2024; 22(1): 53-66  https://doi.org/10.9758/cpn.22.1035
The Importance of Low-frequency Alpha (8−10 Hz) Waves and Default Mode Network in Behavioral Inhibition
Yong-Wook Kim1,2, Sungkean Kim3, Min Jin Jin1,4, Chang-Hwan Im2, Seung-Hwan Lee1,5
1Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
2Department of Biomedical Engineering, Hanyang University, Seoul, Korea
3Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
4Institute of General Education, Kongju National University, Gongju, Korea
5Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
Correspondence to: Seung-Hwan Lee
Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea
E-mail: lshpss@paik.ac.kr
ORCID: https://orcid.org/0000-0003-0305-3709

Chang-Hwan Im
Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
E-mail: ich@hanyang.ac.kr
ORCID: https://orcid.org/0000-0003-3795-3318
Received: October 20, 2022; Revised: January 13, 2023; Accepted: February 17, 2023; Published online: May 30, 2023.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Objective: Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state.
Methods: We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated.
Results: Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN.
Conclusion: Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.
Keywords: Low alpha; Default mode network; Behavioral inhibition; Functional network
INTRODUCTION

The behavioral inhibition is an active inhibitory mechanism allowing us to withhold unwanted or prepotent responses that are triggered by internal or external stimuli [1]. Behavioral inhibition is associated with decision making, error correction, response inhibition, and ordinary life without danger [2]. In addition, attention deficit hyperactivity disorder (ADHD) patients showed low behavioral inhibition [3] and anxiety disorder patients show-ed high behavioral inhibition [4]. In addition, the failure of the inhibitory processes could also result in impulsive action [5]. Several studies investigated the behavioral inhibition using electroencephalography (EEG) with a high temporal resolution, adequate for analyzing the spontaneous changes in brain activity [6-11].

It has been repeatedly reported that large alpha wave amplitudes were mainly observed during inhibition control [6-9]. Knyazev and Slobodskaya [7] mentioned that the alpha power, which was prepared for unusual external stimulation, should be related to behavioral inhibition, characterized by inattention and impulsivity. Händel et al. [8] observed strong hemispheric specific alpha increase during an inhibitory task with an unattended stimulus. Klimesch et al. [9] suggested that synchronized alpha activity reflected a state of inhibition, and it was related to a certain top-down inhibitory control of cortical activation.

Although many studies explored the neuroscientific background of behavioral inhibition, further studies were needed to uncover the origin of the inhibition [4,12]. While most inhibition studies were conducted under a specific task condition, a resting-state task-free study is necessary to explore underline mechanisms of trait inhibition and personal functioning, which have not been studied much [13]. Some of the innate visceral inhibition factors associated with a person’s self-control will differ slightly from the outcome of specific tasks [14]. In order to study person’s trait inhibition, several studies have evaluated questionnaires related with inhibition [11,14,15]. Because these questionnaires reflected person’s trait without any tasks, task-free resting-state brain activity will be appropriate for this study. The research investigating behavioral inhibition in resting-state is necessary for uncovering the mechanisms of trait inhibition.

For investigating brain functions and activities during the resting-state, default mode network (DMN) studies have been performed with functional magnetic resonance imaging (fMRI), positron emission tomography, and EEG [16-20]. The DMN is the functional network whose activity is decreased during a goal-directed task when compared with activity during resting-state [21]. The DMN generally was known as associate with several mental states such as relaxed state of quiet repose, daydreaming, and mind wandering [21]. The DMN regions include medial prefrontal cortex, precuneus, anterior/posterior cingulate cortex, lateral temporal cortex, inferior parietal lobe, and hippocampus [21,22]. Graph theory has been introduced recently as a method to construct human brain networks [23]. Several fMRI studies investigated relationships between behavioral inhibition and DMN functional networks [24-26] which were calculated with graph theory. In Li et al. [24], the activation or decreased deactivation of DMN regions was observed before stop-errors, which reflected deficient response inhibition during the stop-signal task. Also, the stop-signal reaction time, inversely correlated with inhibition, showed positive correlations on activations of DMN regions [25]. According to Bonnelle et al. [26], during a stop-signal task, the group of patients with traumatic brain injury who showed more activation in the DMN regions, had slower response inhibition than the DMN deactivated group. Although these articles show-ed the relationships between behavioral inhibition and DMN, most of them also were inhibition task researches.

To study the mechanisms of the trait inhibition, this research investigates the relations among alpha wave, DMN, and behavioral inhibition in resting-state. We hypothesize that alpha power and alpha functional network of DMN could reflect trait behavioral inhibition. We will conduct our exploratory study, researching behavioral inhibition, by using resting-state EEG-source networks such as DMN. We will study only DMN because the DMN is primarily related to resting-state. Moreover, the DMN nodes will be mapped into Destrieux atlas to discover the detailed functional connectivity. Also, in Fink et al. [27], alpha with lower frequency band (8−10 Hz) was presumably reflecting general task demands and alpha with higher frequency band (10−12 Hz) was more likely to reflect specific task requirements. Therefore, the alpha band with total/lower/higher frequency should be researched, which might show a different activity in our task-free study. In this study, the relationships between alpha band powers and behavioral inhibition will be observed first. After then, the relationships between functional network measures of DMN and behavioral inhibition will be calculated for investigating brain functional connectivity. Furthermore, the correlations among the behavioral inhibition, alpha power, and DMN functional networks will be explored.

METHODS

Participants

A 104 healthy volunteers participated in our study (45 males and 59 females; mean age: 27.39 ± 6.34 years). The participants were recruited from the local community posting the opportunity in local newspapers and posters. A researcher conducted a face-to-face screening interview in a semi-structured fashion. The exclusion criteria for the study were: the presence of neurological (subjective cognitive decline, history of head trauma, loss of consciousness, and any central nervous system illness) and psychiatric (depressive or anxiety disorders, and any psychotic episodes) history and treatment. This study and all experimental protocols were approved by the Institutional Review Board and Ethics Committee of Inje University Ilsan Paik Hospital (IRB no. 2015-07-026-001). The study was performed in accordance with approved guidelines. Informed consent was obtained from all study participants.

Psychological Measures

The validated Korean version of behavioral inhibition/ behavioral activation scale (BIS/BAS) [28,29], and Barratt impulsivity scale [30,31] were used to evaluate the level of inhibition. The BIS/BAS is a self-report scale measuring the dysregulations of behavioral inhibition and activation. The BIS/BAS comprises 20 self-rating items, including the Behavioral Inhibition System (7 items) and 3 subtypes of the Behavioral Activation System: reward responsiveness (5 items), drive (4 items), and fun-seeking (4 items). The higher scores of BAS reflect higher sensitivity to rewards associated with the behavior. The higher scores of BIS reflect higher sensitivity to the potential risk associated with the behavior. The Barratt impulsivity scale is a self-report scale measuring the personality and behavioral constructs of impulsiveness. It comprises 30 items divided by 3 sub- factors: attentional, motor, and non-planning impulsivity. The higher scores on the Barratt Impulsivity scale reflect higher impulsivity.

Electroencephalography Recording and Pre-processing

Resting-state EEG was recorded and analyzed in this study. The participants were noticed not to smoke and drink coffee and alcohol before the EEG recording. Every experiment was performed in a slightly dim room. During the experiments, every participant was seated on a chair and was asked not to move or sleep. The EEG was recorded for 3 minutes while the participants closed their eyes. The EEG was recorded using the NeuroScan SynAmps amplifier (Compumedics USA) with 62 Ag/AgCl electrodes. Each electrode was placed according to the extended international 10−20 system on NeuroScan Quik-cap. The sampling rate of this device was set at 1,000 Hz, and 0.1−100 Hz analog band-pass filter was applied. The impedance of all the electrodes was carefully maintained below 5 kW. The ground electrode was placed on the forehead, and the reference electrodes were placed on both mastoids. Electrooculography (EOG) was also recorded from electrodes placed above and below the right eye.

The EEG data were pre-processed using CURRY 7 software (Compumedics USA) and Matlab R2016b (MathWorks). Baseline correction was performed by removing each channel’s DC offset (the mean amplitude of a waveform). Unexpectedly recorded eyeblink artifacts were removed using the regression methods implemented in CURRY 7 [32]. The eyeblink artifacts were first detected by each participant’s specific thresholds (200 mV). In every sample of each channel data, eyeblink artifacts were calculated as covariance (EOG, EEGch)/variance (EOG) × EOG where EOG means averaged detected eyeblink artifacts and EEGch means each channel EEG, and were subtracted from baseline corrected data. Then the remaining gross artifacts were rejected based on visual inspection by a trained person without any prior information regarding the data. The mean length of EEG was 175.67 ± 18.34 seconds after gross artifact rejection. The pre-processed EEG data were then filtered using a 1−50 Hz cutoff 6th order butterworth band-pass filter using Matlab R2016b (Math-Works). After the application of common average reference, the processed EEG data were divided into several two-second length epochs. Epochs with large physiological artifacts (± 100 mV) at any electrodes were ex-cluded. For each epoch, the relative powers of total-alpha band (TA band; 8−12 Hz), low-alpha band (LA band; 8−10 Hz), high-alpha band (HA band; 10−12 Hz), theta band (4−8 Hz), beta band (12−30 Hz), and gamma band (30−50 Hz) relative to the whole-band power (1−50 Hz) were calculated for each channel using the Fast Fourier Transform and then averaged across all channels. Sixty epochs were randomly selected for each participant for further analyses.

Go/no-go Experiment

All the participants were asked to remain seated on a chair approximately 60 cm away from a computer screen (Mitsubishi, 22-inch CRT monitor). For each trial, a fixation cross was first presented on the monitor for 100 ms, and 700−1,000 ms intervals followed. The visual go/ no-go stimuli were randomly presented for 500 ms after that. The inter-trial interval was 500 ms. The visual stimuli of the go/no-go task were the number 1−8. The participants were instructed to press a space bar when the go stimuli (even numbers: 2, 4, 6, and 8) were presented but not to respond to no-go stimuli (odd numbers: 1, 3, 5, and 7). The numbers of go and no-go stimuli were 240 (80%) and 60 (20%), respectively. This paradigm was run by E-Prime 2.0 (Psychology Software Tools).

Default Mode Network

The DMN regions and its coordinates were selected ahead from 17 articles related to DMN [16-21,33-43]. Each article was found at Google ScholarTM with a keyword “default mode network,” and the selection criteria were articles with over 500 citations or references that were directly related to DMN coordinates of the articles. The articles without DMN coordinates were excluded from the study. The coordinates from these articles were first converted from Talairach to MNI coordinates (http:// imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach) and placed onto the Colin27 standard template. After that, according to the Destrieux atlas and Aseg atlas, all the regions of each coordinate were redefined. Only regions mentioned more than 3 times in the research articles, regardless of hemisphere, were selected as DMN because too many regions were included in DMN. The right posterior-dorsal cingulate gyrus was excluded from DMN because there were no coordinates referred to in these articles belonging to that region. Finally, 25 DMN regions were selected for further analyses (Fig. 1). The selected DMN regions included anterior cingulate cortex (number 1, 2), middle-posterior cingulate cortex (3, 4), posterior-dorsal cingulate gyri (5), middle frontal gyrus (6, 7), superior frontal gyrus (8, 9), lingual gyrus (10, 11), middle occipital gyrus (12, 13), angular gyrus (14, 15), precuneus (16, 17), subparietal sulcus (18, 19), middle temporal gyrus (20, 21), superior temporal sulcus (22, 23), hippocampus (24, 25) from both hemispheres except right posterior-dorsal cingulate gyrus. The coordinates in the same region were integrated as one, by calculating the center of the coordinates. The figure was created with Matlab R2016b (MathWorks).

Source Localization

To evaluate a time-series of source-reconstructed EEG, the depth-weighted L2 minimum norm estimator implemented in Brainstorm toolbox was used [44]. Because source analysis of the hippocampal activity required at least 45 epochs [45], we used 60 epochs to analyze it. A new template composed of Colin27 standard template with deep brain structure offered by Brainstorm was used to analyze deep brain sources in the hippocampus [45]. A three-layer boundary element method model was constructed from the integrated template by using OpenMEEG project software [46]. The lead field matrix was computed with this boundary element method model. Cortical current density values at 30,020 vertices were evaluated for every time-point of each epoch with constrained source orientations. Twenty-three cortical source-reconstructed EEG and 2 deep source-reconstructed EEG (bilateral hippocampus) were calculated based on the DMN coordinates. The representative value of each node was evaluated by using principal component analysis of the cortical sources located in a 5 mm radius of each DMN coordinates.

Connectivity and Network Analysis

The functional connectivity between each pair of nodes was evaluated using phase-locking values (PLVs). PLVs were used to measure the phase synchronization between signals from each pair of nodes regardless of its amplitude and were used to form a weighted functional connectivity network.

In this study, we performed a weighted network analysis based on graph theory. The use of weighted networks instead of binarized networks preserves the unique characteristics of the original network without distortion [47]. A network is composed of several nodes that are connected at their edges. Four different global level weighted network measures (clustering coefficient [CC], path length [PL], global efficiency [GE], global strength [GS]) were evaluated [23]. CC indicates the degree of which a node is clustered with its neighboring nodes. The formula of CC is:

CC =1n∑i∈N2tiki(ki− 1)

where n means the number of nodes, N is the set of all links in the network, i is nodes, ti means geometric mean of triangles around node i, and ki means weighted degree. PL shows the overall connectedness of the whole network and is calculated as the sum of lengths between 2 nodes in the entire network, as follows:

PL =1n∑i∈N∑j∈N, j≠idij(n− 1)

where dij means distance between nodes i and j. GE refers to the efficiency of information processing in the brain with this formula below:

GE=1n∑i∈N∑j∈N, ji(dij)−1(n− 1)

GS also known as degree, refers to the degree of connection strength in the network. It is estimated by summing up the weight of links connected to the brain regions:

GS=1n∑i∈N∑j∈Nwij

where wij is connection weight (PLV value) between nodes i and j. The weighted nodal CC alone was evaluated the formula inside sigma in (1) for each node. All the functional network measures were calculated by Matlab toolbox; brain connectivity toolbox [23].

Statistical Analyses

The number of participants was calculated by power analysis with G*power 3.1.9.7 software [48]. The minimum sample size was calculated with effect size (eta square; large effect size): 0.14, alpha: 0.05, power: 0.8, and 4 response variables since we analyzed 4 global network measures. The calculated minimum total sample size was 60. Because we divided all participants into 3 groups, the minimum sample size for each group was 20, which is equal to 60 divided into 3 groups. We divided all participants into 3 groups according to the relative alpha band power (i.e., LA band, HA band, and TA band), theta band power, beta band power, and gamma band power. The number of participants in each group was set to be as equivalent as possible. Because the total number of participants was 104, the number of participants in the 3 groups was set to be 35, 34, and 35. In order to divide participants into 3 groups, participants were first sorted by the relative alpha power in ascending order, respectively. From the first to the 35th participants, based on the relative band power, were set as low band power group. From the 36th to the 69th participants were set as middle band power group. From the 70th to the last participants were set as high band power group. Statistical tests were applied to investigate whether there were any statistical differences in demographic/behavioral data among the 3 groups. The statistical tests were performed for LA band, HA band, and TA band, respectively.

The chi-square test was conducted to compare the sex ratio of the 3 groups. A one-way analysis of variance (ANOVA) was conducted to compare age, years of education, scores of psychological/behavioral data among the 3 groups with permutation test [49] to adjust for multiple testing. For the permutation test, the members of the groups were randomly shuffled 5,000 times across the groups. The distribution of F statistics is obtained by calculating F statistics for each random shuffling. The p values were calculated as proportions of the number of F statistics that were greater than or equal to the original F statistics to overall distributions. Because the global/nodal network measures could not pass the Shapiro–Wilk test for normality, the Kruskal–Wallis test was conducted to compare the global/nodal network measures among the 3 groups with the permutation test. The variables showing significant differences among the 3 groups were further analyzed using post-hoc pairwise comparisons with Mann– Whitney U test and the Bonferroni correction. Besides, the relationships between network indices and inhibition- related measures were analyzed by Spearman’s correlation across 104 subjects with the permutation test [50] (Fig. 2; which was created with Matlab R2016b [MathWorks]). The random shuffling was performed 5,000 times, and rho distributions were used for this permutation test. Statistical analyses were performed using SPSS 21 (IBM Co.) and Matlab 2016b (MathWorks).

RESULTS

Demographic Results

We divided all participants into 3 groups according to the relative band power. Three different types of groups were tested because we evaluated the relative powers of the TA band, LA band, and HA band. For each group type, chi-square test and one-way ANOVA were applied to investigate whether there were any statistical differences in demographic/behavioral data among the 3 groups. Be-cause the behavioral inhibition measures showed significant differences among groups divided based on LA power alone (Table 1, Supplementary Tables 1-5, Fig. 3 [51], Supplementary Fig. 1), the following analysis was conducted based on LA power alone. The participants were divided into 3 groups based on LA power: low LA power group (n = 35, mean relative LA power = 11.16 ± 2.79 dbm), middle LA power group (n = 34, mean relative LA power = 14.65 ± 1.65 dbm), and high LA power group (n = 35, mean relative LA power = 16.66 ± 1.94 dbm). Table 1 showed the demographic and psychological characteristics of the low, middle, and high LA groups’ participants. The BIS from Gray’s BIS/BAS [28] was significantly higher in the high LA group than in the low LA group (21.80 ± 2.79 vs. 20.31 ± 2.39, p = 0.036; one-way ANOVA). The no-go hit rate was significantly higher in the high LA group than in the middle LA group and in the low LA group (90.71 ± 8.00 vs. 85.74 ± 9.82 vs. 84.76 ± 12.51, p = 0.038; one-way ANOVA). The other demographic and psychological characteristics showed no significant differences.

Global/nodal Level Differences in the Functional Network of Default Mode Network

We selected 25 DMN regions (Fig. 1) for calculating source level functional connectivity and network measures of DMN. The functional connectivity was evaluated using PLVs and 4 different weighted network measures (CC, PL, GE, GS) [23] were evaluated. For the nodal level tests, only the weighted nodal CCs were evaluated. The functional network measures for DMN were analyzed in the LA band because the LA band alone showed significant group differences (Table 2). The global CC, GE, and GS were significantly increased in the high LA group than in the low and middle LA groups (CC: 0.532 ± 0.020 vs. 0.592 ± 0.045 vs. 0.659 ± 0.060, p < 0.001; GE: 0.500 ± 0.020 vs. 0.560 ± 0.045 vs. 0.625 ± 0.059, p < 0.001; GS: 12.991 ± 0.471 vs. 14.428 ± 1.074 vs. 16.009 ± 1.419, p < 0.001; Kruskal–Wallis test). The low LA group and middle LA group also had significant differences in CC, GE, and GS after post-hoc analysis. The PL was significantly decreased in the high LA group than in the other groups (PL: 2.070 ± 0.075 vs. 1.868 ± 0.131 vs. 1.672 ± 0.157, p < 0.001; Mann–Whitney U test). The middle LA group also showed significantly longer PL than the low LA group after post-hoc analysis. Table 2 also presents comparisons of nodal CCs among the high, middle, low LA groups. All the 25 DMN regions showed significant increases in nodal CCs after post-hoc in the high LA group than in the other groups. All the p values of the entire nodal CCs were < 0.001.

The average LA band nodal CCs and their average PLVs are depicted in a graph in Figure 4. Across all the subjects, left precuneus showed the highest CC values, and left angular gyrus, left middle occipital gyrus, right lingual gyrus, and right subparietal sulcus showed the next highest CC values. The high PLV values were generally observed among the parietal, occipital, and temporal regions, including the left superior temporal sulcus, right lingual gyrus, and left middle occipital gyrus. These trends were similarly observed in all groups (Fig. 4B).

Correlations between Network Measures with Behavioral Inhibition Scale Score

All the global network measures significantly correlated with the BIS score (CC: rho = 0.249, p = 0.011; PL: rho = −0.248, p = 0.010; GE: rho = 0.248, p = 0.011; GS: rho = 0.248, p = 0.011; Spearman’s correlation) (Fig. 2). The correlations between the nodal CCs and BIS scores were also evaluated (Table 3). Among the 25 correlations, 23 correlations with CC showed a significant difference (p < 0.05) after the permutation test. Especially, the left middle frontal gyrus and left superior frontal gyrus presented a correlation coefficient over medium effect size (r > 0.3) [52]. The comparison of correlation between BIS score and source power, and between BIS score and CC are presented in Table 3. There were no significant correlations between TA or HA band global network measures and the BIS score. 

DISCUSSION

Our study explored the relationships among the behavioral inhibition, power of alpha bands (LA, HA, and TA bands), and DMN. The principal outcomes were revealed only in the LA band: (1) the high LA group showed a significantly higher score of BIS and a higher no-go hit rate than the low LA group; (2) the increased values of global CC, GE, and GS, and decreased value of PL were observed in the high LA group alone compared with the low and middle LA groups; (3) significant correlations were found between BIS and almost all network measures of DMN of the LA band; and (4) the LA band appears to be a major representative of inhibitory function, compared with HA frequency bands.

The high LA group showed a significantly higher score of the BIS, and higher no-go hit rate than the low LA group. The BIS and no-go hit rate are directly representing behavioral inhibition [2,28], and the BIS could reflect the personality trait of behavioral inhibition and include items about introversion and anxiety [7,53]. According to the outcomes and these articles, the LA power would be positively related to the behavioral inhibition function and the personality trait of behavioral inhibition. Several articles support this. Knyazev et al. [53] reported the higher ratio of LA power to HA power (over 1) in subjects with high trait-anxiety before (i.e., resting-state) and during the alarm session. In this article, they also reported that the ratio of LA power to HA power was less than 1 in low trait- anxiety subjects before and during the alarm session. Since the anxious individuals should be more prepared for new or strange environments (i.e., alarm session), they did better with behavioral inhibition. Furthermore, Arns et al. [54] reported that slow individual alpha peak frequency (< 9 Hz) was observed in children with ADHD compared with healthy controls. Because the children with ADHD showed impairments in response inhibition, and indivi-dual alpha peak frequency was considered to be a trait (3), this observation supports our finding that the LA band was positively related to BIS.

The high LA group showed stronger functional network measures of DMN at both global and nodal levels than the low and middle LA groups. The global CC, GE, and GS were significantly increased, and PL was significantly decreased in the high LA group. In the present study, the PLVs were calculated for assessing the network measures. Although the PLV was the absolute value for measuring phase differences of signals alone [55], significant correlations between band power and network measures were also reported [56]. Because groups were divided based on LA power differences, the between-group differences in functional network measures of DMN might be considered a natural result. The notable thing is, however, the DMN networks were more significantly correlated with BIS scores than the source level power. These results support that the DMN network better reflects the behavioral inhibition than source level LA power.

In our results, the BIS score was positively correlated with the global CC, GE, and GS of DMN and negatively correlated with global PL of DMN. Moreover, the 23 nodal CCs out of 25 DMN nodes showed significant positive correlations with the BIS score. These results were consistent with several fMRI findings. Olivo et al. [57] showed that the BIS prominence participants (with high BIS/BAS ratio and risk allele) had more active connectivity of the DMN evaluated in resting-state. Furthermore, Janssen et al. [58] also reported that healthy individuals showed a significant increase in LA power in some DMN regions, such as the right inferior frontal region and bilateral cuneus, than ADHD patients during resting-state. In our results, among 23 significant correlations, 2 correlation coefficients from the left middle frontal gyrus and left superior frontal gyrus were over the medium effect size (r > 0.3). Several previous studies reported that these 2 regions were associated with inhibition [59-62]. A lesion study showed that patients with focal lesions on left superior frontal regions (Brodmann area 6) responded more impulsively to no-go stimuli (false alarm) than healthy participants during the go/no-go task [59]. Previous studies compared brain activations between the healthy controls and people who had difficulty inhibiting their behavior (lysergic acid diethylamide intake participants [60], abstinent male heroin-dependent patients [61], and substance users with a family history of substance use disorder [62]) during go/no-go task. Their results showed more fMRI activations in the left superior frontal and left middle frontal regions during the no-go task compared with a go task. Furthermore, the healthy controls showed more fMRI activations than the people with inhibition difficulty.

There are some limitations in this study. Because we divided all participants into 3 groups, the participants on the border could be overlapped. However, in this case, the low LA group and high LA group were more clearly distinguished in terms of LA power because the middle LA group could be regarded as the buffer. In addition, several socio-demographic (e.g., working status or marital status) and clinical variables that could be associated with behavioral inhibition were not evaluated in this study. Handedness of participants, which could affect resting- state EEG laterality, also was not checked. Although this study evaluated some demographic variables (age, gender, educational background) and behavioral measures (BIS/BAS, Barratt Impulse Scale, hard-hit rate), study with more socio-demographic and clinical information could be firmly supported the results. Also, only the healthy population were participating in this study. Therefore, these results may not be seen in patients suffering from mental disorders.

Our findings suggested that LA power and LA functional DMN could represent trait behavioral inhibition, and LA power and DMN of LA could represent people’s function of behavioral inhibition. Because behavioral inhibition is related with several mental illness such as ADHD and anxiety disorder, our findings will help the researchers to understand and investigate these diseases. Further studies are required to compare the relationships among the behavior inhibition, functional network measures between DMN and other functional networks (e.g., central executive networks).

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: Chang-Hwan Im, Seung-Hwan Lee. Data acquisition: Min Jin Jin. Formal analysis: Yong-Wook Kim, Sungkean Kim. Writing—original draft: Yong-Wook Kim. Writing—review & editing: Chang-Hwan Im, Seung- Hwan Lee.

Figures
Fig. 1. The 25 default mode network regions and seed points are based on the Destrieux atlas. The Colin27 stand-ard template was from the Brain-storm toolbox (http://neuroimage.usc.edu/brainstorm).
Fig. 2. Significant correlations of BIS score with global network measures of LA band (8−10 Hz). The p values are adjusted with the permutation test.
Fig. 3. Violin plots of demographic characteristics: BIS, no-go hit rate, TA, LA, and HA power from each group participants. BIS score in BIS, no-go hit rate in no-go, and relative power (%) in TA, LA, and HA. The violin plots were created with external toolbox [51].
BIS, Behavioral Inhibition Scale; TA, total-alpha; LA, low-alpha; HA, high-alpha.
*Indicates its p value < 0.05, ***indicates its p value < 0.001.
Fig. 4. A glance of the 25 regions of DMN represented by nodal CCs and PLVs. (A) CCs and PLVs of total participants. (B) CCs and PLVs of each group in the LA band (8−10 Hz). colour-code areas represent the CCs, and the blue lines represent the PLV linking 2 regions. The purple, red, green, brown, blue, and black colours indicate the cingulate, front-al, occipital, parietal, temporal, and hippocampus regions, respectively. The height and brightness of the area indicate the CC strength of each region, and the line width and bright-ness also indicate the PLV strength linking 2 regions. For visual simpli-city, only the top 10% of the average PLVs are presented.
DMN, default mode network; LA, low-alpha; CC, clustering coefficient; PLV, phase-locking values.
Tables

Demographic and behavioral characteristics of groups by LA (8−10 Hz) power

Characteristic Low LA groupa (n = 35) Middle LA groupb (n = 34) High LA groupc (n = 35) p value Post-hoc (Bonferroni)
Age (yr) 26.20 ± 5.02 28.94 ± 7.85 27.09 ± 5.73 0.188
Sex 0.462
Male 18 (51.4) 14 (41.2) 13 (37.1)
Female 17 (48.6) 20 (58.8) 22 (62.9)
Education (yr) 14.40 ± 1.67 14.09 ± 1.75 14.54 ± 1.84 0.548
BIS/BAS
BAS reward responsiveness 17.00 ± 6.45 16.14 ± 2.15 16.23 ± 2.25 0.640
BIS 20.31 ± 2.39 20.97 ± 1.85 21.80 ± 2.79 0.036* a < c
BAS drive 8.09 ± 1.70 8.03 ± 1.36 8.83 ± 1.79 0.079
BAS fun-seeking 10.54 ± 2.25 10.32 ± 2.28 10.83 ± 2.29 0.653
Barratt impulsivity scale
Attentional impulsivity 16.51 ± 3.69 16.47 ± 3.45 16.66 ± 3.67 0.975
Motor impulsivity 25.74 ± 4.34 26.17 ± 4.11 23.91 ± 4.72 0.080
Non-planning impulsivity 17.83 ± 3.40 18.12 ± 4.04 18.69 ± 3.44 0.606
Total 60.09 ± 9.06 60.76 ± 9.59 59.26 ± 8.83 0.791
No-go hit rate 84.76 ± 12.51 85.74 ± 9.82 90.71 ± 8.00 0.038* a < b, b < c

Values are presented as mean ± standard deviation or number (%).

The chi-square test was conducted to compare the sex ration of the 3 groups. A one-way ANOVA was conducted to compare age, years of education, scores of psychological/behavioral data among the 3 groups with permutation test.

LA, low-alpha; BIS/BAS, Gray’s behavioral inhibition/behavioral activation scale.

a,b,cThe 3 groups of participants in the study.

*The significant p values (p < 0.05).

Comparisons of the global network and nodal CCs among groups by LA (8−10 Hz) power

Network measures Low LA groupa (n = 35) Middle LA groupb (n = 34) High LA groupc (n = 35) p value* Post-hoc (Bonferroni)
Global network measures
CC 0.53 ± 0.02 0.59 ± 0.04 0.69 ± 0.06 < 0.001 a < b, b < c
PL 2.07 ± 0.07 1.86 ± 0.13 1.62 ± 0.15 < 0.001 a > b, b > c
GE 0.50 ± 0.02 0.56 ± 0.04 0.65 ± 0.05 < 0.001 a < b, b < c
GS 12.99 ± 0.47 14.42 ± 1.07 16.09 ± 1.41 < 0.001 a < b, b < c
Nodal network CC
L anterior cingulate cortex 0.52 ± 0.01 0.55 ± 0.04 0.55 ± 0.04 < 0.001 a < b, b < c
R anterior cingulate cortex 0.52 ± 0.02 0.58 ± 0.05 0.67 ± 0.07 < 0.001 a < b, b < c
L middle-posterior cingulate cortex 0.53 ± 0.02 0.56 ± 0.04 0.62 ± 0.06 < 0.001 a < b, b < c
R middle-posterior cingulate cortex 0.54 ± 0.02 0.60 ± 0.04 0.67 ± 0.06 < 0.001 a < b, b < c
L posterior-dorsal cingulate gyrus 0.53 ± 0.02 0.57 ± 0.05 0.64 ± 0.06 < 0.001 a < b, b < c
L middle frontal gyrus 0.51 ± 0.01 0.56 ± 0.05 0.64 ± 0.07 < 0.001 a < b, b < c
R middle frontal gyrus 0.52 ± 0.02 0.58 ± 0.05 0.66 ± 0.07 < 0.001 a < b, b < c
L superior frontal gyrus 0.51 ± 0.02 0.55 ± 0.04 0.62 ± 0.05 < 0.001 a < b, b < c
R superior frontal gyrus 0.50 ± 0.01 0.54 ± 0.03 0.60 ± 0.07 < 0.001 a < b, b < c
L lingual gyrus 0.52 ± 0.02 0.58 ± 0.06 0.65 ± 0.06 < 0.001 a < b, b < c
R lingual gyrus 0.55 ± 0.03 0.63 ± 0.05 0.69 ± 0.06 < 0.001 a < b, b < c
L middle occipital gyrus 0.54 ± 0.03 0.62 ± 0.05 0.70 ± 0.06 < 0.001 a < b, b < c
R middle occipital gyrus 0.49 ± 0.01 0.54 ± 0.03 0.60 ± 0.06 < 0.001 a < b, b < c
L angular gyrus 0.55 ± 0.02 0.63 ± 0.06 0.70 ± 0.06 < 0.001 a < b, b < c
R angular gyrus 0.52 ± 0.02 0.59 ± 0.05 0.67 ± 0.06 < 0.001 a < b, b < c
L precuneus 0.56 ± 0.02 0.63 ± 0.05 0.69 ± 0.06 < 0.001 a < b, b < c
R precuneus 0.54 ± 0.02 0.60 ± 0.05 0.65 ± 0.06 < 0.001 a < b, b < c
L subparietal sulcus 0.54 ± 0.02 0.61 ± 0.04 0.66 ± 0.06 < 0.001 a < b, b < c
R subparietal sulcus 0.55 ± 0.02 0.63 ± 0.05 0.68 ± 0.06 < 0.001 a < b, b < c
L middle temporal gyrus 0.52 ± 0.01 0.55 ± 0.04 0.61 ± 0.06 < 0.001 a < b, b < c
R middle temporal gyrus 0.50 ± 0.01 0.55 ± 0.04 0.62 ± 0.06 < 0.001 a < b, b < c
L superior temporal sulcus 0.54 ± 0.02 0.60 ± 0.05 0.68 ± 0.07 < 0.001 a < b, b < c
R superior temporal sulcus 0.52 ± 0.02 0.59 ± 0.05 0.67 ± 0.06 < 0.001 a < b, b < c
L hippocampus 0.54 ± 0.02 0.61 ± 0.05 0.69 ± 0.06 < 0.001 a < b, b < c
R hippocampus 0.54 ± 0.02 0.62 ± 0.05 0.70 ± 0.06 < 0.001 a < b, b < c

Values are presented as mean ± standard deviation.

The Kruskal–Wallis test was conducted to compare the global/nodal network measures among the 3 groups with the permutation test and post-hoc pairwise comparisons was performed with Mann–Whitney U test.

CC, clustering coefficient; PL, path length; GE, global efficiency; GS, global strength; LA, low-alpha; L, left; R, right.

a,b,cThe 3 groups of participants in the study.

*p value with permutation test.

Comparison of the correlation scores using BIS between regional source power and network measure (CC) in LA band (8−10 Hz) by performing Spearman’s correlation

Regions BIS/source power BIS/CC
Rho p value Rho p value
L anterior cingulate cortex 0.185 0.053 0.180 0.063
R anterior cingulate cortex 0.222 0.020* 0.280 0.004*
L middle-posterior cingulate cortex 0.216 0.025* 0.198 0.043*
R middle-posterior cingulate cortex 0.188 0.052 0.196 0.043*
L posterior-dorsal cingulate gyrus 0.215 0.027* 0.253 0.008*
L middle frontal gyrus 0.244 0.010* 0.304* 0.002*
R middle frontal gyrus 0.166 0.090 0.244 0.012*
L superior frontal gyrus 0.199 0.042* 0.300* 0.002*
R superior frontal gyrus 0.165 0.088 0.259 0.008*
L lingual gyrus 0.245 0.010* 0.297 0.001*
R lingual gyrus 0.164 0.096 0.219 0.026*
L middle occipital gyrus 0.221 0.022* 0.246 0.012*
R middle occipital gyrus 0.182 0.060 0.229 0.021*
L angular gyrus 0.235 0.012* 0.242 0.014*
R angular gyrus 0.186 0.053 0.213 0.027*
L precuneus 0.192 0.050 0.229 0.020*
R precuneus 0.193 0.047* 0.244 0.014*
L subparietal sulcus 0.144 0.143 0.199 0.044*
R subparietal sulcus 0.162 0.098 0.205 0.038*
L middle temporal gyrus 0.194 0.042* 0.283 0.004*
R middle temporal gyrus 0.198 0.039* 0.192 0.042*
L superior temporal sulcus 0.175 0.072 0.176 0.072
R superior temporal sulcus 0.182 0.062 0.226 0.018*
L hippocampus 0.194 0.046* 0.226 0.021*
R hippocampus 0.191 0.050 0.221 0.026*

BIS, Behavioral Inhibition Scale; CC, clustering coefficient; LA, low-alpha; L, left; R, right.

The p value is adjusted with permutation tests.

*The significant p values (p < 0.05) and correlation coefficients over medium effect size (r > 0.3).
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