Maelle Moranges

Using exceptional attributed subgraph mining to explore interindividual variability in odor pleasantness processing in the piriform cortex and amygdala

By Maelle Moranges, Arnaud Fournel, Marc Thévenet, Marc Plantevit, Moustafa Bensafi

2024-08-10

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.

Continue reading

Peripheral nervous system responses to food stimuli: Analysis using data science approaches

By Maelle Moranges, Marc Plantevit, Moustafa Bensafi

2023-01-05

In Basic protocols on emotions, senses, and foods

Abstract

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.

Continue reading

Using subgroup discovery to relate odor pleasantness and intensity to peripheral nervous system reactions

By Maelle Moranges, Marc Plantevit, Moustafa Bensafi

2022-07-24

In IEEE Transactions on Affective Computing

Abstract

Activation of the autonomic nervous system is a primary characteristic of human hedonic responses to sensory stimuli. For smells, general tendencies of physiological reactions have been described using classical statistics. However, these physiological variations are generally not quantified precisely; each psychophysiological parameter has very often been studied separately and individual variability was not systematically considered. The current study presents an innovative approach based on data mining, whose goal is to extract knowledge from a dataset. This approach uses a subgroup discovery algorithm which allows extraction of rules that apply to as many olfactory stimuli and individuals as possible. These rules are described by intervals on a set of physiological attributes. Results allowed both quantifying how each physiological parameter relates to odor pleasantness and perceived intensity but also describing the participation of each individual to these rules. This approach can be applied to other fields of affective sciences characterized by complex and heterogeneous datasets.

Continue reading

Search

Tags