ArticleIEEE transactions on medical imaging2020
Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.
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 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 182 citations in OpenAlex.
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- Wearable-Compatible Detection of Mild Cognitive Impairment Using Novel Features Based on Sleep Stage Dynamics.Brain sciences · 2026Article
- Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation.Human brain mapping · 2025Article
- Navigation in brain networks is possible using only local measures.Scientific reports · 2025Article
- Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.The journal of prevention of Alzheimer's disease · 2025Article
- MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease.IEEE transactions on emerging topics in computational intelligence · 2025Article
- Quantitative magnetic resonance imaging in Alzheimer's disease: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- A Speech-Based Mobile Screening Tool for Mild Cognitive Impairment: Technical Performance and User Engagement Evaluation.Bioengineering (Basel, Switzerland) · 2025Article
- Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations.Frontiers in neuroscience · 2025Article
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.Brain communications · 2025Article
- Aberrant functional connectivity between the retrosplenial cortex and hippocampal subregions in amnestic mild cognitive impairment and Alzheimer's disease.Brain communications · 2025Article
- Classifying disorders of consciousness using a novel dual-level and dual-modal graph learning model.Journal of translational medicine · 2024Article
- Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.Expert systems with applications · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI.Human brain mapping · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.Journal of digital imaging · 2023Article
- Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.Human brain mapping · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, the voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, the dynamic BFN features can complement static BFN features and, at the meantime, different BFNs can help each other toward a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in early-stage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for a better-individualized diagnosis of various neurological diseases.
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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.