Longitudinal Brain Atrophy Patterns in Dementia and Cognitive Decline: the Framingham Heart Study

BackgroundCharacterizing longitudinal patterns of brain atrophy that distinguish Alzheimers disease (AD) and related neurodegeneration along with normative aging remains a major challenge. We aimed to identify data-driven longitudinal brain atrophy components and evaluate their associations with plasma AD biomarkers and cognitive outcomes in a community-based cohort. MethodsWe analyzed 756 MRI scans from 300 participants in the Framingham Heart Study (mean 2.52 scans per participant; range 2-4). Linear mixed-effects models were used to identify MRI features associated with diagnostic group (cognitively normal [CN], mild cognitive impairment [MCI], and dementia). Significant features (n=211) were entered into a longitudinal multivariate decomposition framework (ANOVA Simultaneous Component Analysis with Assorted Linear functions; ALASCA) to derive principal components (PCs) capturing patterns of structural change over time. Associations between PCs and plasma AD biomarkers (p-Tau181, total Tau(t-Tau), glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL], amyloid-{beta}40 [A{beta}40], and amyloid-{beta}42 [A{beta}42]) were evaluated using multivariable mixed-effects models adjusted for age, sex, education, and APOE {varepsilon}4 status. Cognitive measures and neuroethological measures in a subset were used to assess the functional relevance and biological associations, respectively. ResultsThe first three PCs explained [~]95% of the variance within the modeled MRI feature (n=211) set (PC1: 75.8%, PC2: 13.8%, PC3: 5.4%). PC1 captured medial temporal atrophy involving hippocampal subfields and basolateral amygdala and was associated with worse cognition and higher plasma AD biomarkers. Neuropathological analyses showed stronger associations of PC1-related atrophy with AD-related tau pathology in the absence of concomitant TDP-43 pathology. In contrast, PC2 reflected diffuse cortical gray-white matter contrast alterations across association cortices and showed distinct associations with biomarkers and cognition compared to PC1, consistent with overlapping aging- and neurodegeneration-related processes. PC3 showed limited variance and no consistent associations. ConclusionLongitudinal MRI-derived components capture distinct patterns of brain structural change associated with neurodegeneration. Medial temporal trajectories are closely associated with AD and related dementia, whereas cortical alterations likely reflect mixed aging- and disease-related processes. Integration of structural MRI with plasma biomarkers provides complementary information on disease expression and heterogeneity, supporting multimodal approaches for disease characterization and risk stratification.