In recent years,the application of deep learning in task functional Magnetic Resonance Imaging (tfMRI) decoding has led to significant advancements. However,most studies remain constrained by assumption of temporal stationarity in neural activity,resulting in predominantly block-wise analysis with limited temporal resolution on the order of tens of seconds. This limitation restricts the ability to decode cognitive functions in detail. To address these limitations, this study proposes a deep neural network designed for volume-wise identification of task states within tfMRI data,thereby overcoming the constraints of conventional methods. Evaluated on Human Connectome Project (HCP) motor and gambling tfMRI datasets,the model achieved impressive mean accuracy rates of 94.0% and 79.6%,respectively. These results demonstrate a substantial enhancement in temporal resolution,enabling more detailed exploration of cognitive processes. The study further employs visualization algorithms to investigate dynamic brain mappings during different tasks,marking a significant step forward in deep learning-based frame-level tfMRI decoding. This approach offers new methodologies and tools for examining dynamic changes in brain activities and understanding the underlying cognitive mechanisms.
@article{arxiv.2503.01925,
title = {Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis},
author = {Yueyang Wu and Sinan Yang and Yanming Wang and Jiajie He and Muhammad Mohsin Pathan and Bensheng Qiu and Xiaoxiao Wang},
journal= {arXiv preprint arXiv:2503.01925},
year = {2025}
}