Brain hubs defined in the group do not overlap with regions of high inter-individual variability.

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Trait-like variants in human functional brain networks.

Resting-state functional magnetic resonance imaging (fMRI) has provided converging descriptions of group-level functional brain organization. Recent work has revealed that functional networks identified in individuals contain local features that differ from the group-level description. We define these features as network variants. Building on these studies, we ask whether distributions of network variants reflect stable, trait-like differences in brain organization. Across several datasets of highly-sampled individuals we show that 1) variants are highly stable within individuals, 2) variants are found in characteristic locations and associate with characteristic functional networks across large groups, 3) task-evoked signals in variants demonstrate a link to functional variation, and 4) individuals cluster into subgroups on the basis of variant characteristics that are related to differences in behavior. These results suggest that distributions of network variants may reflect stable, trait-like, functionally relevant individual differences in functional brain organization.

Network variants are similar between task and rest states.

Recent work has demonstrated that individual-specific variations in functional networks (termed "network variants") can be identified in individuals using resting state functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time, suggesting that they may be trait-like markers of individual differences in brain organization. However, while networks variants are reliable at rest, is is still untested whether they are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in task and rest states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.

Interdependent self-construal predicts reduced sensitivity to norms under pathogen threat: An electrocortical investigation.

Prior evidence suggests that external threat motivates people to monitor norm violations. However, the effect of threat may be attenuated for those high in interdependent self-construal (SC) because this SC affords a sense of protection against the threat. Here, we tested this possibility by priming or not priming young American adults with a pathogen threat. We then had participants read norm-violating or normal behaviors while assessing two electrocortical markers: N400 (indexing the detection of norm violations) and suppression of upper α-band power (indexing vigilance to the violations). In the threat priming condition, interdependent SC predicted decreased responsiveness to norm violations. In the control priming condition, however, interdependent SC predicted increased responsiveness. Our work suggests that interdependent SC may provide a sense of security under threat.

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry.

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Alpha band frequency differences between low-trait and high-trait anxious individuals.

Trait anxiety has been shown to cause significant impairments on attentional tasks. Current research has identified alpha band frequency differences between low-trait and high-trait anxious individuals. Here, we further investigated the underlying alpha band frequency differences between low-trait and high-trait anxious individuals during their resting state and the completion of an inhibition executive functioning task. Using human participants and quantitative electroencephalographic recordings, we measured alpha band frequency in individuals both high and low in trait anxiety during their resting state, and while they completed an Eriksen Flanker Task. Results indicated that high-trait anxious individuals exhibit a desynchronization in alpha band frequency from a resting state to when they complete the Eriksen Flanker Task. This suggests that high-trait anxious individuals maintain fewer attentional resources at rest and must martial resources for task performance as compared with low-trait anxious individuals, who appear to maintain stable cognitive resources between rest and task performance. These findings add to the cognitive neuroscience literature surrounding the role of alpha band frequency in low-trait and high-trait anxious individuals.