ArticleMedical image analysis2021
Multi-scale graph-based grading for Alzheimer's disease prediction.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The future of Alzheimer's disease risk prediction: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.Human brain mapping · 2025Article
- Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.medRxiv : the preprint server for health sciences · 2025Article
- Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.Alzheimer's research & therapy · 2024Article
- Consensus on rapid screening for prodromal Alzheimer's disease in China.General psychiatry · 2024Review
- Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.Research square · 2023Article
- Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach.Alzheimer's research & therapy · 2022Article
- Distinct profiles of functional connectivity density aberrance in Alzheimer's disease and mild cognitive impairment.Frontiers in psychiatry · 2022Article
- Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer's Disease Using Structural MRI.Frontiers in aging neuroscience · 2022Article
- Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features.Frontiers in neuroscience · 2022Article
- Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer's disease.Frontiers in aging neuroscience · 2022Article
- Hippocampal morphological atrophy and distinct patterns of structural covariance network in Alzheimer's disease and mild cognitive impairment.Frontiers in psychology · 2022Article
- Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis.Brain connectivity · 2021Review
- Anatomical texture patterns identify cerebellar distinctions between essential tremor and Parkinson's disease.Human brain mapping · 2021Article
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Authors and funding
6 authors.
Funding
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 accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.