ArticleHuman brain mapping2020
Decoding and mapping task states of the human brain via deep learning.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- MAMBAxBrain: A Multi-task Neural Framework Linking Brain Functional Dynamics to Individual Fingerprints, Cognitive and Disease States.bioRxiv : the preprint server for biology · 2026Article
- Reconstructing brain causal dynamics for subject and task fingerprints using fMRI time-series data.Health information science and systems · 2025Article
- Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks.Scientific reports · 2025Article
- An attention-based fuzzy CNN-LTSM network for visual object recognition from fMRI images.Journal of computational neuroscience · 2025Article
- Multi-task machine learning reveals the functional neuroanatomy fingerprint of mental processing.bioRxiv : the preprint server for biology · 2025Article
- Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification.Frontiers in neuroscience · 2025Article
- Causality-based Subject and Task Fingerprints using fMRI Time-series Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 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
- Article
- High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding.Human brain mapping · 2023Article
- A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.Sensors (Basel, Switzerland) · 2023Article
- Benchmarking explanation methods for mental state decoding with deep learning models.NeuroImage · 2023Article
- Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.Human brain mapping · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness.Aperture neuro · 2023Article
- Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.Frontiers in neuroscience · 2023Article
- On the benefits of self-taught learning for brain decoding.GigaScience · 2022Article
- The Role of Extra-Operative Cortical Stimulation and Mapping in the Surgical Management of Intracranial Gliomas.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
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