ArticleMachine learning in medical imaging. MLMI (Workshop)2020
Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder Using Resting-State fMRI.
Article in Machine learning in medical imaging. MLMI (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 31 citations in OpenAlex.
- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder.Psychoradiology · 2025Review
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.Frontiers in psychiatry · 2023Article
- Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.NeuroImage · 2022Article
- Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.Brain sciences · 2022Article
- Extracting default mode network based on graph neural network for resting state fMRI study.Frontiers in neuroimaging · 2022Article
- Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network.Frontiers in neuroimaging · 2022Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Extensive studies focus on analyzing human brain functional connectivity from a network perspective, in which each network contains complex graph structures. Based on resting-state functional MRI (rs-fMRI) data, graph convolutional networks (GCNs) enable comprehensive mapping of brain functional connectivity (FC) patterns to depict brain activities. However, existing studies usually characterize static properties of the FC patterns, ignoring the time-varying dynamic information. In addition, previous GCN methods generally use fixed group-level (e.g., patients or controls) representation of FC networks, and thus, cannot capture subject-level FC specificity. To this end, we propose a Temporal-Adaptive GCN (TAGCN) framework that can not only take advantage of both spatial and temporal information using resting-state FC patterns and time-series but also explicitly characterize subject-level specificity of FC patterns. Specifically, we first segment each ROI-based time-series into multiple overlapping windows, then employ an adaptive GCN to mine topological information. We further model the temporal patterns for each ROI along time to learn the periodic brain status changes. Experimental results on 533 major depressive disorder (MDD) and health control (HC) subjects demonstrate that the proposed TAGCN outperforms several state-of-the-art methods in MDD vs. HC classification, and also can be used to capture dynamic FC alterations and learn valid graph representations.
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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.