Predicting individual traits from unperformed tasks

Neuroimage. 2022 Apr 1:249:118920. doi: 10.1016/j.neuroimage.2022.118920. Epub 2022 Jan 18.

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.

Keywords: Functional-connectivity; Individual traits; Machine-learning; Prediction; Resting-state fMRI; Task fMRI.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain / diagnostic imaging
  • Brain / physiology*
  • Connectome / methods*
  • Humans
  • Individuality*
  • Machine Learning*
  • Magnetic Resonance Imaging*
  • Task Performance and Analysis*