A generative adversarial optimization strategy for predicting counterfactual trajectories of grey matter atrophy
This study introduces a novel counterfactual generation method that provides interpretable, anatomically grounded explanations of MS progression. The framework serves as a powerful tool for hypothesis generation and model validation in biomedical imaging studies, particularly where understanding disease mechanisms is crucial.
Authors
- Berardino Barile (ORCID: https://orcid.org/0000-0002-4016-9172)
- Dominique Sappey‐Marinier (ORCID: https://orcid.org/0000-0003-4104-1514)
- Thomas Grenier (ORCID: https://orcid.org/0000-0002-3630-5856)
- Enyi Chen
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Institut National des Sciences Appliquées de Lyon (FR)
- McGill University (CA)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-10-13
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109095
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00