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- Vogel, Jacob W., et al.
(author)
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Four distinct trajectories of tau deposition identified in Alzheimer’s disease
- 2021
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In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 27:5, s. 871-881
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Journal article (peer-reviewed)abstract
- Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These ‘subtypes’ were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of ‘typical AD’ and a revisiting of tau pathological staging. © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
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| 2. |
- Elman, Jeremy A., et al.
(author)
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Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve
- 2022
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In: Alzheimer's Research and Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 14:1
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Journal article (peer-reviewed)abstract
- Background: Cognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances. Methods and results: We describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against “pre-regression” strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience. Conclusions: Caution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline.
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| 6. |
- Vogel, Jacob W., et al.
(author)
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Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease
- 2019
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In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 40:2, s. 638-651
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Journal article (peer-reviewed)abstract
- Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published “pathology-driven” ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.
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| 8. |
- Groot, Colin, et al.
(author)
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Latent atrophy factors related to phenotypical variants of posterior cortical atrophy
- 2020
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In: Neurology. - 1526-632X. ; 95:12, s. 1672-1685
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Journal article (peer-reviewed)abstract
- OBJECTIVE: To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition. METHODS: We employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% β-amyloid positive, 29% β-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models. RESULTS: The model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiologic phenotype. CONCLUSION: Our results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.
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| 9. |
- Leuzy, Antoine, et al.
(author)
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Biomarker-Based Prediction of Longitudinal Tau Positron Emission Tomography in Alzheimer Disease
- 2022
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In: JAMA Neurology. - : American Medical Association (AMA). - 2168-6149. ; 79:2, s. 149-158
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Journal article (peer-reviewed)abstract
- Importance: There is currently no consensus as to which biomarkers best predict longitudinal tau accumulation at different clinical stages of Alzheimer disease (AD). Objective: To describe longitudinal [18F]RO948 tau positron emission tomography (PET) findings across the clinical continuum of AD and determine which biomarker combinations showed the strongest associations with longitudinal tau PET and best optimized clinical trial enrichment. Design, Setting, and Participants: This longitudinal cohort study consecutively enrolled amyloid-β (Aβ)-negative cognitively unimpaired (CU) participants, Aβ-positive CU individuals, Aβ-positive individuals with mild cognitive impairment (MCI), and individuals with AD dementia between September 2017 and November 2020 from the Swedish BioFINDER-2 (discovery cohort) and BioFINDER-1 (validation cohort) studies. Exposures: Baseline plasma and cerebrospinal fluid Aβ42/Aβ40, tau phosphorylated at threonine-217 (p-tau217), p-tau181 and neurofilament light, magnetic resonance imaging, amyloid PET ([18F]flutemetamol), and tau PET ([18F]RO948 in the BioFINDER-2 study; [18F]flortaucipir in the BioFINDER-1 study). Main Outcomes and Measures: Baseline tau PET standardized uptake value ratio (SUVR) and annual percent change in tau PET SUVR across regions of interest derived using a data-driven approach combining clustering and event-based modeling. Regression models were used to examine associations between individual biomarkers and longitudinal tau PET and to identify which combinations best predicted longitudinal tau PET. These combinations were then entered in a power analysis to examine how their use as an enrichment strategy would affect sample size in a simulated clinical trial. Results: Of 343 participants, the mean (SD) age was 72.56 (7.24) years, and 157 (51.1%) were female. The clustering/event-based modeling-based approach identified 5 regions of interest (stages). In Aβ-positive CU individuals, the largest annual increase in tau PET SUVR was seen in stage I (entorhinal cortex, hippocampus, and amygdala; 4.04% [95% CI, 2.67%-5.32%]). In Aβ-positive individuals with MCI and with AD dementia, the greatest increases were seen in stages II (temporal cortical regions; 4.45% [95% CI, 3.41%-5.49%]) and IV (certain frontal regions; 5.22% [95% CI, 3.95%-6.49%]), respectively. In Aβ-negative CU individuals and those with MCI, modest change was seen in stage I (1.38% [95% CI, 0.78%-1.99%] and 1.80% [95% CI, 0.76%-2.84%], respectively). When looking at individual predictors and longitudinal tau PET in the stages that showed most change, plasma p-tau217 (R2= 0.27, P <.005), tau PET (stage I baseline SUVR; R2= 0.13, P <.05) and amyloid PET (R2= 0.10, P <.05) were significantly associated with longitudinal tau PET in stage I in Aβ-positive CU individuals. In Aβ-positive individuals with MCI, plasma p-tau217 (R2= 0.24, P <.005) and tau PET (stage II baseline SUVR; R2= 0.44, P <.001) were significantly associated with longitudinal tau PET in stage II. Findings were replicated in BioFINDER-1 using longitudinal [18F]flortaucipir. For the power analysis component, plasma p-tau217 with tau PET resulted in sample size reductions of 43% (95% CI, 34%-46%; P <.005) in Aβ-positive CU individuals and of 68% (95% CI, 61%-73%; P <.001) in Aβ-positive individuals with MCI. Conclusions and Relevance: In trials using tau PET as the outcome, plasma p-tau217 with tau PET may prove optimal for enrichment in preclinical and prodromal AD. However, plasma p-tau217 was most important in preclinical AD, while tau PET was more important in prodromal AD..
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| 10. |
- Leuzy, Antoine, et al.
(author)
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Comparison of Group-Level and Individualized Brain Regions for Measuring Change in Longitudinal Tau Positron Emission Tomography in Alzheimer Disease
- 2023
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In: JAMA Neurology. - 2168-6149. ; 80:6, s. 614-623
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Journal article (peer-reviewed)abstract
- Importance: Longitudinal tau positron emission tomography (PET) is a relevant outcome in clinical trials evaluating disease-modifying therapies in Alzheimer disease (AD). A key unanswered question is whether the use of participant-specific (individualized) regions of interest (ROIs) is superior to conventional approaches where the same ROI (group-level) is used for each participant. Objective: To compare group- and participant-level ROIs in participants at different stages of the AD clinical continuum in terms of annual percentage change in tau-PET standardized uptake value ratio (SUVR) and sample size requirements. Design, Setting, and Participants: This was a longitudinal cohort study with consecutive participant enrollment between September 18, 2017, and November 15, 2021. Included in the analysis were participants with mild cognitive impairment and AD dementia from the prospective and longitudinal Swedish Biomarkers For Identifying Neurodegenerative Disorders Early and Reliably 2 (BioFINDER-2) study; in addition, a validation sample (the AVID 05e, Expedition-3, Alzheimer's Disease Neuroimaging Initiative [ADNI], and BioFINDER-1 study cohorts) was also included. Exposures: Tau PET (BioFINDER-2, [18F]RO948; validation sample, [18F]flortaucipir), 7 group-level (5 data-driven stages, meta-temporal, whole brain), and 5 individualized ROIs. Main Outcomes and Measures: Annual percentage change in tau-PET SUVR across ROIs. Sample size requirements in simulated clinical trials using tau PET as an outcome were also calculated. Results: A total of 215 participants (mean [SD] age, 71.4 (7.5) years; 111 male [51.6%]) from the BioFINDER-2 study were included in this analysis: 97 amyloid-β (Aβ)-positive cognitively unimpaired (CU) individuals, 77 with Aβ-positive mild cognitive impairment (MCI), and 41 with AD dementia. In the validation sample were 137 Aβ-positive CU participants, 144 with Aβ-positive MCI, and 125 with AD dementia. Mean (SD) follow-up time was 1.8 (0.3) years. Using group-level ROIs, the largest annual percentage increase in tau-PET SUVR in Aβ-positive CU individuals was seen in a composite ROI combining the entorhinal cortex, hippocampus, and amygdala (4.29%; 95% CI, 3.42%-5.16%). In individuals with Aβ-positive MCI, the greatest change was seen in the temporal cortical regions (5.82%; 95% CI, 4.67%-6.97%), whereas in those with AD dementia, the greatest change was seen in the parietal regions (5.22%; 95% CI, 3.95%-6.49%). Significantly higher estimates of annual percentage change were found using several of the participant-specific ROIs. Importantly, the simplest participant-specific approach, where change in tau PET was calculated in an ROI that best matched the participant's data-driven disease stage, performed best in all 3 subgroups. For the power analysis, sample size reductions for the participant-specific ROIs ranged from 15.94% (95% CI, 8.14%-23.74%) to 72.10% (95% CI, 67.10%-77.20%) compared with the best-performing group-level ROIs. Findings were replicated using [18F]flortaucipir. Conclusions and Relevance: Finding suggest that certain individualized ROIs carry an advantage over group-level ROIs for assessing longitudinal tau changes and increase the power to detect treatment effects in AD clinical trials using longitudinal tau PET as an outcome.
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