用Swin Transformer预测大脑未来状态，实现高精度实时脑活动建模
Voxel-Level Brain States Prediction Using Swin Transformer
- 采用4D移位窗口Transformer编码器捕捉fMRI时空特征
- 基于23.04秒历史数据，准确预测7.2秒后脑状态，匹配真实BOLD信号
- 适用于脑机接口与缩短fMRI扫描时间，适合神经科学与影像算法研究者
理解脑动态对神经科学和心理健康至关重要。功能磁共振成像（fMRI）通过血氧水平依赖（BOLD）信号测量神经活动，反映脑状态。本研究旨在基于fMRI预测未来人脑静息态脑活动。由于fMRI数据具有三维体素空间结构和时间依赖性，我们提出一种新架构：使用4D移位窗口（Swin）Transformer作为编码器，高效学习时空信息，并采用卷积解码器实现与输入相同空间与时间分辨率的脑状态预测。模型在来自人类连接组计划（HCP）的100名无关受试者上训练与测试，可基于前23.04秒的fMRI时间序列，高精度预测7.2秒后的静息态脑活动，预测结果高度吻合真实BOLD对比度与动态变化。该工作表明，基于Swin Transformer可在高分辨率下学习人脑时空组织，为减少fMRI扫描时间及脑机接口发展提供潜力。
原文摘要 · Abstract (English)
Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.
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