Joint prediction of multiple scores captures better individual traits from brain images.
Neuroimage
; 158: 145-154, 2017 09.
Article
en En
| MEDLINE
| ID: mdl-28676298
ABSTRACT
To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Esquizofrenia
/
Simulación por Computador
/
Mapeo Encefálico
/
Enfermedad de Alzheimer
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2017
Tipo del documento:
Article