Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive...

Area of Science:

• Cognitive neuroscience
• Psychology
• Neuroimaging

Background:

• Measuring individual differences in cognitive control is crucial but challenging due to high within-person variability in standard neuroimaging measures.
• Existing methods like response times and task-based fMRI activity struggle to reliably capture these individual distinctions.

Purpose of the Study:

• To introduce and validate an analytic framework combining hierarchical Bayesian modeling and multivariate decoding for trial-level fMRI data.
• To assess the test-retest reliability of neural responses related to cognitive control across several months.

Main Methods:

• Utilized longitudinal data from the Dual Mechanisms of Cognitive Control project.
• Applied a novel framework integrating hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data during a color-word Stroop task.
• Estimated individual neural responses and their stability over time.

Main Results:

• Hierarchical models revealed near-maximal test-retest reliability in prefrontal and parietal brain regions for cognitive control-related neural responses.
• Multivariate decoding significantly improved the precision of estimating individual-level correlations compared to traditional univariate contrasts.
• Improvements in precision were linked to the suppression of cross-trial variability.

Conclusions:

• Cognitive control-related neural responses stably individuate individuals over time.
• The integrated hierarchical and multivariate modeling approach offers a powerful solution for studying individual differences in cognitive control by effectively managing high-variability measures.