Vöglein, Jonathan

Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Aβ deposition in the basal ganglia is common in autosomal dominant Alzheimer’s disease. Vöglein et al. report an increased severity of motor symptoms in autosomal dominant versus sporadic disease in advanced disease stages. Motor symptoms are more severe in post-codon 200 presenilin 1 mutation carriers and correlate with basal ganglia Aβ.

AbstractIntroductionLittle is known about effects of physical activity (PA) in genetically driven early‐onset autosomal dominant Alzheimer's disease (AD).MethodsA total of 372 individuals participating at the Dominantly Inherited Alzheimer Network study were examined to evaluate the cross‐sectional relationship of PA with cognitive performance, functional status, cognitive decline, and AD biomarkers in cerebrospinal fluid. Mutation carriers were categorized as high or low exercisers according to WHO recommendations.ResultsMutation carriers with high PA showed significantly better cognitive and functional performance and significantly less AD‐like pathology in cerebrospinal fluid than individuals with low PA. Mutation carriers with high PA scored 3.4 points better on Mini Mental State Examination at expected symptom onset and fulfilled the diagnosis of very mild dementia 15.1 years later compared with low exercisers.DiscussionThese results support a beneficial effect of PA on cognition and AD pathology even in individuals with genetically driven autosomal dominant AD.

AbstractIntroductionThe objective of this study was to evaluate the relationship between self‐reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers.MethodsIn 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self‐reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ42, and CSF tau levels was evaluated using linear regression.ResultsNo differences in brain amyloid load, CSF Aβ42, or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels.DiscussionOur findings indicate a relationship exists between self‐reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers.