ArticleIEEE transactions on bio-medical engineering2020
Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.
Article in IEEE transactions on bio-medical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 152 citations in OpenAlex.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- 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
- Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.Biomedical engineering online · 2025Review
- ADMGCN: graph convolutional network for Alzheimer's disease diagnosis with a meta-learning paradigm.Bioinformatics (Oxford, England) · 2025Article
- Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.BMC medical imaging · 2025Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations.Frontiers in neuroscience · 2025Article
- Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.Sensors (Basel, Switzerland) · 2024Article
- The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.Interdisciplinary sciences, computational life sciences · 2024Article
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.Human brain mapping · 2023Article
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.Frontiers in aging neuroscience · 2023Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases.Brain sciences · 2022Article
- MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network.BMC medical informatics and decision making · 2022Article
- A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning.Journal of neuroscience methods · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.
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