Analysing heterogeneity in Alzheimer Disease using multimodal normative
  modelling on ATN biomarkers
Sayantan Kumara Thomas Earnest Braden Yang Deydeep Kothapalli Tammie L. S. Benzinger Brian A. Gordon Philip Payne Aristeidis Sotiras
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.