# Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

arXiv: https://arxiv.org/abs/2404.05748 · HTML: https://mlsanity.com/paper/2404.05748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 76 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities](https://mlsanity.com/paper/2303.12706/) (2023)
- [Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data](https://mlsanity.com/paper/1812.04998/) (2018)
- [Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease](https://mlsanity.com/paper/2110.04903/) (2021)
- [Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images](https://mlsanity.com/paper/2006.15255/) (2020)
- [Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders](https://mlsanity.com/paper/2312.00992/) (2023)

## Built on by (most cited first, 1 of 1)

- [Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease](https://mlsanity.com/paper/2608.24931/) (2026)

## Abstract (arXiv)

INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTS: Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION: Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.