State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State
Linda Geerligs1, Mikail Rubinov2, Cam-Can3
1Medical Research Council (MRC) Cognition and Brain Sciences Unit, Cambridge CB2 7EF, United Kingdom, Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom lindageerligs@gmail.com.
Summary
Brain functional connectivity has both stable, trait-like characteristics and state-dependent components. Individual differences in brain function are shaped equally by stable traits and current mental states, not just one or the other.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Brain Imaging
Background:
- Resting-state functional connectivity (rs-fMRI) is often analyzed as a stable individual trait.
- However, brain functional connectivity can fluctuate based on an individual's current mental state.
- Understanding both trait and state components is crucial for interpreting individual differences.
Purpose of the Study:
- To investigate the relative contributions of state and trait components to individual differences in functional connectivity.
- To examine how mental state influences patterns of individual variation in brain functional architecture.
- To determine if state effects are as significant as trait effects in shaping functional connectivity.
Main Methods:
- Utilized functional magnetic resonance imaging (fMRI) data from a large, population-based sample (N=587, ages 18-88) from the Cam-CAN project.
- Collected fMRI data across three distinct mental states: resting, performing a sensorimotor task, and watching a movie.
- Analyzed the impact of mental state on individual differences in functional connectivity patterns, controlling for average connectivity.
Main Results:
- Both state-dependent and trait-like factors significantly contribute to individual differences in functional connectivity.
- State effects and trait effects explained approximately equal amounts of variance in functional connectivity patterns.
- These findings held true for both aging-related differences and general individual variations.
Conclusions:
- Individual differences in brain functional connectivity are a combination of stable traits and dynamic, state-dependent variations.
- Analyzing functional connectivity in a single state (e.g., resting state) captures only a partial view of individual brain function.
- Comprehensive understanding of individual brain function requires studying functional connectivity across multiple mental states to distinguish transient from stable characteristics.
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