Interpretable and integrative deep learning for discovering brain-behaviour associations
Authors/Creators
- 1. NeuroSpin, CEA, Université Paris-Saclay
- 2. Pôle de Psychiatrie, AP-HP, Faculté de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor
Description
This record contains the data of the paper "Interpretable and integrative deep learning for discovering brain-behaviour associations". In this paper, we employ a digital avatar procedure as an interpretability module capable of reporting the relationships learned within a multi-view variational autoencoder. We integrate this procedure into a novel framework that utilises stability selection to identify meaningful and reproducible associations between brain imaging and behaviour.
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