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Alzheimer's Disease: Overview

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Updated: Dec 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Multi-scale graph-based grading for Alzheimer's disease prediction.

Kilian Hett¹, Vinh-Thong Ta², Ipek Oguz³

¹CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France; Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA.

Medical Image Analysis

|October 19, 2020

Summary

This summary is machine-generated.

This study introduces a novel MRI-based biomarker to predict Alzheimer's disease progression in mild cognitive impairment (MCI) patients. The new method accurately identifies individuals likely to convert to Alzheimer's disease (AD) within three years.

Keywords:

Alzheimer’s disease classification Graph-based method Hippocampal subfields Inter-subject similarity Intra-subject variability Mild cognitive impairment Patch-based grading Whole brain analysis

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Neuroimaging
Biomarkers
Alzheimer's Disease Research

Background:

Predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for treatment development.
Current methods require improved accuracy in identifying individuals at high risk of conversion.

Purpose of the Study:

To develop and validate a new MRI-based biomarker for predicting MCI to AD conversion.
To enhance the accuracy of early Alzheimer's disease detection using advanced neuroimaging analysis.

Main Methods:

A novel graph-based grading framework combining inter-subject similarity and intra-subject variability features.
Patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships.
An innovative multiscale brain analysis using a cascade of classifiers to assess alterations at various anatomical levels, including the hippocampus.

Main Results:

The proposed multiscale graph-based grading method achieved an Area Under the Curve (AUC) of 81% for predicting MCI to AD conversion within three years on the ADNI-1 dataset.
Integration with cognitive scores further improved prediction accuracy, reaching 85% AUC.
Performance is competitive with existing state-of-the-art methods.

Conclusions:

The developed MRI-based biomarker demonstrates high accuracy in predicting Alzheimer's disease progression in MCI subjects.
The multiscale graph-based approach effectively captures the signature of Alzheimer's disease.
This biomarker holds potential for accelerating clinical trials and improving patient outcomes.