Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer's disease

Maaß, Anne; Landau, Susan; Baker, Suzanne L.; Horng, Andy; Lockhart, Samuel N.; Joie, Renaud La; Rabinovici, Gil D.; Jagust, William J.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.neuroimage.2017.05.058

Cited by 379 publications

(448 citation statements)

References 54 publications

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“…the subject component score for IC-5) rather than using three separate numbers for each Braak ROI. This observation may explain why Braak ROIs do not outperform global measures of τ-PET (Maass et al, 2017). The other components do not meet these criteria and are referred to as atypical, or ‘non-Braak-like’ patterns.…”

Section: Resultsmentioning

confidence: 99%

“…While this may not necessarily indicate that the signal simultaneously becomes abnormal in all of these regions, it does argue against a sequential involvement of Braak regions with limbic structures becoming involved before neocortical regions, as one would expect from a seed-based templating mechanism following the Braak staging gradient. Sequential spreading not fully detected by τ-PET until later stages could also produce these observations, but using Braak-staging ROIs, or any other regional approach, would not confer any particular advantage over global measures in such a case (Maass et al, 2017). The lack of utility in using Braak-staging ROIs is also highlighted by the fact that the τ-PET signal in preclinical AD is more widespread than predicted by pathologic staging as was recently shown by others (Mishra et al, 2017) and also observed in this study (see Figure 6 and Supplementary Figures 11–13).…”

Section: Discussionmentioning

confidence: 99%

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Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum

Jones

Graff‐Radford

Lowe

et al. 2017

Cortex

203

194

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Functionally related brain regions are selectively vulnerable to Alzheimer’s disease pathophysiology. However, molecular markers of this pathophysiology (i.e., beta-amyloid and tau aggregates) have discrepant spatial and temporal patterns of progression within these selectively vulnerable brain regions. Existing reductionist pathophysiologic models cannot account for these large-scale spatiotemporal inconsistencies. Within the framework of the recently proposed cascading network failure model of Alzheimer’s disease, however, these large-scale patterns are to be expected. This model postulates the following: 1) a tau-associated, circumscribed network disruption occurs in brain regions specific to a given phenotype in clinically normal individuals; 2) this disruption can trigger phenotype independent, stereotypic, and amyloid-associated compensatory brain network changes indexed by changes in the default mode network; 3) amyloid deposition marks a saturation of functional compensation and portends an acceleration of the inciting phenotype specific, and tau-associated, network failure. With the advent of in vivo molecular imaging of tau pathology, combined with amyloid and functional network imaging, it is now possible to investigate the relationship between functional brain networks, tau, and amyloid across the disease spectrum within these selectively vulnerable brain regions. In a large cohort (n = 218) spanning the Alzheimer’s disease spectrum from young, amyloid negative, cognitively normal subjects to Alzheimer’s disease dementia, we found several distinct spatial patterns of tau deposition, including ‘Braak-like’ and ‘non-Braak-like’, across functionally related brain regions. Rather than arising focally and spreading sequentially, elevated tau signal seems to occur system-wide based on inferences made from multiple cross-sectional analyses we conducted looking at regional patterns of tau signal. Younger age-of-disease-onset was associated with ‘non-Braak-like’ patterns of tau, suggesting an association with atypical clinical phenotypes. As predicted by the cascading network failure model of Alzheimer’s disease, we found that amyloid is a partial mediator of the relationship between functional network failure and tau deposition in functionally connected brain regions. This study implicates large-scale brain networks in the pathophysiology of tau deposition and offers support to models incorporating large-scale network physiology into disease models linking tau and amyloid, such as the cascading network failure model of Alzheimer’s disease.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum

Jones

Graff‐Radford

Lowe

et al. 2017

Cortex

203

194

View full text Add to dashboard Cite

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“…Quality control of the output was carried out. The thickness of the entorhinal cortex was selected in this study as a measure of medial temporal atrophy, based on previous findings with another tau PET tracer [40] showing that entorhinal thickness rather than hippocampal volumes is more closely related to local tau PET tracer binding.…”

Section: Methodsmentioning

confidence: 99%

Dual tracer tau PET imaging reveals different molecular targets for 11C-THK5351 and 11C-PBB3 in the Alzheimer brain

Chiotis

Stenkrona

Almkvist

et al. 2018

Eur J Nucl Med Mol Imaging

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PurposeSeveral tau PET tracers have been developed, but it remains unclear whether they bind to the same molecular target on the heterogeneous tau pathology. In this study we evaluated the binding of two chemically different tau-specific PET tracers (11C-THK5351 and 11C-PBB3) in a head-to-head, in vivo, multimodal design.MethodsNine patients with a diagnosis of mild cognitive impairment or probable Alzheimer’s disease and cerebrospinal fluid biomarker evidence supportive of the presence of Alzheimer’s disease brain pathology were recruited after thorough clinical assessment. All patients underwent imaging with the tau-specific PET tracers 11C-THK5351 and 11C-PBB3 on the same day, as well as imaging with the amyloid-beta-specific tracer 11C-AZD2184, a T1-MRI sequence, and neuropsychological assessment.ResultsThe load and regional distribution of binding differed between 11C-THK5351 and 11C-PBB3 with no statistically significant regional correlations observed between the tracers. The binding pattern of 11C-PBB3, but not that of 11C-THK5351, in the temporal lobe resembled that of 11C-AZD2184, with strong correlations detected between 11C-PBB3 and 11C-AZD2184 in the temporal and occipital lobes. Global cognition correlated more closely with 11C-THK5351 than with 11C-PBB3 binding. Similarly, cerebrospinal fluid tau measures and entorhinal cortex thickness were more closely correlated with 11C-THK5351 than with 11C-PBB3 binding.ConclusionThis research suggests different molecular targets for these tracers; while 11C-PBB3 appeared to preferentially bind to tau deposits with a close spatial relationship to amyloid-beta, the binding pattern of 11C-THK5351 fitted the expected distribution of tau pathology in Alzheimer’s disease better and was more closely related to downstream disease markers.Electronic supplementary materialThe online version of this article (10.1007/s00259-018-4012-5) contains supplementary material, which is available to authorized users.

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“…A cutoff value of 1.5 was used to define positive binding voxel on SUVP, representing 1.5 times SD from whole brain mode. For comparison, SUVR PBP was also calculated by using previous published cutoff values, 1.5 for THK5351 PET and 1.19 for AV1451 PET [13–16]. The acquisition and reconstruction of THK5351 scans were identical as in the papers that report the cut-off values, and more or less similar for AV1451.…”

Section: Methodsmentioning

confidence: 99%

Quantitative evaluation of tau PET tracers 18F-THK5351 and 18F-AV-1451 in Alzheimer’s disease with standardized uptake value peak-alignment (SUVP) normalization

Chen

Pirraglia

et al. 2018

Eur J Nucl Med Mol Imaging

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Purpose Off-target binding in the reference region is a challenge for recent tau tracers 18F-AV-1451 and 18F-THK5351. The conventional standardized uptake value ratio (SUVR) method relies on the average uptake from an unaffected tissue sample, and therefore is susceptible to biases from off-target binding as well as variability among individuals in the reference region. We propose a new method, standardized uptake value peak-alignment (SUVP), to reduce the bias of the SUVR, and improve the quantitative assessment of tau deposition. Methods The SUVP normalizes uptake values by their mode and standard deviation. Instead of using a reference region, the SUVP derives the contrast from unaffected voxels over the whole brain. Using SUVP and SUVR methods, we evaluated the global and regional tau binding of 18F-THK5351 and 18F-AV-1451 on two independent cohorts (N = 18 and 32, respectively), each with cognitively normal (NL) subjects and Alzheimer’s disease (AD) subjects. Results Both tracers showed significantly increased binding for AD in the targeted cortical areas. In the temporal cortex, SUVP had a higher classification success rate (CSR) than SUVR (0.96 vs 0.89 for 18F-THK5351; 0.86 vs 0.75 for 18F-AV-1451), as well as higher specificity under fixed sensitivity around 0.80 (0.70 vs 0.45 specificity for 18F-THK5351; 1.00 vs 0.78 for 18F-AV-1451). In the cerebellar cortex, an AD-NL group difference with effect size (Cohen’s d) of 0.62 was observed for AV-1451, confirming the limitation of the SUVR approach using this region as a reference. A smaller cerebellar effect size (0.09) was observed for THK5351. Conclusion The SUVP method reduces the bias of the reference region and improves the NL-AD classification compared to the SUVR approach.

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Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer's disease

Cited by 379 publications

References 54 publications

Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum

Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum

Dual tracer tau PET imaging reveals different molecular targets for 11C-THK5351 and 11C-PBB3 in the Alzheimer brain

Quantitative evaluation of tau PET tracers 18F-THK5351 and 18F-AV-1451 in Alzheimer’s disease with standardized uptake value peak-alignment (SUVP) normalization

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