多主题fMRI数据对齐的局部判别超对齐
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
摘要：
多变量模式（MVP）分类可以将不同的认知状态映射到大脑任务。MVP分析的主要挑战之一是验证跨主题生成的结果。然而，分析多受试者的功能磁共振成像数据需要准确的神经元活动之间的功能对齐不同的主题，这可以迅速提高最终结果的性能和鲁棒性。超序列比对（HA）是最有效的功能序列比对方法之一，它可以通过典型相关分析（CCA）方法进行数学表达。由于HA主要使用无监督CCA技术，因此其解决方案可能不会针对MVP分析进行优化。将局部判别分析（LDA）的思想引入CCA，提出了局部判别超对齐（LDHA）方法，该方法能够为MVP分析提供更好的功能对齐。事实上，局部性是基于训练集中的刺激类别来定义的，其中相同类别中的所有刺激之间的相关性将被最大化，并且不同类别的刺激之间的相关性接近于零。多主题MVP分析的实验研究证实，LDHA方法实现了优于其他国家的最先进的HA算法的性能。
英文摘要：
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. Hyperalignment (HA) is one of the most effective functional alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.
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