Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
- Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis
- Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data
- Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease
- Network neuroscience for optimizing brain–computer interfaces
- Quality Assessment of Single-Channel EEG for Wearable Devices
- Reduction of recruitment costs in preclinical AD trials. Validation of automatic pre-screening algorithm for brain amyloidosis
- Learning low-dimensional representations of shape data sets with diffeomorphic autoencoders
- Learning disease progression models with longitudinal data and missing values
- Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories
- Auto-encoding meshes of any topology with the current-splatting and exponentiation layers
- Riemannian Geometry Learning for Disease Progression Modelling
- How many patients are eligible for disease-modifying treatment in Alzheimer’s disease? A French national observational study over 5 years.
- EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease
- Latent class analysis identifies functional decline with Amsterdam IADL in preclinical Alzheimer's disease
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
- Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis
- Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data
- Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease
- Network neuroscience for optimizing brain–computer interfaces
- Quality Assessment of Single-Channel EEG for Wearable Devices
- Reduction of recruitment costs in preclinical AD trials. Validation of automatic pre-screening algorithm for brain amyloidosis
- Learning low-dimensional representations of shape data sets with diffeomorphic autoencoders
- Learning disease progression models with longitudinal data and missing values
- Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories
- Auto-encoding meshes of any topology with the current-splatting and exponentiation layers
- Riemannian Geometry Learning for Disease Progression Modelling
- How many patients are eligible for disease-modifying treatment in Alzheimer’s disease? A French national observational study over 5 years.
- EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease
- Latent class analysis identifies functional decline with Amsterdam IADL in preclinical Alzheimer's disease
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
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