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

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.

Original languageEnglish
Article number118920
JournalNeuroImage
Volume249
DOIs
StatePublished - 1 Apr 2022

Keywords

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

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