Here we propose to jointly predict behavioral scores that make up the individual profiles from neuroimaging data with multi-output models. This approach boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two rs-fMRI datasets targeting different brain disorders (Alzheimer’s Disease and schizophrenia).
Multi-output learning performance on R-fMRI studies
