Search

Abstract Background Leveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. Methods We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). Results The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. Conclusion These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Abstract Background Sleep-wake regulating circuits are affected during prodromal stages in the pathological progression of both Alzheimer’s disease (AD) and Parkinson’s disease (PD), and this disturbance can be measured passively using wearable devices. Our objective was to determine whether accelerometer-based measures of 24-h activity are associated with subsequent development of AD, PD, and cognitive decline. Methods This study obtained UK Biobank data from 82,829 individuals with wrist-worn accelerometer data aged 40 to 79 years with a mean (± SD) follow-up of 6.8 (± 0.9) years. Outcomes were accelerometer-derived measures of 24-h activity (derived by cosinor, nonparametric, and functional principal component methods), incident AD and PD diagnosis (obtained through hospitalization or primary care records), and prospective longitudinal cognitive testing. Results One hundred eighty-seven individuals progressed to AD and 265 to PD. Interdaily stability (a measure of regularity, hazard ratio [HR] per SD increase 1.25, 95% confidence interval [CI] 1.05–1.48), diurnal amplitude (HR 0.79, CI 0.65–0.96), mesor (mean activity; HR 0.77, CI 0.59–0.998), and activity during most active 10 h (HR 0.75, CI 0.61–0.94), were associated with risk of AD. Diurnal amplitude (HR 0.28, CI 0.23–0.34), mesor (HR 0.13, CI 0.10–0.16), activity during least active 5 h (HR 0.24, CI 0.08–0.69), and activity during most active 10 h (HR 0.20, CI 0.16–0.25) were associated with risk of PD. Several measures were additionally predictive of longitudinal cognitive test performance. Conclusions In this community-based longitudinal study, accelerometer-derived metrics were associated with elevated risk of AD, PD, and accelerated cognitive decline. These findings suggest 24-h rhythm integrity, as measured by affordable, non-invasive wearable devices, may serve as a scalable early marker of neurodegenerative disease.

Abstract Background APOE variants are strongly associated with abnormal amyloid aggregation and additional direct effects of APOE on tau aggregation are reported in animal and human cell models. The degree to which these effects are present in humans when individuals are clinically unimpaired (CU) but have abnormal amyloid (Aβ+) remains unclear. Methods We analyzed data from CU individuals in the Anti-Amyloid Treatment in Asymptomatic AD (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies. Amyloid PET data were available for 4486 participants (3163 Aβ-, 1323 Aβ+) and tau PET data were available for a subset of 447 participants (55 Aβ-, 392 Aβ+). Linear models examined APOE (number of e2 and e4 alleles) associations with global amyloid and regional tau burden in medial temporal lobe (entorhinal, amygdala) and early neocortical regions (inferior temporal, inferior parietal, precuneus). Consistency of APOE4 effects on regional tau were examined in 220 Aβ + CU and mild cognitive impairment (MCI) participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results APOE2 and APOE4 were associated with lower and higher amyloid positivity rates, respectively. Among Aβ+ CU, e2 and e4 were associated with reduced (−12 centiloids per allele) and greater (+15 centiloids per allele) continuous amyloid burden, respectively. APOE2 was associated with reduced regional tau in all regions (-0.05 to -0.09 SUVR per allele), whereas APOE4 was associated with greater regional tau (+0.02 to +0.07 SUVR per allele). APOE differences were confirmed by contrasting e3/e3 with e2/e3 and e3/e4. Mediation analyses among Aβ+ s showed that direct effects of e2 on regional tau were present in medial temporal lobe and early neocortical regions, beyond an indirect pathway mediated by continuous amyloid burden. For e4, direct effects on regional tau were only significant in medial temporal lobe. The magnitude of protective e2 effects on regional tau was consistent across brain regions, whereas detrimental e4 effects were greatest in medial temporal lobe. APOE4 patterns were confirmed in Aβ+ ADNI participants. Conclusions APOE influences early regional tau PET burden, above and beyond effects related to cross-sectional amyloid PET burden. Therapeutic strategies targeting underlying mechanisms related to APOE may modify tau accumulation among Aβ+ individuals.

Abstract Background The recent promise of disease-modifying therapies for Alzheimer’s disease (AD) has reinforced the need for accurate biomarkers for early disease detection, diagnosis and treatment monitoring. Advances in the development of novel blood-based biomarkers for AD have revealed that plasma levels of tau phosphorylated at various residues are specific and sensitive to AD dementia. However, the currently available tests have shortcomings in access, throughput, and scalability that limit widespread implementation. Methods We evaluated the diagnostic and prognostic performance of a high-throughput and fully-automated Lumipulse plasma p-tau181 assay for the detection of AD. Plasma from older clinically unimpaired individuals (CU, n = 463) and patients with mild cognitive impairment (MCI, n = 107) or AD dementia (n = 78) were obtained from the longitudinal Stanford University Alzheimer’s Disease Research Center (ADRC) and the Stanford Aging and Memory Study (SAMS) cohorts. We evaluated the discriminative accuracy of plasma p-tau181 for clinical AD diagnosis, association with amyloid β peptides and p-tau181 concentrations in CSF, association with amyloid positron emission tomography (PET), and ability to predict longitudinal cognitive and functional change. Results The assay showed robust performance in differentiating AD from control participants (AUC 0.959, CI: 0.912 to 0.990), and was strongly associated with CSF p-tau181, CSF Aβ42/Aβ40 ratio, and amyloid-PET global SUVRs. Associations between plasma p-tau181 with CSF biomarkers were significant when examined separately in Aβ+ and Aβ− groups. Plasma p-tau181 significantly increased over time in CU and AD diagnostic groups. After controlling for clinical diagnosis, age, sex, and education, baseline plasma p-tau181 predicted change in MoCA overall and change in CDR Sum of Boxes in the AD group over follow-up of up to 5 years. Conclusions This fully-automated and available blood-based biomarker assay therefore may be useful for early detection, diagnosis, prognosis, and treatment monitoring of AD.

Abstract INTRODUCTION The National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) neuropsychological battery is being used to track cognition in participants across the country, but it is unknown if scores obtained through remote administration can be combined with data obtained in person. METHODS The remote UDS battery includes the blind version of the Montreal Cognitive Assessment (MoCA), Number Span, Semantic and Phonemic Fluency, and Craft Story. For these tests, we assessed intraclass correlation coefficients (ICCs) between in‐person and remote scores in 3838 participants with both in‐person and remote UDS assessments, and we compared annual score changes between modalities in a subset that had two remote assessments. RESULTS All tests exhibited moderate to good reliability between modalities (ICCs = 0.590–0.787). Annual score changes were also comparable between modalities except for Craft Story Immediate Recall, Semantic Fluency, and Phonemic Fluency. DISCUSSION Our findings generally support combining remote and in‐person scores for the majority of UDS tests.

Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer’s disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N = 886) between 42 and 95 years of age (mean = 70 years). We separated individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the proportion of time each subject spent in each state, called occupancy rate or OCR and frequency of visits. We compared both sFNC and dFNC features across individuals with different genetic risks and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. We also found that AD genetic risk affects whole-brain sFNC and dFNC in women but not men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

Abstract Scalable cognitive paradigms that provide metrics such as the Computerized Cognitive Composite (C3) may be sensitive enough to relate to Alzheimer's disease biomarkers in the preclinical clinically unimpaired (CU) stage. We examined CU older adults (n = 3287) who completed alternate versions of the C3 approximately 51 days apart. A subset of CU with abnormal amyloid also completed tau positron emission tomography (PET) imaging. C3 initial performance and practice effects were examined in relation to amyloid status and continuous regional tau burden. Initial C3 performance was associated with amyloid status across all participants, and with tau burden in the medial temporal lobe and early cortical regions in CU with abnormal amyloid. Short‐term practice effects were associated with reduced tau in these regions in CU with abnormal amyloid, but were not associated with amyloid status. Thus, computerized cognitive testing repeated over a short follow‐up period provides additional insights into early Alzheimer's disease processes.

Abstract Background Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer’s disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials. Methods Baseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1

Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects' future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model's prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.

Tau PET has allowed for critical insights into in vivo patterns of tau accumulation and change in individuals early in the Alzheimer's disease (AD) continuum. A key methodological step in tau PET analyses is the selection of a reference region, but there is not yet consensus on the optimal region especially for longitudinal tau PET analyses. This study examines how reference region selection influences results related to disease stage at baseline and over time. Longitudinal flortaucipir ([18F]-AV1451) PET scans were examined using several common reference regions (e.g., eroded subcortical white matter, inferior cerebellar gray matter) in 62 clinically unimpaired amyloid negative (CU A-) individuals, 73 CU amyloid positive (CU A+) individuals, and 64 amyloid positive individuals with mild cognitive impairment (MCI A+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cross-sectionally, both reference regions resulted in robust group differences between CU A-, CU A+, and MCI A+ groups, along with significant associations with CSF phosphorylated tau (pTau-181). However, these results were more focally specific and akin to Braak Staging when using eroded white matter, whereas effects with inferior cerebellum were globally distributed across most cortical regions. Longitudinally, utilization of eroded white matter revealed significant accumulation greater than zero across more regions whereas change over time was diminished using inferior cerebellum. Interestingly, the inferior temporal target region seemed most robust to reference region selection with expected cross-sectional and longitudinal signal across both reference regions. With few exceptions, baseline tau did not significantly predict longitudinal change in tau in the same region regardless of reference region. In summary, reference region selection deserves further evaluation as this methodological step may lead to disparate findings. Inferior cerebellar gray matter may be more sensitive to cross-sectional flortaucipir differences, whereas eroded subcortical white matter may be more sensitive for longitudinal analyses examining regional patterns of change.

Abstract Introduction We aimed to examine the contribution of subjective cognitive decline (SCD) to reduce the number of β‐amyloid (Aβ) positron emission tomography scans required for recruiting Aβ+ clinically normal individuals in clinical trials. Methods Three independent cohorts (890 clinically normal: 72 yrs ± 6.7; Female: 43.4%; SCD+: 24%; apolipoprotein E [APOE] ε4+: 28.5%; Aβ+: 32%) were used. SCD was dichotomized from one question. Using logistic regression, we classified Aβ+ using the SCD dichotomy, APOEε4, sex, and age. Results SCD increased odds of Aβ+ by 1.58 relative to non‐SCD. Female APOEε4 carriers with SCD exhibited higher odds of Aβ+ (OR = 3.34), whereas male carriers with SCD showed a weaker, opposing effect (OR = 0.37). SCD endorsement reduces the number of Aβ positron emission tomography scans to recruit Aβ+ individuals by 13% and by 9% if APOEε4 status is known. Conclusion SCD helps to classify those with high Aβ, even beyond the substantial effect of APOE genotype. Collecting SCD is a feasible method for targeting recruitment for those likely on the AD trajectory.

BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

Abstract Introduction In Alzheimer's disease, asymptomatic patients may have amyloid deposition, but predicting their progression rate remains a substantial challenge with implications for clinical trial enrollment. Here, we demonstrate an artificial intelligence approach to use baseline clinical information and images to predict changes in quantitative biomarkers of brain pathology on future images. Methods Patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent positron emission tomography (PET) with the amyloid radiotracer 18F‐AV45 (florbetapir) were included. We identified important baseline PET image features using a deep convolutional neural network based on ResNet. These were combined with eight clinical, demographic, and genetic markers using a gradient‐boosted decision tree (GBDT) algorithm to predict future quantitative standardized uptake value ratio (SUVR), an established biomarker of brain amyloid deposition. We used this model to better identify individuals with the highest positive change in amyloid deposition on future images and compared this to typical inclusion criteria for clinical trials. We also compared the model's performance to other methods such as multivariate linear regression and GBDT without imaging features. Findings Using 2577 PET scans from 1224 unique individuals, we showed that the GBDT with deep image features was significantly more accurate than the other approaches, reaching a root mean squared error of 0.0339 ± 0.0027 for future SUVR prediction. Using this approach, we could identify individuals with the highest 10% SUVR accumulation at rates 2‐ to 4‐fold higher than by random pick or existing inclusion criteria. Discussion Predicting quantitative biomarkers on future images using machine learning methods consisting of deep image features combined with clinical data may allow better targeting of treatments or enrollment in clinical trials.

Abstract Introduction Alzheimer's disease (AD) is the most common form of dementia, characterized primarily by abnormal aggregation of two proteins, tau and amyloid beta. We assessed tau pathology and white matter connectivity changes in subfields of the hippocampus simultaneously in vivo in AD. Methods Twenty‐four subjects were scanned using simultaneous time‐of‐flight 18F‐PI‐2620 tau positron emission tomography/3‐Tesla magnetic resonance imaging and automated segmentation. Results We observed extensive tau elevation in the entorhinal/perirhinal regions, intermediate tau elevation in cornu ammonis 1/subiculum, and an absence of tau elevation in the dentate gyrus, relative to controls. Diffusion tensor imaging showed parahippocampal gyral fractional anisotropy was lower in AD and mild cognitive impairment compared to controls and strongly correlated with early tau accumulation in the entorhinal and perirhinal cortices. Discussion This study demonstrates the potential for quantifiable patterns of 18F‐PI2620 binding in hippocampus subfields, accompanied by diffusion and volume metrics, to be valuable markers of AD.

Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.

Age-related episodic memory decline is characterized by striking heterogeneity across individuals. Hippocampal pattern completion is a fundamental process supporting episodic memory. Yet, the degree to which this mechanism is impaired with age, and contributes to variability in episodic memory, remains unclear. We combine univariate and multivariate analyses of fMRI data from a large cohort of cognitively normal older adults (N=100) to measure hippocampal activity and cortical reinstatement during retrieval of trial-unique associations. Trial-wise analyses revealed that (a) hippocampal activity scaled with reinstatement strength, (b) cortical reinstatement partially mediated the relationship between hippocampal activity and associative retrieval, (c) older age weakened cortical reinstatement and its relationship to memory behaviour. Moreover, individual differences in the strength of hippocampal activity and cortical reinstatement explained unique variance in performance across multiple assays of episodic memory. These results indicate that fMRI indices of hippocampal pattern completion explain within- and across-individual memory variability in older adults.

We describe the clinical and neuropathologic features of patients with Lewy body spectrum disorder (LBSD) carrying a nonsense variant, c.604C>T; p.R202X, in the glucocerebrosidase 1 (GBA) gene. While this GBA variant is causative for Gaucher’s disease, the pathogenic role of this mutation in LBSD is unclear. Detailed neuropathologic evaluation was performed for one index case and a structured literature review of other GBA p.R202X carriers was conducted. Through the systematic literature search, we identified three additional reported subjects carrying the same GBA mutation, including one Parkinson’s disease (PD) patient with early disease onset, one case with neuropathologically-verified LBSD, and one unaffected relative of a Gaucher’s disease patient. Among the affected subjects carrying the GBA p.R202X, all males were diagnosed with Lewy body dementia, while the two females presented as PD. The clinical penetrance of GBA p.R202X in LBSD patients and families argues strongly for a pathogenic role for this variant, although presenting with a striking phenotypic heterogeneity of clinical and pathological features.

Abstract Introduction Alzheimer's disease (AD) patients exhibit temporally graded memory loss with remote memories remaining more intact than recent memories. It is unclear whether this temporal pattern is observable in clinically normal adults with amyloid pathology (i.e. preclinical AD). Methods Participants were asked to recall the names of famous figures most prominent recently (famous after 1990) and remotely (famous from 1960–1980) and were provided with a phonemic cue to ensure that memory failure was not purely due to verbal retrieval weaknesses. In addition, participants identified line drawings of objects. Clinically normal older adults (n = 125) were identified as amyloid β positive or negative (Aβ+/−) using Pittsburgh compound B positron emission tomography. The relationship between Aβ+/− and recall of remote and recent famous face‐names and objects was examined using repeated measures analyses and general linear models controlling for demographics and media usage. Results When provided with a phonemic cue, Aβ+ participants recalled the names of fewer recent famous faces compared with Aβ− participants. However, recall of remote famous face‐names and objects did not differ by Aβ group. Discussion Relative sparing of remotely learned information compared with recently learned information is (1) detectable in the preclinical stages of AD and (2) related to amyloid pathology. Both this temporal gradient and assessment of person‐centered rather than object‐centered semantic information may be particularly meaningful for tracking early memory changes in the AD trajectory.