Most of the work investigating the representation of concrete nouns in the brain has focused on the locations that code the information. We present a model to study the contributions of perceptual and semantic features to the neural code representing concepts over time and space. The model is evaluated using magnetoencephalography data from different paradigms and not only corroborates previous findings regarding a distributed code, but provides further details about how the encoding of different subcomponents varies in the space-time spectrum. The model also successfully generalizes to novel concepts that it has never seen during training, which argues for the combination of specific properties in forming the meaning of concrete nouns in the brain. The results across paradigms are in agreement when the main differences among the experiments (namely, the number of repetitions of the stimulus, the task the subjects performed, and the type of stimulus provided) were taken into consideration. More specifically, these results suggest that features specific to the physical properties of the stimuli, such as word length and right-diagonalness, are encoded in posterior regions of the brain in the first hundreds of milliseconds after stimulus onset. Then, properties inherent to the nouns, such as is it alive? and can you pick it up?, are represented in the signal starting at about 250 ms, focusing on more anterior parts of the cortex. The code for these different features was found to be distributed over time and space, and it was common for several regions to simultaneously code for a particular property. Moreover, most anterior regions were found to code for multiple features, and a complex temporal profile could be observed for the majority of properties. For example, some features inherent to the nouns were encoded earlier than others, and the extent of time in which these properties could be decoded varied greatly among them. These findings complement much of the work previously described in the literature, and offer new insights about the temporal aspects of the neural encoding of concrete nouns. This model provides a spatiotemporal signature of the representation of objects in the brain. Paired with data from carefully-designed paradigms, the model is an important tool with which to analyze the commonalities of the neural code across stimulus modalities and tasks performed by the subjects.

Le cancer du sein est le cancer le plus répandu chez les femmes de plus de 40 ans. En effet, des études ont montré qu'une détection précoce et un traitement approprié du cancer du sein augmentent de manière significative les chances de survie. La mammographie constitue le moyen d'investigation le plus utilisé dans le diagnostic des lésions mammaires. Cependant, cette technique peut être insuffisante pour montrer les structures du sein et faire apparaître les anomalies présentes et le médecin peut faire appel à d'autres modalités d'imagerie telle que l'imagerie IRM. Ces modalités sont généralement complémentaires. Par conséquent, le médecin procède à une fusion mentale des différentes informations sur les deux images dans le but d'effectuer le diagnostic adéquat. Pour assister le médecin et l'aider dans ce processus, nous proposons une solution permettant de fusionner les deux images. Bien que l'idée de la fusion paraisse simple, sa mise en oeuvre pose de nombreux problèmes liés non seulement au problème de fusion en général mais aussi à la nature des images médicales qui sont généralement des images mal contrastées et présentant des données hétérogènes, imprécises et ambigües. Notons que les images mammographiques et les images IRM présentent des représentations très différentes des informations, étant donnée qu'elles sont prises dans des conditions distinctes. Ce qui nous amène à poser la question suivante: Comment passer de la représentation hétérogène des informations dans l'espace image, à un autre espace de représentation uniforme. Afin de traiter cette problématique, nous optons pour une approche de traitement multi-niveaux : niveau pixel, niveau primitives, niveau objet et niveau scène. Nous modélisons les objets pathologiques extraits des différentes images par des ontologies locales. La fusion est ensuite effectuée sur ces ontologies locales et résulte en une ontologie globale contenant les différentes connaissances sur les objets pathologiques du cas étudié. Cette ontologie globale sert à instancier une ontologie de référence modélisant les connaissances du diagnostic médical des lésions mammaires. Un raisonnement à base de cas est exploité pour fournir les rapports diagnostic des cas les plus similaires pouvant aider le médecin à prendre la meilleure décision. Dans le but de modéliser l'imperfection des informations traitées, nous utilisons la théorie des possibilités avec les différentes ontologies. Le résultat fourni est présenté sous forme de rapports diagnostic comportant les cas les plus similaires au cas étudié avec des degrés de similarité exprimés en mesures de possibilité. Un modèle virtuel 3D complète le rapport diagnostic par un aperçu simplifié de la scène étudiée. / The breast cancer is the most prevalent cancer among women over 40 years old. Indeed, studies evinced that an early detection and an appropriate treatment of breast cancer increases significantly the chances of survival. The mammography is the most tool used in the diagnosis of breast lesions. However, this technique may be insufficient to evince the structures of the breast and reveal the anomalies present. The doctor can use additional imaging modalities such as MRI (Magnetic Reasoning Image). Therefore, the doctor proceeds to a mental fusion of the different information on the two images in order to make the adequate diagnosis. To assist the doctor in this process, we propose a solution to merge the two images. Although the idea of the fusion seems simple, its implementation poses many problems not only related to the paradigm of fusion in general but also to the nature of medical images that are generally poorly contrasted images, and presenting heterogeneous, inaccurate and ambiguous data. Mammography images and IRM images present very different information representations, since they are taken under different conditions. Which leads us to pose the following question: How to pass from the heterogeneous representation of information in the image space, to another space of uniform representation from the two modalities? In order to treat this problem, we opt a multilevel processing approach : the pixel level, the primitive level, the object level and the scene level. We model the pathological objects extracted from the different images by local ontologies. The fusion is then performed on these local ontologies and results in a global ontology containing the different knowledge on the pathological objects of the studied case. This global ontology serves to instantiate a reference ontology modeling knowledge of the medical diagnosis of breast lesions. Case-based reasoning (CBR) is used to provide the diagnostic reports of the most similar cases that can help the doctor to make the best decision. In order to model the imperfection of the treated information, we use the possibility theory with the ontologies. The final result is a diagnostic reports containing the most similar cases to the studied case with similarity degrees expressed with possibility measures. A 3D symbolic model complete the diagnostic report with a simplified overview of the studied scene.