ArticleIEEE transactions on medical imaging2019
Recognizing Brain States Using Deep Sparse Recurrent Neural Network.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Building an auxiliary diagnostic and treatment efficacy prediction model for adolescent depression using machine learning based on electroencephalography technology.Frontiers in human neuroscience · 2026Article
- Alterations of multilayer network correlated with cognitive impairment and gene expression profiles in children with idiopathic generalized epilepsy.Scientific reports · 2025Article
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.Psychoradiology · 2025Review
- Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction.IEEE transactions on medical imaging · 2022Article
- Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.Brain sciences · 2022Article
- Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network.Human brain mapping · 2022Article
- Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder.Brain imaging and behavior · 2021Article
- Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.IEEE journal of biomedical and health informatics · 2021Article
- Interpretation of Frequency Channel-Based CNN on Depression Identification.Frontiers in computational neuroscience · 2021Article
- A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.IEEE transactions on medical imaging · 2020Article
- Modeling task-based fMRI data via deep belief network with neural architecture search.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.
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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.