ArticleMedical image analysis2022
Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural correlates of subjective cognitive decline in Alzheimer's disease: a systematic review of structural and functional brain changes for early diagnosis and intervention.Frontiers in aging neuroscience · 2025Pooled it
- Baseline cortical thinning in the visual cortex as a predictor of early mild cognitive impairment progression: a population-based follow-up study.European archives of psychiatry and clinical neuroscience · 2026Article
- HIBMatch: Hypergraph Information Bottleneck for Semi-Supervised Alzheimer's Progression.IEEE journal of biomedical and health informatics · 2026Article
- Generative Adversarial Networks Based on Fine-Grained Image Recognition for the Progression Prediction of Progressive Mild Cognitive Impairment.Interdisciplinary sciences, computational life sciences · 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
- Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Fully Incomplete Information for Multiview Clustering in Postoperative Liver Tumor Diagnoses.Sensors (Basel, Switzerland) · 2025Article
- A lightweight triple-modal fusion network for progressive mild cognitive impairment prediction in Alzheimer's disease.Frontiers in neuroscience · 2025Article
- Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease.Frontiers in aging neuroscience · 2025Article
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing.Frontiers in neuroinformatics · 2024Article
- Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI.IEEE journal of biomedical and health informatics · 2023Article
- Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review.Frontiers in artificial intelligence · 2022Article
- Article
- Review
- Recent contributions to the field of subjective cognitive decline in aging: A literature review.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline. Multi-modal neuroimaging data such as T1-weighted magnetic resonance imaging (MRI) and positron emission tomography (PET), help provide objective and supplementary disease biomarkers for computer-aided diagnosis of MCI. However, there are few studies dedicated to SCD progression prediction since subjects usually lack one or more imaging modalities. Besides, one usually has a limited number (e.g., tens) of SCD subjects, negatively affecting model robustness. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework for SCD conversion prediction using incomplete multi-modal neuroimages. The JSRL contains two components: 1) a generative adversarial network to synthesize missing images and generate multi-modal features, and 2) a classification network to fuse multi-modal features for SCD conversion prediction. The two components are incorporated into a joint learning framework by sharing the same features, encouraging effective fusion of multi-modal features for accurate prediction. A transfer learning strategy is employed in the proposed framework by leveraging model trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI and fluorodeoxyglucose PET from 863 subjects to both the Chinese Longitudinal Aging Study (CLAS) with only MRI from 76 SCD subjects and the Australian Imaging, Biomarkers and Lifestyle (AIBL) with MRI from 235 subjects. Experimental results suggest that the proposed JSRL yields superior performance in SCD and MCI conversion prediction and cross-database neuroimage synthesis, compared with several state-of-the-art methods.
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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 Socratic graph.