用Transformer预测大脑活动状态，可提前5秒准确预判脑区功能连接。
Predicting Human Brain States with Transformer
- 基于HCP fMRI数据，用Transformer建模脑区活动序列
- 21.6秒历史数据可预测未来5.04秒的脑状态
- 生成结果保持功能性连接结构，适合神经科学与生成模型研究者
人脑是复杂且高度动态的系统，当前对其功能机制的理解仍有限。借助功能磁共振成像（fMRI），我们可通过血氧水平依赖（BOLD）信号观测神经活动，推断脑状态与动态变化。本文探讨能否利用区域脑fMRI数据预测脑状态。受自注意力机制和Transformer在序列建模（如语言建模、音乐生成）中成功启发，本研究基于人类连接组计划（HCP）的大规模高质量fMRI数据，探索用Transformer预测静息态脑状态的可行性。结果显示，模型可利用前21.6秒数据准确预测未来5.04秒的脑状态。尽管长期预测误差累积，但生成的fMRI脑状态仍保留了功能性连接组的结构特征。这些初步成果证明了基于自注意力机制的生成模型在学习人脑功能组织方面的潜力。代码已公开：https://github.com/syf0122/brain_state_pred。
原文摘要 · Abstract (English)
The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics. In this paper, we ask the question of whether the brain states rep-resented by the regional brain fMRI can be predicted. Due to the success of self-attention and the transformer architecture in sequential auto-regression problems (e.g., language modelling or music generation), we explore the possi-bility of the use of transformers to predict human brain resting states based on the large-scale high-quality fMRI data from the human connectome project (HCP). Current results have shown that our model can accurately predict the brain states up to 5.04s with the previous 21.6s. Furthermore, even though the prediction error accumulates for the prediction of a longer time period, the gen-erated fMRI brain states reflect the architecture of functional connectome. These promising initial results demonstrate the possibility of developing gen-erative models for fMRI data using self-attention that learns the functional or-ganization of the human brain. Our code is available at: https://github.com/syf0122/brain_state_pred.
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