State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State

Abstract

Resting-state functional connectivity, as measured by functional magnetic resonance imaging (fMRI), is often treated as a trait, used, for example, to draw inferences about individual differences in cognitive function, or differences between healthy or diseased populations. However, functional connectivity can also depend on the individual's mental state. In the present study, we examined the relative contribution of state and trait components in shaping an individual's functional architecture. We used fMRI data from a large, population-based human sample (N ϭ 587, age 18 -88 years), as part of the Cambridge Centre for Aging and Neuroscience (Cam-CAN), which were collected in three mental states: resting, performing a sensorimotor task, and watching a movie. Whereas previous studies have shown commonalities across mental states in the average functional connectivity across individuals, we focused on the effects of states on the pattern of individual differences in functional connectivity. We found that state effects were as important as trait effects in shaping individual functional connectivity patterns, each explaining an approximately equal amount of variance. This was true when we looked at aging, as one specific dimension of individual differences, as well as when we looked at generic aspects of individual variation. These results show that individual differences in functional connectivity consist of state-dependent aspects, as well as more stable, trait-like characteristics. Studying individual differences in functional connectivity across a wider range of mental states will therefore provide a more complete picture of the mechanisms underlying factors such as cognitive ability, aging, and disease.

Building a Science of Individual Differences from fMRI

“…Yet, a discussion of motion would have been appropriate in the previous section on validity as well: some subjects are generally more fidgety than others in the scanner (some have argued that this constitutes a trait in and of itself, with a neurobiological basis [93]). Motion arguably contributes more to inter-subject variance than to intra-subject variance (e.g., men tend to exhibit more head movements than women [90]; older people move more than younger people [94]; and people with autism move more than controls [95]). Motion artifacts have complex effects on fMRI statistics, and incompletely correcting for them can lead to erroneous conclusions in individual differences research [95–97].…”

“…Functional connectivity can also be estimated from task fMRI data, usually following the removal of stimulus-evoked activity [72], and shares about half its variance with the functional connectivity estimated from RS-fMRI data [166]. The remaining half of unshared variance could complicate the interpretation of individual differences in functional connectivity [94]. …”

From connectome to cognition: The search for mechanism in human functional brain networks

“…Firstly, encouraging the analysis of networks across careful task manipulations can bridge the gap between the largely segregated task activation and FC literatures (with the latter largely focusing on task-free rest; Biswal et al, 1995). Secondly, elucidating network mechanisms underlying “healthy” cognitive function will by extension clarify how dysfunction of these mechanisms contributes to clinical conditions (Cole, Repov, & Anticevic, 2014; Craddock et al, 2013; Geerligs, Rubinov, Cam-CAN, & Henson, 2015). Finally, testing precise mechanistic hypotheses would help constrain the sizeable methodological “model space” in brain network analysis, spanning choices between multiple network node definitions, clustering algorithms and FC estimation methods.…”

And the best task is …? Using Task potency to infer task specificity

“…Here, we show how this functional baseline architecture can be used to index task-dependent modulations, providing a means for quantitatively comparing evoked effects across different cognitive domains. This model incorporates the idea that functional connectivity observed under cognitive manipulation is task-specific with respect to its underlying resting state functional connectivity (Cole et al, 2014;Geerligs et al, 2015;Shirer et al, 2012;Smith et al, 2009). To facilitate understanding the building blocks of cognition we demonstrate that differential levels of localised sensitivity to task manipulation inform about the relative potency of a specific task.…”