Resting-state BOLD signal variability is associated with individual differences in metacontrol

<jats:title>Abstract</jats:title><jats:p>Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.</jats:p>

section_content: Neural activity is highly variable from moment to moment at every level of neural organization. Traditionally, variability of this kind is considered to be "noise" that tends to mask, overshadow, or even distort the neural signals that are assumed to represent the relevant neural processing. Accordingly, functional magnetic resonance imaging (fMRI) research typically focuses on mean activity within a voxel or brain region, but considers variance in blood oxygen leveldependent (BOLD) signal as to-be-neglected "noise" 1. The same logic applies to other neuroscienti c and behavioral measurements indicative of human cognitive functioning 2. 

However, accumulating evidence suggests that intra-individual variability might be functional and bene cial for cognitive performance [3] [4] [5] [6] [7], so that a better understanding of its functional role might strongly improve the diagnosis and treatment of mental disorders such as ADHD [8] [9] [10] [11]. For example, higher BOLD signal variability is associated with younger age, higher accuracy, faster and more stable responses across a number of cognitive tasks spanning perception, attention, working memory, and task switching [4] [5] [6] [12] [13] [14]. Notabl, previous work suggests that more pronounced brain variability might allow the brain to explore among different functional network con gurations, which in turn supports cognitive exibility -the ability to explore variable opportunities and exibly adapt to changing circumstances 3, 12, 15. 

The present study was motivated by the idea that individual differences in cortical variability might be systematically related to individual cognitive-control styles, to what Hommel (2015) has called "metacontrol" 16. This term refers to the control of cognitive functioning to deal with a fundamental dilemma of human cognition [17] [18] [19] : the fact that we sometimes need to be "cognitively conservative" by sticking with our present mindset and our present goal, but to be exible and more open to alternative goals on other occasions. Hence, people need both cognitive persistence and cognitive exibility: while cognitive exibility helps them to switch between alternative opportunities, intentional agents also need cognitive persistence to avoid distractions and to stick with the current goal as long as pursuing it is worthwhile 16, [20] [21] [22]. As Hommel's Metacontrol State Model (MSM) suggests, cognitive control emerges from the interplay of two counteracting forces or systems, one promoting cognitive persistence and the other promoting cognitive exibility 16. A metacontrol bias towards persistence is characterized by a strong top-down in uence from the current goal and restricting processing to task-relevant information. In contrast, a metacontrol bias towards exibility is characterized by a stronger bottom-up in uence and openness to alternative goals and opportunities 16. Truly adaptive control requires humans to nd a balance between persistence and exibility, an ability called metacontrol. Interestingly, there are systematic individual differences with respect to the metacontrol default: while some people tend to have a persistence bias, so that they perform better than others on tasks that require persistence but less well than others on tasks that require exibility, other individuals tend to have a exibility bias, resulting in the opposite performance pro le 22. The basic idea driving the present study was that such individual biases in metacontrol might be related to individual differences in brain variability, that is, in the individual level of the BOLD signal variability of people's brains. 

We assessed our key hypothesis by testing whether an indicator of the individual degree of brain variability, our noise measure, is statistically correlated to behavior in tasks that have been shown to be diagnostic for individual biases towards metacontrol persistence or exibility. We used resting-state fMRI (rsfMRI) measures as indicators of the individual variability level. RsfMRI is a spontaneous low frequency (< 0. 1Hz) BOLD signal within the brain in the absence of external stimulation. Noise (at an optimal level) in rsfMRI is thought to re ect greater network connectivity 23, 24 and enables the exploration of the brain among various functional con gurations representing its dynamic repertoire 15. It thus seems possible that cortical noise is systematically related to metacontrol. 

Various temporal variability estimation approaches for rsfMRI data have been introduced and used in previous studies 4, 6, 25, 26. The simplest and most prominent measure of variability is the standard deviation (SD), which re ects the distributional width of a BOLD signal time series. The SD of a BOLD signal is related to age and cognitive performance in both younger and older adults 4, 6. However, SD overestimates the true dispersion when the (mean) signal varies because the calculation of SD is based on the difference between single data points and the overall mean 27. To circumvent this problem, some researchers have suggested an alternative measure -the mean squared successive difference (MSSD) 25, 28, 29. The MSSD captures the BOLD signal difference between successive time points and thus can adapt to changing expected (mean) signals. More recently, Wang et al. applied a sliding-time window correlation (SWC) analysis on rsfMRI temporal variability estimation (see Materials and Methods for details, submitted for publication) 30. This technique segments the BOLD time series into multiple overlapping time windows (typically 30-60 s) and then estimates the correlation between brain activity over these time windows 31. 

Although the advantages and disadvantages of different measures have been discussed in the literature 4, 25, 28, it is unknown whether different parameters that can be estimated on the basis of rsfMRI data reveal differences in their predictability to cognitive control. Given that we had no a-priori reason to favor one measure over another, we considered all three of them, assuming that a systematic comparison would lay the grounds for identifying the 'best' variability measure and choosing proper measurement approaches in future studies. Therefore, the present study employed three different brain variability measures and tested which of them, if any, would best predict performance in metacontrol-sensitive tasks. 

We used two tasks in which high performance requires cognitive persistence (i. e., the Stroop task and the RAT task), and a task in which high performance depends on cognitive exibility (i. e., the AUT task). Given that metacontrol biases cannot (yet) be assessed directly, we followed the previous experimental logic of comparing individual differences in tasks that rely (more) on persistence with tasks that rely (more) on exibility 22. Persistence is assumed to lead to a strong focus on the present goal and information strictly related to that goal, which suggests that a high degree of persistence would lead to better performance in tasks that require a strong focus on some stimuli and neglect of others. The Stroop task 32 is an excellent example for such a task. In the classical Stroop task, participants are to respond to the color of colored words while ignoring the word meaning (e. g., responding "green" to the word "RED" written in green ink [32] [33] [34] ). To be successful in this task, one has to process task-relevant information (i. e., color "green") and ignore task-irrelevant information (i. e., word "RED"). Individuals usually respond slower in incongruent trials (in which the color of the word and meaning are different) than in congruent trials (in which the color of the word and meaning are same), which is known as the Stroop effect. A smaller Stroop effect can be taken to indicate a better ability in reducing cognitive con ict, which is supposed to bene t from a metacontrol bias towards persistence (e. g., Dreisbach & Goschke, 2004, who applied this logic to similar tasks 35 ). In comparison, a larger Stroop effect implies a stronger impact from task-irrelevant information, which indicates a metacontrol bias towards exibility. As some researchers argue that reaction time (RT) difference scores are sometimes unreliable in individual differences research 36, we also considered intra-individual variability (IIV) of Stroop performance, which can be taken to re ect the stability of metacontrol over time. More trial-to-trial variability which was potentially induced by more frequent strategy readjustments, would indicate lesser stability of metacontrol states, i. e., higher exibility. 

Conversely, less trial-to-trial variability in Stroop performance would indicate more persistence. 

A second persistence-heavy task we considered was the Remote Associates Task (RAT). RAT is typically used to measure convergent thinking, which is one aspect or component of human creativity 37. It requires participants to nd a single solution under highly constrained search conditions: they are presented with three words and are requested to specify the one word that can be combined with either of them (e. g., "Market", "Glue", and "Man", with the solution "Super"). While this task does require a certain degree of exibility (in repeatedly searching through memory and considering novel possible targets), its reliance on persistence is much stronger than in tasks testing divergent thinking 22, 38. Accordingly, participants with comparably better performance in the RAT would be considered to have a stronger bias towards persistence than participants with worse performance 39. 

As a exibility-heavy task, we employed the Alternate Uses Task (AUT) 40, 41. This task is traditionally used to assess divergent thinking, another component of human of creativity, requiring to generate new ideas and to overcome more familiar, but currently misleading ideas 40, 41. As an example, a participant might be presented with the label or picture of a brick and asked to report all kinds of uses that a brick might have, including very uncommon ones. Like a kind of mirror image of the RAT, the AUT does need some degree of persistence (in keeping the original concept active to check it for possible uses) but it relies much more on exibility 22, 38. Accordingly, participants with comparably better performance in the AUT would be considered to have a stronger bias towards exibility than participants with worse performance 39. 

In sum, the present study explored whether and how resting-state BOLD signal variability is associated with inter-individual differences in metacontrol biases towards persistence or exibility. We examined different indicators of brain variability and three different tasks drawing on cognitive persistence or exibility. 

Our main question was whether these indicators are signi cantly related to performance in the three behavioral tasks and whether these associations would differ between tasks tapping into persistence biases and tasks tapping into exibility biases. We were also interested in possible differences between the three indicators in the way they are associated with such behavioral differences but had no speci c hypothesis regarding such differences.
section_name: Introduction

section_name: Results

section_content: The analysis of the Stroop data (n = 32) yielded a standard Stroop effect, with longer mean RTs in incongruent trials (1100 ms, SD = 317 ms) than in congruent trials (797 ms, SD = 234 ms), t(31) = 4. 34, p < 0. 001, d = 1. 09) (see Fig. 2a ). Performance accuracy and speed were not signi cantly correlated (congruent trials: r = 0. 181, p = 0. 323; incongruent trials: r = 0. 260, p = 0. 151), which rules out a speed-accuracy trade-off. Intra-individual variability of Stroop performance (RT-CV) was 0. 315 ± 0. 062 ms. In the RAT, participants solved 6. 22 items correctly on average (SD = 4. 09). In the AUT, inter-rater reliability was assessed by intraclass correlation coe cients (ICC), which were moderate for exibility scores (ICC shoe = 0. 571, ICC stone = 0. 650) and for uency scores (ICC shoe = 0. 705, ICC stone = 0. 665). The averaged AUT exibility scores from both raters were 7. 50 ± 2. 04, and the averaged AUT uency scores were 9. 78 ± 2. 26. 

In order to test whether metacontrol-bias parameters extracted from various tasks and different measures were related, we applied an inter-correlation analysis between the size of Stroop effect, RT-CV of Stroop task, RAT scores, AUT exibility scores, and AUT uency scores. As displayed in Fig. 2b, the size of Stroop effect was signi cantly positively correlated with RT-CV (r = 0. 403, p = 0. 022). A highly positive correlation was also found between AUT exibility scores and AUT uency scores (r = 0. 709, p < 0. 001). Correlations between other measures were not signi cant. These results may indicate that participants are biased towards persistence or exibility to a different extent, depending on the task demands.
section_name: Behavioral ndings

section_content: The spatial maps at the threshold of Z > 1. 0 and time courses of our selected ICs are shown in Fig. 3. IC1 and IC4 mainly re ect activities in bilateral precuneus, superior and inferior parietal regions, within the parietal cortex. IC2 includes bilateral inferior prefrontal gyrus, middle temporal gyrus, and angular gyrus. Bilateral inferior parietal regions, postcentral and precentral areas are involved in IC3, which was de ned as a parietal and motor network. IC5 re ects the left-sided executive control network, including the left prefrontal and parietal cortex, while IC7 represents the right executive control network 42. IC6 mainly includes the bilateral middle part of orbital frontal gyrus and precuneus which was de ned as the frontal and parietal network. IC8 represents activity in the anterior cingulate cortex, the prefrontal cortex, and the bilateral insular, which were denoted as the attention network 43. IC9 mainly re ects activity in the prefrontal cortex and extends to the anterior cingulate cortex. 

To assess whether brain variability correlated between different measures, we tested the inter-correlations between SD, MSSD, and SWC. SD and MSSD of BOLD signals were highly positively correlated for all ICs (see Table 1for details). In contrast, brain variability of most ICs measured by SWC was not signi cantly correlated with SD or MSSD, suggesting that sliding-time window correlation analysis assessed aspects of brain variability that differ from those assessed by MSSD and SD measures.
section_name: Resting-state independent components ndings

section_content: The analysis of the SD measure revealed that the SD of all selected components was positively correlated with the size of the Stroop effect. A pattern of positive correlations was also obtained between MSSD of all components and the size of Stroop effect. An almost signi cant positive correlation was found between MSSD of IC8 (i. e., attention network) and the size of Stroop effect (r = 0. 468, p uncorrected = 0. 007, p corrected = 0. 062) (Fig. 4a ; see Table 2 for details). 

The analysis of the SWC measure showed that SWC of IC5 (i. e., left executive control network) is signi cantly negatively correlated with the size of Stroop effect (r = -0. 513, p uncorrected = 0. 003, p corrected < 0. 05) (see Table 1 ). However, when examining this negative correlation in more detail, we found that it was driven by one participant exhibiting very small brain variability and relatively large Stroop effect at the same time (Fig. 4b ). To rule out the in uence of extreme values, we conducted a control analysis in which an outlier was discarded. Results showed that the correlation between SWC of IC5 and the size of Stroop effect was no longer signi cant (r = -0. 387, p uncorrected = 0. 032, p corrected = 0. 288). No signi cant correlations were found between RT-CV of Stroop task and brain variability as measured by SD, MSSD, or SWC. Regarding RAT performance, the SD of all ICs revealed negative correlations. SD of IC3 (i. e., parietal and motor network) and IC6 (i. e., parietal and frontal network) was signi cantly negatively correlated with RAT performance (IC3: r = -0. 505, p uncorrected = 0. 003, p corrected < 0. 05; IC6: r = -0. 508, p uncorrected = 0. 003, p corrected < 0. 05) (see Fig. 5a and 5b ). A similar pattern of negative correlations was displayed between MSSD of all components and RAT scores. Most signi cant negative correlations were found between MSSD of IC6 (i. e., parietal and frontal network), IC9 (i. e., frontal and ACC network) and RAT performance (IC6: r = -0. 543, p uncorrected = 0. 001, p corrected < 0. 05; IC9: r = -0. 510, p uncorrected = 0. 003, p corrected < 0. 05) (see Fig. 5c and 5d ). We found an almost signi cant negative correlation between MSSD of IC3 and RAT scores (r = -0. 470, p uncorrected = 0. 007, p corrected = 0. 059) (see Fig. 5e ). The brain variability of all ICs estimated by the SWC approach was not signi cantly correlated with RAT performance. 

AUT exibility and uency scores were not signi cantly related to brain variability.
section_name: Resting-state BOLD variability and individual difference in metacontrol policies

section_content: The present study explored the relationship between the individual's resting-state BOLD signal variability and individual differences in metacontrol biases towards persistence or exibility. Three BOLD signal variability measures were compared. We found that resting-state BOLD signal variability measured by SD and MSSD was highly positively correlated, but variability measured by SWC was not related with the other two. Notably, our results suggest that higher levels of resting-state BOLD variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and variability measured by SD in the parietal and motor network, parietal and frontal network were associated with lesser persistence (or more exibility) (denoted by larger Stroop effect or worse RAT performance) than lower levels of brain variability in these networks. 

Correlations between three brain variability measures suggest that resting-state BOLD signal variability estimated by SD and MSSD is highly correlated, but variability measured by SWC is different from the other two. The high correlation between the SD measure and MSSD measure is consistent with ndings from Garrett and colleagues 4. Although SD as a measure of brain variability has been criticized for its dependence on shifts in the mean and MSSD was recommended to prevent this problem, our ndings where MSSD and SD show highly consistent results suggest that SD is an appropriate variability measure in resting-state fMRI data where (mean) signals are relatively constant. 

In addition to SD and MSSD, which assess the dispersion of signal amplitude, SWC measures the dynamic correlations where a succession of pairwise correlations between the time series from segmented time windows was computed. Inconsistent results between SWC on the one hand and SD and MSSD on the other suggest that the sliding-time window correlation analysis may re ect characteristic of brain uctuations that are different from those re ected in SD and MSSD. The little contributions of SWC in predicting performance in tasks related to cognitive control also suggest that this measure may not be overly suitable for tapping into cognitive-control functions. 

We found that brain signal variability measured by SD and MSSD in a range of resting-state networks was positively associated with metacontrol biases towards exibility but negatively associated with metacontrol biases towards persistence. In contrast, SWC measured brain variability was not signi cantly associated with metacontrol biases. Our ndings extend previous knowledge of the relationship between brain variability and human behavior in three ways:

First, resting-state BOLD signal variability is meaningful and can tentatively be taken as a neural marker of metacontrol biases towards persistence or exibility. Previous investigations have identi ed the on-task brain variability, which varies between cognitive demands 5, attentional states 14, task conditions 44, and perceptual input 45. We suggest that off-task variability can also be used as a trait-like neural marker of the individual metacontrol bias and, thus, as a predictor of individual cognitive control performance. 

Second, although numerous studies demonstrate general positive effects of higher brain variability on cognitive performance 3-6, 13,45,46, our results suggest that the bene cial effect of brain variability may depend on cognitive demands and metacontrol states involved. Our ndings are in line with the previous taskbased fMRI study suggesting that higher brain signal variability levels are bene cial for task switching but detrimental for distractor inhibition 12. Hence, brain variability should not be considered as a general performance booster, but as a factor that can be bene cial for some tasks but impair performance in others. 

Third, we found metacontrol to be correlated with SD and MSSD measures, but not with SWC. Hence, metacontrol is not related to all aspects and kinds of brain variability but only to those that are re ected in SD and MSSD. 

How might signal variability in the brain translate into metacontrol biases towards persistence or exibility? Researchers have proposed that dopamine (DA) and inter-individual differences in DA levels and/or the dynamics of these levels over time are promising candidates for linking characteristics of neural processing, like differences in neural variability, to behavior 3, [47] [48] [49] and some evidence suggests that dopaminergic (or catecholamine system activity) is associated with metacontrol [50] [51] [52] [53]. 

According to the computational model proposed by Durstewitz and Seamans 54, D2-dominated state related to a low energy barrier among activity states would allow easier and faster transition between different cortical network states 12. This D2-dominated state facilitates switching among representations at the behavioral level and supports metacontrol biases towards exibility 22, 54. Conversely, D1-dominated states are associated with a high energy barrier leading to more stable brain activity patterns and a more di cult transition between different network states 12, 54. 

At the same time, this D1-dominated state boosts the robustness of items in working memory and promotes metacontrol biases towards persistence 22, 54. 

Evidence from simulation research suggests that dynamics of the brain's intrinsic properties may help keep the system in a state where different subnetworks compete with each other 23. Such an active resting-state (at an optimal level) can be sensitive to external signals, which can trigger brain activity during different tasks, thus supporting behavioral exploring and switching. In contrast, sensitivity to external stimuli makes people more likely to be distracted by task-irrelevant stimuli. 

We found that resting-state BOLD variability of parietal and motor network (IC3), parietal and frontal network (IC6), attention network (IC8), frontal and ACC network (IC9) was positively associated with metacontrol biases towards exibility but negatively associated with metacontrol biases towards persistence. 

Previous work suggests that distractor inhibition and task switching rely on a shared frontoparietal network, and brain activity varies depending on the exact cognitive processing involved 55. As a control network, the frontoparietal network plays a crucial role in task adaptation, implementation and exible modulation of cognitive control 56. Moreover, the frontoparietal network is a globally functional hub that exibly interacts with other brain networks. Higher variability in frontal and parietal regions may indicate more dynamic connectivity between brain networks with the frontoparietal network as the hub, and thus supports the exibility of metacontrol, but hamper persistence of metacontrol 57, 58. The attention network which mainly includes ACC, prefrontal cortex and insular has been shown to be involved in sustained focus on task-relevant information and con ict resolution 59, 60. A variable attention network may reveal exible attention resources allocation, which is bene cial for exibility but detrimental for persistence. 

Whereas the analyses of the Stroop and the RAT data provide a rather consistent picture, this is not the case with respect to the AUT ndings. On the one hand, previous studies have rarely found RAT performance to be an exact mirror image of AUT performance; rather, various manipulations affected either only one of the two tasks or at least one of the more than the other 61,62. This suggests that both tasks are likely to capture aspects of metacontrol persistence and exibility, but they can hardly be viewed as a direct measure of the respective metacontrol states. It is also likely that they differ in sensitivity, presumably depending on the experimental setting. Hence, it does not seem to be odd per se that only one of the two creativity tasks showed systematic effects. On the other hand, however, it is also possible that our particular assessment of divergent thinking was suboptimal. Due to the time limit in Qualtrics, our AUT task only allowed up to 6 responses within a short time duration for each item. This might have created ceiling effects, so that especially the uency and exibility scores were likely to be less sensitive to interindividual differences than the standard versions of the AUT. This must have reduced the variability of the data, which in turn could have worked against nding signi cant correlations. Accordingly, we are reluctant to draw strong conclusions from the absence of correlations related to the AUT. 

Another potential limitation of our explorative study is the sample size, which in turn resulted from our use of already collected data. Larger sample sizes would be bene cial for probing brain-behavior relationships. Accordingly, we consider the outcomes of the present study as preliminary and in need of replication, but at the same time encouraging for further studies on the relationship between brain variability and metacontrol policies. 

To conclude, we aimed to explore the relationship between resting-state BOLD signal variability and metacontrol policies and compared three previously used brain variability estimation metrics. We demonstrated that temporal brain variability during resting-state is associated with metacontrol biases towards persistence or exibility, highlighting the importance of temporal variability of brain activity in understanding the neural underpinnings of cognitive control. Moreover, we found that BOLD signal variability is antagonistically related to metacontrol biases towards persistence or exibility, suggesting that the bene cial effect of brain variability on cognitive control may depend on the metacontrol modes involved. Last but not least, different brain variability measures may imply different relationships between the brain and behavior. Our ndings may open a new line of inquiry into how different brain variability measures impact or be re ected by brain-behavior relationships.
section_name: Discussion

section_name: Materials And Methods

section_content: Our sample consisted of thirty-two right-handed adults (21 females; age 18-35 years; M = 23. 81, SD = 3. 53). The raw dataset, which has been reported in a previous study 63, included 40 participants reporting no history of psychiatric or neurological disorders. Six participants were excluded because of missing data for the Stroop task, RAT or AUT, or resting-state fMRI scanning; two participants were excluded because of extremely large or small Stroop effect size (i. e., exceeding group mean ± 2 standard deviations). Framewise displacement (FD) of all remaining participants was smaller than 0. 5 mm. The present study was approved by the Psychology Research Ethics Committee of Leiden University. The original study was approved by the Internal Review Board of the Erasmus Research Institute of Management, and all participants provided written informed consent for their participation. The current study and original study were conducted in accordance with the Declaration of Helsinki.
section_name: Participants

section_name: Behavioral assessment

section_content: An adapted version of the Stroop task 32 was used. In this task, two rows of letters appeared on screen, and participants were instructed to decide as quickly as possible whether the color of the top row letters correspond to the color name written at the bottom row by pressing one of two buttons (see Fig. 1 ). In congruent trials, the top row consisted of a color word ("RED," "GREEN," "BLUE," or "YELLOW") printed in a color that matches its semantic meaning (e. g., "RED" presented in red ink), and the bottom row consisted of a color word printed in white ink. For incongruent trials, the color word in the top row printed in a color that mismatches its semantic meaning (e. g., "RED" presented in green ink). The bottom row letters were identical to the congruent condition. Participants performed 72 trials in the MRI scanner, containing 36 congruent trials and 36 incongruent trials. In half of the trials, the color of top row word corresponded to bottom color word (corresponding trials), while the color of top row word not corresponded to bottom word in the other half (not corresponding trials). 

Each trial started with a xation period of 2000-4000ms, followed by the stimuli presented for a maximum time of 3000ms. Afterwards feedback appeared for 1000ms. To prevent participants from focusing on the bottom word and not attending the word in the top row, the top-row word was presented 100ms before the bottom word. If no response was given within 3000ms from the onset of the stimulus presentation, an incorrect response was registered. 

We calculated two parameters from the Stroop task as estimations of metacontrol biases: First, the size of Stroop effect (mean RT for incongruent trials minus mean RT for congruent trials). As we mentioned before, a smaller Stroop effect indicates bias towards persistence, while a larger Stroop effect indicates bias towards exibility. Note that in our word-matching version of the Stroop task, the size of the Stroop effect may depend on the type of answer (yes or no), i. e., on the color-word correspondence 64. Speci cally, in non-corresponding trials (when the answer was 'NO'), the con ict generated by the Stroop effect may facilitate a 'no response', which may work against the Stroop effect. Hence, a standard Stroop effect may only occur with correspondence (when the answer was 'YES'). Therefore, we calculated the Stroop effect by subtracting the mean RT for corresponding congruent trials from the mean RT for corresponding incongruent trials. As RT difference scores are sometimes unreliable in assessing individual differences 36, we also calculated the intra-individual variability (IIV) of Stroop RT as a second metacontrol measure. The IIV of Stroop RT was estimated by the RT coe cient of variation across all trials (RT-CV: SD divided by mean). Greater RT-CV would re ect lesser stability of metacontrol states in the Stroop task, i. e., a bias towards exibility. In contrast, smaller RT-CV would be taken as a bias towards metacontrol persistence.
section_name: Color-word matching Stroop task

section_content: In each trial of this task, participants were to nd a single word that can be combined with each of the three presented stimulus words (e. g., cottage, swiss, cake = "cheese"; Mednick & Mednick, 1967). Participants had to complete 17 trials within 5 minutes. This task was completed via Qualtrics outside the scanner. To complete the RAT, participants were assumed to engage in convergent thinking, which was assumed to rely on a persistence bias 65.
section_name: Remote Associates Task (RAT)

section_content: Participants were presented with two everyday objects (i. e., shoe, stone) and asked to name as many possible uses (up to 6 uses) for each object as they can. 

This task was completed via the Qualtrics outside the scanner. Performance on AUT was scored by two independent raters from four dimensions: exibility (number of ideas in different categories), uency (number of uses one can think of), originality (uniqueness of responses), and elaboration (the level of details in responses). As exibility and uency require switching between different ideas and considering multiple solutions 41, we used exibility scores and uency scores which were averaged between two raters as metacontrol biases measures. Higher scores indicated more tendency towards exibility, while lower scores indicated more tendency towards persistence 41.
section_name: Alternate Uses Task (AUT)

section_content: MRI scanning was performed on a 3T Siemens MRI system. Resting-state functional data were acquired by a T2*-weighted gradient-echo, echo-planar pulse sequence in descending interleaved order (repetition time (TR) = 2030ms, echo time (TE) = 30ms, ip angle = 75°, slice thickness = 3. 0 mm, in-plane resolution = 3. 0 × 3. 0 mm, 64 × 64 voxels per slice). In addition to functional imaging, a T1-weighted image was acquired at the resolution of 1. 0 × 0. 5 × 0. 5 mm for anatomical reference (192 sagittal slices, TR = 1900ms, TE = 2. 26ms, ip angle = 9°). 

Resting-state functional data preprocessing Data preprocessing was using DPASF (http://rfmri. org/DPARSF), a Matlab toolbox for resting-state fMRI data processing & analysis 66,67. The rst 10 volumes were discarded, and then slice-time correction and realignment were performed. Head motion was assessed by frame-wise displacement (FD) 68. 

All participants' FD were smaller than 0. 5 mm. Individual T1-weighted images were co-registered to the mean functional image and then segmented into gray matter, white matter (WM), and cerebrospinal uid (CSF). Transformations from individual native space to MNI space were computed with the DARTEL tool 69, and then the functional images were normalized to MNI space with warped parameters. Lastly, all functional images were smoothed with a 6 mm full width at half maximum (FWHM) Gaussian kernel.
section_name: MRI data acquisition

section_content: As previous studies note brain signal variability is region-speci c 12, 44, we only selected control-related networks (i. e., independent components) which were obtained from the independent component analysis (ICA). ICA was performed using the GIFT Toolbox (https://www. nitrc. org/projects/gift) to identify temporally coherent networks which are spatially distinct. Following the processing protocol used in the previous study 70, pre-processed functional images were rstly intensity-normalized. Subsequently, each participant's data were reduced to 70 principal components. Then, group-level decomposition was performed using the Infomax algorithm 71, which resulted in 25 spatially independent components (ICs) and associated time courses. To improve the reliability of IC-decomposition, the Infomax ICA algorithm was repeated 20 times using the ICASSO toolbox 72. Afterward, the obtained 25 ICs were visually inspected to exclude noise components. We then compared all non-noise components' spatial topology to the pre-de ned resting-state network templates 42, 43. The ICs re ecting activities in the executive control network, attention network, prefrontal, and parietal regions were identi ed and used for further analyses. 

Participant-speci c spatial maps and time courses were then estimated using the dual regression back-reconstruction method 73. We did not further scale the components due to the preprocessing step of intensity normalization, which returns back-reconstructed maps in units of percent signal change. 

Resting-state BOLD signal variability calculation estimated resting-state BOLD signal variability using within-participant measures. For each component and each participant, BOLD variability was calculated. Here, we used three brain signal variability measures listed below. 

First, we calculated the standard deviation (SD) of BOLD signals for each component and each participant. 

As a second measure, we estimated the variability of time courses in selected ICs via mean squared successive difference (MSSD) 25, 26. As a non-biased estimation to SD, MSSD re ects moment-to-moment BOLD signal variability that is less sensitive to low-frequency drift 74 and independent from shifts in the mean 4. For each IC and each participant, we subtracted BOLD signals in time point t from time point t + 1, and then squared the average of all subtractions across the entire time series. (Eq. (1): t and t + 1 are two successive time points belonging to the same component time course, n is the number of time points in each component).
section_name: Group independent component analysis

section_content: n -1 t = 1 (x t + 1 -x t ) 2 n -1

As a third measure, the dynamic correlations of brain activity within a component were calculated using a sliding-time window correlation (SWC) approach. 

We selected a window length of 54 TRs (window length > 1/(TR•f min )) to resolve the trade-off between capturing faster dynamic correlations (with shorter windows) and reducing spurious uctuations (with longer windows) 75, 76. The full-length time series (224 TRs) were divided into sliding windows of 54 TRs
section_name: MSSD = ∑

section_content: and shifted with a step size of 10 TRs, resulting in 18 windows for each IC and participant. To estimate dynamic uctuations, we calculated the Pearson correlation between brain signals of all 18 window pairs, averaged across all correlation values, and then subtracted the averaged correlation value from 1 77.
section_name: √

section_content: To examine the relationship between resting-state BOLD signal variability and individual differences in metacontrol policies, we correlated the size of Stroop effect, Stroop RT-CV, RAT scores, AUT exibility scores, and AUT uency scores with brain variability estimated by SD, MSSD, and SWC, respectively. As nine components were included for correlation analysis, Bonferroni correction was used to control for the increased risk of a type I error. Note that the theoretical meaning of the signs/directions of the correlations varies with task scores: Whereas higher scores in the two Stroop measures and the AUT scores imply stronger bias towards exibility (and lower scores stronger bias towards persistence), higher scores in the RAT imply stronger bias towards persistence (and lower scores bias towards exibility).
section_name: Statistical analysis

section_content: Examples
section_name: Declarations Figures

section_name: Acknowledgements

section_content: This work was supported by an Advanced Grant of the European Research Council to B. H. ( ERC-2015-AdG-694722 ) and a 100 Double Talent Grant of the Province of Shandong, China to B. H. and C. B. . C. Z. is funded by the PhD scholarship (201806990039) of the Chinese Scholarship Council.
section_name: Acknowledgements

section_name: Data availability statement

section_content: Data is publicly available in a repository which can be accessed by the following link: https://datarepository. eur. nl/articles/dataset/Individual_differences_in_dis_honesty_are_represented_in_the_brain_s_functional_connectivity_at_rest_/1709132
section_name: Data availability statement

section_name: Author contributions

section_content: C. Z. carried out fMRI data processing and statistical analyses, interpreted the data, drafted the original manuscript. C. B. provided feedbacks on data analysis, interpreted the data, critically revised the manuscript. L. P. carried out behavioral data analysis, contributed to the data interpretation and revision of the manuscript. K. W. contributed to the analysis of fMRI data, data interpretation and revision of the manuscript. S. S., A. S. and M. B. provided behavioral and neuroimaging data, revised the manuscript. B. H. initiated and conceived the study, interpreted the data, and substantively revised the work.
section_name: Author contributions

section_content: In the present study, we used existing data initially collected for a project reported in the article Individual differences in (dis)honesty are represented in the brain's functional connectivity at rest 63. Note that this study, which served other theoretical purposes, included the collection of additional data and measurements that are not reported here. Also note that our study/analytical design and our hypotheses were developed and submitted for ethical approval after the data collection was completed but before we had access to the data. 

The authors declare no con icts of interest. Figure 5 RAT performance was signi cantly (or, in the case of e, almost signi cantly) negatively correlated with brain variability of the parietal and motor network (i. e., IC3), parietal and frontal network (i. e., IC6), frontal and ACC network (i. e., IC9). Brain variability was calculated using SD in (a), (b); brain variability was measured by MSSD in (c) (d) and (e).
section_name: Additional Information