# Decoding and mapping task states of the human brain via deep learning

arXiv: https://arxiv.org/abs/1801.09858 · HTML: https://mlsanity.com/paper/1801.09858/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 16 of 16)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network](https://mlsanity.com/paper/2110.14927/) (2021)
- [Transfer learning to decode brain states reflecting the relationship between cognitive tasks](https://mlsanity.com/paper/2206.03950/) (2022)
- [Challenges for cognitive decoding using deep learning methods](https://mlsanity.com/paper/2108.06896/) (2021)
- [Evaluating deep transfer learning for whole-brain cognitive decoding](https://mlsanity.com/paper/2111.01562/) (2021)
- [Attention module improves both performance and interpretability of 4D fMRI decoding neural network](https://mlsanity.com/paper/2110.00920/) (2021)
- [Comparing interpretation methods in mental state decoding analyses with deep learning models](https://mlsanity.com/paper/2205.15581/) (2022)
- [On the benefits of self-taught learning for brain decoding](https://mlsanity.com/paper/2209.10099/) (2022)
- [High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding](https://mlsanity.com/paper/2211.07507/) (2022)
- [Causality-based Subject and Task Fingerprints using fMRI Time-series Data](https://mlsanity.com/paper/2409.18298/) (2024)
- [Emotional Brain State Classification on fMRI Data Using Deep Residual and Convolutional Networks](https://mlsanity.com/paper/2210.17015/) (2022)
- [Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks](https://mlsanity.com/paper/2305.09057/) (2023)
- [Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data](https://mlsanity.com/paper/2305.13226/) (2023)
- [Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks](https://mlsanity.com/paper/2412.19329/) (2024)
- [Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis](https://mlsanity.com/paper/2503.01925/) (2025)
- [Nonlocal operator learning for fMRI encoding and decoding tasks](https://mlsanity.com/paper/2605.20389/) (2026)

## Abstract (arXiv)

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=1034). 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.