Millar, Peter R

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.

Abstract INTRODUCTION Preclinical Alzheimer's disease (AD) can be described relative to biomarker positivity onset time. METHODS We estimated time from amyloid positivity (A+) using sampled iterative local approximation (SILA) in a longitudinal autosomal dominant AD (ADAD) sample ( N = 379) with amyloid positron emission tomography. We compared (1) predicted age at A+ to imputed age, (2) estimated age at A+ to estimated age at symptom onset, and (3) variance in cognitive performance explained. RESULTS Mean error between imputed and SILA‐estimated age at A+ ( N = 26) was 1.15 years. Age at A+ explained 39% of estimated years to symptom onset (EYO) variance. Time from A+ explained 19% of cognitive composite variance and 14% of Clinical Dementia Rating Sum of Boxes CDR‐SB variance; EYO explained 43% and 57%, respectively. DISCUSSION SILA estimates A+ age in ADAD with reasonably good accuracy. SILA‐estimated time from A+ describes the start of pathology, but the time from A+ onset to symptoms is variable in ADAD and better described by EYO. Highlights Amyloid chronicity predicts a 14‐year preclinical AD phase in ADAD. SILA accurately estimates age at A+ (MAE < 2 years). EYO outperforms chronicity in predicting symptom onset. APP mutation carriers show atypical amyloid accumulation. Chronicity models help reveal AD heterogeneity in preclinical stages.

AbstractBackgroundAlzheimer disease (AD) involves neurodegenerative disorders with progressive cognitive decline. Atypical presentations like Posterior Cortical Atrophy (PCA) and Logopenic Variant Primary Progressive Aphasia (lvPPA) exhibit distinct clinical profiles. PCA affects the posterior parietal and occipital lobes, causing visuospatial deficits, while lvPPA manifests as language impairment in the temporoparietal region. Understanding the biological underpinnings of these variants is crucial for deciphering AD’s heterogeneity.MethodParticipants diagnosed with AD dementia (n = 54, female = 25, Age: 75.23±6.52), PCA (n = 9, female = 8, Age: 62.78±7.21), and lvPPA (n = 6, female = 2, Age: 66.33±6.38) from the Knight Alzheimer Disease Research Center (Knight ADRC) underwent 18F‐AV‐1451 tau‐PET imaging. Tau deposition, along with nine biological properties, was mapped to the cortical surface (Figure 1A): gene expression, myelin, cortical thickness, sensorimotor association axis, evolutionary expansion, cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of glucose (CMRglu) (Figure 1B). Correlation matrices and generalized linear models (GLMs) assessed relationships between tau accumulation and biological properties.ResultA correlation matrix (Figure 2A) shows significant positive associations (p<0.05) between evolutionary expansion patterns and tau accumulation in AD (r = 0.53), PCA (r = 0.29), and lvPPA (r = 0.56); and gene expression in PCA (r = 0.51). Conversely, strongest negative associations were observed in AD (r = ‐0.38, r = ‐0.15) and lvPPA (r = ‐0.18, r = ‐0.28) for CBF and myelin mapping, respectively, and in PCA (r = ‐0.25, ‐0.23) for cortical thickness and sensorimotor association axis. GLMs demonstrated significant relationships between cerebrovascular health and evolutionary expansion with tau measures across all groups. AD (r = ‐0.52) and PCA (r = ‐0.19) exhibited negative associations with gene expression and myelin mapping, respectively. Additionally, lvPPA (r = 0.39) displayed positive association with the sensorimotor association cortex.ConclusionResults elucidate distinct tau deposition patterns in AD, PCA, and lvPPA, providing insights into subtype‐specific pathophysiology. The correlation matrix and GLMs highlight the significance of evolutionary expansion and cerebrovascular health in tau accumulation. Associations underscore the complex interplay of gene expression, myelin mapping, and cortical thickness with tau pathology. Findings contribute to understanding heterogeneity within AD spectrum disorders, emphasizing the multifaceted nature of neurodegenerative processes. Further research promises refined diagnostic and therapeutic strategies tailored to distinct neurobiological profiles in AD, PCA, and lvPPA.

Abstract Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer’s disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages, 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepen the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

Background: Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).