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• APOE ϵ 4 carriers and non-carriers have significantly different disease timelines • Progression along imaging-based data-driven disease timelines is predictive of AD • Disease timelines were found to be generalizable to a population-based cohort • Incident AD and controls have significantly different disease stage trajectories Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE -specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ 4 non-carriers and carriers were found to be significantly different from one another (p < 0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOE ϵ 4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ 4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p < 0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOE ϵ 4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials. [ABSTRACT FROM AUTHOR]

Gyrification of the cerebral cortex changes with aging and relates to development of cognitive function during early life and midlife. Little is known about how gyrification relates to age and cognitive function later in life. We investigated this in 4397 individuals (mean age: 63.5 years, range: 45.7 to 97.9) from the Rotterdam Study, a population-based cohort. Global and local gyrification were assessed from T 1 -weighted images. A measure for global cognition, the g-factor, was calculated from five cognitive tests. Older age was associated with lower gyrification (mean difference per year ​= ​−0.0021; 95% confidence interval ​= ​−0.0025; −0.0017). Non-linear terms did not improve the models. Age related to lower gyrification in the parietal, frontal, temporal and occipital regions, and higher gyrification in the medial prefrontal cortex. Higher levels of the g-factor were associated with higher global gyrification (mean difference per g-factor unit ​= ​0.0044; 95% confidence interval ​= ​0.0015; 0.0073). Age and the g-factor did not interact in relation to gyrification (p ​> ​0.05). The g-factor bilaterally associated with gyrification in three distinct clusters. The first cluster encompassed the superior temporal gyrus, the insular cortex and the postcentral gyrus, the second cluster the lingual gyrus and the precuneus, and the third cluster the orbitofrontal cortex. These clusters largely remained statistically significant after correction for cortical surface area. Overall, the results support the notion that gyrification varies with aging and cognition during and after midlife, and suggest that gyrification is a potential marker for age-related brain and cognitive decline beyond midlife. • Older age associates with lower cortical gyrification during and after midlife. • Age and gyrification relate linearly during adulthood, not non-linearly. • Gyrification of the medial prefrontal cortex increases with age after midlife. • Cognitive function associates with gyrification in the frontal and temporal lobes. [ABSTRACT FROM AUTHOR]