# Deep learning of fMRI big data: a novel approach to subject-transfer decoding

arXiv: https://arxiv.org/abs/1502.00093 · HTML: https://mlsanity.com/paper/1502.00093/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2015.

## Built on by (most cited first, 7 of 7)

- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (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)
- [On the benefits of self-taught learning for brain decoding](https://mlsanity.com/paper/2209.10099/) (2022)
- [Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging](https://mlsanity.com/paper/2210.05835/) (2022)
- [Explainable Emotion Decoding for Human and Computer Vision](https://mlsanity.com/paper/2408.00493/) (2024)
- [Condition-Invariant fMRI Decoding of Speech Intelligibility with Deep State Space Model](https://mlsanity.com/paper/2511.01868/) (2025)

## Abstract (arXiv)

As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individuals has limited the scope of many studies to the development of individual-specific decoders. In this study, we used deep neural network (DNN), a nonlinear hierarchical model, to construct a subject-transfer decoder. Our decoder is the first successful DNN-based subject-transfer decoder. When applied to a large-scale functional magnetic resonance imaging (fMRI) database, our DNN-based decoder achieved higher decoding accuracy than other baseline methods, including support vector machine (SVM). In order to analyze the knowledge acquired by this decoder, we applied principal sensitivity analysis (PSA) to the decoder and visualized the discriminative features that are common to all subjects in the dataset. Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.