887 citations
- Centre National de la Recherche ScientifiqueFR81 papers
- Sorbonne UniversitéFR79 papers
- InsermFR75 papers
- Pitié-Salpêtrière HospitalFR52 papers
- Assistance Publique – Hôpitaux de ParisFR43 papers
- Institut national de recherche en sciences et technologies du numériqueFR25 papers
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- ARAMIS: Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain sain et pathologiqueFR17 papers
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16 papers · 1 filter
Low-dimensional controllability of brain networks
Remy Ben Messaoud, Vincent Le Du, Brigitte Charlotte Kaufmann +5
Network controllability is a powerful tool to study causal relationships in complex systems and identify the driver nodes for steering the network dynamics into desired states. How…
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Maëlys Solal, Ravi Hassanaly, Ninon Burgos
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on b…
Identification of a DAGLB Mutation in a Non-Chinese Patient with Parkinson's Disease
Christelle Tesson, Mohamed Sofiane Bouchetara, Mélanie Ferrien +2
Liu et al. recently reported that biallelic mutations in DAGLB are responsible for autosomal recessive early-onset Parkinson's disease. They identified six patients carrying DAGLB…
Intentional binding enhances hybrid BCI control
Tristan Venot, Arthur Desbois, Marie-Constance Corsi +3
Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and a…
HappyFeat -- An interactive and efficient BCI framework for clinical applications
Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani +1
Brain-Computer Interface (BCI) systems allow users to perform actions by translating their brain activity into commands. Such systems usually need a training phase, consisting in t…
Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
R Valabregue, F Girka, A Pron +2
Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the…