Analysing heterogeneity in Alzheimer Disease using multimodal normative modelling on ATN biomarkers

Alzheimer Disease (AD) is a multi-faceted disorder, with each modality providing unique and complementary info about AD. In this study, we used a deep-learning based multimodal normative model to assess the heterogeneity in regional brain patterns for ATN (amyloid-tau-neurodegeneration) biomarkers. We selected discovery (n = 665) and replication (n = 430) cohorts with simultaneous availability of ATN biomarkers: Florbetapir amyloid, Flortaucipir tau and T1-weighted MRI (magnetic resonance imaging) imaging. A multimodal variational autoencoder (conditioned on age and sex) was used as a normative model to learn the multimodal regional brain patterns of a cognitively unimpaired (CU) control group. The trained model was applied on individuals on the ADS (AD Spectrum) to estimate their deviations (Z-scores) from the normative distribution, resulting in a Z-score regional deviation map per ADS individual per modality. ADS individuals with moderate or severe dementia showed higher proportion of regional outliers for each modality as well as more dissimilarity in modality-specific regional outlier patterns compared to ADS individuals with early or mild dementia. DSI was associated with the progressive stages of dementia, (ii) showed significant associations with neuropsychological composite scores and (iii) related to the longitudinal risk of CDR progression. Findings were reproducible in both discovery and replication cohorts. Our is the first study to examine the heterogeneity in AD through the lens of multiple neuroimaging modalities (ATN), based on distinct or overlapping patterns of regional outlier deviations. Regional MRI and tau outliers were more heterogenous than regional amyloid outliers. DSI has the potential to be an individual patient metric of neurodegeneration that can help in clinical decision making and monitoring patient response for anti-amyloid treatments.