# Multi-scale Graph-based Grading for Alzheimer's Disease Prediction

arXiv: https://arxiv.org/abs/1907.06625 · HTML: https://mlsanity.com/paper/1907.06625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Large language models improve Alzheimer's disease diagnosis using multi-modality data](https://mlsanity.com/paper/2305.19280/) (2023)
- [Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans](https://mlsanity.com/paper/2307.16366/) (2023)
- [Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis](https://mlsanity.com/paper/2505.00627/) (2025)
- [A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images](https://mlsanity.com/paper/2203.01944/) (2022)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)

## Abstract (arXiv)

The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerate the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to predict conversion of MCI subjects to AD accurately. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.