AbstractIndividuals with Alzheimer’s disease (AD) dementia exhibit significant heterogeneity across clinical symptoms, atrophy patterns, and spatial distribution of Tau deposition. Most previous studies of AD heterogeneity have focused on atypical clinical subtypes, defined subtypes with a single modality, or restricted their analyses to a priori brain regions and cognitive tests. Here, we considered a data-driven hierarchical Bayesian model to identify latent factors from atrophy patterns and cognitive deficits simultaneously, thus exploiting the rich dimensionality within each modality. Unlike most previous studies, our model allows each factor to be expressed to varying degrees within an individual, in order to reflect potential multiple co-existing pathologies.By applying our model to ADNI-GO/2 AD dementia participants, we found three atrophy-cognitive factors. The first factor was associated with medial temporal lobe atrophy, episodic memory deficits and disorientation to time/place (“MTL-Memory”). The second factor was associated with lateral temporal atrophy and language deficits (“Lateral Temporal-Language”). The third factor was associated with atrophy in posterior bilateral cortex, and visuospatial executive function deficits (“Posterior Cortical-Executive”). While the MTL-Memory and Posterior Cortical-Executive factors were discussed in previous literature, the Lateral Temporal-Language factor is novel and emerged only by considering atrophy and cognition jointly. Several analyses were performed to ensure generalizability, replicability and stability of the estimated factors. First, the factors generalized to new participants within a 10-fold cross-validation of ADNI-GO/2 AD dementia participants. Second, the factors were replicated in an independent ADNI-1 AD dementia cohort. Third, factor loadings of ADNI-GO/2 AD dementia participants were longitudinally stable, suggesting that these factors capture heterogeneity across patients, rather than longitudinal disease progression. Fourth, the model outperformed canonical correlation analysis at capturing associations between atrophy patterns and cognitive deficits.To explore the influence of the factors early in the disease process, factor loadings were estimated in ADNI-GO/2 mild cognitively impaired (MCI) participants. Although the associations between the atrophy patterns and cognitive profiles were weak in MCI compared to AD, we found that factor loadings were associated with inter-individual regional variation in Tau uptake. Taken together, these results suggest that distinct atrophy-cognitive patterns exist in typical Alzheimer’s disease, and are associated with distinct patterns of Tau depositions before clinical dementia emerges.HighlightsBayesian model reveals 3 atrophy-cognitive factors in typical AD from ADNI-GO/2Replicated in independent ADNI-1 cohort; longitudinally stable within individualsTriple cognitive dissociations among atrophy patterns suggest subtypes, not stagesOutperforms canonical correlation analysisFactor loadings associated with spatial patterns of Tau uptake in MCI
Hyper-graph based Adaptive Sparse Multi-view Canonical Correlation Analysis with Application to Neuroimaging Genetics Study of Alzheimer’s Disease
