SpectralOT：基于几何感知的全脑快速功能对齐用于跨被试解码
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding
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.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include SpectralOT, fMRI, 功能对齐, 脑活动解码.
Source attribution: curated from arXiv cs.LG (@Pierre-Louis Barbarant, Florent Meyniel, Bertrand Thirion).