By considering task-induced brain state and sex, the best-performing task-based fMRI model explained over 20% of the variance in fluid intelligence scores, compared to <6% for rest-based models.
Observational (n=1,086)
Yes
Do predictive models built from task fMRI data improve the prediction of individual traits like fluid intelligence compared to resting-state fMRI data?
Task-based fMRI significantly outperforms resting-state fMRI in predicting individual traits such as fluid intelligence, suggesting a paradigm shift in functional connectivity analyses.
Absolute Event Rate: 20.3% vs 5.9%
p-value: p=<0.001
Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.
Greene et al. (2018) conducted an observational in Healthy individuals (fluid intelligence prediction) (n=1,086). Task-based fMRI predictive modeling vs. Resting-state fMRI predictive modeling was evaluated on Variance in fluid intelligence (gF) explained by the predictive model (p=<0.001). By considering task-induced brain state and sex, the best-performing task-based fMRI model explained over 20% of the variance in fluid intelligence scores, compared to <6% for rest-based models.