Vincent Dore

In vivo evaluation of a novel tau imaging tracer for Alzheimer’s disease

PurposeDiagnosis of tauopathies such as Alzheimer’s disease (AD) still relies on post-mortem examination of the human brain. A non-invasive method of determining brain tau burden in vivo would allow a better understanding of the pathophysiology of tauopathies. The purpose of the study was to evaluate 18F-THK523 as a potential tau imaging tracer.MethodsTen healthy elderly controls, three semantic dementia (SD) and ten AD patients underwent neuropsychological examination, MRI as well as 18F-THK523 and 11C-Pittsburgh compound B (PIB) positron emission tomography (PET) scans. Composite memory and non-memory scores, global and hippocampal brain volume, and partial volume-corrected tissue ratios for 18F-THK523 and 11C-PIB were estimated for all participants. Correlational analyses were performed between global and regional 18F-THK523, 11C-PIB, cognition and brain volumetrics.Results18F-THK523 presented with fast reversible kinetics. Significantly higher 18F-THK523 retention was observed in the temporal, parietal, orbitofrontal and hippocampi of AD patients when compared to healthy controls and SD patients. White matter retention was significantly higher than grey matter retention in all participants. The pattern of cortical 18F-THK523 retention did not correlate with Aβ distribution as assessed by 11C-PIB and followed the known distribution of tau in the AD brain, being higher in temporal and parietal areas than in the frontal region. Unlike 11C-PIB, hippocampal 18F-THK523 retention was correlated with several cognitive parameters and with hippocampal atrophy.Conclusion18F-THK523 does not bind to Aβ in vivo, while following the known distribution of paired helical filaments (PHF)-tau in the brain. Significantly higher cortical 18F-THK523 retention in AD patients as well as the association of hippocampal 18F-THK523 retention with cognitive parameters and hippocampal volume suggests 18F-THK523 selectively binds to tau in AD patients. Unfortunately, the very high 18F-THK523 retention in white matter precludes simple visual inspection of the images, preventing its use in research or clinical settings.

Cross-sectional and Longitudinal Analysis of the Relationship Between Aβ Deposition, Cortical Thickness, and Memory in Cognitively Unimpaired Individuals and in Alzheimer Disease

IMPORTANCE β-amyloid (Aβ) deposition is one of the hallmarks of Alzheimer disease. Aβ deposition accelerates gray matter atrophy at early stages of the disease even before objective cognitive impairment is manifested. Identification of at-risk individuals at the presymptomatic stage has become a major research interest because it will allow early therapeutic interventions before irreversible synaptic and neuronal loss occur. We aimed to further characterize the cross-sectional and longitudinal relationship between Aβ deposition, gray matter atrophy, and cognitive impairment. OBJECTIVE To investigate the topographical relationship of Aβ deposition, gray matter atrophy, and memory impairment in asymptomatic individuals with Alzheimer disease pathology as assessed by Pittsburgh compound B positron emission tomography (PiB-PET). DESIGN Regional analysis was performed on the cortical surface to relate cortical thickness to PiB retention and episodic memory. SETTING The Australian Imaging, Biomarkers, and Lifestyle Study of Aging, Austin Hospital, Melbourne, Australia. PARTICIPANTS Ninety-three healthy elderly control subjects (NCs) and 40 patients with Alzheimer disease from the Australian Imaging, Biomarkers, and Lifestyle Study of Aging cohort. INTERVENTION Participants underwent neuropsychological evaluation as well as magnetic resonance imaging and PiB-PET scans. Fifty-four NCs underwent repeated scans and neuropsychological evaluation 18 and 36 months later. MAIN OUTCOMES AND MEASURES Correlations between cortical thickness, PiB retention, and episodic memory. RESULTS There was a significant reduction in cortical thickness in the precuneus and hippocampus associated with episodic memory impairment in the NC PiB-positive (NC+) group when compared with the NC- group. Cortical thickness was also correlated negatively with neocortical PiB in the NC+ group. Longitudinal analysis showed a faster rate of gray matter (GM) atrophy in the temporal lobe and the hippocampi of the NC+ group. Over time, GM atrophy became more extensive in the NC+ group, especially in the temporal lobe. CONCLUSIONS AND RELEVANCE In asymptomatic individuals, Aβ deposition is associated with GM atrophy and memory impairment. The earliest signs of GM atrophy were detected in the hippocampus and the posterior cingulate and precuneus regions, and with disease progression, atrophy became more extensive in the temporal lobes. These findings support the notion that Aβ deposition is not a benign process and that interventions with anti-Aβ therapy at these early stages have a higher chance to be effective.

High performance plasma amyloid-β biomarkers for Alzheimer’s disease

To facilitate clinical trials of disease-modifying therapies for Alzheimer’s disease, which are expected to be most efficacious at the earliest and mildest stages of the disease, supportive biomarker information is necessary. The only validated methods for identifying amyloid-β deposition in the brain—the earliest pathological signature of Alzheimer’s disease—are amyloid-β positron-emission tomography (PET) imaging or measurement of amyloid-β in cerebrospinal fluid. Therefore, a minimally invasive, cost-effective blood-based biomarker is desirable. Despite much effort, to our knowledge, no study has validated the clinical utility of blood-based amyloid-β markers. Here we demonstrate the measurement of high-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry. The ability of amyloid-β precursor protein (APP)669–711/amyloid-β (Aβ)1–42 and Aβ1–40/Aβ1–42 ratios, and their composites, to predict individual brain amyloid-β-positive or -negative status was determined by amyloid-β-PET imaging and tested using two independent data sets: a discovery data set (Japan, n = 121) and a validation data set (Australia, n = 252 including 111 individuals diagnosed using 11C-labelled Pittsburgh compound-B (PIB)-PET and 141 using other ligands). Both data sets included cognitively normal individuals, individuals with mild cognitive impairment and individuals with Alzheimer’s disease. All test biomarkers showed high performance when predicting brain amyloid-β burden. In particular, the composite biomarker showed very high areas under the receiver operating characteristic curves (AUCs) in both data sets (discovery, 96.7%, n = 121 and validation, 94.1%, n = 111) with an accuracy approximately equal to 90% when using PIB-PET as a standard of truth. Furthermore, test biomarkers were correlated with amyloid-β-PET burden and levels of Aβ1–42 in cerebrospinal fluid. These results demonstrate the potential clinical utility of plasma biomarkers in predicting brain amyloid-β burden at an individual level. These plasma biomarkers also have cost–benefit and scalability advantages over current techniques, potentially enabling broader clinical access and efficient population screening.

Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer's disease pathophysiology (SNAP) or Alzheimer's disease pathology: a longitudinal study

BACKGROUND Brain amyloid β (Aβ) deposition and neurodegeneration have been documented in about 50-60% of cognitively healthy elderly individuals (aged 60 years or older). The long-term cognitive consequences of the presence of Alzheimers disease pathology and neurodegeneration, and whether they have an independent or synergistic effect on cognition, are unclear. We aimed to characterise the long-term clinical and cognitive trajectories of healthy elderly individuals using a two-marker (Alzheimers disease pathology and neurodegeneration) imaging construct. METHODS Between Nov 3, 2006, and Nov 25, 2014, 573 cognitively healthy individuals in Melbourne and Perth, Australia, (mean age 73·1 years [SD 6·2]; 58% women) were enrolled in the Australian Imaging, Biomarker and Lifestyle (AIBL) study. Alzheimers disease pathology (A) was determined by measuring Aβ deposition by PET, and neurodegeneration (N) was established by measuring hippocampal volume using MRI. Individuals were categorised as A(-)N(-), A(+)N(-), A(+)N(+), or suspected non-Alzheimers disease pathophysiology (A(-)N(+), SNAP). Clinical progression, hippocampal volume, standard neuropsychological tests, and domain-specific and global cognitive composite scores were assessed over 6 years of follow-up. Linear mixed effect models and a Cox proportional hazards model of survival were used to evaluate, compare, and contrast the clinical, cognitive, and volumetric trajectories of patients in the four AN categories. FINDINGS 50 (9%) healthy individuals were classified as A(+)N(+), 87 (15%) as A(+)N(-), 310 (54%) as A(-)N(-), and 126 (22%) as SNAP. APOE ε4 was more frequent in participants in the A(+)N(+) (27; 54%) and A(+)N(-) (42; 48%) groups than in the A(-)N(-) (66; 21%) and SNAP groups (23; 18%). The A(+)N(-) and A(+)N(+) groups had significantly faster cognitive decline than the A(-)N(-) group (0·08 SD per year for AIBL-Preclinical AD Cognitive Composite [PACC]; p<0·0001; and 0·25; p<0·0001; respectively). The A (+)N(+) group also had faster hippocampal atrophy than the A(-)N(-) group (0·04 cm(3) per year; p=0·02). The SNAP group generally did not show significant decline over time compared with the A(-)N(-) group (0·03 SD per year [p=0·19] for AIBL-PACC and a 0·02 cm(3) per year increase [p=0·16] for hippocampal volume), although SNAP was sometimes associated with lower baseline cognitive scores (0·20 SD less than A(-)N(-) for AIBL-PACC). Within the follow-up, 24% (n=12) of individuals in the A(+)N(+) group and 16% (n=14) in the A(+)N(-) group progressed to amnestic mild cognitive impairment or Alzheimers disease, compared with 9% (n=11) in the SNAP group. INTERPRETATION Brain amyloidosis, a surrogate marker of Alzheimers disease pathology, is a risk factor for cognitive decline and for progression from preclinical stages to symptomatic stages of the disease, with neurodegeneration acting as a compounding factor. However, neurodegeneration alone does not confer a significantly different risk of cognitive decline from that in the group with neither brain amyloidosis or neurodegeneration. FUNDING CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF), National Health and Medical Research Council, the Dementia Collaborative Research Centres programme, McCusker Alzheimers Research Foundation, and Operational Infrastructure Support from the Government of Victoria.

Aβ-amyloid and Tau Imaging in Dementia

The introduction of in vivo imaging of Aβ-amyloid (Αβ) pathology more than a decade ago, transformed the assessment of Alzheimer disease (AD) allowing the evaluation of Aβ deposition over time by providing highly accurate, reliable, and reproducible quantitative statements of regional or global Aβ burden in the brain to the extent that Aβ imaging has already been approved for clinical use and is being used for both patient recruitment and outcome measure in current anti-Αβ therapeutic trials. Aβ imaging studies have deepened our insight into Aβ deposition, showing that Aβ accumulation is a slow and protracted process extending for more than two decades before the onset of the clinical phenotype. Although cross-sectional evaluation of Αβ burden does not strongly correlate with cognitive impairment in AD, Αβ burden does correlate with memory impairment and a higher risk for cognitive decline in the aging population and mild cognitive impairment subjects. These associations suggest that Αβ deposition is not a benign process. The recent addition of selective tau imaging will allow to elucidate if these effects are directly associated with Αβ deposition or if they are mediated, in toto or in parte, by tau as it spreads out of the mesial temporal lobe into neocortical association areas. The combination of Aβ and tau imaging studies would likely help elucidate the relationship or interplay between the two pathologic hallmarks of the disease. Longitudinal observations to assess their potential independent or synergistic, sequential or parallel effects on cognition, disease progression, and other disease-specific biomarkers of neurodegeneration would be required to further clarify the respective role of Αβ and tau deposition play in the course of AD.

MR-Less Surface-Based Amyloid Assessment Based on 11C PiB PET

Background β-amyloid (Aβ) plaques in brains grey matter (GM) are one of the pathological hallmarks of Alzheimers disease (AD), and can be imaged in vivo using Positron Emission Tomography (PET) with 11C or 18F radiotracers. Estimating Aβ burden in cortical GM has been shown to improve diagnosis and monitoring of AD. However, lacking structural information in PET images requires such assessments to be performed with anatomical MRI scans, which may not be available at different clinical settings or being contraindicated for particular reasons. This study aimed to develop an MR-less Aβ imaging quantification method that requires only PET images for reliable Aβ burden estimations. Materials and Methods The proposed method has been developed using a multi-atlas based approach on 11C-PiB scans from 143 subjects (75 PiB+ and 68 PiB- subjects) in AIBL study. A subset of 20 subjects (PET and MRI) were used as atlases: 1) MRI images were co-registered with tissue segmentation; 2) 3D surface at the GM-WM interfacing was extracted and registered to a canonical space; 3) Mean PiB retention within GM was estimated and mapped to the surface. For other participants, each atlas PET image (and surface) was registered to the subjects PET image for PiB estimation within GM. The results are combined by subject-specific atlas selection and Bayesian fusion to generate estimated surface values. Results All PiB+ subjects (N = 75) were highly correlated between the MR-dependent and the PET-only methods with Intraclass Correlation (ICC) of 0.94, and an average relative difference error of 13% (or 0.23 SUVR) per surface vertex. All PiB- subjects (N = 68) revealed visually akin patterns with a relative difference error of 16% (or 0.19 SUVR) per surface vertex. Conclusion The demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.

En Attendant Centiloid

Aims: Test the robustness of a linear regression transformation of semiquantitative values from different Aβ tracers into a single continuous scale. Study Design: Retrospective analysis. Place and Duration of Study: PET imaging data acquired in Melbourne and Perth, Australia, between August 2006 and May 2014. Methodology: Aβ imaging in 633 participants was performed with four different radiotracers: flutemetamol (n=267), florbetapir (n=195), florbetaben (n=126) and NAV4694 (n=45). SUVR were generated with the methods recommended for each tracer, and classified as high (Aβ+) or low (Aβ-) based on their respective thresholds. Linear regression transformation based on reported head-to-head comparisons of each tracer with PiB was applied to each tracer result. Each tracer native classification was compared with the classification derived from the transformed data into PiB-like SUVR units (or BeCKeT: Before the Centiloid Kernel Transformation) using 1.50 as a cut-off. Results: Misclassification after transformation to PiB-like SUVR compared to native classification was extremely low with only 3/267 (1.1%) of flutemetamol, 1/195 (0.5%) of florbetapir, 1/45 (2.2%) of NAV4694, and 1/126 (0.8%) of florbetaben cases assigned into the wrong category. When misclassification occurred (<1% of all cases) it was restricted to an extremely narrow margin (±0.02 BeCKeT) around the 1.50 BeCKeT threshold. Conclusion: While a definitive transformation into centesimal units is being established, application of linear regression transformations provide an interim, albeit robust, way of converting results from different Aβ imaging tracers into more familiar PiB-like SUVR units.

Subjective Memory Complaints in APOEɛ4 Carriers are Associated with High Amyloid-β Burden.

BACKGROUND APOEɛ4 genotype and aging have been identified as risk factors for Alzheimers disease (AD). In addition, subjective memory complaints (SMC) might be a first clinical expression of the effect of AD pathology on cognitive functioning. OBJECTIVE To assess whether APOEɛ4 genotype, age, SMC, and episodic memory are risk factors for high amyloid-β (Aβ) burden in cognitively normal elderly. METHODS 307 cognitively normal participants (72.7 ± 6.8 years, 53% female, 55% SMC) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study underwent amyloid PET and APOE genotyping. Logistic regression analyses were performed to determine the association of APOEɛ4 genotype, age, SMC, and episodic memory with Aβ pathology. RESULTS Odds of high Aβ burden were greater at an older age (OR = 3.21; 95% CI = 1.68-6.14), when SMC were present (OR = 1.90; 95% CI = 1.03-3.48), and for APOEɛ4 carriers (OR = 7.49; 95% CI = 3.96-14.15), while episodic memory was not associated with odds of high Aβ burden. Stratified analyses showed that odds of SMC for high Aβ burden were increased in specifically APOEɛ4 carriers (OR = 4.58, 95% CI = 1.83-11.49) and younger participants (OR = 3.73, 95% CI = 1.39-10.01). CONCLUSION Aging, APOEɛ4 genotype, and SMC were associated with high Aβ burden. SMC were especially indicative of high Aβ burden in younger participants and in APOEɛ4 carriers. These findings suggest that selection based on the presence of SMC, APOEɛ4 genotype and age may help identify healthy elderly participants with high Aβ burden eligible for secondary prevention trials.

Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions.

Most neurodegenerative disorders are associated with aggregated protein deposits. In the case of Alzheimer disease (AD), extracellular amyloid-β (Aβ) aggregates and intracellular tau neurofibrillary tangles are the two neuropathological hallmarks of the disease. Aβ-PET imaging has already been approved for clinical use and is being used in clinical trials of anti-Aβ therapies both for patient recruitment and as an outcome measure. These studies have shown that Aβ accumulation is a protracted process that can extend for more than 2 decades before the onset of clinical AD. This Review describes how in vivo brain imaging of Aβ pathology has revolutionized the evaluation of patients with clinical AD by providing robust and reproducible statements of global or regional brain Aβ burden and enabling the monitoring of disease progression. The role of selective tau imaging is discussed, focusing on how longitudinal tau and Aβ imaging studies might reveal the various effects (sequential and/or parallel, independent and/or synergistic) of these proteins on progression, cognition and other disease-specific biomarkers of neurodegeneration. Finally, imaging studies are discussed in the context of elucidating the respective roles of Aβ and tau in AD and in advancing our understanding of the relationship and/or interplay between these two proteinopathies.

Standardized Expression of 18F-NAV4694 and 11C-PiB β-Amyloid PET Results with the Centiloid Scale.

A common quantitative output value for PET measures of β-amyloid (Aβ) binding across tracers and methods would allow better comparison of data across sites and application of universal diagnostic and prognostic values. A method has recently been developed that generates a unit of measurement called the centiloid. We applied this method to 2-[2-18F-fluoro-6-(methylamino)-3-pyridinyl]-1-benzofuran-5-ol (18F-NAV4694) and 11C-Pittsburgh compound B (11C-PiB) Aβ images to derive the scaling factor required to express tracer binding in centiloids. Methods: Fifty-five participants, including 10 young controls (33 ± 7 y old), underwent both 11C-PiB and 18F-NAV4694 imaging no more than 3 mo apart, with the images acquired 50–70 min after tracer injection. The images were spatially normalized and analyzed using the standard centiloid method and regions (cortex and whole-cerebellum reference) downloaded from the Global Alzheimer Association Interactive Network website. Results: SUV ratios (SUVRs) showed a strong correlation in tracer binding (18F-NAV4694 SUVR = 1.09 × 11C-PiB SUVR – 0.08, R2 = 0.99). The equation to convert 18F-NAV4694 to centiloids [100 × (18F-NAV4694 SUVR – 1.028)/1.174] was similar to a published equation for 11C-PiB [100 × (11C-PiB SUVR – 1.009)/1.067]. In the young controls, the variance ratio (18F-NAV4694 centiloid SD divided by 11C-PiB centiloid SD) was 0.85. Conclusion: The results for both 11C-PiB and 18F-NAV4694 can now be expressed in centiloids, an important step that should allow better clinical and research use of Aβ imaging. The standard centiloid method also showed that 18F-NAV4694 has slightly higher Aβ binding and lower variance than 11C-PiB, important properties for detecting early Aβ deposition and change over time.