Using exceptional attributed subgraph mining to explore interindividual variability in odor pleasantness processing in the piriform cortex and amygdala
In Intelligent Computing
Abstract
In humans, the amygdala and piriform cortex are 2 important brain structures involved in hedonic odor processing. Although the affective processing of odors in these 2 structures has been extensively studied in the past, the way in which each tested individual contributes to the observed global pattern remains little understood at this stage. The purpose of this study is to examine whether exceptional pattern extraction techniques can improve our understanding of hedonic odor processing in these brain areas while paying particular attention to individual variability. A total of 42 volunteers participated in a functional magnetic resonance imaging (fMRI) study in which they were asked to smell 6 odors and describe their hedonic valence. Classical univariate analyses (statistical parametric mapping) and data mining were performed on the fMRI data. The results from both analyses showed that unpleasant odors preferentially activate the anterior part of the left piriform cortex. Moreover, the data mining approach revealed specific patterns for pleasant and unpleasant odors in the piriform cortex but also in the amygdala. The approach also revealed the contribution of each of the 42 individuals to the observed patterns. Taken together, these results suggest that the data mining approach can be used—with standard fMRI analyses—to provide complementary information regarding spatial location and the contribution of individuals to the observed patterns.