LinkRbrain
Une plateforme collaborative pour l’intégration multi-échelle des connaissances sur le cerveau.
Contexte
LinkRbrain est une plateforme web en libre accès pour l’intégration et la visualisation de données sur le cerveau humain à plusieurs échelles. Cette plateforme intègre les connaissances anatomiques, fonctionnelles et génétiques produites par la communauté scientifique.
Nouvelle méthode
La plateforme LinkRbrain comporte deux composantes principales : (1) un composant d’agrégation de données qui intègre plusieurs bases de données ouvertes en une seule plateforme avec une représentation unifiée ; et (2) un site web qui permet une intégration et une visualisation rapides et à plusieurs échelles de ces données et qui rend les résultats immédiatement disponibles.
Résultats
LinkRbrain permet aux utilisateurs de visualiser les réseaux fonctionnels et/ou l’expression génétique sur un modèle de cerveau standard (MNI152). Les interrelations entre ces composantes basées sur le chevauchement topographique sont affichées à l’aide de graphiques relationnels. En outre, LinkRbrain permet de comparer les nouveaux résultats expérimentaux avec les travaux publiés précédemment.
Comparaison avec les méthodes existantes
Les outils et études précédents illustrent les possibilités d’exploration de données à travers de multiples niveaux de neuroscience et d’information génétique. Cependant, il manque encore une approche systématique globale pour rassembler les connaissances cognitives, topographiques et génétiques dans un cadre commun afin de faciliter leur visualisation, leur comparaison et leur intégration.
Dans les Médias
Au coeur du cerveau, Pour la Science.
Libérez les images du cerveau, Le Monde.
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1.
Mesmoudi, S., Lapina, C., Rodic, M. & Peschanski, D. Multi-Data Integration Towards a Global Understanding of the Neurological Impact of Human Brain Severe Acute Respiratory Syndrome Coronavirus 2 Infection. Front. Integr. Neurosci. 16, 756604 (2022).
1.
Mesmoudi, S. et al. LinkRbrain: Multi-scale data integrator of the brain. Journal of Neuroscience Methods 241, 44–52 (2015).
1.
Cioli, C., Abdi, H., Beaton, D., Burnod, Y. & Mesmoudi, S. Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks. PLoS ONE 9, e115913 (2014).
1.
Mesmoudi, S. et al. Coupling geometric analysis and viability theory for system exploration: Application to a living food system. JOURNAL OF PROCESS CONTROL 24, 18–28 (2014).
1.
Mesmoudi, S. et al. Resting State Networks’ Corticotopy: The Dual Intertwined Rings Architecture. PLoS ONE 8, e67444 (2013).
Équipe
Salma MESMOUDI, Responsable de projet
Université Paris 1Anciens membres :
- Claudia CIOLI
- Yves BRUNO
- Mathieu RODIC
Formation
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