# Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching

arXiv: https://arxiv.org/abs/2509.16973 · HTML: https://mlsanity.com/paper/2509.16973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [The Implicit Bias of Gradient Descent on Separable Data](https://mlsanity.com/paper/1710.10345/) (2017)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Implicit Regularization in Deep Learning](https://mlsanity.com/paper/1709.01953/) (2017)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)

## Abstract (arXiv)

Deep learning has advanced fMRI analysis, yet it remains unclear which architectural inductive biases are most effective at capturing functional patterns in human brain activity. This issue is particularly important in small-sample settings, as most datasets fall into this category. We compare models with three major inductive biases in deep learning including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and Transformers for the task of biological sex classification. These models are evaluated within a unified pipeline using parcellated multivariate fMRI time series from the Human Connectome Project (HCP) 7-Tesla cohort, which includes four resting-state runs and four movie-watching task runs. We assess performance on Whole-brain, subcortex, and 12 functional networks. CNNs consistently achieved the highest discrimination for sex classification in both resting-state and movie-watching, while LSTM and Transformer models underperformed. Network-resolved analyses indicated that the Whole-brain, Default Mode, Cingulo-Opercular, Dorsal Attention, and Frontoparietal networks were the most discriminative. These results were largely similar between resting-state and movie-watching. Our findings indicate that, at this dataset size, discriminative information is carried by local spatial patterns and inter-regional dependencies, favoring convolutional inductive bias. Our study provides insights for selecting deep learning architectures for fMRI time series classification.