Predicting individual traits from unperformed tasks
| Autor: | Ido Tavor, Shachar Gal, Michal Bernstein-Eliav, Niv Tik |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Adult
Brain activation Computer science Cognitive Neuroscience Individuality Neurosciences. Biological psychiatry. Neuropsychiatry Task (project management) Machine Learning Task Performance and Analysis Connectome medicine Humans Functional-connectivity Resting-state fMRI Machine-learning Human Connectome Project medicine.diagnostic_test Brain Cognition Magnetic Resonance Imaging Neurology Functional organization Functional magnetic resonance imaging Prediction Individual traits Cognitive psychology Task fMRI RC321-571 |
| Zdroj: | NeuroImage, Vol 249, Iss, Pp 118920-(2022) |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2022.118920 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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