提出几何感知的快速脑功能对齐方法，提升跨人脑解码性能。
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding
- 利用拉普拉斯-贝尔特拉米特征模态融合皮层几何结构
- 在10秒内完成全脑功能对齐，计算效率显著提升
- 适合需要高效跨被试解码的研究者使用
脑活动解码有助于揭示认知过程的功能架构，但个体间脑响应模式差异限制了通用解码器的发展。功能对齐是解决此问题的关键：在训练群体级解码器前对齐不同个体的功能数据。核心挑战在于平衡功能特征对齐与解剖结构保留，并兼顾计算效率。本文提出一种新的fMRI功能对齐方法SpectralOT，通过将皮层几何结构嵌入拉普拉斯-贝尔特拉米特征模态来正则化对齐过程，实现高效且结构感知的功能对齐。
原文摘要 · Abstract (English)
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.
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。