Graph of brain structures grading for early detection of Alzheimer's disease

TL;DR

This paper introduces a novel graph-based framework that combines inter-subject similarity and intra-subject variability to improve early detection of Alzheimer's disease, showing competitive performance against existing methods.

Contribution

The work presents a new graph of brain structures grading method that integrates multiple approaches for early Alzheimer's diagnosis.

Findings

Demonstrates competitive accuracy with state-of-the-art methods

Combines inter-subject and intra-subject analysis in a unified framework

Enhances early detection capabilities of Alzheimer's disease

Abstract

Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies have proposed biomarkers to perform early detection of Alzheimer's disease. Some of them have proposed methods based on inter-subject similarity while other approaches have investigated framework using intra-subject variability. In this work, we propose a novel framework combining both approaches within an efficient graph of brain structures grading. Subsequently, we demonstrate the competitive performance of the proposed method compared to state-of-the-art methods.