Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition.

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

Introduction

The ability to refrain from the action when a readied response is no longer adequate is one of the crucial survival skills for an individual. Therefore, it is important to evaluate an individual’s ability to withhold an action [1]. In the laboratory, one of the conventional paradigms developed to evaluate an individual’s such inhibition efficacy is the stop-signal task (SST) [2, 3]. The SST assesses the motor inhibition by instructing participants to withhold a motor response, and how effectively the participants can withhold this response can be calculated as the stop-signal reaction time (SSRT) [4]. Utilizing this paradigm, researchers have observed decreased motor inhibition in various clinical disorders, such as attention deficit hyperactivity disorder [5] and substance use disorders [6, 7]. The deficiency in motor inhibition can also be found in normal aging [8-13].

Neuroimaging research has identified brain regions associated with motor inhibition, such as the right inferior frontal cortex (rIFC; [14, 15]), right frontal-opercular regions (including the right inferior frontal cortex and the right anterior insula; [16]), subcortical regions (thalamus and basal ganglia; [17]), and presupplementary motor areas [18]. These studies employed the task-evoked functional magnetic resonance image (fMRI) technique, in which an individual’s brain activations were quantified and correlated with task performance. However, in some scenarios, individuals, such as clinical patients or the elderly, may have difficulties in completing the SST, a time-consuming computer task, in a scanner. Therefore, developing an alternative neuroimaging technique to evaluate an individual’s performance variance without time-consuming SST inside an MRI scanner is useful for clinical applications (e.g., [19, 20]) as well as for the assessment of individual differences among the normal population (e.g., [21, 22]). The resting-state fMRI measures brain activity in a task-free setting, estimates an individual’s intrinsic functional organization of the brain [23]. Acquisition of neuroimaging data at rest (i.e., resting-state fMRI) may be helpful to capture the individual differences of performance variance in cognitive performance (e.g., [24]).

Resting-state fMRI (rs-fMRI) reflects the spontaneous but organized synchrony of the low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signals emerging from some brain regions at rest [25]. The analysis of temporal correlations between the spontaneous BOLD-signals in different brain regions allows for the quantification of functional connectivity [26] and the investigation of intrinsic connectivity networks [27]. Important advantages of the rs-fMRI approach are that rs-fMRI can minimize confounds of differences in the level of task engagement, be used to detect differences in brain function between certain patients and normal populations, and correlate the differences in functional connectivity to clinical applications.

Given the advantages of the rs-fMRI approach, some (but not many) prior research has employed this method to correlate the intrinsic functional connectivity derived from rs-fMRI data with motor inhibition performance measured by the SST. However, this research used different analysis methods. For example, Tian and Ren [28] used regional homogeneity (ReHo) as a method to examine the relationship between performances of motor inhibition and changes of spontaneous fluctuations in brain activity. ReHo is a voxel-based measure of brain activity that evaluates the similarity or synchronization between the time series of a given voxel and its nearest neighbors [29, 30]. Tian and Ren [28] found local synchronization of the spontaneous fluctuations in brain activity changes in three brain regions within the default mode network––that is, the bilateral medial prefrontal cortex, the bilateral precuneus, and the left inferior parietal lobule were associated with performance measured by SST. Their results based on ReHo can be understood as an index of network centrality for characterizing the voxel-wise consistency of signals in a region in the human functional connectome.

Other analysis methods, such as the fractional amplitude of low-frequency spontaneous fluctuations (fALFF) in brain activity have also been employed and associated with SSRT. For example, Hu and Chao [31] used the fALFF analysis method and found that the pre-supplementary motor area (pre-SMA) and sensorimotor area were negatively correlated to SSRT. More recently, Lee and Hsieh [12] further jointly combined these two analysis methods (i.e., ReHo and fALFF) and have reported that bilateral inferior frontal gyrus and parts of the default mode network were correlated with individual differences in SSRT.

In addition to within-cluster and whole-brain spontaneous fluctuations, changes correspondingly measured by ReHo and fALFF––or other methods, such as independent component analysis (ICA), which decomposes the signal from whole-brain voxels to spatially and temporally independent components––were also reported regarding the association between individual brain differences and the motor inhibition. For example, Tian and Kong [32] applied the ICA method and found the components of the motor network, motor control network, visual network, dorsal attention network, and task-activation network were associated with individual differences of SSRT. Their results suggest that spontaneous fluctuations in brain activity changes are associated with individual performance differences in motor inhibition. However, neither intrinsic brain activity in clusters of voxels (e.g., ReHo) [28] nor whole-brain spontaneous fluctuations (e.g., fALFF and ICA) [12, 31, 32] offer information regarding the organization of the functional network. Comprehension of the organizational principles in brain networks may provide a key to understanding the interplay between functional segregation and integration of large-scale networks and ultimately the emergence of cognition and adaptive behaviors. Investigations of large-scale dynamic functional networks derived from brain imaging data (e.g., see Fig 1A) have revealed valuable insights about the topological organization of the human brain in health and disease [33]. The application of graph-theoretic methods can identify the topological organization of brain networks for assessing the brain organization and the functional network properties [34, 35]. In a recent study by Kumar and colleagues [24], they used the graph-theory quantity (i.e., maximum flow between nodes, whose capacities are defined with transfer entropy) to predict the attention ability of SST. However, their study did not investigate the association between functional network organization properties and motor inhibition. As such, the goal of this study is to investigate whether functional networks organization properties are associated with the performance of motor inhibition.

Fig 1

Experimental method.

(a) Nodes in the atlas: nodes used from Schaefer and Kong [36] atlas. (b) Indexes extraction: indexes calculation for each node, including within-module-degree (WMD) and participation coefficient (PC). WMD represents the degree of a node’s connectome level within a module, while the PC represents the degree of a node’s connectome level between other networks. (c) Multiple linear regression: we set PCs/WMDs of each network as a pattern and used multiple linear regression to find the relationship between the linear combination of network property pattern and the SSRT. (d) Result assessment: all the prediction models were tested by Pearson’s correlation between predicted stop-signal reaction time (SSRT) and the actual SSRT.

Graph-theoretic network analysis [37] provides a set of calculations to estimate and analyze the properties and functions of complex networks (see Fig 1B). The brain can be modeled as a complex network for graph-theoretic analysis, in which brain regions are treated as nodes in the network, and between-regional connections are treated as edges. Graph-theoretic network analysis can assess network properties and functions of different brain regions. For example, the PCs of each node in each network represent the degree of the between-network connectivity. The nodes with high PC are called “connector hub” and the networks having more and stronger “connector hubs” are presumed to mediate interactions between networks. In contrast, the node that has more within-networks connectivity, which is assessed by the WMD, is called the “provincial hub”, and the network with more provincial hubs is presumed to promote the within-network interaction for conducting the particular function of the network [38]. These two indexes (i.e., PC and WMD) can not only reveal the connectivity properties of networks in healthy, normal population (e.g., [39]) but also distinguish normative brain origination from the abnormal ones, such as frontal lobe epilepsy [40]. The principal contribution of our study is to examine the within- and between-module network connectivity in relation to motor inhibition. To this end, we implemented multiple linear regression (see Fig 1C) to identify the relationship between brain network properties and motor inhibition.

The goal of this study was to examine the relationship between the properties of topological organization in the functional brain network and inter-individual differences in motor inhibition. Specifically, we first performed a graph-theoretic network analysis on rs-fMRI data for measuring functional network organization properties. We then used multiple linear regression with leave-one-out cross validation to estimate the relationship between graph metrics and SSRT (see Fig 1C). Finally, we performed Pearson’s correlation to estimate if, across subjects, the predicted SSRT is correlated to the actual SSRT (see Fig 1D). The result was also verified by permutation test with 10000 iterations to calculate the p-value from the empirically derived null distribution. We aimed to test whether within-network or between-network module connectivity in a large-scale functional network explains the performance variances on individual differences of motor inhibition.

Method and materials

This study protocol was approved by the Human Research Ethics Committee of the National Cheng Kung University, Tainan, Taiwan, R.O.C. [Contract No. 104–004] to protect the participants’ right according to the Declaration of Helsinki and the rule of research at the University. All participants signed an informed consent form before participating in the experiments.

Participants

One hundred eighty-three right-handed participants without a history of psychological and neurological disorders were recruited. Montreal Cognitive Assessment (MoCA; [41]) and Beck Depression Inventory-II (BDI-II; [42]) were assessed for participants, and those with scores lower than 22 in MoCA (n = 4) or higher than 13 in BDI-II (n = 7) were excluded before the final analysis. Additionally, 31 participants were excluded due to MRI technical problems, failed to learn the behavioral task (SST), incomplete data, or outlier calculated by Whisker method (below 25th percentile– 1.5 x interquartile range or over 75th percentile + 1.5 x interquartile range) in behavioral performance. Therefore, a total of 141 healthy volunteers aged 20–78 years (67 females, mean age 46.34, SD = 16.18) were included in the final analysis. Table 1 showed the demographic information on the participants included in the final analysis. All participants acquired their behavioral task and rs-fMRI during day time and were asked to star at a white cross during rs-fMRI acquisition.

Table 1

141 participants’ demographic information.

Age group (years)	Age (mean/SD)	Sample size (male/female)	MoCA (mean/SD)	BDI-II (mean/SD)
20–30	24.09/2.91	19/13	28.56/0.98	5.06/3.75
30–40	33.64/2.82	9/9	27.39/2.00	6.39/4.38
40–50	44.70/3.01	14/9	26.39/2.15	6.35/4.05
50–60	55.19/3.06	14/21	26.86/1.88	5.23/4.02
60–70	64.50/2.36	12/13	27.12/1.90	4.08/4.30
70–80	73.13/2.52	6/2	25.88/2.30	2.88/2.36

SD: standard deviation; MoCA: Montreal Cognitive Assessment; BDI-II: Beck Depression Inventory II

Behavioral task: Stop-signal task

Participants were instructed to respond as quickly and correctly as possible to the on-screen target stimuli “O” and “X” by correspondingly pressing the “z” and “/” buttons on a keyboard with their left and right index fingers, respectively. Also, they were asked to inhibit their responses if they heard auditory stop signal “beeps”, which were 500 Hz and lasted for 300 ms after the stimulus. The target stimulus was colored white with a size of 2 cm and at a visual angle of 0.64° in the center of a black screen.

Two practice blocks were conducted before the formal experiment started. In the first practice block, participants were instructed to perform a choice reaction-time task and tried their best to respond to the stimulus as soon and accurately as possible. The “beep” sounds were still presented in the background, but participants were asked to ignore this sound. In the second practice block, participants were instructed to react to the “beep” sound following the stimulus onset by stopping the response immediately. Participants were told not to slow down their reaction to waiting for the stop signal to occur.

After the practice, the formal experiment commenced, and all of the settings and rules were the same as for the second practice block. In the formal experiment, 5 blocks were included (40 stop-trials and 100 go-trials per block). The start stop-signal delay (SSD) was first selected from one of two interleaved staircases, 150 ms and 350 ms. The SSD then varied according to the participants’ response on stop-trials, which made the following SSD increased by 50 ms while one successfully stopped and made the SSD decreased by 50 ms while one did not successfully stop [43]. The SSD range was fixed between 0 and 800 ms. The staircase procedure ensured that the subject's likelihood of stopping converged to a 50% chance. The inter-stimulus interval varied from 1,300 to 4,800 ms, and the completion time was approximately 30 min, including instruction and practice time.

The block-wised integration method [44] was used for the SSRT calculation. Recently, Verbruggen and Chambers [44] suggested that a block-wise integration method for SSRT calculation could reduce bias from skewness and slow the degree of response. In light of the improvement of this method, a block-wise calculation was performed in this study, which is the least biased approach currently available for SSRT calculation. We subtracted the nth go RT from the mean SSD block by block, where n was the number of RTs in the go-RT distribution multiplied by the p(response|stop). The mean of the five-block values was the block-wise integration SSRT.

Image parameters and data analysis

Image acquisition

MRI images were acquired using a GE MR750 3T scanner (GE Healthcare, Waukesha, WI, USA) at the Mind Research Imaging center at National Cheng Kung University. Resting-state functional images were acquired with a gradient-echo echo-planar imaging (EPI) pulse sequence (TR = 2,000 ms, TE = 30 ms, flip angle = 77, 64 × 64 matrices, FOV = 22 × 22 cm2, slice thickness = 4 mm, no gap, voxel size = 3.4375 mm × 3.4375 mm × 4 mm, 32 axial slices covering the entire brain). A total of 245 volumes were acquired; the first five served as dummy scans and were discarded to avoid T1 equilibrium effects. Participants were instructed to remain awake with their eyes open and to fixate on a central white cross shown on the screen during the scans. High-resolution anatomical T1 images were acquired using fast-SPGR which consisted of 166 axial slices (TR = 7.6 ms, TE = 3.3 ms, flip angle = 12°, 224 × 224 matrices, slice thickness = 1 mm) lasting 218 seconds.

For quality control, the subjects with 2.5 mm and 2.5 degrees in max head motion were asked to be scanned again. No maximum numbers of censored volumes were set as criteria for including a subject, but volumes of the scrubbing over 0.9 mm subject-motion were included as a covariate for avoiding confounding.

Image pre-processing

Functional images were preprocessed using CONN toolbox 18a (www.nitrc.org/projects/conn) and SPM 12 (http://www.fil.ion.ucl.ac.uk/spm) implemented in Matlab (The MathWorks, Inc., Natick, MA, USA). We employed a pre-processing protocol modified from Geerligs and Tsvetanov [45]. First, slice timing, realignment, normalization, and smoothing with an 8-mm Gaussian kernel were conducted. Second, we calculated nuisance covariates (denoted as R), including movement parameters (translations along the x, y, and z axes and translations in three rotation angles: roll, yaw, and pitch), white matter signal (WM), and cerebral spinal fluid (CSF). Third, we regressed out bad frames at the subject level detected by “head motion censoring” [46] and [R R2 Rt-1 R2t-1], where t and t-1 refer to the current and immediately preceding timepoint, and R refers to nuisance covariates [47]. Finally, a band-pass filter at 0.008–0.1 Hz was applied to nuisance covariates and fMRI data simultaneously [48]. To further control for potential motion confounds, we ran partial Pearson correlations between observed scores with predicted scores while controlling for head motion (defined as the average of the mean frame-to-frame motion).

Identifying functional networks and organization and their properties

We used a whole-brain parcellation template [36] to define cortical regions of interests (ROI), and calculated functional connectivity between these ROIs. It contains 400 nodes of brain regions, and these nodes can be further categorized into 17 networks, including four subnetworks of the default mode network, two subnetworks of the somatomotor network, three subnetworks of the control network, two subnetworks of the visual network, two subnetworks of the dorsal attention network, two subnetworks of the salient ventral attention network, and two subnetworks of the limbic network (see S1 Table for more detail). The PC and WMD of each node were calculated by the method mentioned by Guimera and Amaral [49]. For a review of these four measures, please see Rubinov and Sporns [50].

Specifically, PC can be estimated as the degree to which a node is connected to external networks, with values ranging from 0 to 1. Nodes that are associated solely with other nodes within a single network would have a PC of 0, while nodes with many distributed associates with many different networks would have a PC closer to 1 [51]. PC is normalized by the degree of the node. The PC value of a region is defined as follows: , where Ki is the sum of the connectivity weight of i, K is the sum of the connectivity weight between i and the cortical networks, and NM is the total number of networks [38, 39]. Moreover, WMD is calculated as follows: binarized correlation matrices were setting weights above the density threshold to 1. WMD values were calculated across a range of density thresholds (ranged from 0.1 to 0.15), and averaged across thresholds. WMD is calculated as: , where is the average number of connections among all cortical ROIs within cortical network s, and σCWs is the SD of the number of connections of all ROIs in networks. Ki is the number of connections between i and all cortical ROIs in networks [38, 39]. WMD scores of each network’s ROI were calculated using the mean and SD of the within-network degree (number of intra-network connections) calculated from each cortical functional network. WMD is normalized by the number of regions within the associated functional network. Higher WMD values reflect more within-network connections of the voxels within the network it was assigned to.

Multiple linear regression

For the organization and properties of functional networks in the whole brain, the participation coefficient (PC) and within-module-degree (WMD) measures of each node in each network were independently extracted as predictors and used with multiple linear regression. For example, there were 12 nodes in the Control C network, with 12 PCs and 12 WMDs contained in the Control C network and these PCs and WMDs in each network were individually calculated by multiple linear regression (Eq 1). The multiple linear regression model was conducted repeatedly for each of the 34 network properties, including 17 PC and 17 WMD networks. The equation for each network model is as follows: where is the SSRT to be predicted with the N network, and Node, …, Node are the property P of k nodes in the N network. The property P represents either PC or WMD. The K nodes represent one of the nodes in the N network, which represents one of the 17 networks. Each of the predictor variables is numerical. The coefficients β, …, β measure the effect of each predictor after considering the effects of all the other predictors in the model. Thus, the coefficients measure the marginal effects of the predictor variables.

We conducted post-hoc power analysis for all the regression models to estimate the observed power of our analyses. For this analysis, the degree of freedoms and R-squared was extracted from each multiple linear regression model, and the alpha was set as 0.05. The desired observed power for the motion-controlled partial correlation models should conventionally be at least higher than 0.80 [52]. Also, all the predictions in each network were assessed with Pearson’s correlation (head-motion was partialled out) to estimate if, across subjects, the predicted SSRT is correlated with the actual SSRT (see Fig 1D). Leave-one-out cross-validation was applied to the model training for each individual, using n-1 subjects for model training and predicting the leaved-out subject’s behavior [53]. To assess the significance between actual SSRT and predicted SSRT generated from leave-one-out cross-validation, the non-parametric p-value using a permutation test with 10000 iterations with an alpha of 0.05 for significance. For the permutation test, we ran a leave-one-out cross-validation 10000 times, and for each time, random-shuffled actual SSRT was used for model training, and then the Pearson’s r value was calculated as mentioned above. This resulted in a null distribution composed of 10000 r values, and the p values were calculated by the percentile where the generated r values were equal to or larger than the null values [54]. Only the networks that had a significant result in the permutation test and a positive correlation result were retained, which was considered to be a robust and successful prediction and would be interpreted in the Discussion section.

Also, for those retained networks, the Bayes Factor (BF10) was further conducted to evaluate the strength of evidence for the presence or absence of correlation between predicted and observed SSRT [55]. The BF10 is the ratio of the likelihood of an alternative hypothesis (the presence of correlation) to the likelihood of the null hypothesis (the absence of correlation). For instance, BF10 = 4 may be interpreted as the data being 4 times more likely to occur under the alternative hypothesis than under the null hypothesis. The interpretation of BF10 can base on Wetzels and Wagenmakers [56], which were built from methods proposed by Jeffreys [57]. They suggested that the criteria of 1 < BF10 < 3 can only be anecdotal evidence for the alternative hypothesis, while the criteria of 3 < BF10 < 10 can be interpreted as moderate evidence sufficiently supporting the alternative hypothesis. For a detailed explanation of BF10, see [58].

Results

Behavior performance

Table 2 displays behavioral data. For the go trials, the mean accuracy was 90.98 ± 0.81%, and the mean nth RT was 611.51 ± 9.21 ms. For stop trials, the stop inhibition rate (stop success rate) was 54.57 ± 0.60%, which was close to the 50% aimed at the staircase algorithm. The average of SSRT was 219.56 ± 7.02 ms.

Table 2

Behavioral data.

Go trials	% of accuracy	RT (ms)	Choice Error (%)	Omission (%)
	90.98 (0.81)	611.51 (9.21)	1.53 (0.12)	7.49 (0.75)
Stop trials	% of inhibit	False inhibit RT (ms)	SSD (ms)	SSRT (ms)
	54.57 (0.60)	563.55 (8.78)	391.95 (13.38)	219.56 (7.02)

(standard error (SE) between parentheses) (1) Mean reaction time of five blocks’ nth go reaction times (RT), percentage of choice error (%) and omission (%) associated with go-trials; (2) mean percentage of inhibition (%), mean RT associated with stop-failure trials, mean stop-signal delay of five blocks’ mean stop-signal delay (SSD; ms) and stop-signal RT (SSRT; ms) associated with stop-success trials.

Table 3 and Fig 2 showed the result of Pearson’s correlation and permutation test. The top two highest correlations between predicted and actual SSRT were participation coefficients (PCs) of salient attention A network and PCs of dorsal attention A network. The permutation test showed that Pearson’s correlation results were statistically significant. The results implied that the individual network property of salient ventral attention A network (BF10 = 4.01) and dorsal attention A network (BF10 = 1.96) could predict motor inhibition ability.

Table 3

Summary of correlation results.

Property	Network	r value	Permutation p-value	Observed Power (%)
PC	ContA	-5.76 x10-2	6.01 x10-1	59.52
	ContB	-1.51 x10-1	8.48 x10-1	48.58
	ContC	3.82 x10-2	2.72 x10-1	39.69
	DefaultA	-2.20 x10-2	5.14 x10-1	86.36
	DefaultB	1.11 x10-1	1.31 x10-1	91.81
	DefaultC	-2.10 x10-3	3.94 x10-1	36.66
	DefaultD	-2.47 x10-2	4.79 x10-1	43.32
	DorsAttnA*	1.85 x10-1	4.07 x10-2	90.36
	DorsAttnB	-4.07 x10-2	5.63 x10-1	60.74
	LimbicB	-3.31 x10-1	9.80 x10-1	10.52
	LimbicA	-1.00 x10-1	6.86 x10-1	29.38
	SalVentAttnA*	2.11 x10-1	2.39 x10-2	94.47
	SalVentAttnB	-2.91 x10-1	9.74 x10-1	20.80
	SomMotA	3.82 x10-2	3.28 x10-1	90.88
	SomMotB	-4.47 x10-3	4.60 x10-1	77.95
	VisCent	-1.21 x10-1	7.88 x10-1	43.48
	VisPeri	-2.14 x10-1	9.33 x10-1	32.03
WMD	ContA	7.86 x10-2	1.94 x10-1	80.44
	ContB	-3.00 x10-2	5.27 x10-1	64.50
	ContC	8.65 x10-2	1.55 x10-1	41.37
	DefaultA	-1.09 x10-1	7.72 x10-1	73.60
	DefaultB	-4.67 x10-2	5.78 x10-1	80.26
	DefaultC	-4.27 x10-2	5.20 x10-1	29.56
	DefaultD	-1.87 x10-1	8.85 x10-1	23.55
	DorsAttnA	-1.24 x10-1	7.94 x10-1	52.21
	DorsAttnB	-1.75 x10-1	8.80 x10-1	41.63
	LimbicB	2.53 x10-2	3.06 x10-1	31.71
	LimbicA	3.42 x10-2	2.77 x10-1	34.17
	SalVentAttnA	7.18 x10-2	2.43 x10-1	89.71
	SalVentAttnB	-3.84 x10-1	9.95 x10-1	15.66
	SomMotA	1.69 x10-1	5.60 x10-2	97.22
	SomMotB	-1.51 x10-1	8.55 x10-1	63.90
	VisCent	-9.28 x10-2	7.08 x10-1	56.96
	VisPeri	-3.81 x10-2	5.45 x10-1	55.58

Note: (1) Network: Cont: control network; Default: default mode network; DorsAttn: dorsal attention network; Limbic: limbic network; SalVentAttn: salience ventral attention network; SomMot: somatomotor network; VisCent: visual central network; VisPeri: visual peripheral; (2) assessment: r value, Pearson’s correlation coefficient; Permutation p-value, the p value was estimated by permutation test with 10000 iterations; observed power, the power of the correlation.

* represent the significance of uncorrected p-value at alpha level < 0.05.

Fig 2

Result plot.

(a) Pearson’s r of networks: summary of the Pearson’s correlation between prediction stop-signal reaction time (SSRT) and the actual SSRT. Error bars were plotted with 0.95 CI of 10000-iteration bootstrapping estimation and the asterisks (i.e., “*”) showed the significant result of the permutation test. Here, PCs of salient ventral attention A network (SalVentAttnA) and dorsal attention A network (DorsAttnA) showed significant correlation between the predicted and actual SSRT in the permutation test. (b) Brain regions of salient attention A network (SalVentAttnA) and dorsal attention A network (DorsAttnA).

Discussion

The current study aimed to examine whether brain functional network properties, within-network connectivity (i.e., within-module-degree, WMD), or between-network connectivity (i.e., participation coefficient, PC), among whole brain 17 networks in rs-fMRI are associated with performance on motor inhibition. Our results showed that the between-networks connectivity in the regions of the salient attention A network and in the dorsal attention A network explained the most individual variances in motor inhibition. Specifically, results showed significant correlations with SSRT performance in nodal PCs of those two networks. This suggests that functional connectivity for salient attention A and dorsal attention A networks with the other functional networks exhibited a strong association with inhibitory control. Noted that according to the Bayes Factor, evidence for salient attention A network was stronger for supporting the presence of correlation, while for the dorsal attention A network evidence is relatively weak. On the other hand, we found top highest positive correlations results, including nodal PCs of salient ventral attention A network, nodal PCs of dorsal attention A network, nodal WMDs of somatomotor A network, nodal PCs of default B network, and nodal WMDs of Control C network, covered most regions within the frontal-cingulate-parietal cortices, which contributes to cognitive control [16, 59].

The between-network connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

Frontal-cingulate-parietal network

Previous studies investigating the association between rs-fMRI and SSRT showed mixed results regarding which brain networks in the resting state are most likely involved in motor inhibition. These networks include the control network (frontoparietal regions), default mode network (medial frontal and posterior cingulate gyrus), motor control network (pre-supplementary motor area (pre-SMA) and sensorimotor), visual network, and dorsal attention network [12, 27, 31, 32]. Those studies used the methods contained in the independent component analysis, fALFF, and ReHo that were similar to the present study, indicating that not only one network but multiple networks are associated with the motor inhibition. However, despite the discrepancy of methodology and results, we found the highest correlations with motor inhibition for properties in salient ventral attention A network, dorsal attention A network, somatomotor A network, default B network, and Control C network, although only salient ventral attention A network and dorsal attention A network showed significance. These five networks cover most regions within the frontal-cingulate-parietal cortices, which is considered important to cognitive control [16, 59], and most of these five networks properties resulted in their between-network connectivity (i.e., PC), suggesting that as motor inhibition is a high-level cognitive control function, the interactions across these five networks within the frontal-cingulate-parietal network are keys to motor inhibition.

Salient ventral attention a network

The salient ventral A attention covered the brain regions of the salience network and ventral attention network. The salience network plays an important role in the tasks involving attention and response to unexpected but salient stimuli (e.g., stop signal) [60, 61], while the ventral attention network plays an important role in the bottom-up attentional process [62]. Therefore, salient attention A network may be responsible for the unexpected but salient stimuli from the environment, such as the stop signal in SST. Also, our result, which the between-network connectivity (e.g., PC) in the salient ventral attention A network is associated with motor inhibition, echoes a previous study conducted by Seely and Menon [27]. Using the ICA method, Seely and Menon [27] further separated the intrinsic connectome of the frontal-cingulate-parietal network into two networks: salient network and executive-control network, which are anchored in the prefrontal, insular, cingulate, and parietal cortexes. Our result echoed their result, suggesting that the salient network is also important to the motor inhibition [16, 59], and further suggested that the importance of the salient network in motor inhibition is contributed by its connectivity to the other networks.

Dorsal attention a network

The dorsal attention network is responsible for the top-down attentional process [62]. Our another significant result in the present study, which the between-network connectivity (i.e., PC) of dorsal attention A network is associated with motor inhibition, may imply the top-down attentional control with other network is important for motor inhibition. Also, this result of the present study echoes a previous study conducted by Tian and Kong [32]. Tian and Kong [32] applied the ICA method and found the components of the motor network, motor control network, visual network, dorsal attention network, and task-activation network were associated with individual differences of SSRT. For motor inhibition, our result showed not only the importance of the dorsal attention A network but also the contribution of between-network connectivity of dorsal attention A network to the other networks.

Limitations and contributions

In this study, a few limitations of experimental design may undermine the results. First, the indirect link between spontaneous brain activity and behavior performance needs to be confirmed by task fMRI. Future studies may use task fMRI combined with network analysis to test if our results would be replicated using task-evoked functional connectivity. Second, the scope of interpretability on the machine learning prediction model is limited by feature selection and training dataset, hence another learning model may provide an alternative insight into prediction results. This requires further study to clarify. Third, the generalizability of the prediction model may be varied by the number of datasets, sample sizes and populations that completely separated from ours. Despite these limitations, our results provide a new perspective of the clinical implication that application of machine learning prediction of task-free fMRI data on motor inhibition, accelerating screening impulsive behavior related neurological and psychiatric disorders. Our findings suggested that whole-brain functional integration and between-network connection instead of functional segregation and within-network connection could predict motor inhibition.

Conclusion

In conclusion, we investigated the relationship between network properties with graphic-theoretic metrics and applied a multiple linear regression model to predict SSRT using resting-state fMRI data. Our findings showed that the between-network connectivity of the salient ventral attention network and dorsal attention network explain the majority of individual differences in motor inhibition. In addition, the between-network connectivity within the frontal-cingulate-parietal network can also moderately explain individual differences in motor inhibition. Our study provides new insight into the predictive value of rs-fMRI and its implication for assessing motor inhibition deficit in the clinical population.

Centroid-nodal information of networks.

[(1) Network: Cont: control network; Default: default mode network; DorsAttn: dorsal attention network; Limbic: limbic network; SalVentAttn: salience ventral attention network; SomMot: somatomotor network; VisCent: visual central network; VisPeri: visual peripheral; (2) AAL, automated anatomical labeling atlas].

(DOCX)

Click here for additional data file.

Final dataset.

Minimal dataset for Table 2 and Fig 2.

(XLSX)

Click here for additional data file.

24 Jul 2020

PONE-D-20-17526

Within- and between-module functional connectivity in the somatomotor and the frontal-cingulate-parietal networks associates with motor inhibition

PLOS ONE

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Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Overview

In this paper, Hsu and colleagues use resting-state fMRI in distributed brain networks to predict individual differences in task performance (in this case, the stop-signal task). In particular, for each of the seventeen brain networks they analyzed, they calculated two graph-theoretic statistics (Participation Coefficient, PC, which measures between network connectivity, and Within-module-degree, WMD, which measures within network connectivity) and trained a multiple linear regression model to predict SST performance across subjects. The paper’s motivations and scope are, in general, well-written and clear. However, there are serious concerns with the statistical methods that were employed. Therefore, I believe that the manuscript is not currently suited for publication in its current form. If the authors can address these all of these concerns, I would be happy to look at a revised version. The following contains my specific concerns with the current publication in order of how important I think they are.

1. Problem with training paradigm of regression model:

The authors train a linear regression model to predict stop-signal task performance across subjects using graph-theoretic statistics of the functional brain networks. It seems that the authors did not evaluate the linear regression model on a set of held-out subjects. If this is the case, then the authors will not be able to claim that these network statistics can predict individual differences in behavior. The current results can only suggest that there is a potential linear association between these network statistics and the individual differences in behavior. In order to show brain data can predict individual differences in behavior for a particular dataset, one must employ some kind of cross-validation paradigm where predictions from the regression model are evaluated on subjects that were not included in the training of the regression model. This is a standard procedure for studies that use functional brain data to explain individual differences in behavior. For example, Rosenberg et al. 2016 Nature Neuroscience use leave-one-out cross validation in their internal validation analyses to demonstrate their model accurately predict behavioral scores of unseen subjects. Additionally, they also show that their models have strong generalizability by showing their linear regression models trained on a dataset with a particular behavior measure and set of subjects generalize to a dataset with a completely different set of subjects and a different behavioral measure. Generalizing to different task performance measures might be out of the scope in the current study, but I think it is reasonable to ask the authors to employ a more principled approach in terms of having test sets of held-out subjects during training.

2. Reporting p-values

I was not able to find details on how the p-values were determined for reporting the Pearson correlation that determined model performance in the current study. I understand that they were corrected for using Bonferroni corrections, but how exactly were the original uncorrected p-values determined before they were subsequently corrected? Were they determined parametrically or non-parametrically? I recommend the p-values be determined non-parametrically, because a non-parametric approach would not make any assumptions on the data and thus one would avoid possibly violating certain assumptions that invalidate the p-value. If the authors decide to employ cross-validation to address my first point above, then this point will be even more relevant. Here is an example of calculating a p-value non-parametrically (the permutation test): shuffle the participants’ task performance, perform the whole analysis pipeline, calculate regression performance (in this study’s case, Pearson’s correlation), and repeat procedure many times and report the percentage of iterations that had a Pearson’s correlation as high as the one obtained from unshuffled data. This procedure should be rigorous enough to obtain reliable p-values in the current study regardless of the analysis pipeline.

3. Addressing head motion during analysis stage in addition to the preprocessing stage

The effect of head motion on functional connectivity analyses is well documented. It’s good that head motion parameters were regressed out in the preprocessing stage, but I think it is important that head motion is ensured to not be affecting the main analyses. Hsu et al. 2018 Social Cognitive and Affective Neuroscience is a similar study to the current study in that the authors are also using strength of functional brain network to predict individual differences in behavior and assess their predictions by correlating predicted behavior scores from brain activity to actual behavior scores (in their case, the behaviors were personality traits). This study addressed head motion by using partial Pearson correlations between observed scores and predicted scores to control for head motion. A similar kind of control for head motion in both the preprocessing and analysis stage (rather than only in the preprocessing stage) would greatly strengthen the current work by ensuring head motion is not confounding the main results.

4. Clarity on describing the brain network statistics

Although the authors point to previous work explaining how to calculate PC and WMD and include useful high-level descriptions in the current manuscript, I think it would be useful for them to include a specific section with precise mathematical descriptions of how these graph-theory statistics are calculated. Doing so will make it clearer for readers interested in reproducing the study’s results.

5. Clarity on results figure.

Figure 2a contains the effect sizes (Pearson correlations) from the statistical analysis of each of the brain networks (predicting SST performance from graph-theoretic statistics). It would be useful for the figure to mark which brain networks yielded statistically significant results (possibly with a star next to the network name). Additionally, in order to have a better sense of the variance of the correlation, it would be nice if the authors included error bars to each of the bars in Figure 2a. I recommend these error bars be 95% confidence intervals calculated non-parametrically using the bootstrap method (for the same reasons I addressed in point 2 when I suggested using non-parametric p-value calculations).

6. Performing a multiple regression across all brain networks

The authors did a regression for each individual brain network to predict subjects’ individual differences in SST performance. Why not do a multiple linear regression that uses all 17 brain networks to predict the behavior scores? This could give interesting information on how much each brain network is contributing to the prediction (using the magnitude of the regression weights that have been assigned to each of the brain networks).

7. Clarity on preprocessing section

Section 2.3.2 is a little unclear to me. Nuisance covariates were regressed out in the second step, and then certain parameters are set as nuisance covariates in the third step and a filter is applied on them in the fourth step. I’m a little confused on what role these third and fourth steps are playing.

8. Small error in describing previous work.

Line 124 indicates “there is no prior research employing graph-theoretic analysis methods on network properties of rs-fMRI data to associate with SSRT.” However, Kumar et al. 2019 Brain and Behavior, which is cited previously in the introduction as citation 24, use a graph theory quantity (maximum flow) to predict task performance in a dataset that employs the Stop Signal Task. The difference is that the current study is using different graph-theoretic quantities and focuses on motor inhibition (whereas Kumar and colleagues focus on attention) but this detail should be emphasized.

9. Minor points

In line 149, I think “form” should be “from.”

References

1. Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature neuroscience, 19(1), 165-171.

2. Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social cognitive and affective neuroscience, 13(2), 224-232.

3. Kumar, S., Yoo, K., Rosenberg, M. D., Scheinost, D., Constable, R. T., Zhang, S., ... & Chun, M. M. (2019). An information network flow approach for measuring functional connectivity and predicting behavior. Brain and behavior, 9(8), e01346.

Reviewer #2: In this manuscript, the authors used resting-state fMRI to investigate associations between motor inhibition and connectomics. The methods are sound, the manuscript clearly written and the conclusions supported by the data. The large sample size is a strength of this study. I have nevertheless some comments/suggestions which I strongly believe would improve the manuscript and clarify its impact

Major points:

Introduction: The rationale for a graph-theory analysis in this context needs further elaboration; while, the authors present the characteristics of this analysis and its potential to unravel topological features, it is not clear why/how this approach will bring new insights to answer this specific research question

Methods: Please add reference of ethical approval

Participants: How did the authors decide on sample size? The manuscript does not contain any evidence of power calculation

MRI: Can the authors expand on the specific instructions given to the participants for the resting-state scan? Did the authors check by any mean alertness during the scan? Were the scans acquired at the same time of the day? Further information on this extra sources of variability would be interesting to know

Preprocessing: Did the authors perform any QC on movement beyond censoring "bad volumes"? Did they exclude any subject because of excessive movement? What were the maximum number of censored volumes the authors thought to be acceptable for including a subject?

Connectomics: The description of the methods for calculating the PC and WMD is highly insufficient; in the absence of this information, the reader cannot follow or scrutinize the approach taken by the authors to derive these metrics

Multiple regression: Were the assumptions of this statistical model verified? If yes, how? Also, given the large sample size i feel it would strength the message if the authors would consider including a further cross-validation analysis (k-folds) to examine how well the model will generalize to new observations; Given their focus on the somatomotor network, are the authors confident that this association is not explained by inter-individual differences in head movement? Did authors control for head movement (i.e. mean framewise displacement) in their regression models?

Discussion/Conclusion: I am afraid the reader is left without a clear understanding of what this study brings that is new; please consider expanding on the contribution and implication of these findings

Minor points:

Line 398: "shows more explanations" - please rephrase

**********

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8 Sep 2020

Reviewer #1: Overview

In this paper, Hsu and colleagues use resting-state fMRI in distributed brain networks to predict individual differences in task performance (in this case, the stop-signal task). In particular, for each of the seventeen brain networks they analyzed, they calculated two graph-theoretic statistics (Participation Coefficient, PC, which measures between network connectivity, and Within-module-degree, WMD, which measures within network connectivity) and trained a multiple linear regression model to predict SST performance across subjects. The paper’s motivations and scope are, in general, well-written and clear. However, there are serious concerns with the statistical methods that were employed. Therefore, I believe that the manuscript is not currently suited for publication in its current form. If the authors can address these all of these concerns, I would be happy to look at a revised version. The following contains my specific concerns with the current publication in order of how important I think they are.

Reply0: The reviewer’s understanding of this study is correct. Regarding the concerns about methodology employed, we have adjusted the use of statistical models and provided new results to address your concerns.

1. Problem with training paradigm of regression model:

The authors train a linear regression model to predict stop-signal task performance across subjects using graph-theoretic statistics of the functional brain networks. It seems that the authors did not evaluate the linear regression model on a set of held-out subjects. If this is the case, then the authors will not be able to claim that these network statistics can predict individual differences in behavior. The current results can only suggest that there is a potential linear association between these network statistics and the individual differences in behavior. In order to show brain data can predict individual differences in behavior for a particular dataset, one must employ some kind of cross-validation paradigm where predictions from the regression model are evaluated on subjects that were not included in the training of the regression model. This is a standard procedure for studies that use functional brain data to explain individual differences in behavior. For example, Rosenberg et al. 2016 Nature Neuroscience use leave-one-out cross validation in their internal validation analyses to demonstrate their model accurately predict behavioral scores of unseen subjects. Additionally, they also show that their models have strong generalizability by showing their linear regression models trained on a dataset with a particular behavior measure and set of subjects generalize to a dataset with a completely different set of subjects and a different behavioral measure. Generalizing to different task performance measures might be out of the scope in the current study, but I think it is reasonable to ask the authors to employ a more principled approach in terms of having test sets of held-out subjects during training.

Reply1: We thank the reviewer for this suggestion on strengthening the rationale behind the use of regression model to predict behavioral performance. We agree with the reviewer’s view to use leave-one-out cross validation for the prediction. Therefore, we have revised the manuscript accordingly.

2. Reporting p-values

I was not able to find details on how the p-values were determined for reporting the Pearson correlation that determined model performance in the current study. I understand that they were corrected for using Bonferroni corrections, but how exactly were the original uncorrected p-values determined before they were subsequently corrected? Were they determined parametrically or non-parametrically? I recommend the p-values be determined non-parametrically, because a non-parametric approach would not make any assumptions on the data and thus one would avoid possibly violating certain assumptions that invalidate the p-value. If the authors decide to employ cross-validation to address my first point above, then this point will be even more relevant. Here is an example of calculating a p-value non-parametrically (the permutation test): shuffle the participants’ task performance, perform the whole analysis pipeline, calculate regression performance (in this study’s case, Pearson’s correlation), and repeat procedure many times and report the percentage of iterations that had a Pearson’s correlation as high as the one obtained from unshuffled data. This procedure should be rigorous enough to obtain reliable p-values in the current study regardless of the analysis pipeline.

Reply2: We thank the reviewer’s suggestion. We have included non-parametrical p-values (see Table 3) for both association and prediction results as you suggested. We find out that participation coefficients (i.e., PC) of salient attention A network and dorsal attention A network show significance (salient attention A network: p = 0.02; dorsal attention A network: p = 0.04).

3. Addressing head motion during analysis stage in addition to the preprocessing stage. The effect of head motion on functional connectivity analyses is well documented. It’s good that head motion parameters were regressed out in the preprocessing stage, but I think it is important that head motion is ensured to not be affecting the main analyses. Hsu et al. 2018 Social Cognitive and Affective Neuroscience is a similar study to the current study in that the authors are also using strength of functional brain network to predict individual differences in behavior and assess their predictions by correlating predicted behavior scores from brain activity to actual behavior scores (in their case, the behaviors were personality traits). This study addressed head motion by using partial Pearson correlations between observed scores and predicted scores to control for head motion. A similar kind of control for head motion in both the preprocessing and analysis stage (rather than only in the preprocessing stage) would greatly strengthen the current work by ensuring head motion is not confounding the main results.

Reply3: We thank the reviewer’s suggestion. We have included an additional step for assessing the prediction by running the partial correlation between observed and predicted scores to control for head motion (see p.12, section “Image pre-processing”, lines 239-241).

4. Clarity on describing the brain network statistics

Although the authors point to previous work explaining how to calculate PC and WMD and include useful high-level descriptions in the current manuscript, I think it would be useful for them to include a specific section with precise mathematical descriptions of how these graph-theory statistics are calculated. Doing so will make it clearer for readers interested in reproducing the study’s results.

Reply4: We thank the reviewer’s suggestion for improving readability. As you suggested, we have incorporated more detailed descriptions on the calculation of PC and WMD in the introduction and methods sections of the manuscript.

5. Clarity on results figure.

Figure 2a contains the effect sizes (Pearson correlations) from the statistical analysis of each of the brain networks (predicting SST performance from graph-theoretic statistics). It would be useful for the figure to mark which brain networks yielded statistically significant results (possibly with a star next to the network name). Additionally, in order to have a better sense of the variance of the correlation, it would be nice if the authors included error bars to each of the bars in Figure 2a. I recommend these error bars be 95% confidence intervals calculated non-parametrically using the bootstrap method (for the same reasons I addressed in point 2 when I suggested using non-parametric p-value calculations).

Reply5: We thank the reviewer’s suggestion on figure presentation. We have included error bars in the figures throughout the manuscript. Moreover, your suggestion together with your previous comments on non-parametric p-values were also calculated and presented in the figures.

6. Performing a multiple regression across all brain networks

The authors did a regression for each individual brain network to predict subjects’ individual differences in SST performance. Why not do a multiple linear regression that uses all 17 brain networks to predict the behavior scores? This could give interesting information on how much each brain network is contributing to the prediction (using the magnitude of the regression weights that have been assigned to each of the brain networks).

Reply6: We thank the reviewer’s suggestion. We did originally consider using a multiple linear regression model across all 17 brain networks to predict individual differences in SST performance. However, it would occur a statistical problem (i.e. Multicollinearity) in which independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. The high degree of correlations between networks would cause the coefficients not reliable to interpret/inference the results of networks’ contributions in explaining SST performance.

7. Clarity on preprocessing section

Section 2.3.2 is a little unclear to me. Nuisance covariates were regressed out in the second step, and then certain parameters are set as nuisance covariates in the third step and a filter is applied on them in the fourth step. I’m a little confused on what role these third and fourth steps are playing.

Reply7: We thank the reviewer’s suggestion. We have modified this section to clarify the preprocessing steps, especially the third and fourth steps in the section of image pre-processing (see p.12, section “Image pre-processing”, line 235-238). Combining with the third and fourth steps in the previous version, in the third step of the new version, we regressed out bad frames at the subject level detected by “head motion censoring” and [R R2 Rt-1 R2t-1], where t and t-1 refer to the current and immediately preceding timepoint, and R refer to nuisance covariates.

8. Small error in describing previous work.

Line 124 indicates “there is no prior research employing graph-theoretic analysis methods on network properties of rs-fMRI data to associate with SSRT.” However, Kumar et al. 2019 Brain and Behavior, which is cited previously in the introduction as citation 24, use a graph theory quantity (maximum flow) to predict task performance in a dataset that employs the Stop Signal Task. The difference is that the current study is using different graph-theoretic quantities and focuses on motor inhibition (whereas Kumar and colleagues focus on attention) but this detail should be emphasized.

Reply8: We thank the reviewer’s suggestion. We have included a paper by Kumar et al. (2019) in this paragraph and focused on differences between their work and the current study.

9. Minor points

In line 149, I think “form” should be “from.”

Reply9: We thank reviewer’s correction. We have corrected the typo.

References

1. Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature neuroscience, 19(1), 165-171.

2. Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social cognitive and affective neuroscience, 13(2), 224-232.

3. Kumar, S., Yoo, K., Rosenberg, M. D., Scheinost, D., Constable, R. T., Zhang, S., ... & Chun, M. M. (2019). An information network flow approach for measuring functional connectivity and predicting behavior. Brain and behavior, 9(8), e01346.

Reply: We have added the first and second references in the method section (see p.14, line 293-295; p.15, line 295-302) and modified the study interpretation of Kumar et al. (2019) in the introduction section (see p.6, line 112-117).

Reviewer #2: In this manuscript, the authors used resting-state fMRI to investigate associations between motor inhibition and connectomics. The methods are sound, the manuscript clearly written and the conclusions supported by the data. The large sample size is a strength of this study. I have nevertheless some comments/suggestions which I strongly believe would improve the manuscript and clarify its impact.

Reply: We thank the reviewer’s positive feedbacks. We have incorporated your suggestions for improving this manuscript.

Major points:

Introduction: The rationale for a graph-theory analysis in this context needs further elaboration; while, the authors present the characteristics of this analysis and its potential to unravel topological features, it is not clear why/how this approach will bring new insights to answer this specific research question

Reply: We thank the reviewer’s suggestion about the rationale behind the use of graph-theory analysis on motor inhibition. As we reviewed in the introduction, previous works on association between functional networks and motor inhibition have shown potential functional brain involvement at network level. However, such attempts did not consider its association between functional network organization properties and performance of motor inhibition. Moreover, how the results on topological features of functional network properties associated with previous findings remain unclear. Therefore, our goal of this study is to explore whether within- or between- network connection properties associated with motor inhibition. Specifically, how do these topological features associate with motor inhibition and its relation to previous findings. These results would substantially enhance our understanding of functional networks involvement in motor inhibition.

Methods: Please add reference of ethical approval

Reply: We thank the reviewer’s suggestion. We have reported the case number of ethical approvals by the National Cheng Kung University Research Ethics Committee.

Participants: How did the authors decide on sample size? The manuscript does not contain any evidence of power calculation.

Reply: We add the power analysis result of each network property model in table 3. In the power analysis, the degree of freedoms and R-squared were extracted from each multiple linear regression model, and the alpha is set as 0.05. The desired power for the motion-controlled partial correlation models should conventionally be at least higher than 0.80. Our result showed that the power for salient attention A network and dorsal attention A network result in 0.95 and 0.90 respectively, which fit our criteria, suggesting the model of these two network properties were sufficient.

MRI: Can the authors expand on the specific instructions given to the participants for the resting-state scan? Did the authors check by any mean alertness during the scan? Were the scans acquired at the same time of the day? Further information on this extra sources of variability would be interesting to know

Reply: The participants were not limited to be scanned at the same time of the day, but they were all scanned during the day time. During the resting-state functional scans, the participants were instructed to remain awake and relax with their eyes open and stare at the white cross as shown on the screen (each scan lasted for 8min for each participant).

Preprocessing: Did the authors perform any QC on movement beyond censoring "bad volumes"? Did they exclude any subject because of excessive movement? What were the maximum number of censored volumes the authors thought to be acceptable for including a subject?

Reply: The screening criteria for imaging quality control were based on head motion parameters and frame‐wise displacement (FD). None of the remaining participants' max head motion exceeded 2.5 mm, or means FD exceeded 0.25. We also visual inspection of all images after normalization and coregistration steps to make sure there was no bad warping. The subjects with 2.5 mm and 2.5 degree in max head motion would be asked for re-scanning. No maximum numbers of censored volumes were set as criteria for excluding a subject, but volumes of the subject motion scrubbing over 0.9 mm would be set as a covariate at the preprocessing level.

Connectomics: The description of the methods for calculating the PC and WMD is highly insufficient; in the absence of this information, the reader cannot follow or scrutinize the approach taken by the authors to derive these metrics

Reply: We thank the reviewer’s suggestion for readability. To address this, we have included an additional section to describe its mathematical logics behind the PC and WMD.

Multiple regression: Were the assumptions of this statistical model verified? If yes, how? Also, given the large sample size i feel it would strength the message if the authors would consider including a further cross-validation analysis (k-folds) to examine how well the model will generalize to new observations; Given their focus on the somatomotor network, are the authors confident that this association is not explained by inter-individual differences in head movement? Did authors control for head movement (i.e. mean framewise displacement) in their regression models?

Reply: We add the head motion in the regression model and conduct the leave-one-out cross-validation.

Discussion/Conclusion: I am afraid the reader is left without a clear understanding of what this study brings that is new; please consider expanding on the contribution and implication of these findings

Reply: We thank the reviewer’s suggestion. As you suggested, we have expanded contribution and implication of current findings in this study.

Minor points:

Line 398: "shows more explanations" - please rephrase

Reply: We thank the reviewer’s correction. We have rephrased the sentence.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

9 Oct 2020

PONE-D-20-17526R1

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I strongly encourage you to address the issue of correcting for multiple comparisons. As pointed out by Reviewer 1, Bonferroni correction is likely to be overly conservative. You could consider correcting for the false discovery rate, as an alternative.  Also, as stated by Reviewer 2, please address the concerns about post-hoc power analysis.

Please submit your revised manuscript by Nov 23 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I believe the authors have provided a very comprehensive revision and have addressed the majority of my concerns.The paper is much stronger than before, but there are three main issues that I think need to be addressed before this paper is appropriate for publication. If these can be addressed, I think this paper will be well-suited for publication. The following are the aforementioned issues, in order of how crucial I believe they are:

1. Accounting for multiple comparisons in the determination of statistical significance

The authors have done a great job in using cross-validation to generate and evaluate predictions on held-out subjects as well as utilizing permutation testing and non-parametric p-values. However, since there are many of these statistical tests being done (seventeen brain networks and two graph-theory statistics for 34 statistical tests in total). It is absolutely necessary to adjust the statistical tests for the multiple comparisons issue. The previous iteration of the manuscript used the Bonferroni method, which is a great method to use for multiple comparisons, but it seems that this has been taken out in the current manuscript.

Providing a statistical test that incorporates both non-parametric permutation testing as well as takes into account for multiple comparisons will be the best way to ensure the study’s results are statistically solid enough for publication.

The authors could use Bonferroni in this situation, but I do recognize that Bonferroni is highly conservative and it may be that none of the authors’ results may end up being statistically significant if they use Bonferroni. I would be fine if the authors used a less conservative method of multiple comparisons, but I think it is absolutely necessary to at least incorporate some kind of statistical correction for multiple comparisons.

2. Error in displaying error bars in Figure 2a.

The authors have added error bars in Figure 2a to give an estimate of the effect size of the regression models’ performance, which is a great addition. However, I think there has been a mistake in adding these error bars. The error bars are at the beginning of each bar in the barplot (where it starts at 0) instead of the end of the bar (which is where the calculated R value lies). This should be fixed immediately as it makes the figure very confusing.

3. Clarity in displaying which brain networks/graph-theory statistics are significant

Table 3 displays r and p-values for each of the statistical tests. It is difficult to find which one is significant, so it would be helpful for the authors to bold-face the rows in which the p-value was significant. Related to this, it is similarly difficult to find which brain network/graph theory statistic was significant in Figure 2. A similar bold-facing on the network name would be really helpful here as well.

Reviewer #2: The authors have addressed most of my previous comments. However, I am afraid that the power analyses the authors now added need some further contextualization. The authors used the degrees of freedom and effect sizes from their own data to provide a picture of actual achieved power for each regression model. This does not inform the reader on how the sample size was decided, but can provide information for appraising each individual finding. This needs to be better explained, otherwise these analyses sound rather seldom. Also, if it was the case that a priori power analyses to decide on sample size were not conducted, please state this openly - I don't see a major problem here, but for clarity it is important that the reader is aware of such circumstance.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

24 Oct 2020

Review Comments to the Author

Reviewer #1: I believe the authors have provided a very comprehensive revision and have addressed the majority of my concerns. The paper is much stronger than before, but there are three main issues that I think need to be addressed before this paper is appropriate for publication. If these can be addressed, I think this paper will be well-suited for publication. The following are the aforementioned issues, in order of how crucial I believe they are:

1. Accounting for multiple comparisons in the determination of statistical significance

The authors have done a great job in using cross-validation to generate and evaluate predictions on held-out subjects as well as utilizing permutation testing and non-parametric p-values. However, since there are many of these statistical tests being done (seventeen brain networks and two graph-theory statistics for 34 statistical tests in total). It is absolutely necessary to adjust the statistical tests for the multiple comparisons issue. The previous iteration of the manuscript used the Bonferroni method, which is a great method to use for multiple comparisons, but it seems that this has been taken out in the current manuscript.

Providing a statistical test that incorporates both non-parametric permutation testing as well as takes into account for multiple comparisons will be the best way to ensure the study’s results are statistically solid enough for publication.

The authors could use Bonferroni in this situation, but I do recognize that Bonferroni is highly conservative and it may be that none of the authors’ results may end up being statistically significant if they use Bonferroni. I would be fine if the authors used a less conservative method of multiple comparisons, but I think it is absolutely necessary to at least incorporate some kind of statistical correction for multiple comparisons.

Reply: We thank the reviewer for this advice. The difference in statistical analyses between the first submission and first revision is that the first manuscript reported the parametric p values of correlations between the predicted and observed SSRT, while the second manuscript reported partial correlation between predicted and observed SSRT, which partial out effects from motion parameters, and reported non-parametric permutation value. After controlling for multiple comparisons for all the 34 models tested, with more liberal methods, we did not find a corrected p value less than .05.

However, we still think it is worthwhile to report our findings. Therefore, we implemented Bayes Factor (BF10), which used thresholding methods to make their inference more conservative (Han et al. 2019). Specifically, the Bayesian procedure is more conservative, making fewer claims with confidence and not overestimating effect sizes. In this case, we used Bayes Factors for this revised version to evaluate the evidence strength of each model. The BF10 is the ratio of the likelihood of an alternative hypothesis (in the present case, the presence of the correlation) to the likelihood of the null hypothesis (in the present case, the absence of the correlation). Bayesian inference based on BF10 would enable us evaluate the relative strength of evidence supporting the null or alternative hypothesis, which could not be easily done within the frequentist inference. Moreover, Bayesian statisticians have argued that it is relatively freer from issues associated with inflated false positives and multiple comparison correction. This is because Bayesian statistics do not rely on frequentist assumptions associated with false positives but focuses on updating the posterior probability distributions of parameters of interest based on data (Gelman, Hill, & Yajima, 2012; Han & Park, 2018). Furthermore, researchers have suggested that Bayes factor can also solve the issue of multiple comparisons (please see Neath & Cavanaugh, 2006 for details). For instance, BF10 = 4 may be interpreted as the data being 4 times more likely to occur under the alternative hypothesis than under the null hypothesis. The interpretation of BF10 can base on Wetzels & Wagenmakers (2012), which edited from Jeffreys (1961). They suggested that the criteria of 1 < BF10 < 3 can only be anecdotal evidence for the alternative hypothesis, while the criteria of 3 < BF10 < 10 can be interpreted as moderate evidence sufficiently supporting the alternative hypothesis (for review, please see Ly, A., et al., 2016). Therefore, the BF10 values of significant models in partial correlation are reported. The result finds that the PC of salient ventral attention A network (BF10 = 4.01) is sufficiently supporting the alternative hypothesis, while the PC of dorsal attention A network (BF10 = 1.96) is only weak and less sufficient evidence of alternative attention. We have added the description of this new result in the revised manuscript. Please refer to following pages: [p.15, line 316-325; p.17, line 345-346, p.19, line 374-376].

Reference

Gelman, A., Hill, J., & Yajima, M. (2012). Why We (Usually) Don’t Have to Worry About Multiple Comparisons. J. Res. Educ. Eff. 5 (2), 189–211. doi:10.1080/19345747.2011.618213

Han, H., Glenn, A. L., & Dawson, K. J. (2019). Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research. Brain Sci. 9 (8), 198. doi:10.3390/brainsci9080198.

Han, H. & Park, J. (2018). Using SPM 12’s Second-level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users. Frontiers in Neuroinformatics, 12, 1. doi:10.3389/FNINF.2018.00001

Jeffreys, H. (1961) Theory of Probability. 3rd Edition, Clarendon Press, Oxford.

Ly, A., et al. (2016). "An evaluation of alternative methods for testing hypotheses, from the perspective of Harold Jeffreys." Journal of Mathematical Psychology 72: 43-55.

Neath, A. A., & Cavanaugh, J. E. (2006). A Bayesian approach to the multiple comparisons problem. Journal of Data Science, 4(2), 131-146.

Wetzels, R. and E.-J. Wagenmakers (2012). "A default Bayesian hypothesis test for correlations and partial correlations." Psychonomic Bulletin & Review 19(6): 1057-1064.

2. Error in displaying error bars in Figure 2a.

The authors have added error bars in Figure 2a to give an estimate of the effect size of the regression models’ performance, which is a great addition. However, I think there has been a mistake in adding these error bars. The error bars are at the beginning of each bar in the barplot (where it starts at 0) instead of the end of the bar (which is where the calculated R value lies). This should be fixed immediately as it makes the figure very confusing.

Reply: We thank the reviewer for this suggestion, and we have corrected Fig 2a according to the reviewer’s comments.

3. Clarity in displaying which brain networks/graph-theory statistics are significant

Table 3 displays r and p-values for each of the statistical tests. It is difficult to find which one is significant, so it would be helpful for the authors to bold-face the rows in which the p-value was significant. Related to this, it is similarly difficult to find which brain network/graph theory statistic was significant in Figure 2. A similar bold-facing on the network name would be really helpful here as well.

Reply: We thank the reviewer for this suggestion, and we modify the Table 3 and Fig 2a according to the suggestion.

Reviewer #2:

The authors have addressed most of my previous comments. However, I am afraid that the power analyses the authors now added need some further contextualization. The authors used the degrees of freedom and effect sizes from their own data to provide a picture of actual achieved power for each regression model. This does not inform the reader on how the sample size was decided, but can provide information for appraising each individual finding. This needs to be better explained, otherwise these analyses sound rather seldom. Also, if it was the case that a priori power analyses to decide on sample size were not conducted, please state this openly - I don't see a major problem here, but for clarity it is important that the reader is aware of such circumstance.

Reply: We thank you for this comment. We did not conduct the priori power analysis to decide on sample size, yet we are willing to clarify this circumstance openly in our research [see p.14, line 287 – 288]. In our previous revision, we have incorporated the post-hoc power analysis on each network property model in table 3. In the power analysis, the degree of freedoms and R-squared was extracted from each multiple linear regression model, and the alpha is set as 0.05. The desired power for the motion-controlled partial correlation models should conventionally be at least higher than 0.80. Our result showed that the power for salient attention A network and dorsal attention A network result in 0.95 and 0.90 respectively, which fit our criteria, suggesting the model of these two network properties was sufficient. Moreover, any power analysis question requires consideration of effect sizes, which we have detailed in the manuscript. Moreover, commonly presume that the required sample size for linear regression analysis is based on predictor variables or participant numbers (e.g. above 50 participants) or both (5 participants/ten participants for each predictor variable). Studies (Green, 1991; Jenkins & Quintana-Ascencio, 2020; Wilson Van Voorhis & Morgan, 2007) have shown that a minimum N>25 is essential (Jenkins & Quintana-Ascencio, 2020) for accurate inference but sample size ranges from N ≥ 50 + 8m (recommendation of Green (1991)) for the multiple regression or N ≥ 104 + m for partial correlation where m is the number of predictor variables. Moreover, Knofczynski & Mundfrom (2008) proposed a guideline for determining the minimum required sample size for using multiple regressions for prediction. They proposed that the sample should vary from small to large, according to the effect sizes. As different guidelines provide some useful reference for deciding sample size, the larger sample size would indeed provide more robust insight into this study. As of now, our inference based on current sample size may be still justified.

References:

Green, S. B. (1991). How Many Subjects Does It Take To Do A Regression Analysis? Multivariate Behavioral Research. https://doi.org/10.1207/s15327906mbr2603_7.

Jenkins, D. G., & Quintana-Ascencio, P. F. (2020). A solution to minimum sample size for regressions. PLoS ONE. https://doi.org/10.1371/journal.pone.0229345.

Knofczynski, G. T., & Mundfrom, D. (2008). Sample Sizes When Using Multiple Linear Regression for Prediction. Educational and Psychological Measurement, 68(3), 431–442. https://doi.org/10.1177/0013164407310131

Wilson Van Voorhis, C. R., & Morgan, B. L. (2007). Understanding Power and Rules of Thumb for Determining Sample Sizes. Tutorials in Quantitative Methods for Psychology. https://doi.org/10.20982/tqmp.03.2.p043

Submitted filename: R2_Response to Reviewers.docx

Click here for additional data file.

10 Nov 2020

PONE-D-20-17526R2

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please address the comment from Review 1 regarding the error bars. If there is some reason that you wish to keep the Figure as-is, please provide a specific rationale.

Please submit your revised manuscript by Dec 25 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I believe the major comments on the statistics have been met, but I do request that the error bars in Figure 2 for SalVentAttnB in PC be corrected. It seems the bars aren't centered on the end of the bar. It is very important that these error bars are placed correctly, so I implore the authors to double check this figure.

Reviewer #2: As far as i can tell, the authors have addressed all the comments neatly. I commend the authors for the Bayesian analyses included in the revised version of the manuscript, which are important to interpret some of their null findings. I believe the current version meets the standards of Plos One; hence, i am delighted to recommend this manuscript for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

11 Nov 2020

Response to Editor and Reviewers’ comments

PONE-D-20-17526R2

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLOS ONE

Editor’s comment:

Please address the comment from Review 1 regarding the error bars. If there is some reason that you wish to keep the Figure as-is, please provide a specific rationale.

REPLY: We have modified the figure as requested by Reviewer 1.

Comments to the Author

6. Review Comments to the Author

Reviewer #1: I believe the major comments on the statistics have been met, but I do request that the error bars in Figure 2 for SalVentAttnB in PC be corrected. It seems the bars aren't centered on the end of the bar. It is very important that these error bars are placed correctly, so I implore the authors to double check this figure.

REPLY: We have modified the figure as requested. Please see the revised Figure 2. Please note the bootstrapped confidence intervals are not symmetric, because the empirical null distribution is skewed.

Submitted filename: R3_Response to Reviewers.docx

Click here for additional data file.

13 Nov 2020

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PONE-D-20-17526R3

Dear Dr. Hsieh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Niels Bergsland

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

24 Nov 2020

PONE-D-20-17526R3

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

Dear Dr. Hsieh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Niels Bergsland

Academic Editor

PLOS ONE