让脑影像模型跨任务通用，无需重新训练
Towards Zero-Shot Task-Generalizable Learning on fMRI
- 设计任务感知网络，分离通用编码与任务特异性信息
- 在多个任务数据上实现零样本迁移，提升泛化能力
- 可适配任意神经网络，适合脑功能研究者使用
功能磁共振成像（fMRI）通过测量血氧水平依赖信号，在研究脑功能和神经疾病中日益重要。相比静息态fMRI，任务态fMRI在受试者执行特定任务时采集，能增强与任务相关的脑活动信号，信息量更丰富。然而，由于任务设计多样，不同任务的fMRI数据难以融合以训练通用模型。为此，我们提出监督式任务感知网络TA-GAT，联合学习通用编码器与任务特异性上下文信息。编码器生成的嵌入与学习到的上下文信息结合，作为输入供给多个下游任务模块。我们认为，该任务感知架构可无缝嵌入任意神经网络，将fMRI任务先验知识融入功能性脑模式捕捉中。
原文摘要 · Abstract (English)
Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task designed to enhance study-related brain activities. Consequently, it generally has more informative task-dependent signals. However, due to the variety of task designs, it is much more difficult than in resting state to aggregate task-based fMRI acquired in different tasks to train a generalizable model. To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information. The encoder-generated embedding and the learned contextual information are then combined as input to multiple modules for performing downstream tasks. We believe that the proposed task-aware architecture can plug-and-play in any neural network architecture to incorporate the prior knowledge of fMRI tasks into capturing functional brain patterns.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。