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Representational Similarity Analysis (RSA) aims to explore similaritiesbetween neural activities of different stimuli. Classical RSA techniques employthe inverse of the covariance matrix to explore a linear model between theneural activities and task events. However, calculating the inverse of alarge-scale covariance matrix is time-consuming and can reduce the stabilityand robustness of the final analysis. Notably, it becomes severe when thenumber of samples is too large. For facing this shortcoming, this paperproposes a novel RSA method called gradient-based RSA (GRSA). Moreover, theproposed method is not restricted to a linear model. In fact, there is agrowing interest in finding more effective ways of using multi-subject andwhole-brain fMRI data. Searchlight technique can extend RSA from the localizedbrain regions to the whole-brain regions with smaller memory footprint in eachprocess. Based on Searchlight, we propose a new method called SpatiotemporalSearchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm tothe whole-brain data. Further, our approach can handle some computationalchallenges while dealing with large-scale, multi-subject fMRI data.Experimental studies on multi-subject datasets confirm that both proposedapproaches achieve superior performance to other state-of-the-art RSAalgorithms.

Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data