ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020
Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Pooled it
- Macroscale Gradient Dysfunction in Alzheimer's Disease: Patterns With Cognition Terms and Gene Expression Profiles.Human brain mapping · 2024Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- A neuroimaging biomarker for disease staging in clinically diagnosed Alzheimer's disease.BMC medicine · 2026Article
- Innovations in Alzheimer's disease diagnostic technologies: clinical prospects of novel biomarkers, multimodal integration, and non-invasive detection.Frontiers in neurology · 2025Review
- Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.Brain communications · 2025Article
- A multispatial information representation model emphasizing key brain regions for Alzheimer's disease diagnosis with structural magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach.Scientific reports · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.Human brain mapping · 2024Article
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
- Never-Ending Learning for Explainable Brain Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.Neuroinformatics · 2024Article
- Applications of interpretable deep learning in neuroimaging: A comprehensive review.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer's disease progression from longitudinal MRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.EClinicalMedicine · 2023Article
- MRAβ: A multimodal MRI-derived amyloid-β biomarker for Alzheimer's disease.Human brain mapping · 2023Article
- Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease.Neuroscience bulletin · 2023Article
- Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 9 institutions in 2 countries.
Funding
Abstract
Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (
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What OpenQuestion holds
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.