用深度学习实现全体积任务fMRI解码，提升时间分辨率以更精细分析认知功能。
Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis
- 设计深度神经网络，直接对每一体积帧进行任务状态识别。
- 在HCP数据集上达到94.0%和79.6%的平均准确率，显著优于传统块状分析。
- 适用于需要高时间精度的认知神经科学研究，尤其适合动态脑活动建模。
近年来，深度学习在任务功能性磁共振成像（tfMRI）解码中取得显著进展。然而，多数研究受限于神经活动时间平稳性假设，导致主要采用块状分析，时间分辨率仅在数十秒量级，难以精细解码认知功能。为解决此问题，本文提出一种用于tfMRI数据体积分辨任务状态的深度神经网络，突破传统方法限制。在人类连接组计划（HCP）运动与赌博任务数据集上，模型分别达到94.0%和79.6%的平均准确率。结果表明，该方法大幅提升了时间分辨率，可更细致地探索认知过程。研究还通过可视化算法分析不同任务下的动态脑映射，标志着基于深度学习的帧级tfMRI解码的重要进展。该方法为研究脑活动动态变化及认知机制提供了新工具。
原文摘要 · Abstract (English)
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.
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