyloid levels were different for each group (85.3% of patients with probable AD, 46.6% of patients with MCI, and 28.1% of OHCs; P < 1.0 × 10−7) (Figure 4). The mean percentage of individuals with cortical florbetapir levels falling between PATHAMY and any-amyloid levels did not differ substantially among diagnostic groups (4.4% of patients with probable AD, 6.7% of patients with MCI, and 2.4% of OHCs; P = .76) (Figure 2). APOE4 carriers among the OHC group had more than twice the percentage of florbetapir-PET positivity compared with noncarriers: of 13 APOE4 carriers, 4 (30.8%) had PATHAMY levels, and 6 (46.1%) had any-amyloid levels; of 41 APOE4 noncarriers, 6 (14.6%) had PATHAMY levels, and 9 (21.9%) had any-amyloid levels (Figure 4). However, these differences did not reach statistical significance (P = .19 for PATHAMY level and P = .09 for any-amyloid level). Lastly, florbetapir positivity increased by age decile among OHCs for both positivity thresholds (P = .05 for PATHAMY level and P = .01 for any-amyloid level) (Figure 5). The percentage of positivity by age decile among OHCs for PATHAMY levels of florbetapir was 5.9% for those 55 to 60 years of age, 15.8% for those 61 to 70 years of age, 27.3% for those 71 to 80 years of age, and 29.2% for those 81 years of age or older. For any-amyloid levels above normal, the percentage of positivity by age decile was 11.8% for those 55 to 60 years of age, 15.8% for those 61 to 70 years of age, 36.4% for those 71 to 80 years of age, and 41.7% for those 81 years of age or older (Figure 5). Within the MCI and AD groups, there were no statistical differences in the proportion of florbetapir-PET positivity using the PATHAMY threshold vs the any-amyloid threshold (P = .25 for AD group and P = .13 for MCI group). For the OHC group, however, more positivity was detected when using the more liberal any-amyloid threshold than when using the PATHAMY threshold (P = .03) (Figure 4). However, OHC APOE subgroups did not show this difference in percentage of positivity between thresholds.

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Figure 4. Percentages of florbetapir positivity by diagnostic group. The percentages of positivity in all 3 diagnostic groups and in 2 subgroups of older healthy controls (APOE ε4 carriers [OHC E4+] and noncarriers [OHC E4−]) are based on meeting criteria for florbetapir F 18 levels associated with intermediate or high likelihood neuropathological Alzheimer disease (AD) (hereafter referred to as PATHAMY levels, with a standard uptake value ratio [SUVR] of ≥1.17) or the presence of any identifiable cortical Aβ (hereafter referred to as any-amyloid levels, with SUVR > 1.08). The probable AD (PAD), mild cognitive impairment (MCI), and OHC diagnostic groups differed significantly in their percentage of positivity for both thresholds (P < 1.0 × 10−7) but not between the 2 OHC subgroups, likely owing to the small sample size. Only the full OHC group shows significant differences in their percentage of positivity based on threshold used (P = .03).

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Figure 5. Percentages of florbetapir positivity by age in older healthy controls (OHCs). The percentages increased by age decile in OHCs (P = .05 for pathological amyloid threshold and P = .01 for presence of any identifiable cortical Aβ). SUVR indicates standard uptake value ratio.

To our knowledge, our study presents the largest analysis of multicenter 18F amyloid PET data currently reported. These results robustly support the ability of florbetapir-PET SUVRs to characterize amyloid levels in clinically probable AD, MCI, and OHC groups using both continuous and binary quantitative measures of amyloid burden. Between-group comparisons revealed increased florbetapir F 18 positivity associated with clinical severity in distribution patterns consistent with known fibrillar amyloid deposition in AD.15,16 In addition, clinical severity was distinguishable by florbetapir PET even when controlling for the effects of APOE ε4 gene status. However, OHCs had higher levels of mean cortical florbetapir SUVRs among APOE4 carriers compared with noncarriers, and normal aging was associated with an increase in florbetapir F 18 binding. By using florbetapir positivity cutoff thresholds that were based on patients with neuropathologically confirmed AD and low-risk young individuals, we introduce criteria to determine whether an image is consistent with amyloid levels associated with an intermediate-to-high likelihood of pathologic AD or with having any identifiable presence of cortical amyloid.

Although the etiology of AD has not been definitively established, converging evidence suggests that the Aβ peptide plays an important role in its pathogenesis.14,1821 Given the recently reported correlation of florbetapir F 18 with underlying cortical amyloid burden,5 we applied criteria established from the florbetapir autopsy and the young specificity cohort study to this larger data set. Our findings are consistent with previous clinical-pathological comparisons in AD and with previous amyloid imaging studies. Clinical-pathological comparison studies demonstrate that between 10% and 30% of patients with clinically diagnosed AD lack AD pathology at autopsy.2224 We found 19.1% of patients with clinical AD to have florbetapir levels below that associated with pathologic AD and 14.7% of patients with clinical AD to have no evidence of amyloid at all. Similarly, in cognitively normal older adults, although large autopsy studies are not available, previous carbon 11–labeled Pittsburgh Compound B [11C]PiB studies report that 20% to 51% of healthy elderly adults have elevated levels of amyloid binding on PET imaging,13,2528 compared with our findings of 21% to 28%.

Advanced age and the APOE ε4 gene are the most potent known risk factors for AD and the subsequent presence of neurofibrillary tangles and amyloid plaques in the brain.2933 Our findings with florbetapir are consistent with previous [11C]PIB imaging studies that demonstrate an association between the APOE ε4 gene and amyloid PET load in cognitively normal adults.34,35 Although mean cortical SUVRs were higher in APOE4 carriers compared with noncarriers, the proportion of florbetapir-PET positivity between carriers and noncarriers did not reach statistical significance. This may have been due to the small sample size of APOE4 carriers. Similar to our findings, increasing age among healthy elderly controls has been shown to be associated with increasing amyloid binding in recent large studies of [11C]PiB PET.27,35

The choice of thresholds for determining florbetapir positivity should be hypothesis driven and applied within the context of the clinical or scientific question at hand. For instance, a more conservative threshold (such as that associated with pathologic AD) might be used in the clinical setting to determine whether or not a person meets florbetapir-PET criteria for neuropathologically significant cerebral amyloidosis. However, a more liberal criteria (eg, florbetapir levels above those seen in low-risk young individuals) might identify those individuals in the earliest stages of amyloid accumulation, providing a group that might be especially responsive to presymptomatic amyloid-modifying treatments for AD. Establishing standards for image acquisition, cerebral and reference ROIs, and cutoff thresholds is needed to facilitate the comparison of data among different subjects and the comparison of findings from different investigators.

In our report, we proposed 2 thresholds for determining positivity based on 2 separate convenience samples: one threshold intended to be consistent with the neuropathological diagnosis of intermediate or high likelihood AD and another consistent with any substantial identifiable cortical amyloid. Statistical methods such as interquartile ranges13,36 or hierarchical cluster analysis27 can be used to establish positivity thresholds based on clinically defined cohorts of healthy elderly adults or patients with probable AD. Herein, however, we had the opportunity to base thresholds on a pathological gold standard and on a cohort extremely unlikely to have any cortical amyloid pathology.5,9 Notably, we found no statistical outliers in the mean cortical SUVR distribution for either of these 2 cohorts. Despite this, these convenience samples may not represent the full range of florbetapir F 18 levels seen in a larger community sample. Clinicopathologic correlative studies in larger community-based samples with a broader distribution of SUVRs may be needed to more definitively establish standard thresholds.

Use of the whole cerebellum as a reference region for SUVR calculations has not been proven to be superior to other noncortical brain regions, such as the pons or cerebellar gray matter. A whole-cerebellar reference region was chosen here because it rarely contains fibrillar amyloid plaques, and cortical regions on PET scans typically contain a mixture of white and gray matter tissue, which is matched by the whole-cerebellar region. Therefore, theoretically, the whole cerebellum should be a suitable measure of neutral nonspecific florbetapir binding.37,38 The use of pons or cerebellar gray matter as reference regions may potentially further minimize the effects of nonneocortical amyloid and nonspecific white matter binding, respectively.5,15,3638 In addition, it is certainly possible that healthy elderly controls have different nonspecific binding compared with the young cohort used here as an exemplar of normal.

Another limitation to our study may include a potential cohort selection bias. This cohort is a combined sample from several clinical studies with specific selection criteria intended to identify relatively homogenous diagnostic samples. Although data collection was done in a consistent prospective manner, this cohort was not intended to represent a broad community-based clinical practice setting. Guidance for appropriate clinical use of florbetapir requires further community-based data.

In conclusion, our analysis provides additional support for the emerging role of florbetapir-PET imaging in the assessment of fibrillar Aβ burden and underscores the need for standardization of definitions for positive amyloid scans. Herein, we introduced the use of positivity thresholds associated with an intermediate-to-high likelihood of fibrillar Aβ pathology vs one related to at least minimal elevations in fibrillar Aβ above that seen in young low-risk individuals. Our study analyzes pooled data in an effort to better understand and optimize the use of florbetapir PET for clinical research and diagnostic assessments in AD.

Correspondence: Adam S. Fleisher, MD, Banner Alzheimer's Institute, 901 E Willetta St, Phoenix, AZ 85006 (adam.fleisher@bannerhealth.com).

Accepted for Publication: May 10, 2011.

Published Online: July 11, 2011. doi:10.1001/archneurol.2011.150

Author Contributions:Study concept and design: Fleisher, Clark, Mintun, Pontecorvo, and Skovronsky. Acquisition of data: Fleisher, Joshi, Mintun, Doraiswamy, Johnson, and Skovronsky. Analysis and interpretation of data: Fleisher, Chen, Liu, Roontiva, Thiyyagura, Ayutyanont, Joshi, Clark, Mintun, Pontecorvo, Johnson, and Reiman. Drafting of the manuscript: Fleisher, Chen, Ayutyanont, Clark, Johnson, and Reiman. Critical revision of the manuscript for important intellectual content: Fleisher, Liu, Roontiva, Thiyyagura, Joshi, Clark, Mintun, Pontecorvo, Doraiswamy, Johnson, Skovronsky, and Reiman. Statistical analysis: Chen, Liu, Roontiva, Thiyyagura, Ayutyanont, Mintun, Johnson, and Reiman. Obtained funding: Skovronsky. Administrative, technical, and material support: Joshi, Mintun, Doraiswamy, Johnson, Skovronsky, and Reiman. Study supervision: Fleisher, Joshi, Clark, Johnson, and Reiman.

Financial Disclosure: Dr Fleisher has received research support for nonrelated projects from Avid Radiopharmaceuticals and Eli Lilly and is a consultant for Eli Lilly. Mr Joshi and Drs Clark, Mintun, Pontecorvo, and Skovronsky are employees of Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly. Dr Doraiswamy has received research support or advisory fees from Avid, Lundbeck, Medivation, TauRx, Bayer, GE Healthcare, BMS, Astra, Shering, Neuroptix, Neuronetrix, Alzheimer's Foundation, and Sonexa and owns stock in Sonexa. Dr Johnson has received research support or advisory fees from GE Healthcare, Bayer, Janssen Alzheimer Immunotherapy, and Avid Radiopharmaceuticals. Dr Reiman has received research support for nonrelated projects from Avid Radiopharmaceuticals and Eli Lilly.

Funding/Support: This study was supported by funding from Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly.

Additional Information: All data analyses were performed independently by the Banner Alzheimer's Institute (a 503c not-for-profit organization) with consultation provided by Avid Radiopharmaceuticals. Drs Fleisher, Chen, Ayutyanont, and Reiman, Ms Liu, and Messrs Roontiva and Thiyyagura had full access to the data set reported herein and unrestricted publication rights. All data collection was done as part of multisite-registered trials funded by Avid Radiopharmaceuticals, many of which Banner Alzheimer's Institute participated in. No financial compensation was obtained from Avid Radiopharmaceuticals for the analyses reported herein.

Zhang W, Oya S, Kung MP, Hou C, Maier DL, Kung HF. F-18 polyethyleneglycol stilbenes as PET imaging agents targeting Abeta aggregates in the brain.  Nucl Med Biol. 2005;32(8):799-809
PubMed   |  Link to Article
Zhang W, Oya S, Kung MP, Hou C, Maier DL, Kung HF. F-18 stilbenes as PET imaging agents for detecting beta-amyloid plaques in the brain.  J Med Chem. 2005;48(19):5980-5988
PubMed   |  Link to Article
Zhang W, Kung MP, Oya S, Hou C, Kung HF. 18F-labeled styrylpyridines as PET agents for amyloid plaque imaging.  Nucl Med Biol. 2007;34(1):89-97
PubMed   |  Link to Article
Wong DF, Rosenberg PB, Zhou Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir F 18) [published correction appears in J Nucl Med. 2010;51(8):1327].  J Nucl Med. 2010;51(6):913-920
PubMed   |  Link to Article
Clark CM, Schneider JA, Bedell BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology [published correction appears in JAMA. 2011;305(11):1096].  JAMA. 2011;305(3):275-283
PubMed   |  Link to Article
The National Institute on Aging and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer's Disease.  Consensus recommendations for the postmortem diagnosis of Alzheimer's disease.  Neurobiol Aging. 1997;18(4):(suppl)  S1-S2
PubMed   |  Link to Article
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 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. 1984;34(7):939-944
PubMed   |  Link to Article
Tzourio-Mazoyer N, Landeau B, Papathanassiou D,  et al.  Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.  Neuroimage. 2002;15(1):273-289
PubMed   |  Link to Article
Valla J, Yaari R, Wolf AB,  et al.  Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE ε4 allele, the major late-onset Alzheimer's susceptibility gene.  J Alzheimers Dis. 2010;22(1):307-313
PubMed
Braak H, Braak E. Evolution of the neuropathology of Alzheimer's disease.  Acta Neurol Scand Suppl. 1996;165:3-12
PubMed
Mirra SS, Heyman A, McKeel D,  et al.  The Consortium to Establish a Registry for Alzheimer's Disease (CERAD), part II: standardization of the neuropathologic assessment of Alzheimer's disease.  Neurology. 1991;41(4):479-486
PubMed   |  Link to Article
Hyman BT, Trojanowski JQ. Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease.  J Neuropathol Exp Neurol. 1997;56(10):1095-1097
PubMed   |  Link to Article
Aizenstein HJ, Nebes RD, Saxton JA,  et al.  Frequent amyloid deposition without significant cognitive impairment among the elderly.  Arch Neurol. 2008;65(11):1509-1517
PubMed   |  Link to Article
Jack CR Jr, Lowe VJ, Weigand SD,  et al; Alzheimer's Disease Neuroimaging Initiative.  Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease.  Brain. 2009;132(pt 5):1355-1365
PubMed   |  Link to Article
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes.  Acta Neuropathol. 1991;82(4):239-259
PubMed   |  Link to Article
Ikonomovic MD, Klunk WE, Abrahamson EE,  et al.  Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease.  Brain. 2008;131(pt 6):1630-1645
PubMed   |  Link to Article
Klunk WE, Engler H, Nordberg A,  et al.  Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B.  Ann Neurol. 2004;55(3):306-319
PubMed   |  Link to Article
Shaw LM, Vanderstichele H, Knapik-Czajka M,  et al; Alzheimer's Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.  Ann Neurol. 2009;65(4):403-413
PubMed   |  Link to Article
Selkoe DJ. Soluble oligomers of the amyloid beta-protein impair synaptic plasticity and behavior.  Behav Brain Res. 2008;192(1):106-113
PubMed   |  Link to Article
Oddo S, Caccamo A, Kitazawa M, Tseng BP, LaFerla FM. Amyloid deposition precedes tangle formation in a triple transgenic model of Alzheimer's disease.  Neurobiol Aging. 2003;24(8):1063-1070
PubMed   |  Link to Article
Braak H, Braak E. Staging of Alzheimer-related cortical destruction.  Int Psychogeriatr. 1997;9:(suppl 1)  257-261, discussion 269-272
PubMed   |  Link to Article
Jobst KA, Barnetson LP, Shepstone BJ. Accurate prediction of histologically confirmed Alzheimer's disease and the differential diagnosis of dementia: the use of NINCDS-ADRDA and DSM-III-R criteria, SPECT, X-ray CT, and Apo E4 in medial temporal lobe dementias: Oxford Project to Investigate Memory and Aging.  Int Psychogeriatr. 1998;10(3):271-302
PubMed   |  Link to Article
Mayeux R, Saunders AM, Shea S,  et al; Alzheimer's Disease Centers Consortium on Apolipoprotein E and Alzheimer's Disease.  Utility of the apolipoprotein E genotype in the diagnosis of Alzheimer's disease.  N Engl J Med. 1998;338(8):506-511
PubMed   |  Link to Article
Ranginwala NA, Hynan LS, Weiner MF, White CL III. Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years.  Am J Geriatr Psychiatry. 2008;16(5):384-388
PubMed   |  Link to Article
Pike KE, Savage G, Villemagne VL,  et al.  Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease.  Brain. 2007;130(pt 11):2837-2844
PubMed   |  Link to Article
Rowe CC, Ng S, Ackermann U,  et al.  Imaging beta-amyloid burden in aging and dementia.  Neurology. 2007;68(20):1718-1725
PubMed   |  Link to Article
Rowe CC, Ellis KA, Rimajova M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283
PubMed   |  Link to Article
Gomperts SN, Rentz DM, Moran E,  et al.  Imaging amyloid deposition in Lewy body diseases.  Neurology. 2008;71(12):903-910
PubMed   |  Link to Article
Fratiglioni L, Ahlbom A, Viitanen M, Winblad B. Risk factors for late-onset Alzheimer's disease: a population-based, case-control study.  Ann Neurol. 1993;33(3):258-266
PubMed   |  Link to Article
Corder EH, Lannfelt L, Bogdanovic N, Fratiglioni L, Mori H. The role of APOE polymorphisms in late-onset dementias.  Cell Mol Life Sci. 1998;54(9):928-934
PubMed   |  Link to Article
Corder EH, Ghebremedhin E, Taylor MG, Thal DR, Ohm TG, Braak H. The biphasic relationship between regional brain senile plaque and neurofibrillary tangle distributions: modification by age, sex, and APOE polymorphism.  Ann N Y Acad Sci. 2004;1019:24-28
PubMed   |  Link to Article
Ghebremedhin E, Schultz C, Braak E, Braak H. High frequency of apolipoprotein E epsilon4 allele in young individuals with very mild Alzheimer's disease-related neurofibrillary changes.  Exp Neurol. 1998;153(1):152-155
PubMed   |  Link to Article
Ghebremedhin E, Schultz C, Thal DR,  et al.  Gender and age modify the association between APOE and AD-related neuropathology.  Neurology. 2001;56(12):1696-1701
PubMed   |  Link to Article
Reiman EM, Chen K, Liu X,  et al.  Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease.  Proc Natl Acad Sci U S A. 2009;106(16):6820-6825
PubMed   |  Link to Article
Morris JC, Roe CM, Xiong C,  et al.  APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131
PubMed   |  Link to Article
Mintun MA, Larossa GN, Sheline YI,  et al.  [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease.  Neurology. 2006;67(3):446-452
PubMed   |  Link to Article
Joachim CL, Morris JH, Selkoe DJ. Diffuse senile plaques occur commonly in the cerebellum in Alzheimer's disease.  Am J Pathol. 1989;135(2):309-319
PubMed
Choi SR, Golding G, Zhuang Z,  et al.  Preclinical properties of 18F-AV-45: a PET agent for Abeta plaques in the brain.  J Nucl Med. 2009;50(11):1887-1894
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Cortical region of interest template, which includes the orbital frontal cingulate (A), the temporal cingulate (B), the anterior cingulate (C), the posterior cingulate (D), the parietal lobe (E), and the precuneus (F).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Scatterplots of mean cortical standard uptake value ratios (SUVRs) for persons having florbetapir F 18 levels associated with intermediate or high likelihood neuropathological Alzheimer disease (AD) (hereafter referred to as the PATHAMY levels) and young adult APOE ε4 noncarriers (YNC), and for the following clinical diagnostic groups: probable AD (PAD), mild cognitive impairment (MCI), and older healthy control (OHC) group. The black line represents a florbetapir threshold associated with minimal SUVRs seen in the PATHAMY group (≥1.17). The red line represents the minimal SUVR associated with the presence of any identifiable Aβ (hereafter referred to as the any-amyloid levels; >1.08). Mean cortical SUVRs differed significantly among diagnostic groups (P = 2.9 × 10−14).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Mean between-group differences in cerebral-to-cerebellar florbetapir standard uptake value ratios (SUVRs). Representative horizontal, coronal, and sagittal sections show mean between-group increases. The images are displayed as mean differences (P ≤ .05). Typical patterns of amyloid distribution in Alzheimer disease (AD) are demonstrated with preferential uptake in the parietal, frontal, and temporal lobes. MCI indicates mild cognitive impairment group of patients; OHC, older healthy control group; PAD, probable AD group.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 4. Percentages of florbetapir positivity by diagnostic group. The percentages of positivity in all 3 diagnostic groups and in 2 subgroups of older healthy controls (APOE ε4 carriers [OHC E4+] and noncarriers [OHC E4−]) are based on meeting criteria for florbetapir F 18 levels associated with intermediate or high likelihood neuropathological Alzheimer disease (AD) (hereafter referred to as PATHAMY levels, with a standard uptake value ratio [SUVR] of ≥1.17) or the presence of any identifiable cortical Aβ (hereafter referred to as any-amyloid levels, with SUVR > 1.08). The probable AD (PAD), mild cognitive impairment (MCI), and OHC diagnostic groups differed significantly in their percentage of positivity for both thresholds (P < 1.0 × 10−7) but not between the 2 OHC subgroups, likely owing to the small sample size. Only the full OHC group shows significant differences in their percentage of positivity based on threshold used (P = .03).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 5. Percentages of florbetapir positivity by age in older healthy controls (OHCs). The percentages increased by age decile in OHCs (P = .05 for pathological amyloid threshold and P = .01 for presence of any identifiable cortical Aβ). SUVR indicates standard uptake value ratio.

Tables

Table Graphic Jump LocationTable. Characteristics of Participants in Study Designed to Characterize Quantitative Florbetapir F 18 Positron Emission Tomographic Measurements of Fibrillar Aβ Burden

References

Zhang W, Oya S, Kung MP, Hou C, Maier DL, Kung HF. F-18 polyethyleneglycol stilbenes as PET imaging agents targeting Abeta aggregates in the brain.  Nucl Med Biol. 2005;32(8):799-809
PubMed   |  Link to Article
Zhang W, Oya S, Kung MP, Hou C, Maier DL, Kung HF. F-18 stilbenes as PET imaging agents for detecting beta-amyloid plaques in the brain.  J Med Chem. 2005;48(19):5980-5988
PubMed   |  Link to Article
Zhang W, Kung MP, Oya S, Hou C, Kung HF. 18F-labeled styrylpyridines as PET agents for amyloid plaque imaging.  Nucl Med Biol. 2007;34(1):89-97
PubMed   |  Link to Article
Wong DF, Rosenberg PB, Zhou Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir F 18) [published correction appears in J Nucl Med. 2010;51(8):1327].  J Nucl Med. 2010;51(6):913-920
PubMed   |  Link to Article
Clark CM, Schneider JA, Bedell BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology [published correction appears in JAMA. 2011;305(11):1096].  JAMA. 2011;305(3):275-283
PubMed   |  Link to Article
The National Institute on Aging and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer's Disease.  Consensus recommendations for the postmortem diagnosis of Alzheimer's disease.  Neurobiol Aging. 1997;18(4):(suppl)  S1-S2
PubMed   |  Link to Article
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 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. 1984;34(7):939-944
PubMed   |  Link to Article
Tzourio-Mazoyer N, Landeau B, Papathanassiou D,  et al.  Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.  Neuroimage. 2002;15(1):273-289
PubMed   |  Link to Article
Valla J, Yaari R, Wolf AB,  et al.  Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE ε4 allele, the major late-onset Alzheimer's susceptibility gene.  J Alzheimers Dis. 2010;22(1):307-313
PubMed
Braak H, Braak E. Evolution of the neuropathology of Alzheimer's disease.  Acta Neurol Scand Suppl. 1996;165:3-12
PubMed
Mirra SS, Heyman A, McKeel D,  et al.  The Consortium to Establish a Registry for Alzheimer's Disease (CERAD), part II: standardization of the neuropathologic assessment of Alzheimer's disease.  Neurology. 1991;41(4):479-486
PubMed   |  Link to Article
Hyman BT, Trojanowski JQ. Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease.  J Neuropathol Exp Neurol. 1997;56(10):1095-1097
PubMed   |  Link to Article
Aizenstein HJ, Nebes RD, Saxton JA,  et al.  Frequent amyloid deposition without significant cognitive impairment among the elderly.  Arch Neurol. 2008;65(11):1509-1517
PubMed   |  Link to Article
Jack CR Jr, Lowe VJ, Weigand SD,  et al; Alzheimer's Disease Neuroimaging Initiative.  Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease.  Brain. 2009;132(pt 5):1355-1365
PubMed   |  Link to Article
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes.  Acta Neuropathol. 1991;82(4):239-259
PubMed   |  Link to Article
Ikonomovic MD, Klunk WE, Abrahamson EE,  et al.  Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease.  Brain. 2008;131(pt 6):1630-1645
PubMed   |  Link to Article
Klunk WE, Engler H, Nordberg A,  et al.  Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B.  Ann Neurol. 2004;55(3):306-319
PubMed   |  Link to Article
Shaw LM, Vanderstichele H, Knapik-Czajka M,  et al; Alzheimer's Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.  Ann Neurol. 2009;65(4):403-413
PubMed   |  Link to Article
Selkoe DJ. Soluble oligomers of the amyloid beta-protein impair synaptic plasticity and behavior.  Behav Brain Res. 2008;192(1):106-113
PubMed   |  Link to Article
Oddo S, Caccamo A, Kitazawa M, Tseng BP, LaFerla FM. Amyloid deposition precedes tangle formation in a triple transgenic model of Alzheimer's disease.  Neurobiol Aging. 2003;24(8):1063-1070
PubMed   |  Link to Article
Braak H, Braak E. Staging of Alzheimer-related cortical destruction.  Int Psychogeriatr. 1997;9:(suppl 1)  257-261, discussion 269-272
PubMed   |  Link to Article
Jobst KA, Barnetson LP, Shepstone BJ. Accurate prediction of histologically confirmed Alzheimer's disease and the differential diagnosis of dementia: the use of NINCDS-ADRDA and DSM-III-R criteria, SPECT, X-ray CT, and Apo E4 in medial temporal lobe dementias: Oxford Project to Investigate Memory and Aging.  Int Psychogeriatr. 1998;10(3):271-302
PubMed   |  Link to Article
Mayeux R, Saunders AM, Shea S,  et al; Alzheimer's Disease Centers Consortium on Apolipoprotein E and Alzheimer's Disease.  Utility of the apolipoprotein E genotype in the diagnosis of Alzheimer's disease.  N Engl J Med. 1998;338(8):506-511
PubMed   |  Link to Article
Ranginwala NA, Hynan LS, Weiner MF, White CL III. Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years.  Am J Geriatr Psychiatry. 2008;16(5):384-388
PubMed   |  Link to Article
Pike KE, Savage G, Villemagne VL,  et al.  Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease.  Brain. 2007;130(pt 11):2837-2844
PubMed   |  Link to Article
Rowe CC, Ng S, Ackermann U,  et al.  Imaging beta-amyloid burden in aging and dementia.  Neurology. 2007;68(20):1718-1725
PubMed   |  Link to Article
Rowe CC, Ellis KA, Rimajova M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283
PubMed   |  Link to Article
Gomperts SN, Rentz DM, Moran E,  et al.  Imaging amyloid deposition in Lewy body diseases.  Neurology. 2008;71(12):903-910
PubMed   |  Link to Article
Fratiglioni L, Ahlbom A, Viitanen M, Winblad B. Risk factors for late-onset Alzheimer's disease: a population-based, case-control study.  Ann Neurol. 1993;33(3):258-266
PubMed   |  Link to Article
Corder EH, Lannfelt L, Bogdanovic N, Fratiglioni L, Mori H. The role of APOE polymorphisms in late-onset dementias.  Cell Mol Life Sci. 1998;54(9):928-934
PubMed   |  Link to Article
Corder EH, Ghebremedhin E, Taylor MG, Thal DR, Ohm TG, Braak H. The biphasic relationship between regional brain senile plaque and neurofibrillary tangle distributions: modification by age, sex, and APOE polymorphism.  Ann N Y Acad Sci. 2004;1019:24-28
PubMed   |  Link to Article
Ghebremedhin E, Schultz C, Braak E, Braak H. High frequency of apolipoprotein E epsilon4 allele in young individuals with very mild Alzheimer's disease-related neurofibrillary changes.  Exp Neurol. 1998;153(1):152-155
PubMed   |  Link to Article
Ghebremedhin E, Schultz C, Thal DR,  et al.  Gender and age modify the association between APOE and AD-related neuropathology.  Neurology. 2001;56(12):1696-1701
PubMed   |  Link to Article
Reiman EM, Chen K, Liu X,  et al.  Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease.  Proc Natl Acad Sci U S A. 2009;106(16):6820-6825
PubMed   |  Link to Article
Morris JC, Roe CM, Xiong C,  et al.  APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131
PubMed   |  Link to Article
Mintun MA, Larossa GN, Sheline YI,  et al.  [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease.  Neurology. 2006;67(3):446-452
PubMed   |  Link to Article
Joachim CL, Morris JH, Selkoe DJ. Diffuse senile plaques occur commonly in the cerebellum in Alzheimer's disease.  Am J Pathol. 1989;135(2):309-319
PubMed
Choi SR, Golding G, Zhuang Z,  et al.  Preclinical properties of 18F-AV-45: a PET agent for Abeta plaques in the brain.  J Nucl Med. 2009;50(11):1887-1894
PubMed   |  Link to Article

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