提升脑影像解码效率，解决数据少且个体差异大的难题
Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding
- 用逆半监督学习和元迁移学习对齐潜在表示
- 在有限数据下实现接近顶尖的图像重建性能
- 适合脑科学与医学影像研究者快速部署
脑编码与解码旨在揭示外部刺激与脑活动之间的关系，是神经科学的基础问题。本文针对功能性磁共振成像（fMRI）与刺激图像配对数据有限、个体差异显著的情况，研究潜在嵌入对齐方法，以提升样本效率。提出一种轻量级对齐框架，包含两个统计学习组件：利用大量未配对刺激嵌入进行逆映射的逆半监督学习，以及通过稀疏聚合与残差校正从跨被试预训练模型中迁移知识的元迁移学习。两者均仅在对齐阶段运行，保持编码器与解码器冻结，实现高效计算、模块化部署与严谨理论分析。建立了有限样本泛化界与安全保证，并在大规模fMRI-图像重建基准数据上展现出具有竞争力的实证性能。
原文摘要 · Abstract (English)
Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a focus on improving sample efficiency under limited fMRI-stimulus paired data and substantial subject heterogeneity. We propose a lightweight alignment framework equipped with two statistical learning components: inverse semi-supervised learning that leverages abundant unpaired stimulus embeddings through inverse mapping and residual debiasing, and meta transfer learning that borrows strength from pretrained models across subjects via sparse aggregation and residual correction. Both methods operate exclusively at the alignment stage while keeping encoders and decoders frozen, allowing for efficient computation, modular deployment, and rigorous theoretical analysis. We establish finite-sample generalization bounds and safety guarantees, and demonstrate competitive empirical performance on the large-scale fMRI-image reconstruction benchmark data.
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