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.

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain’s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show “core” (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

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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.

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Cognitive control, the ability to engage in goal-related behavior, is linked to frontal, parietal, and cingulate brain regions. However, the underlying function(s) of these regions is still in question, with ongoing discussions about their specificity and/or multifunctionality. These brain regions are also among the most variable across individuals, which may confound multi-functionality with inter-individual heterogeneity. Precision fMRI-extended data acquisition from single individuals-allows for reliable individualized mapping of brain organization. We review examples of recent studies that use precision fMRI to surmount inter-individual variability in functional neuroanatomy. These studies provide evidence of interleaved specialized and multifunctional regions in the frontal cortex. We discuss the potential for these techniques to address outstanding controversies on the neural underpinnings of cognitive control. Highlights

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain’s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. Here our goal was to leverage information about individual-level brain organization to identify locations of high inter-subject consensus. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show “core” (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across datasets with different scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, allowing researchers to apply information about group consistency to their own work in rest- or task-based studies.

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While a dominant assumption is that cortical 'variants' represent boundary shifts in the borders between regions, here we show that variants can also occur at a distance from their typical position, forming ectopic intrusions. Both forms of variants are common across individuals, but the forms differ in their location, network linkages, and activations during tasks. Sub-groups of individuals share similar variant properties, but sub-grouping on the two variant forms appears independent. This work argues that individual differences in brain organization commonly occur in two dissociable forms, border shifts and ectopic intrusions, that must be separately accounted for in the analysis of cortical systems across people. This work expands our knowledge of cortical variation in humans and helps reconceptualize the discussion of how cortical systems variability arises and links to individual differences in behavior.