ArticleNeurobiology of aging2018
Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.
Article in Neurobiology of aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations.Communications biology · 2026Article
- Modeling and application of alzheimer's disease complex trait prediction based on multi-task learning.Scientific reports · 2026Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.Scientific reports · 2025Article
- An imaging and genetic-based deep learning network for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Review
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Paradigm shift required for translational research on the brain.Experimental & molecular medicine · 2024Review
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
- Article
- Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.Journal of translational medicine · 2024Article
- 3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer's disease sub-scores.Brain informatics · 2024Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- A review of brain imaging biomarker genomics in Alzheimer's disease: implementation and perspectives.Translational neurodegeneration · 2022Review
- Deep Learning-Based Diagnosis of Alzheimer's Disease.Journal of personalized medicine · 2022Review
- Predictive classification of Alzheimer's disease using brain imaging and genetic data.Scientific reports · 2022Article
- Associations Between Sub-Threshold Amyloid-β Deposition, Cortical Volume, and Cognitive Function Modulated by APOE ɛ4 Carrier Status in Cognitively Normal Older Adults.Journal of Alzheimer's disease : JAD · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them.
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Registered trials
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.