用大规模脑影像数据训练模型，提升小样本任务脑活动解码效果
Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
- 基于时空分治注意力与自监督对比学习，从995人数据中预训练可迁移的脑区表征
- 在多个感知与认知任务上，仅需极少预处理即可显著提升解码准确率
- 适合脑科学、神经工程领域研究者，尤其关注小样本脑活动分析的团队
神经科学的核心挑战之一是从脑活动解码心理状态。尽管功能磁共振成像（fMRI）能以高空间精度非侵入式捕捉全脑神经动态，但因数据维度高、信噪比低及个体内部数据有限，任务诱发脑活动的解码仍具挑战。本文提出STDA-SwiFT，一种基于Transformer的模型，利用人类连接组计划（HCP）中995名受试者的大型fMRI数据集，通过空间-时间分治注意力和自监督对比学习，学习可迁移的体素级表征。实验表明，该模型在多个感觉与认知任务上，即使采用极简数据预处理，也能显著提升下游任务的解码性能。我们验证了记忆高效注意力机制带来的更大感受野优势，以及预训练数据的功能相关性对小样本微调的影响。本工作展示了迁移学习在克服fMRI脑活动解码挑战中的可行性。
原文摘要 · Abstract (English)
A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a transformer-based model that learns transferable representations from large-scale fMRI datasets via spatial-temporal divided attention and self-supervised contrastive learning. Using pretrained voxel-wise representations from 995 subjects in the Human Connectome Project (HCP), we show that our model substantially improves downstream decoding performance of task-evoked activity across multiple sensory and cognitive domains, even with minimal data preprocessing. We demonstrate performance gains from larger receptor fields afforded by our memory-efficient attention mechanism, as well as the impact of functional relevance in pretraining data when fine-tuning on small samples. Our work showcases transfer learning as a viable approach to harness large-scale datasets to overcome challenges in decoding brain activity from fMRI data.
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