这篇论文讲了一个新的fMRI对齐方法SpectralOT，在保留解剖结构的同时提高了跨被试解码的准确性，做神经科学研究的可以看看。
arXiv 论文提出 SpectralOT，一种针对 fMRI 数据的功能对齐方法。它通过将皮层几何嵌入 Laplace-Beltrami 特征模态来正则化对齐过程，平衡功能特征保留与解剖结构保持。该方法在计算效率上优于传统算法，在跨被试解码任务中验证了性能提升。SpectralOT 解决了个体间脑响应变异导致的解码器泛化问题。
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding
Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional alignment: aligning functional data across individuals before training population-level decoders. The core issue is to strike the balance between aligning functional features and preserving the anatomical structure, while maintaining computational efficiency. We introduce a new functional alignment method for fMRI, SpectralOT, that embeds cortical geometry into Laplace-Beltrami eigenmodes along functional data to regularize the alignment.