ArticleNeuroImage2019
Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Scanning the Horizon of Replicability in Neuroscience: A Recipe of Developing Replicable Deep Models for Functional Neuroimages.IEEE transactions on bio-medical engineering · 2026Article
- Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.PLoS biology · 2025Article
- Precision Neuromodulation with Real-Time Brain Decoding for Working Memory Enhancement.bioRxiv : the preprint server for biology · 2025Article
- SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data.Magma (New York, N.Y.) · 2024Article
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- Design and evaluation of a global workspace agent embodied in a realistic multimodal environment.Frontiers in computational neuroscience · 2024Article
- Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage.Frontiers in neuroinformatics · 2024Article
- Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.Human brain mapping · 2023Article
- Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice.Nature communications · 2023Article
- Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.Frontiers in neuroscience · 2023Article
- Decoding task specific and task general functional architectures of the brain.Human brain mapping · 2022Article
- Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network.Human brain mapping · 2022Article
- Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy.Frontiers in human neuroscience · 2021Article
- Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.Lasers in medical science · 2020Article
- A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Decoding and mapping task states of the human brain via deep learning.Human brain mapping · 2020Article
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2 authors.
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Abstract
Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurofeedback signals. However, it remains challenging to decode subtly distinct brain states for individual fMRI data points due to varying temporal durations and dependency among different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in intrinsic functional network modeling and sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, subject-specific intrinsic functional networks (FNs) are computed from resting-state fMRI data and are used to characterize functional signals of task fMRI data with a compact representation for building brain decoding models, and LSTM RNNs are adopted to learn brain decoding mappings between functional profiles and brain states. Validation results on fMRI data from the HCP dataset have demonstrated that brain decoding models built on training data using the proposed method could learn discriminative latent feature representations and effectively distinguish subtly distinct working memory tasks of different subjects with significantly higher accuracy than conventional decoding models. Informative FNs of the brain decoding models identified as brain activation patterns of working memory tasks were largely consistent with the literature. The method also obtained promising decoding performance on motor and social cognition tasks. Our results suggest that LSTM RNNs in conjunction with FNs could build interpretable, highly accurate brain decoding models.
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