  Representational Similarity Analysis (RSA) aims to explore similarities
between neural activities of different stimuli. Classical RSA techniques employ
the inverse of the covariance matrix to explore a linear model between the
neural activities and task events. However, calculating the inverse of a
large-scale covariance matrix is time-consuming and can reduce the stability
and robustness of the final analysis. Notably, it becomes severe when the
number of samples is too large. For facing this shortcoming, this paper
proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
proposed method is not restricted to a linear model. In fact, there is a
growing interest in finding more effective ways of using multi-subject and
whole-brain fMRI data. Searchlight technique can extend RSA from the localized
brain regions to the whole-brain regions with smaller memory footprint in each
process. Based on Searchlight, we propose a new method called Spatiotemporal
Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
the whole-brain data. Further, our approach can handle some computational
challenges while dealing with large-scale, multi-subject fMRI data.
Experimental studies on multi-subject datasets confirm that both proposed
approaches achieve superior performance to other state-of-the-art RSA
algorithms.
