Predicting individual traits from unperformed tasks.
Neuroimage
; 249: 118920, 2022 04 01.
Article
em En
| MEDLINE
| ID: mdl-35051583
ABSTRACT
Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise e Desempenho de Tarefas
/
Encéfalo
/
Imageamento por Ressonância Magnética
/
Conectoma
/
Aprendizado de Máquina
/
Individualidade
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article