ArticleEuropean journal of radiology2024
Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition.
Article in European journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.PloS one · 2026Pooled it
- Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.Journal of imaging informatics in medicine · 2026Article
- AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.Global epidemiology · 2026Article
- [Progress on prognostic assessment methods for mild cognitive impairment].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Review
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- An interpretable nomogram with SHAP analysis predicts thrombotic failure of forearm arteriovenous fistulas.Frontiers in surgery · 2026Article
- Machine learning with multitype functional connectivity uncovers whole-brain network disruption in primary angle-closure glaucoma.Brain informatics · 2025Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.Skeletal radiology · 2025Review
- Explainable CNN-Radiomics Fusion and Ensemble Learning for Multimodal Lesion Classification in Dental Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer's disease continuum using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer's Disease Classification.Brain sciences · 2025Article
- A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer's Disease Detection in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Predicting microvascular invasion in solitary hepatocellular carcinoma: a multi-center study integrating clinical, MRI assessments, and radiomics indicators.Frontiers in oncology · 2025Article
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.PloS one · 2025Article
- An explainable AI approach for mapping multivariate regional brain age and clinical severity patterns in Alzheimer's disease.Biology methods & protocols · 2025Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships.
methodWe integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients.
resultsDeep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter.
conclusionDeep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.
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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.