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Aggregating multi-subject functional magnetic resonance imaging (fMRI) datais indispensable for generating valid and general inferences from patternsdistributed across human brains. The disparities in anatomical structures andfunctional topographies of human brains warrant aligning fMRI data acrosssubjects. However, the existing functional alignment methods cannot handle wellvarious kinds of fMRI datasets today, especially when they are nottemporally-aligned, i.e., some of the subjects probably lack the responses tosome stimuli, or different subjects might follow different sequences ofstimuli. In this paper, a cross-subject graph that depicts the(dis)similarities between samples across subjects is used as a priori fordeveloping a more flexible framework that suits an assortment of fMRI datasets.However, the high dimension of fMRI data and the use of multiple subjects makesthe crude framework time-consuming or unpractical. To address this issue, wefurther regularize the framework, so that a novel feasible kernel-basedoptimization, which permits nonlinear feature extraction, could betheoretically developed. Specifically, a low-dimension assumption is imposed oneach new feature space to avoid overfitting caused by thehighspatial-low-temporal resolution of fMRI data. Experimental results on fivedatasets suggest that the proposed method is not only superior to severalstate-of-the-art methods on temporally-aligned fMRI data, but also suitable fordealing `with temporally-unaligned fMRI data.

Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data