ArticleIEEE transactions on bio-medical engineering2018
Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Discovering hierarchical common brain networks via multimodal deep belief network.Medical image analysis · 2019Pooled it
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks.Scientific reports · 2025Article
- Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.Psychoradiology · 2025Review
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Detection of ASD Children through Deep-Learning Application of fMRI.Children (Basel, Switzerland) · 2023Article
- [Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network.Brain sciences · 2022Article
- Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2022Article
- Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network.Frontiers in neuroscience · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder.Brain imaging and behavior · 2021Article
- Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis.Neuroinformatics · 2021Article
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network.Frontiers in neuroinformatics · 2021Article
- Design of Deep Learning Model for Task-Evoked fMRI Data Classification.Computational intelligence and neuroscience · 2021Article
- 4D Modeling of fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN).IEEE transactions on cognitive and developmental systems · 2020Article
- Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.IEEE transactions on medical imaging · 2020Article
- Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network.IEEE journal of biomedical and health informatics · 2019Article
- Deep Learning Models Unveiled Functional Difference Between Cortical Gyri and Sulci.IEEE transactions on bio-medical engineering · 2019Article
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Authors and funding
10 authors.
Funding
Abstract
Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as independent component analysis and sparse coding methods, can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective, and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project fMRI data showed that the proposed deep 3-D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labeled training instances. This study provides a new deep learning approach for modeling functional connectomes based on fMRI data.
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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.