Predicting individual traits from unperformed tasks

Shachar Gal, Niv Tik, Michal Bernstein-Eliav, Ido Tavor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number118920
StatePublished - 1 Apr 2022


  • Functional-connectivity
  • Individual traits
  • Machine-learning
  • Prediction
  • Resting-state fMRI
  • Task fMRI


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