# Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuum

arXiv: https://arxiv.org/abs/2406.13305 · HTML: https://mlsanity.com/paper/2406.13305/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 19 Jun 2024.

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

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans](https://mlsanity.com/paper/2307.16366/) (2023)
- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)

## Built on by (most cited first, 2 of 2)

- [4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis](https://mlsanity.com/paper/2504.16798/) (2025)
- [Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI](https://mlsanity.com/paper/2604.12574/) (2026)

## Abstract (arXiv)

Amyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762±0.04. The specificity of the information brought by each modality is assessed by textitpost-hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shading light on modality-specific possibly unknown Aβ deposition signatures.