Brain-decoding fMRI reveals how wholes relate to the sum of parts

Abstract

The human brain performs many nonlinear operations in order to extract relevant information from local inputs. How can we observe and quantify these effects within and across large patches of cortex? In this paper, we discuss the application of multi-voxel pattern analysis (MVPA) in functional magnetic resonance imaging (fMRI) to address this issue. Specifically, we show how MVPA (i) allows to compare various possibilities of part combinations into wholes, such as taking the mean, weighted mean, or the maximum of responses to the parts; (ii) can be used to quantify the parameters of these combinations; and (iii) can be applied in various experimental paradigms. Through these procedures, fMRI helps to obtain a computational understanding of how local information is integrated into larger wholes in various cortical regions.

Keywords: Functional magnetic resonance imaging; Multi-voxel pattern analysis; Nonlinear.