Jack, Clifford R

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.

ImportanceIncreased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors.ObjectiveTo estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes.Design, Setting, and ParticipantsThis cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers—the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer’s Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019).Main Outcome and MeasuresThe main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH.ResultsData from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = −3.1 [P = .002]; ADNI: t = −5.6 [P < .001]; HABS: t = −2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001).Conclusions and RelevanceThe findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.

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.

AbstractBackgroundPrevious studies have shown that magnetic susceptibility is increased in several subcortical regions in progressive supranuclear palsy (PSP). However, it is still unclear how subcortical and cortical susceptibilities vary across different PSP variants, Parkinson's disease (PD), and corticobasal syndrome (CBS).ObjectiveThis study aims to clarify the susceptibility profiles in the subcortical and cortical regions in different PSP variants, PD, and CBS.MethodsSixty‐four patients, 20 PSP‐Richardson syndrome (PSP‐RS), 9 PSP‐parkinsonism (PSP‐P), 7 PSP‐progressive gait freezing, 4 PSP‐postural instability, 11 PD, and 13 CBS, and 20 cognitively normal control subjects underwent a 3‐Tesla magnetic resonance imaging scan to reconstruct quantitative susceptibility maps. Region‐of‐interest analysis was performed to obtain susceptibility in several subcortical and cortical regions. Bayesian linear mixed effect models were used to estimate susceptibility within group and differences between groups.ResultsIn the subcortical regions, patients with PSP‐RS and PSP‐P showed greater susceptibility than control subjects in the pallidum, substantia nigra, red nucleus, and cerebellar dentate (P < 0.05). Patients with PSP‐RS also showed greater susceptibility than patients with PSP‐progressive gait freezing, PD, and CBS in the red nucleus and cerebellar dentate, and patients with PSP‐P showed greater susceptibility than PD in the red nucleus. Patients with PSP‐postural instability and CBS showed greater susceptibility than control subjects in the pallidum and substantia nigra. No significant differences were observed in any cortical region.ConclusionsThe PSP variants and CBS had different patterns of magnetic susceptibility in the subcortical regions. The findings will contribute to our understanding about iron profiles and pathophysiology of PSP and may provide a potential biomarker to differentiate PSP variants, PD, and CBS. © 2023 International Parkinson and Movement Disorder Society.

AbstractThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co‐pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI‐4 aims to address by enrolling a diverse cohort.

Abstract INTRODUCTION We aimed to describe baseline amyloid‐beta (Aβ) and tau‐positron emission tomograrphy (PET) from Longitudinal Early‐onset Alzheimer's Disease Study (LEADS), a prospective multi‐site observational study of sporadic early‐onset Alzheimer's disease (EOAD). METHODS We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)‐PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ−) based on the combination of visual read by expert reader and image quantification. RESULTS 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid‐PET; 231 (95.1%) of them were tau‐PET positive (A+T+). Tau‐PET signal was elevated across cortical regions with a parietal‐predominant pattern, and higher burden was observed in younger and female EOAD participants. DISCUSSION LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau‐PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. HIGHLIGHTS 72% of patients with clinical EOAD were positive on both amyloid‐ and tau‐PET. Amyloid‐positive patients with EOAD had high tau‐PET signal across cortical regions. In EOAD, tau‐PET mediated the relationship between amyloid‐PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau‐PET.

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.