Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Acta Neuropathol. Author manuscript; available in PMC 2013 December 1.
Published in final edited form as:
PMCID: PMC3566238

Correspondence between in vivo 11C-PiB PET amyloid imaging and post-mortem, region-matched assessment of plaques


The definitive Alzheimer’s disease diagnosis requires post-mortem confirmation of neuropathological hallmarks – amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs). The advent of radiotracers for amyloid imaging presents an opportunity to investigate amyloid deposition in vivo. The 11C-Pittsburgh Compound-B (PiB) PET ligand remains the most widely studied to date; however, regional variations in 11C-PiB binding and the extent of agreement with neuropathological assessment have not been thoroughly investigated. We examined the correspondence among quantitative immunohistological assessments of Aβ and NFTs, regional 11C-PiB load, and brain atrophy (MRI) in six older Baltimore Longitudinal Study of Aging participants who came to autopsy (imaging-autopsy interval range 0.2–2.4 years). The total number of Aβ plaques (6E10) and NFTs (PHF1) in paraffin sections from hippocampus, orbito-frontal cortex, anterior and posterior cingulate gyrus, precuneus and cerebellum were quantified using a technique guided by unbiased stereological principles. We report a general agreement between the regional measures of amyloid obtained via stereological assessment and imaging, with significant relationships evident for the anterior (r=0.87; p=0.02) and posterior (r=0.93; p=0.007) cingulate gyri, and the precuneus (r=0.98; p=0.001). Moreover, higher Aβ count in the hippocampus was associated with lower hippocampal volume (r= −0.86; p=0.03). No associations were observed between 11C-PiB load and NFT count for any of the regions examined (p>0.2 in all regions) or between regional NFT count and corresponding brain volumes. The strong associations of PiB retention with region-matched, quantitative analyses of Aβ in post-mortem tissue offer support for the validity of 11C-PiB-PET imaging as a method for evaluation of plaque burden in vivo.

Keywords: plaques, tangles, stereology, PiB, Alzheimer, neuroimaging


Considerable resources have been focused on developing disease-modifying strategies aimed at delaying the pathophysiological processes in Alzheimer’s disease (AD). Understanding pathologic mechanisms and biological risks is imperative to the discovery of biomarkers, pharmacological targets, and treatments. The preeminent explanation of AD pathogenesis, the ‘amyloid cascade hypothesis’, suggests that amyloid-β plaque (Aβ) deposition underlies neurodegeneration, subsequent brain atrophy, cognitive impairment and ultimately dementia [13].

The definitive diagnosis of AD still requires confirmation either by biopsy or autopsy. Post-mortem confirmation is based on neuropathological hallmarks – Aβ and neurofibrillary tangles (NFTs). Radiotracers for amyloid imaging now provide the opportunity to investigate fibrillar Aβ deposition in vivo. Thus, PET amyloid imaging agents hold promise not only for early diagnosis of AD, but can also add to the understanding of the pathogenesis of AD, the temporal course of amyloid deposition, and its relation to clinical manifestations. The 11C-Pittsburgh Compound-B (PiB) ligand remains the most widely studied PET amyloid imaging agent to date but the validations against post-mortem measures of neuropathology are a few [1, 3, 4, 14, 1617, 3536].

In vitro, 11C-PiB binds specifically to extracellular and vascular fibrillar Aβ [14, 21]. At PET tracer concentrations, 11C-PiB does not significantly bind to other protein aggregates such as NFTs [14, 17, 19,] or Lewy bodies [10, 17]. The first 11C-PiB study in humans performed in mild AD patients [19] reported patterns of PiB retention consistent with the classic pattern of amyloid deposition described in post-mortem studies of AD brains [2] and demonstrated approximately a twofold increase in 11C-PiB retention in AD cases compared to non-demented individuals [19]. Moreover, 11C-PiB shows promise in discriminating individuals with mild cognitive impairment (MCI) who progress vs. those who remain stable [11, 20, 27, 37,] and provides some utility for differential diagnosis of AD from frontotemporal dementia [9, 26, 29].

Regional variations in PiB binding and the extent of agreement with neuropathological assessment however, have not been thoroughly investigated, and unbiased stereological investigations are lacking given that stereology remains a time-consuming process not easily compatible with routine neuropathologic work. In a previous study [35], we reported variable agreement between the mean PiB cortical distribution volume ratio and the neuritic plaque score according to Consortium to Establish a Registry for AD (CERAD), routinely used for pathologic diagnosis of Alzheimer disease. The limited agreement may reflect the inherent methodological limitations and differences between imaging and CERAD semi-quantitative neuropathologic protocols. Here we extend those findings by examining the regional correspondence between PiB retention, quantitative immunohistological regional assessments of Aβ and NFTs, and regional brain atrophy in six older Baltimore Longitudinal Study of Aging (BLSA) participants.



The sample consists of six participants (5 males and 1 female) from the BLSA – Neuroimaging (NI) sub-study [30, 31, 35], who underwent 11C PiB-PET and MRI assessments and eventually came to autopsy. The autopsy cases in this manuscript are the same ones as those studied in a previously published manuscript [35]. Participants were between 78 and 87 years of age at the time of 11C PiB PET imaging and received concurrent MRI scans. Imaging-autopsy intervals ranged between 0.2 to 2.4 years. All studies were approved by the local institutional review boards, and all participants gave written informed consent prior to each assessment. In addition, next of kin or legally designated power of attorney provided consent for autopsy.

Demographic, genetic and cognitive status data are presented in Table 1. Participants received routine neuropsychological and neurological exams. Interval medical history, medication review, and a structured informant and subject interview were documented at each visit [7, 18]. All participants were in general good health at the time of BLSA neuroimaging study enrollment, with exclusionary criteria encompassing CNS disease, severe cardiovascular disease, severe pulmonary disease, or metastatic cancer. None of the participants had Parkinson disease, epilepsy, or clinical stroke at the time of PiB PET imaging.

Table 1
Demographic, genetic and cognitive status information.

Cognitive status was determined based on standardized consensus diagnostic procedures for the BLSA [7]. The CDR Scale [25], typically informant-based, was administered at each visit including the one concurrent with PiB imaging. All six participants were non-demented at the time of scan, three remained cognitively stable, two developed MCI, and only one subject became demented prior to death.

Neuropathologic Evaluations


The brains were examined in the Division of Neuropathology of the Johns Hopkins University. The left hemibrain was fixed in 10% buffered formaldehyde for at least two weeks, then sectioned in the coronal plane. Tissue blocks from medial frontal gyrus (MFG), inferior parietal (IP) region, superior middle temporal gyrus (SMTG), visual cortex, amygdala, and entorhinal cortex were processed, embedded in paraffin, cut at 10 μm and stained using the Hirano modification of Bielchowky’s silver stain. Neuritic plaque burden was assessed on a semi-quantitative age-adjusted scale (CERAD 0, A, B, or C) [24] based on the MFG, IP, and SMTG. Neurofibrillary tangle staging was assigned a score (0–VI) [2]. Presence of CAA was assessed in regions used for CERAD evaluation on hematoxylin and eosin (H & E) stains. Evaluation for Lewy body pathology was also performed [22].


For imaging-pathology correlations, tissue blocks from the hippocampus, orbito-frontal cortex, anterior cingulate gyrus, posterior cingulate gyrus, precuneus and the cerebellum were processed, embedded in paraffin, and cut serially at 10 μm (yielding 50 sections). Using a random start, we selected five sections per region using a systematic 10-section interval. Sections were mounted on slides, deparaffinized, dehydrated in alcohols and xylene, formic acid and heat pretreated for 5 minutes, washed and blocked with normal serum, incubated with the Aβ antibody (6E10, Covance Inc., Princeton, New Jersey; dilution 1:500), washed and blocked with the biotinylated secondary antibody, followed by the avidin-biotin-peroxidase complex (ABC-Elite kit, Vector Laboratories, Burlingame, CA), counterstained with hematoxylin, and coversliped using Permount. Adjacent sections were stained with phosphorylated Tau-protein (PHF-1 clone, a gift from Dr. P. Davies, Albert Einstein College of Medicine, Bronx, NY; dilution 1:100). The Aβ and Tau stained sections were used to assess the respective fractional areas for these proteins. Additional adjacent sections were stained with cresyl violet and used for regional anatomical delineation. Lewy Body pathology was excluded in the diagnostic neuropathology work-up.


Stereological measurements were performed with a Zeiss light microscope equipped with a 100X, NA 1.30, oil Plan neofluor ∞/0.17 objective interfaced with the Stereo-Investigator optical dissector software from MBF Biosciences, MicroBrightField, Inc. (Williston, VT, USA). The images were captured with a microFire video camera (Optronics, Goleta, CA, USA). The actual thickness of each section was measured in the 8 to 9 micron range. Aβ and Tau-protein immunoreactivities were measured using the area-fraction-fractionator probe (counting frame: 150 μm×150 μm), which allowed us to assess the burden of proteins of interest in these sections. We outlined a contour of cortical gray matter and selected sampling sites with a grid size = 800 μm × 800 μm. At each sampling site, a counting frame was superimposed, containing markers equally spaced from one another. The markers that co-localized with immunoreactivity were labeled as positive, whereas remaining markers were labeled negative. The area fraction was calculated as the number of positive markers divided by the total number of markers. All assessments were performed blinded to clinical and neuropathological diagnoses. Values for each region were averaged to arrive at the mean fraction of Aβ or Tau-protein immunoreactivity. Coefficients of error ranged between 0.07–0.12 for both.

PET Imaging

PET acquisition

Dynamic 11C-PiB-PET images were acquired on a GE Advance scanner in three-dimensional mode with 33 time frames (70 min) obtained at rest. PET scanning immediately followed an intravenous bolus injection of a mean (SD) 14.7 (0.9) mCi 11C-PiB with a mean (SD) specific activity of 5.1 (2.8; range 1.9–10.2) Ci/μmol. Participants were fitted with a thermoplastic mask to minimize motion during scanning. Two-dimensional transmission scans were used for attenuation correction of the emission scans. Dynamic images were reconstructed using filtered backprojection with a ramp filter (image size 128 × 128; pixel size, 2mm × 2 mm; slice thickness, 4.25 mm) and a spatial resolution of about 4.50-mm full width at half maximum at the center of the field of view.

MRI-based ROI definition for 11C-PiB-PET

MRI scans acquired concurrently or closest in time to the 11C-PiB-PET scan were used for region of interest (ROI) definition. MRI scans for ROI definition used either the SPGR volumetric scans from the 1.5 T GE Signa or a comparable T1-weighted volumetric imaging protocol on a Philips 1.5 T scanner, which replaced the GE scanner in 2006. Volumetric scans were co-registered to the mean of the first 20-min dynamic PET images for each participant using the mutual information method in the Statistical Parametric Mapping software (SPM2; Wellcome Department of Cognitive Neurology). ROIs were drawn manually on the co-registered MR images [28]. Cerebellum served as a reference region. The parametric images of distribution volume ratio (DVR) and R1 (=K1/K1 (reference tissue), the target to reference tissue ratio of tracer transport rate constant from vascular space to tissue) were generated by using a simplified reference tissue model and linear regression with spatial constraint algorithm [38, 39]. The mean cortical DVR was calculated by averaging DVR values from orbito-frontal, prefrontal, superior frontal, parietal, lateral temporal, occipital, and anterior and posterior cingulate regions [36]. For voxel-wise analysis, parametric images of DVR were spatially normalized using SPM2 with an R1 template [39], which provides better brain boundary definition in the case of low PiB retention. The ROI DVRs were obtained by applying MR-based ROIs to the DVR images.

MR Imaging

MRI acquisition

Scanning was performed on a GE Signa 1.5 Tesla scanner (Milwaukee, WI) using a high-resolution volumetric spoiled-grass (SPGR) axial series (TR = 35 ms, TE=5 ms, FOV= 24 cm, flip angle = 45°, matrix = 256 × 256, NEX= 1, voxel dimensions 0.94 mm × 0.94 mm × 1.5 mm).

MRI image analysis

MRI scans acquired concurrently or closest in time to the 11C-PiB-PET scan were used to obtain regional volumes. Image processing procedures have been previously described in detail and validated [6, 8, 12, 30, 31]. Briefly, images were corrected for head tilt and rotation, and reformatted parallel to the anterior–posterior commissure plane. Extracranial tissue was removed using a semi-automated procedure followed by manual editing. Next, images were segmented into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The final step involves stereotaxic normalization and tissue quantitation for specific regions of interest with a template-based deformation approach. An ICBM (International Consortium for Brain Mapping) standard MRI anatomical template (Montreal Neurologic Institute) served as the template and a hierarchical elastic matching algorithm was used for deformation and determination of volumes of interest [34]. All images were normalized individually to the same template. Voxel-based analysis utilizes the RAVENS approach [12], whereby local values of tissue density maps (for GM, WM, and CSF) reflect the amount of respective tissue in the vicinity of a voxel. Tissue densities are mathematical quantities measuring local tissue volumes and do not reflect any microstructural physical density of brain tissue.


Participants and Cognitive Status

Although all participants were non-demented at the time of scanning, participant F received a diagnosis of probable AD at last imaging visit, which occurred 55 days prior to death. Participant F also received a neuropathologic diagnosis of probable AD at autopsy based on CERAD neuritic plaque score and Braak NFT staging. Mild memory impairment by CDR (0.5) was documented in participants D and F.

Regional Distribution of PiB

Mean cortical DVR values ranged between 0.96 and 1.59. Highest regional DVR values were observed in the precuneus, as well as the anterior and posterior cingulate regions. Regional DVR values were the lowest for the hippocampus and generally low for the OFC (see Table 2; Figure 1).

Figure 1
Comparison of regional distribution of A) amyloid load in vivo (11C-PiB DVR; from Sojkova et al., 2011) and B) corresponding Aβ immunohistochemistry (6E10) on a case-by-case basis. A) Voxel-wise 11C-PiB distribution DVR maps are overlaid on the ...
Table 2
Overview of regional 11C-PiB DVR values and Aβ and Tau quantifications obtained employing techniques guided by unbiased stereological principles (area fractions).

Regional Distribution of Aβ and Tau

Upon immunohistochemical evaluation, the cerebellum was free of both Aβ by 6E10 and Tau by PHF1. Aβ was detected throughout the remaining regions of interest in this sample, with the exception of case A, which was free of Aβ across all sampled regions (see Figure 1). Aβ load was lowest in the hippocampus; intermediate in the posterior cingulate gyrus and the precuneus, and highest in the OFC and the anterior cingulate gyrus. Tau immunostaining was detected primarily in the hippocampus, and little or no Tau was detected in the remaining regions of interest.

PiB-Pathology Correspondence

Regional 11C-PiB DVR values and Aβ and Tau loads are presented in Table 2. In general, there was an agreement between the regional measures of Aβ obtained via stereological assessment and imaging, with significant relationships evident for the anterior (r=0.87; p=0.02) and posterior (r=0.93; p=0.007) cingulate gyri, and the precuneus (r=0.98; p=0.001). Regional correspondence of amyloid by immunohistochemistry (6E10) and amyloid load obtained in vivo (11C-PiB) is presented in Figures 1 and and22 on a case-by-case basis. No significant associations were observed between regional 11C-PiB load and Tau immunohistochemistry for any of the regions examined (p>0.2 for all regions).

Figure 2
Correspondence between regional amyloid load by 11C-PiB and immunohistochemistry (6E10) in A) the hippocampus, B) orbito-frontal cortex, C) anterior cingulate gyrus, D) posterior cingulate gyrus, and E) the precuneus. Regional PiB DVR is represented on ...

Brain Volume–Pathology Correspondence

Higher Aβ count by immunohistochemistry in the hippocampus was associated with lower hippocampal volume (r= −0.86; p=0.03). No significant regional associations were observed between Tau immunohistochemistry or PiB DVR, respectively, or with other respective regional brain volumes investigated, although it should be noted that the cingulate gyrus volume we obtained was for anterior and posterior regions combined.


Fractional areas of Aβ (6E10) and Tau (PHF1) immunoreactivities in 5 random, systematically-selected paraffin sections from each of the following regions: hippocampus, orbito-frontal cortex, anterior cingulate gyrus, posterior cingulate gyrus, precuneus and the cerebellum were quantified employing technique guided by unbiased stereological principles. We compared these measures of Aβ and Tau with Aβ burden measured ante-mortem using PET and 11C-PiB. We report an agreement between regional in vivo 11C-PiB retention and region-matched, quantitative analyses of Aβ in post-mortem tissue.

Only a handful of studies to date have investigated the direct relationship between 11C-PiB binding and neuropathology, one in AD [14], one in Parkinson’s Disease (PD) dementia [3] and two case reports of dementia with Lewy bodies (LBD) [3,17]. Although sample sizes were small (AD: n=1; PDD: n=3; LBD: n=1), all studies demonstrated high selectivity of 11C-PiB for fibrillar Aβ deposits. A recent study also showed good agreement between ante-mortem imaging and autopsy findings for the 18-F amyloid imaging radiotracer, Florbetapir [5].

We recently investigated the correspondence between 11C-PiB DVRs and CERAD-based neuropathological diagnoses in the same six individuals included in the present study. In our earlier study [35], using the Hirano modification of the Bielschowsky silver stain, we found only limited and variable agreement between CERAD score and mean cortical 11C-PiB DVR. This study had a couple of important methodological limitations at the time: 1.) CERAD is a semi-quatitative rating based on a small volume of tissue in specific cortical regions, 2.) cortical PiB DVR, a summary score of eight cortical regions was used, both of which may have contributed to the limited agreement between neuropathological and imaging assessments of amyloid. Our current results suggest that much better agreement is observed in the same individuals using immunohistochemistry and quantitative stereological methodology when evaluating the regions typically showing the greatest 11C-PiB retention. In the present investigation we systematically sampled throughout the regions of interest to better capture the distribution of amyloid deposition. Moreover, we examined the correspondence between imaging and post-mortem measures in regions showing the earliest evidence of 11C-PiB retention, including precuneus and cingulate regions. This is especially important, given that most of the individuals evaluated here were non-demented and in non-demented individuals early Aβ deposition is observed in the precuneus/posterior cingulate and the anterior cingulate gyrus [23], regions not included in the CERAD neuropathological assessment.

Although the ‘amyloid cascade hypothesis’, which suggests that Aβ deposition underlies neurodegeneration, subsequent brain atrophy, cognitive impairment and ultimately dementia, has been the dominant explanation of AD pathogenesis to date [13]. Yet, the tangles often precede amyloid deposition and the tangles that are left in the extracellular space after the death of neurons that contain them (“ghost tangles”) provide direct evidence for neuronal death either caused by or at least associated with tangles [2, 40], posing a challenge for the ‘amyloid cascade hypothesis’. We also investigated the potential association between Tau immunohistochemistry and 11C-PiB and found no relationship. Additionally, we found no relationships between regional brain volumes obtained from MRI scans closest in time to amyloid imaging and PiB retention. Moreover, there were no significant associations of brain volume with Aβ or Tau levels by immunohistochemistry, except for the significant relationships between hippocampal volume and Aβ-6E10 immunohistochemistry. As Jack and colleagues [15] note, both Aβ deposition and NFTs can be present in individuals who do not exhibit any clinical symptoms. The presence of NFTs, however, although widespread in demented individuals tends to be confined to the entorhinal cortex (Braak stage I–II) in asymptomatic individuals [15] and all participants in our study were clinically normal at the time of the scan. The relationships between brain volume and Aβ or Tau respectively will need to be revisited as the autopsy sample with prior imaging assessments grows, as we likely lacked power in the present sample to detect small changes. Moreover, a note of caution is in order considering that shorter MRI-PiB interval in some cases and refinement of image processing techniques that would allow us to separate anterior from posterior cingulate gyrus volumes might lead to positive findings in the future.

There are several limitations to our study. The strength of the relationship between amyloid by PiB vs. immunohistochemistry may be affected by different forms of Aβ. For example, PiB does not bind to a polymorphic form of Aβ [32], which may account for some of the discrepancy – especially in cases of low PiB retention yet moderate scores upon neuropathologic evaluation, particularly in the hippocampus and the orbito-frontal cortex. A recent study by Ikonomovic and colleagues [41] examined Aβ pathology in an individual with clinical diagnoses of probable Lewy body dementia and possible AD but no detectable PiB PET retention, suggesting a need for better defining the Aβ concentrations and binding levels needed to produce a positive PiB PET signal. The inherent problem of varied imaging-autopsy intervals may also affect the correspondence between imaging-neuropathologic assessments. Moreover, the truly unbiased stereological investigation would involve defining, dissecting, and cutting the entire structure of interest from which then random sections would be chosen for immunohistochemistry, while ours were selected from a 0.5 mm of tissue from the region of interest for practical purposes. Not sampling throughout the entire structure may have biased the estimation of Aβ plaques in individuals with potentially sparse or sporadically distributed plaques. Limitations notwithstanding, this study represents the largest of its kind to date and the only series to date including non-demented adults with variable levels of PiB retention. Moreover, a decade of prospective cognitive follow-up and consensus diagnosis makes this sample a unique and valuable resource.

Overall, we report an agreement between ante-mortem 11C-PiB amyloid imaging and post-mortem quantitative assessment of Aβ deposition in a sample of six older, prospectively followed individuals. The strong correlation of in vivo PiB retention with region-matched, quantitative analyses of Aβ in post-mortem tissue offer support for the validity of 11C-PiB-PET imaging as a method for evaluation of plaque burden in vivo. Prospective studies will not only be imperative in furthering our understanding of amyloid deposition and its role in the disease process, but also in guiding preventative and therapeutic strategies.


This work was supported in part by the NIA Intramural Research Program of the National Institutes of Health. Dr. Troncoso was supported by a grant from the JHU ADRC (NIA AG05146) and the Alzheimer’s Association (IIRG-09-134090). We thank Andrew Crabb, MS, for image data management, Beth Nardi, MA, for study management; the staff of the PET facility, the Brain Bank, and the Autopsy Study at The Johns Hopkins University and the neuroimaging staff of the National Institute on Aging for their assistance. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging.


Conflict of Interest: GE Healthcare holds a license agreement with the University of Pittsburgh based on the PiB technology described in this article. Drs. Klunk and Mathis are co-inventors of PiB and, as such, have a financial interest in this license agreement.


1. Bacskai BJ, Frosch MP, Freeman SH, Raymond SB, Augustinack JC, Johnson KA, Irizarry MC, Klunk WE, Mathis CA, Dekosky ST, Greenberg SM, Hyman BT, Growdon JH. Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report. Arch Neurol. 2007;64(3):431–434. [PubMed]
2. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–259. [PubMed]
3. Burack MA, Hartlein J, Flores HP, Taylor-Reinwald L, Perlmutter JS, Cairns NJ. In vivo amyloid imaging in autopsy-confirmed Parkinson disease with dementia. Neurology. 2010;74(1):77–84. [PMC free article] [PubMed]
4. Cairns NJ, Ikonomovic MD, Benzinger T, Storandt M, Fagan AM, Shah AR, Reinwald LT, Carter D, Felton A, Holtzman DM, Mintun MA, Klunk WE, Morris JC. Absence of Pittsburgh Compound B detection of cerebral amyloid beta in a patient with clinical, cognitive, and cerebrospinal fluid markers of Alzheimer disease: a case report. Arch Neurol. 2009;66(12):1557–1562. [PMC free article] [PubMed]
5. Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, Pontecorvo MJ, Hefti F, Carpenter AP, Flitter ML, Krautkramer MJ, Kung HF, Coleman RE, Doraiswamy PM, Fleisher AS, Sabbagh MN, Sadowsky CH, Reiman EP, Zehntner SP, Skovronsky DM. AV45-A07 Study Group. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305(3):275–283. [PubMed]
6. Davatzikos C, Genc A, Xu D, Resnick SM. Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. Neuroimage. 2001;14:1361–1369. [PubMed]
7. Driscoll I, Resnick SM, Troncoso JC, An Y, O’Brien R, Zonderman AB. Impact of Alzheimer’s pathology on cognitive trajectories in nondemented elderly. Ann Neurol. 2006;60:688– 695. [PubMed]
8. Driscoll I, Davatzikos C, An Y, Wu X, Shen D, Kraut M, Resnick SM. Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology. 2009;72(22):1906–1913. [PMC free article] [PubMed]
9. Drzezga A, Grimmer T, Henriksen G, Stangier I, Perneczky R, Diehl-Schmid J, Mathis CA, Klunk WE, Price J, DeKosky S, Wester HJ, Schwaiger M, Kurz A. Imaging of amyloid plaques and cerebral glucose metabolism in semantic dementia and Alzheimer’s disease. Neuroimage. 2008;39(2):619–633. [PubMed]
10. Fodero-Tavoletti MT, Smith DP, McLean CA, Adlard PA, Barnham KJ, Foster LE, Leone L, Perez K, Cortés M, Culvenor JG, Li QX, Laughton KM, Rowe CC, Masters CL, Cappai R, Villemagne VL. In vitro characterization of Pittsburgh compound-B binding to Lewy bodies. J Neurosci. 2007;27(39):10365–10371. [PubMed]
11. Forsberg A, Engler H, Almkvist O, Blomquist G, Hagman G, Wall A, Ringheim A, Långström B, Nordberg A. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging. 2008;29(10):1456–1465. [PubMed]
12. Goldszal AF, Davatzikos C, Pham DL, Yan MX, Bryan RN, Resnick SM. An image- processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomogr. 1998;22:827–837. [PubMed]
13. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science. 2002;297:353–356. [PubMed]
14. Ikonomovic MD, Klunk WE, Abrahamson EE, Mathis CA, Price JC, Tsopelas ND, Lopresti BJ, Ziolko S, Bi W, Paljug WR, Debnath ML, Hope CE, Isanski BA, Hamilton RL, DeKosky ST. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain. 2008;131:1630–1645. [PubMed]
15. Jack CR, Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9(1):119–128. [PMC free article] [PubMed]
16. Kadir A, Almkvist O, Forsberg A, Wall A, Engler H. Långström B, Nordberg A (2012) Dynamic changes in PET amyloid and FDG imaging at different stages of Alzheimer’s disease. Neurobiol Aging. 33(1):198.e1–14. [PubMed]
17. Kantarci K, Yang C, Schneider JA, Senjem ML, Reyes DA, Lowe VJ, Barnes LL, Aggarwal NT, Bennett DA, Smith GE, Petersen RC, Jack CR, Jr, Boeve BF. Ante mortem amyloid imaging and β-amyloid pathology in a case with dementia with Lewy bodies. Neurobiol Aging. 2010 [Epub ahead of print] [PMC free article] [PubMed]
18. Kawas C, Gray S, Brookmeyer R, Fozard J, Zonderman A. Age-specific incidence rates of Alzheimer’s disease: the Baltimore Longitudinal Study of Aging. Neurology. 2000;54(11):2072–2077. [PubMed]
19. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergström M, Savitcheva I, Huang GF, Estrada S, Ausén B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Långström B. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–319. [PubMed]
20. Koivunen J, Pirttilä T, Kemppainen N, Aalto S, Herukka SK, Jauhianen AM, Hänninen T, Hallikainen M, Någren K, Rinne JO, Soininen H. PET amyloid ligand [11C]PIB uptake and cerebrospinal fluid beta-amyloid in mild cognitive impairment. Dement Geriatr Cogn Disord. 2008;26(4):378–83. [PubMed]
21. Lockhart A, Lamb JR, Osredkar T, Sue LI, Joyce JN, Ye L, Libri V, Leppert D, Beach TG. PIB is a non-specific imaging marker of amyloid-beta (Abeta) peptide-related cerebral amyloidosis. Brain. 2007;130:2607–2615. [PubMed]
22. McKeith IG, Galasko D, Kosaka K, Perry EK, Dickson DW, Hansen LA, Salmon DP, Lowe J, Mirra SS, Byrne EJ, Lennox G, Quinn NP, Edwardson JA, Ince PG, Bergeron C, Burns A, Miller BL, Lovestone S, Collerton D, Jansen EN, Ballard C, de Vos RA, Wilcock GK, Jellinger KA, Perry RH. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology. 1996;47(5):1113–1124. [PubMed]
23. Mintun MA, Larossa GN, Sheline YI, Dence CS, Lee SY, Mach RH, Klunk WE, Mathis CA, DeKosky ST, Morris JC. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology. 2006;67(3):446–452. [PubMed]
24. Mirra SS, Hart MN, Terry RD. Making the diagnosis of Alzheimer’s disease. A primer for practicing pathologists. Arch Pathol Lab Med. 1993;117(2):132–144. [PubMed]
25. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43:2412–2414. [PubMed]
26. Ng SY, Villemagne VL, Masters CL, Rowe CC. Evaluating atypical dementia syndromes using positron emission tomography with carbon 11 labeled Pittsburgh Compound B. Arch Neurol. 2007;64:1140–1144. [PubMed]
27. Okello A, Koivunen J, Edison P, Archer HA, Turkheimer FE, Någren K, Bullock R, Walker Z, Kennedy A, Fox NC, Rossor MN, Rinne JO, Brooks DJ. Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study. Neurology. 2009;73(10):754–760. [PMC free article] [PubMed]
28. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, Holt DP, Meltzer CC, DeKosky ST, Mathis CA. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;25(11):1528–1547. [PubMed]
29. Rabinovici GD, Furst AJ, O’Neil JP, Racine CA, Mormino EC, Baker SL, Chetty S, Patel P, Pagliaro TA, Klunk WE, Mathis CA, Rosen HJ, Miller BL, Jagust WJ. 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology. 2007;68(15):1205–1212. [PubMed]
30. Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ, Bryan RN, Zonderman AB. One-year age changes in MRI brain volumes in older adults. Cereb Cortex. 2000;10:464–472. [PubMed]
31. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci. 2003;23:3295– 3301. [PubMed]
32. Rosen RF, Ciliax BJ, Wingo TS, Gearing M, Dooyema J, Lah JJ, Ghiso JA, LeVine H, 3rd, Walker LC. Deficient high-affinity binding of Pittsburgh compound B in a case of Alzheimer’s disease. Acta Neuropathol. 2010;119(2):221–233. [PMC free article] [PubMed]
33. Rowe CC, Ng S, Ackermann U, Gong SJ, Pike K, Savage G, Cowie TF, Dickinson KL, Maruff P, Darby D, Smith C, Woodward M, Merory J, Tochon-Danguy H, O’Keefe G, Klunk WE, Mathis CA, Price JC, Masters CL, Villemagne VL. Imaging beta-amyloid burden in aging and dementia. Neurology. 2007;68:718–1725. [PubMed]
34. Shen D, Davatzikos C. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging. 2002;21:1421–1439. [PubMed]
35. Sojkova J, Driscoll I, Iacono D, Zhou Y, Codispoti KE, Kraut MA, Ferrucci L, Pletnikova O, Mathis CA, Klunk WE, O’Brien RJ, Wong DF, Troncoso JC, Resnick SM. In vivo fibrillar beta-amyloid detected using [11C]PiB positron emission tomography and neuropathologic assessment in older adults. Arch Neurol. 2011;68(2):232–240. [PMC free article] [PubMed]
36. Villemagne VL, McLean CA, Reardon K, Boyd A, Lewis V, Klug G, Jones G, Baxendale D, Masters CL, Rowe CC, Collins SJ. 11C-PiB PET studies in typical sporadic Creutzfeldt-Jakob disease. J Neurol Neurosurg Psychiatry. 2009;80(9):998–1001. [PubMed]
37. Wolk DA, Price JC, Saxton JA, Snitz BE, James JA, Lopez OL, Aizenstein HJ, Cohen AD, Weissfeld LA, Mathis CA, Klunk WE, DeKosky ST. Amyloid imaging in mild cognitive impairment subtypes. Ann Neurol. 2009;65(5):557–568. [PMC free article] [PubMed]
38. Zhou Y, Endres CJ, Brasić JR, Huang SC, Wong DF. Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model. Neuroimage. 2003;18(4):975–989. [PubMed]
39. Zhou Y, Resnick SM, Ye W, Fan H, Holt DP, Klunk WE, Mathis CA, Dannals R, Wong DF. Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer’s disease. Neuroimage. 2007;36(2):298–312. [PMC free article] [PubMed]
40. Duyckaerts C, Potier MC, Delatour B. Alzheimer disease models and human neuropathology: similarities and differences. Acta Neuropathol. 2008;115(1):5–38. [PMC free article] [PubMed]
41. Ikonomovic MD, Abrahamson EE, Price JC, Hamilton RL, Mathis CA, Paljug WR, Debnath ML, Cohen AD, Mizukami K, DeKosky ST, Lopez OL, Klunk WE. Early AD pathology in a [C-11]PiB-negative case: a PiB-amyloid imaging, biochemical, and immunohistochemical study. Acta Neuropathol. 2012;123(3):433–447. [PMC free article] [PubMed]