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

## TL;DR

This paper introduces a novel multiscale graph-based MRI biomarker for predicting Alzheimer's disease progression in MCI patients, achieving high accuracy and outperforming existing methods.

## Contribution

It presents a new graph-based grading framework combined with multiscale brain analysis to improve AD conversion prediction from MRI data.

## Key findings

- Achieved 81% AUC in predicting MCI to AD conversion.
- Combined with cognitive scores, reached 85% AUC.
- Outperforms state-of-the-art methods on ADNI-1 dataset.

## Abstract

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.