Search Results for author:
Found 7 papers, 3 papers with code
Clinica: an open source software platform for reproducible clinical neuroscience studies
It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines.
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI.
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data.
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation.
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
