ArticleIEEE transactions on medical imaging2020
Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 76 citations in OpenAlex.
- Resolving Heterogeneity in the Diagnosis of Alzheimer's Disease and its Progression Using Multimodal Data.Journal of molecular neuroscience : MN · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- Prediction of Alzheimer's Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization.Journal of imaging informatics in medicine · 2025Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline.BMC medical informatics and decision making · 2024Article
- Disease2Vec: Encoding Alzheimer's progression via disease embedding tree.Pharmacological research · 2024Article
- Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.Brain connectivity · 2023Article
- A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.Biomolecules · 2023Article
- REPRESENTATIVE FUNCTIONAL CONNECTIVITY LEARNING FOR MULTIPLE CLINICAL GROUPS IN ALZHEIMER'S DISEASE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.Sensors (Basel, Switzerland) · 2023Article
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.IEEE transactions on bio-medical engineering · 2021Article
- Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images.IEEE transactions on medical imaging · 2021Article
- THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer's disease.Quantitative imaging in medicine and surgery · 2021Article
- Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort.Frontiers in big data · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities (i.e., genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online.11 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc.
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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.