Aggregating multi-subject functional magnetic resonance imaging (fMRI) data
is indispensable for generating valid and general inferences from patterns
distributed across human brains. The disparities in anatomical structures and
functional topographies of human brains warrant aligning fMRI data across
subjects. However, the existing functional alignment methods cannot handle well
various kinds of fMRI datasets today, especially when they are not
temporally-aligned, i.e., some of the subjects probably lack the responses to
some stimuli, or different subjects might follow different sequences of
stimuli. In this paper, a cross-subject graph that depicts the
(dis)similarities between samples across subjects is used as a priori for
developing a more flexible framework that suits an assortment of fMRI datasets.
However, the high dimension of fMRI data and the use of multiple subjects makes
the crude framework time-consuming or unpractical. To address this issue, we
further regularize the framework, so that a novel feasible kernel-based
optimization, which permits nonlinear feature extraction, could be
theoretically developed. Specifically, a low-dimension assumption is imposed on
each new feature space to avoid overfitting caused by the
highspatial-low-temporal resolution of fMRI data. Experimental results on five
datasets suggest that the proposed method is not only superior to several
state-of-the-art methods on temporally-aligned fMRI data, but also suitable for
dealing `with temporally-unaligned fMRI data.

本文提出一种基于跨学科功能磁共振成像数据聚合的方法，使用跨主题图作为开发更灵活框架的先验条件，以处理各种类型的功能磁共振成像数据集，并进一步规范了该框架，提出了一种可行的基于核的优化方法，以避免因 fMRI 数据的空间低且时间分辨率高产生的过拟合问题。实验结果表明，该方法不仅适用于处理时间对齐的 fMRI 数据，而且还适用于处理时间不对齐的 fMRI 数据。