The second MVPA form is the so called similarity-based MVPA. This approach gained momentum in recent years because it provides system neuroscience with the opportunity to quantitatively assess the relationship between brain-activity measurement, behavioral measurement, and computational modeling (Kriegeskorte et al., 2008).

As for classifier-based MVPA, the idea is to consider neuronal representations as a multidimensional space, where the number of dimensions correspond to the number of voxels, and each point in that space corresponds to different activation patterns. The set of all possible mental content corresponds to a vast set of points in the space, where the distance between points indicates their similarity (Kiergeskorte and Kievit, 2013). But, the goal of pattern similarity analysis of fMRI data is to make inferences about the representational geometry of mental states based on the similarity of patterns elicited by those concepts. The dissimilarity (or similarity) of two patterns can be computed with different metrics (correlation distance, euclidean distance, absolute activation difference), and having measured these distances, it is possible to construct a matrix called the representational dissimilarity matrix (RDM). Each RDM cell contains the values of dissimilarity between pairs of representation. Thanks to this conceptualization is possible to compare representational geometries obtained from different type of data, such as brain activity, behavioral measurements, and computational models, by just computing the correlation between the RDMs. This approach is called Representational similarity analysis (RSA).

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For example, Proklova and colleagues (2016), used RSA to disentangle the role in the animate/inanimate distinction of object shape and object category representations in human visual cortex. They mapped out brain regions in which the pairwise neural dissimilarity was successfully predicted by the objects’ pairwise visual dissimilarity (overall appearance, outline dissimilarity, and texture dissimilarity). These analyses revealed several clusters in which categorical dissimilarity, measured as reaction time in a visual search task, predicted neural dissimilarity, even when controlling for visual similarities. Results provide evidence that the animate-inanimate organization of human visual cortex is not fully explained by visual differences (shape or texture) of animate and inanimate objects. RSA has been mostly applied to study perception and cognition. However, it might be an important tool for further applications such as biomarker detection for brain diseases¬† that show different degrees of representational dissimilarity.

Distinct representational models exist other than RSA, each of which has advantages and disadvantages, and better suited for different applications. “More statistical methods remain to be developed to accommodate various analytic parameters, different definition of metrics, reliability and stability assessment, and different similarity measures from multivariate brain-activity data” (Xue et al., 2013).


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