Citations

Abstract Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

AbstractINTRODUCTIONDelay in diagnosis of posterior cortical atrophy (PCA) syndrome is common, and the lack of familiarity with assessment tools for identifying visual cortical dysfunction is a contributing factor. We propose recommendations for the approach to the evaluation of PCA clinical features during the office visit, the neuropsychological evaluation, and the research setting. A recommended screening battery for eye clinics is also proposed.METHODSRecommendations were developed using results from a web‐based survey of members of Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) Atypical Alzheimer's Disease Professional Interest Area (PIA), literature review, and consensus by the PCA assessment working party of the Atypical Alzheimer's Disease PIA.RESULTSSurvey results revealed robust agreement for assessment tool preferences for PCA features, and many respondents indicated that they reserve assessment tools for use only when PCA is suspected. For some PCA features, curated tools were preferred over validated battery tools, particularly for the office visit. Consensus recommendations superseded survey preferences for two core cognitive features within the 2017 PCA diagnostic criteria.DISCUSSIONThese consensus recommendations provide an evaluation framework for PCA clinical features and can facilitate timely and accurate recognition and diagnosis of PCA. Broader use of these tools should be sought, and development and validation of novel PCA clinical outcome assessments are needed to improve our understanding of atypical AD and other dementias and support the inclusion of those with PCA in treatment trials.

BACKGROUND: Cerebral microbleeds (CMBs) are associated with cognitive decline, but their importance outside of cerebral amyloid angiopathy and the mechanisms of their impact on cognition are poorly understood. We evaluated the cross-sectional association between CMB patterns and cerebral Aβ (amyloid-β) deposition, by florbetapir positron emission tomography. METHODS: The longitudinal ARIC study (Atherosclerosis Risk in Communities) recruited individuals from 4 US communities from 1987 to 1989. From 2012 to 2014, the ARIC-PET (Atherosclerosis Risk in Communities - Positron Emission Tomography) ancillary recruited 322 nondemented ARIC participants who completed 3T brain magnetic resonance imaging with T2*GRE as part of ARIC visit 5 to undergo florbetapir positron emission tomography imaging. Magnetic resonance imaging images were read for CMBs and superficial siderosis; on positron emission tomography, global cortical standardized uptake value ratio >1.2 was considered a positive Aβ scan. Multivariable logistic regression models evaluated CMB characteristics in association with Aβ positivity. Effect modification by sex, race, APOE status, and cognition was evaluated. RESULTS: CMBs were present in 24% of ARIC-PET participants. No significant associations were found between CMBs and Aβ positivity, but a pattern of isolated lobar CMBs or superficial siderosis was associated with over 4-fold higher odds of elevated Aβ when compared with those with no CMBs (odds ratio, 4.72 [95% CI, 1.16–19.16]). A similar elevated risk was not observed in those with isolated subcortical or mixed subcortical and either lobar CMBs or superficial siderosis. Although no significant interactions were found, effect estimates for elevated Aβ were nonsignificantly lower ( P >0.10, odds ratio, 0.4–0.6) for a mixed CMB pattern, and odds ratios were nonsignificantly higher for lobar-only CMBs for 4 subgroups: women (versus men); Black participants (versus White participants), APOE ε4 noncarriers (versus carriers), and cognitively normal (versus mild cognitive impairment). CONCLUSIONS: In this community-based cohort of nondemented adults, lobar-only pattern of CMBs or superficial siderosis is most strongly associated with brain Aβ, with no elevated risk for a mixed CMB pattern. Further studies are needed to understand differences in CMB patterns and their meaning across subgroups.

AbstractINTRODUCTIONAtypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them.METHODSWe evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18F‐flortaucipir positron emission tomography were performed at baseline and at 1‐year follow‐up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed‐effects models and voxel‐based analyses.RESULTSPCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation.DISCUSSIONLongitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy.HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.

AbstractBackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, after Alzheimer’s disease (AD). While alpha‐synuclein pathology is the hallmark of DLB, AD and cerebrovascular pathologies are common in DLB patients. However, the contribution of these pathologies towards neurodegeneration in DLB is largely unknown. We investigated associations of amyloid‐beta, tau, and cerebrovascular biomarkers with regional gray matter (GM) volume in DLB patients and cognitively unimpaired (CU) controls.MethodWe included 30 DLB patients (69.3±10.2 years old, 87% men) and 100 CU individuals balanced on age and sex (Table 1). We used 11C‐Pittsburgh Compound‐B (PiB) and 18F‐Flortaucipir positron emission tomography (PET) to assess amyloid‐beta and tau in‐vivo. We used structural MRI to assess white matter hyperintensity (WMH) volume, a marker of cerebrovascular lesion load, and regional GM volume (a marker of neurodegeneration). We used correlations and ANCOVA in the whole dataset and structural equation modelling in the DLB patients to investigate associations of WMH volume and regional amyloid‐beta and tau PET standardized uptake value ratios (SUVRs) with regional GM volume.ResultDLB patients showed lower GM volume across all cortical and subcortical regions except for cuneus, putamen, and pallidum (Fig.1). Greater WMH volume was associated with lower volume in the medial and orbital frontal cortices, insula, fusiform cortex, and thalamus in DLB patients (Fig.2). Greater PiB SUVR was associated with lower volume in the inferior temporal cortex, while Flortaucipir SUVR did not correlate with GM volume (Fig.2). Structural equation modelling showed that higher age and positivity for the APOE e4 genotype were significant predictors of greater WMH, and WMH in turn was a significant predictor of GM volume in medial and orbital frontal cortices, insula, and inferior temporal cortex (Fig.3). In contrast, we observed two distinct paths for the fusiform cortex, with age having an effect through PiB and Flortaucipir SUVRs on the one path, and through WMH on the other path (Fig.3).ConclusionDLB patients have widespread cortical atrophy, most of which likely is influenced by alpha‐synuclein pathology. Although amyloid‐beta, tau, and cerebrovascular pathologies often coexist in DLB, their contributions to neurodegeneration seem to be region specific.

AbstractBackgroundHigher brain amyloid PET is associated with slower gait speed and has been hypothesized to play a role in age‐related mobility decline. With the development of blood‐based biomarkers of Alzheimer’s disease pathology, we examined whether plasma levels of amyloid‐beta (Aβ)40, Aβ42, the Aβ42/Aβ40 ratio, phosphorylated tau 181 (p‐tau181) and 217 (p‐tau217), total tau, and neurofilament light chain (NfL) were associated with gait speed.MethodThis study included 779 cognitively unimpaired participants (median age 75, 54.8% male, median follow‐up 6.8 years) in the Mayo Clinic Study of Aging with concurrent measurement of plasma biomarkers and gait speed. Individuals with chronic kidney disease or a history of stroke were excluded. The plasma markers (pg/mL) were measured on the Quanterix Simoa® HD‐1 analyzer (total tau, Aβ42, Aβ40, NfL) and the Meso Scale Discovery platform (p‐tau181, p‐tau217). Gait speed (m/s) was obtained by timing a self‐paced walk across 25 ft using a stopwatch. Linear mixed effects models, with subject‐specific intercepts and slopes for time since baseline, with z‐scored baseline plasma markers were used to predict cross‐sectional and longitudinal gait speed. All models adjusted for baseline age and sex.ResultAt baseline, higher Aβ42 [Beta = ‐0.019; 95%CI = (‐0.035, ‐0.004)], total‐tau [‐0.026; (‐0.041, ‐0.011)], and p‐tau181 [‐0.021; (‐0.036, ‐0.006)] were associated with slower gait speed. Gait speed decreased, on average, by about 0.015 m/s annually for those in this study. Each one standard deviation (SD) increase in baseline level of plasma Aβ40 [‐0.005; (‐0.007, ‐0.002)], NfL [‐0.006; (‐0.009, ‐0.004)], and p‐tau217 [‐0.004; (‐0.007, ‐0.001)] accelerated this decline over time. In contrast, each one SD increase in Aβ42/Aβ40 ratio [0.003; (0.001, 0.006)] mitigated decline. Additional analyses adjusting for Charlson comorbidity index, Beck Depression Inventory, body mass index, and APOE E4 allele status gave similar results.ConclusionBoth cross‐sectional and longitudinal associations of plasma markers of amyloid and tau pathology and neurodegeneration with gait speed among cognitively unimpaired individuals were present. Therefore, not only are these markers of cognitive changes, they are also related to motor changes such as those affecting gait speed.