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Près d’un français sur cinq souffre de troubles de l’odorat (TO), ou dysosmie. Cette prévalence a augmenté depuis l’apparition de la COVID-19 dont la dysosmie est un symptôme très fréquent et possiblement persistant. Pourtant, la prise en soin des personnes dysosmiques reste insuffisante. Cet article ouvre des pistes pour développer les rééducations olfactives (RO) sur la base de protocoles déjà éprouvés, mais aussi pour améliorer l’accompagnement des patients dysosmiques. Pour cela, une stratégie de prise en charge pluri-professionnelle est préconisée, alliant l’intervention de scientifiques, de médecins (généralistes, ORL) et de professions paramédicales. published

Symposium conducted at the meeting of ARGAGE (Argumentation & Language); International audience

Recent findings indicate that astrocytes might play a functional role in triggering epileptic seizures. To test both the role of astrocytes on neuronal firings under normal conditions and the role they could play in epilepsy, we build here a computational model where four neurons and an astrocyte interact. Based on a mathematical model previously described in the literature, our model allows to investigate the dynamics of neuronal firing in this mini-network. In particular, we describe the conditions that can lead to hypersynchronous firings.

In the present study, we apply the Self Organizing Map (SOM) algorithm for classifying cognitive states from fMRI data without prior selection of spatial or temporal features. In addition, we compare our method with two other models. We applied the method to single-subject as well as multi-subject classification. BOLD signals from subjects viewing emotional pictures of positive, neutral and negative valences were acquired during a block design experiment, and classified with an unsupervised non-linear method, the SOM. We demonstrate here that, in terms of classification performance, the SOM algorithm outperforms an SVM algorithm when processing whole brain data, and performs as well as methods (SVM and KCCA) working with temporal compression or spatial feature selection. Our method presents three phases: data dimensionality reduction : where non functional data are deleted, SOM algorithm training : where statistic regularities relevant for classification are extracted, SOM algorithm test : where the subject's brain state is predicted from his brain activity.