# Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease

arXiv: https://arxiv.org/abs/2408.02018 · HTML: https://mlsanity.com/paper/2408.02018/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 4 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation](https://mlsanity.com/paper/2212.08228/) (2022)
- [Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge](https://mlsanity.com/paper/2405.03328/) (2024)
- [Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia](https://mlsanity.com/paper/1912.01526/) (2019)
- [Variational AutoEncoder For Regression: Application to Brain Aging Analysis](https://mlsanity.com/paper/1904.05948/) (2019)
- [Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression](https://mlsanity.com/paper/1907.02787/) (2019)

## Built on by (most cited first, 7 of 7)

- [Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion](https://mlsanity.com/paper/2502.08560/) (2025)
- [Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation](https://mlsanity.com/paper/2509.03141/) (2025)
- [Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling](https://mlsanity.com/paper/2601.14584/) (2026)
- [Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images](https://mlsanity.com/paper/2506.14560/) (2025)
- [Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis](https://mlsanity.com/paper/2410.13363/) (2024)
- [AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders](https://mlsanity.com/paper/2511.05934/) (2025)
- [Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder](https://mlsanity.com/paper/2603.01552/) (2026)

## Abstract (arXiv)

Neurodegeneration as measured through magnetic resonance imaging (MRI) is recognized as a potential biomarker for diagnosing Alzheimer's disease (AD), but is generally considered less specific than amyloid or tau based biomarkers. Due to a large amount of variability in brain anatomy between different individuals, we hypothesize that leveraging MRI time series can help improve specificity, by treating each patient as their own baseline. Here we turn to conditional variational autoencoders to generate individualized MRI predictions given the subject's age, disease status and one previous scan. Using serial imaging data from the Alzheimer's Disease Neuroimaging Initiative, we train a novel architecture to build a latent space distribution which can be sampled from to generate future predictions of changing anatomy. This enables us to extrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated the model on a held-out set from ADNI and an independent dataset (from Open Access Series of Imaging Studies). By comparing to several alternatives, we show that our model produces more individualized images with higher resolution. Further, if an individual already has a follow-up MRI, we demonstrate a usage of our model to compute a likelihood ratio classifier for disease status. In practice, the model may be able to assist in early diagnosis of AD and provide a counterfactual baseline trajectory for treatment effect estimation. Furthermore, it generates a synthetic dataset that can potentially be used for downstream tasks such as anomaly detection and classification.