We have shown that human intelligence is associated with the dynamic reconfiguration in functional brain networks as indexed by temporal fluctuations in global modularity. Participants with higher intelligence scores demonstrated higher stability of network segregation over time and exhibited lower rates of high‐modularity states. Our results suggest that an intrinsic network architecture exhibiting fluctuations within a more narrow range of modularity may offer an advantage in the face of momentary task‐driven demands related to cognition. Finally, we found that greater network stability associated with higher intelligence was driven primarily by brain regions belonging to the DAN.

4.1 Higher network stability associated with intelligence

Although previous research indicates that integrated and segregated information processing are both essential for human cognition (Cohen & D'Esposito, 2016), neither the general level of network integration (indexed by global efficiency; Hilger et al., 2017a; Kruschwitz et al., 2018) nor the general level of network segregation (indexed by global modularity; Hilger et al., 2017b, and present results) seem to differentiate between high versus low general intelligence — when investigated in static, time‐invariant networks. Rather, we observed here that higher intelligence is associated with more stable (i.e., less variable) levels of network segregation over time. Furthermore, we found that across the ~10 min of the task‐free fMRI scan, individuals with higher intelligence scores settled relatively less often into states of particularly high network segregation. A similar association for low segregation states was, however, only observed when defining these states relative to subject‐specific modularity thresholds. This latter result was driven by only a small subgroup of subjects and should therefore be interpreted with caution, requiring further investigation.

Studies of task fMRI suggest that states of low modularity facilitate network integration (Shine, Bissett, et al., 2016), probably because information can be exchanged more freely across module boundaries (Betzel et al., 2016), which is especially important for complex cognitive tasks requiring the coordination of different subprocesses (Cohen & D'Esposito, 2016; Shine, Bissett, et al., 2016). In contrast, high modularity facilitates network segregation. This is more characteristic of specialized information processing (Betzel et al., 2016), for example, in tasks requiring the unhindered processing of one type of information (e.g., motor information during finger tapping; Cohen & D'Esposito, 2016).

Additional support for the behavioral relevance of network segregation as indexed by modularity comes from clinical studies. Increased modularity has, for example, been observed in persons with Attention Deficit Hyperactivity Disorder (ADHD; Lin et al., 2014) or in patients suffering from major depression (MD; Ye et al., 2015). Graph‐theoretical investigations indicate that enhanced levels of network segregation can lead to a fragmented network organization with sharply isolated modules (Watts & Strogatz, 1998), which may cause a breakdown of communication between major functional subsystems. Interestingly, however, these two clinical conditions present with opposing cognitive deficits: while ADHD is associated with high levels of impulsivity (White, 1999), the executive function deficits observed in MD are associated with reduced cognitive flexibility (Lee, Hermens, Porter, & Redoblado‐hodge, 2012). These studies, however, are difficult to compare to this study because of methodological differences, including (a) that they relied primarily on static functional networks, (b) involved group comparisons that, accordingly, do not require the definition of concrete thresholds for high versus low modularity, and (c) differences in graph analysis methods (e.g., binary vs. weighted graphs).

Irrespective of the specific task content, the brain seems to decrease its general level of network segregation when switching from rest to task (Shine, Bissett, et al., 2016)—with lower levels of network segregation associated with higher cognitive performance (Cohen & D'Esposito, 2016; Shine, Bissett, et al., 2016). Based on recent evidence demonstrating that, during rest, intelligence is not per se associated with the level of segregation or integration (Hilger et al., 2017a, 2017b; Kruschwitz et al., 2018; Pamplona et al., 2015), one can plausibly assume that more intelligent people may invest more effort into reconfiguring their network when switching from rest to task in order to reach better‐suitable network configurations that facilitate high cognitive performance (Cohen & D'Esposito, 2016; Shine, Bissett, et al., 2016). The results of a recent study, however, point into exactly the opposite direction. Here, fewer differences between resting‐state and task‐general network organization were associated with higher levels of general intelligence—which the authors interpreted as indicating that more intelligent subjects need to reconfigure their network less when switching from rest to task (Schultz & Cole, 2016). This study adds a missing piece into this emerging picture as it reveals that during rest (a) higher temporal stability of intrinsic network segregation and (b) fewer states of extremely high network segregation are associated with higher levels of general intelligence.

Task‐related connectivity is assumed to rely critically on connectivity properties measured during rest (see also Amico, Arenas, & Goñi, 2019; Tavor et al., 2016), reflecting individual differences (Cole, Bassett, Power, Braver, & Petersen, 2014). Furthermore, first evidence suggests that individual profiles of connectivity dynamics generalize between rest and task and may therefore represent a task‐invariant common characteristic (Fong et al., 2019). These observations suggest that the association between intelligence and higher stability in network organization over time is not limited to task‐free (resting‐state) conditions, but represents a more general phenomenon. Against this background, we here speculate that during cognitive tasks (with unchanging cognitive demands), higher intelligence may be associated with both, that is, a more effectively reduced degree of network segregation (Cohen & D'Esposito, 2016; Shine, Bissett, et al., 2016), and an overall higher extent of temporal stability within this adapted architecture (this study and Fong et al., 2019). Finally, the association between intelligence and fewer occurrences of high‐modularity states may suggest an intrinsic protection against unintentional shifts toward states of network fragmentation (i.e., particularly high modularity) that are likely to disrupt information processing and hinder ongoing cognition.

Our conclusion is complementary to a recent proposal suggesting that general intelligence depends on the ability to flexibly transition between “easy‐to‐reach” and “difficult‐to‐reach” network states (Barbey, 2018; Girn, Mills, & Christo, 2019). Our finding of higher intelligence associated with greater temporal stability in network organization during rest (see also Fong et al., 2019, for similar finding under task conditions) expands on the intuitively plausible proposal that higher intelligence relies on higher task‐dependent network flexibility. We thus propose that higher intelligence may be associated with both, that is, higher flexibility in network configurations when task demands change, and higher network stability when task demands remain stable across time. The latter may occur when subjects engage in the same task (Fong et al., 2019) or remain within the resting‐state condition (this study and in Fong et al., 2019). However, this suggestion needs to be tested empirically by studies investigating dynamic changes in network organization that occur during the switch from rest into task. The proposal of Barbey (2018) postulates further that the superior “ability” of more intelligent people to adaptively form task‐specific network configurations results from differences in intrinsic small‐world network attributes, specifically, in global network integration or global network segregation levels measured in static functional brain networks (Girn et al., 2019). Contrasting this view, we observed no relation between global modularity and intelligence in static, time‐averaged network metrics (see also Hilger et al., 2017b).