Université Paris-Saclay, Inria, CEA, Palaiseau 91120, France.
Montréal Neurological Institute, McGill University, Montréal, Canada.
Neuroimage. 2021 Dec 15;245:118683. doi: 10.1016/j.neuroimage.2021.118683. Epub 2021 Oct 26.
Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment-a class of methods that matches subjects' neural signals based on their functional similarity-is a promising strategy for addressing this variability. To date, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider three existing methods: piecewise Procrustes, searchlight Procrustes, and piecewise Optimal Transport. We also introduce and benchmark two new extensions of functional alignment methods: piecewise Shared Response Modelling (SRM), and intra-subject alignment. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM and Optimal Transport perform well at both the region-of-interest level of analysis as well as at the whole-brain scale when aggregated through a piecewise scheme. We also benchmark the computational efficiency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of all methods used.
大脑功能组织的个体间变异性是识别可推广的神经编码原则的主要障碍。功能对齐是一种基于功能相似性匹配主体神经信号的方法，是解决这种变异性的一种有前途的策略。然而，迄今为止，已经提出了一系列功能对齐方法，其相对性能仍不清楚。在这项工作中，我们在四个公开可用的数据集上对五种用于跨主体解码的功能对齐方法进行了基准测试。具体来说，我们考虑了三种现有的方法：分段 Procrustes、搜索光 Procrustes 和分段最优传输。我们还介绍并基准测试了两种功能对齐方法的新扩展：分段共享响应建模 (SRM) 和主体内对齐。我们发现功能对齐通常可以提高跨主体解码精度，尽管表现最好的方法取决于研究背景。具体来说，SRM 和最优传输在感兴趣区域水平的分析以及通过分段方案聚合时在全脑范围内都表现良好。我们还对每种被调查方法的计算效率进行了基准测试，深入了解它们的可用性和可扩展性。我们以跨主体解码精度作为跨主体相似性的量化指标，我们的结果支持使用功能对齐来改善面对可变结构-功能组织时的跨主体比较。我们提供了所有使用方法的开源实现。
