Different times TMS over fronto-parietal network regulates visual selective attention

Different times TMS over fronto-parietal network regulates visual selective attention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Different times TMS over fronto-parietal network regulates visual selective attention Qiuzhu Zhang, Danmei Zhang, Gulibaier Alimu, Guragai Bishal, WenJuan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4237359/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Individuals pay attention to meaningful information by using visual selective attention. Top-down attention is goal-driven and requires cognitive effort to guide attention. Bottom-up attention is stimuli-driven and automatically attracted by salient stimuli. The fronto-parietal network (FPN) is involved in visual selective attention, and top-down and bottom-up attention from neuron activation in the FPN at different times. To explore how different times of transcranial magnetic stimulation (TMS) over the nodes of FPN modulate visual selective attention behavior. Methods The single-pulse TMS was applied to stimulate the right dorsolateral prefrontal cortex (rDLPFC) and right superior parietal lobule (rSPL) of two groups (active TMS and sham TMS group) at early times (33ms, 50ms, 66ms, and 83ms) and late times (216ms, 233ms, 250ms, and 266ms) after the pop-out and search stimulus displayed onset. Results The behavior results showed late TMS over rDLPFC decreased ACC of top-down attention. Late TMS over rSPL improved ACC of top-down attention and decreased cognitive load difference between top-down and bottom-up attention. Voxel-based morphometry (VBM) results of T1 images showed that gray matter volumes (GMV) in fronto-parietal cortex correlated with visual selective attention behavior, including bilateral superior frontal gyrus, right precentral gyrus, left supramarginal gyrus, right inferior frontal gyrus (orbital part), and left superior frontal gyrus (medial), especially in the active TMS group. Conclusions Our findings reveal the cause role of the FPN on visual selective attention behavior and the relationship between GMV in the fronto-parietal cortex and visual selective attention. right dorsolateral prefrontal cortex (rDLPFC) right superior parietal lobule (rSPL) single-pulse transcranial magnetic stimulation (single-pulse TMS) visual selective attention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Selective attention helps individuals process information from different sources separately, select meaningful and behaviorally relevant information, and filter meaningless and behaviorally irrelevant information [ 1 ]. Top-down attention is goal-driven and requires intentional arousal and maintenance, while bottom-up attention is stimulus-driven and automatically captures attention. Bottom-up attention control signals were dominant in the parietal cortex, while top-down attention control signals occurred earlier in the frontal cortex [ 2 – 6 ]. The fronto-parietal network (FPN) is involved in information selection and visual selective attention detection of target [ 7 – 10 ]. The DLPFC and superior parietal lobule (SPL) were key nodes of the FPN. The DLPFC was associated with execution control [ 11 , 12 ], activity reflected top-down control mediating task response demands and interfered with contextual memory-guided attention [ 12 , 13 ]. Many studies of the parietal cortex's contribution to attention have focused on the SPL, the major subregion of the PPC [ 14 – 16 ]. The SPL was considered a key brain region in the dorsal attention network [ 6 , 17 , 18 ] and involved in top-down attentional orientations [ 19 – 21 ]. Although research into the neural mechanisms of visual selective attention is abundant, the relationship between brain structure and visual selective attention behavior has been understudied. Voxel-based morphometry (VBM) was used to assess the relationship between gray matter volumes (GMV) of structural MRI and cognition behavior [ 22 , 23 ]. To the best of our knowledge, reduced gray matter volume in the anterior cingulate cortex (ACC) was associated with selective inattention scores in ADHD patients [ 24 ], increased attentional bias to threat in anxious individuals was associated with greater GMV in the middle frontal gyrus and superior frontal gyrus [ 25 ], poor executive shifting attention in the very preterm born children was associated with reduced GM in the right superior temporal cortex and bilateral thalami[ 26 ], systemizing quotient (SQ) questionnaire in the 5–15 years healthy children showed a significant negative correlation with the regional GMV of the left posterior parietal cortex [ 27 ]. However, these studies do not a direct relationship between brain structure and visual selective attention behavior in healthy adults. Transcranial magnetic stimulation (TMS) was applied to cognitive neuroscience to explore the causal relationship between the brain and behavior [ 28 – 32 ]. Different patterns of TMS of fronto-parietal cortex could modulate visual attention behavior [ 33 – 36 ]. The five-pulses repetitive TMS (rTMS) over the SPL interfered with the processing of arousal associated with the informative peripheral cueing paradigm [ 16 ]. The single-pulse TMS was applied 100ms after the probe onset over the DLPFC or the PPC, changing the negative and positive priming effect, which reflects the top-down mechanism in the special TMS time applied to the right FPN [ 37 ]. Although previous studies have confirmed that TMS can modulate attention behavior, the modulation effect of TMS on visual selective attention at different attention times is unclear. The aim is to explore how different times TMS affects visual selective attention by stimulating the FPN. Furthermore, this study was also designed to assess the relationship between brain structure and visual selective attention behavior. Therefore, we used single-pulse TMS to stimulate the right DLPFC (rDLPFC) and right SPL (rSPL) at early and late times after pop-out and search stimuli displayed onset. we hypothesized that different times of single-pulse TMS over FPN can effectively regulate visual selection attention behavior, and TMS delivered to rDLPFC and rSPL improved top-down and bottom-up attention. 2 Methods 2.1 Participants Sixty-six (thirty-six males, age range: 18–26 years, mean age: 22.18 ± 2.27 years) healthy and right-handed volunteers from the University of Electronic Science and Technology of China (UESTC) were recruited. Participants had normal or corrected vision, no color blindness or color weakness, and no history of neurological or psychiatric disorders. Since the behavioral accuracy (ACC) of two females was lower than three standard deviations, sixty-four participants were included in the analysis (active TMS group: n = 32; sham TMS group: n = 32). The sample size was calculated a priori using G*Power 3.1.9.7 software, and the sample size was at least N = 54 at a significance level α = 0.05, a medium effect (f = 0.25), and 95% statistical power. The study was approved by the ethics and human protection committee of the UESTC and conducted in line with the Declaration of Helsinki. 2.2 Stimuli and procedure The pop-out and search tasks were designed based on a previous study [ 3 ]. In the pop-out task, the target and distractors had distinct colors and orientations, whereas in the search task, the target and distractors had different orientations but the same color. Isosceles triangle consisted of two colors (red and green) with eight orientations (clockwise 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). Each mini block began with the target for 1500ms, followed by a "+" fixation for 500ms, and then the stimulus (pop-out or search) lasted for 500ms (Fig. 1 a). Then, intertrial interval (ITI) was presented for 1500ms. After pop-out or search stimuli were displayed, single-pulse TMS stimulated the rDLPFC and rSPL. The formal trials were divided into four sessions, each with thirty-two mini blocks containing four trials, for a total of 512 trials. The E-prime 3.0 (Psychology program Tools, Pittsburgh, USA) was used to present stimuli and record behavioral data. 2.3 Transcranial magnetic stimulation protocol A 70 mm figure-of-eight coil connected Magstim Rapid 2 stimulator (Magstim Company Limited, Whiteland, United Kingdom) to deliver a single TMS pulse. A stereotactic navigation system (BrainSight Frameless, Rogue Research, Montreal, Canada) was used to coil the localization of the target sites. The anatomical T1 image of each participant was obtained in the rest state before TMS. A head model was constructed based on the T1 images, and then the constructed model was matched with a head based on the brow center, eyes, and ears as a reference. The TMS location was marked in Brainsight with T1 images of each participant, and the coil was adjusted to the center of the target location to apply the TMS. The single-pulse TMS intensity was set at 100% of the resting motor threshold (RMT)(71.25 ± 5.56% of the maximum stimulator output), which was defined as the lowest stimulus intensity that single-pulse TMS over M1 (C3 of the 10–20 EEG system) induced visual muscles twitches of the contralateral hand resting in five out of ten consecutive trials at least [ 38 , 39 ]. Ten consecutive pulses were delivered for each intensity, starting at 50% of the maximum stimulator output. The stimulation target locations were centered on Montreal Neurological Institute (MNI) coordinates for rDLPFC (x = 39, y = 39, z = 30) and rSPL (x = 27, y = − 66, z = 51) from activation regions of pop-out and search tasks [ 40 ] ( Fig. 1 b ). Based on the reaction time (RT) of the saccade (visual pop-out: 233 ± 33ms; visual search: 272 ± 43ms) and times of fronto-parietal neurons first began during the pop-out and search tasks (LIP neuron began 170ms before pop-out target saccade; LPFC neuron began 40ms before search target saccade)[ 3 ], we determined the early time (30-90ms) and late time (200-260ms). Because it took the TMS stimulator 16-17ms to receive the stimulated order from E-Prime, we jittered the onset of TMS relative to the stimuli around early time (33ms, 50ms, 66ms, and 83ms) and late time (216ms, 233ms, 250ms, and 266ms). We used single-pulse TMS to stimulate rDLPFC and rSPL at early and late time points (random TMS time points) after pop-out and search stimuli onset. The interval of two TMS sites was fifteen minutes, and each target site received stimulation after completing 256 trials, so each participant completed 512 trials in total. To reduce the practice effect and order effect, the order of TMS sites was counterbalanced with the ABBA design. The operations process of TMS strictly enforced in accordance with safety manual [ 41 – 43 ]. 2.4 structural MRI data collection The 5T GE scanner with an eight-channel head coil (General Electric, Milwaukee, WI, USA) was used to obtain anatomical MRI images to scan the brains of participants within the rest state. T1-weighted sequences were collected with the following parameters: repetition time (TR) = 5.96ms, echo time (TE) = 1.96ms, voxel size = 1×1×1mm 3 , flip angle (FA) = 9°, field of view (FOV) = 256×256mm 2 , 152 slices, slice thickness = 1mm. 2.5 Data analysis 2.5.1 Behavioral data The design was a 2 (TMS group: active TMS and sham TMS) × 2 (TMS sites: rDLPFC and rSPL) × 2 (TMS times: early and late) × 2 (tasks: pop-out and search) mixed design. The between-subject factor was TMS group, and the within-subject factors were TMS sites, TMS times, and tasks. The ACC and reaction time (RT) were analyzed on repeated measurement analysis of variance (ANOVA) and t-test. To explore the TMS effect, we removed trials that RT less than TMS times. ACC was the mean of the number of correct trials and RT was the mean of correct and 200-1200ms (including 200ms and 1200ms) trials. The ΔACC and ΔRT were the differences between pop-out and search tasks (i.e.ΔACC = ACC pop−out −ACC search ;ΔRT = RT search −RT pop−out ). The SPSS 20.0 (IBM Corp, Armonk, NY) performed statistical analyses. Post-test using two-tailed t-tests to compare conditions differences and Bonferroni correction to correct the experiment-wise error rate (type I error) when using multiple t-tests [ 44 ]. The p < 0.05 shows that the difference between different conditions is significant. 2.5.2 Structural MRI data The VBM analysis was used to assess the correlation between the GMV and visual selective attention behavior indexes (ACC and RT)[ 45 ]. The CAT12 Toolbox runs within SPM12 (Statistical Parametric Mapping; Wellcome Trust Centre for Neuroimaging, London, UK) and was used to pre-process the T1 image based on the MATLAB R2013a (MathWorks, Sherborn, MA, USA). The pre-process included five steps: (1) check for orientation artifacts of raw images; (2) T1 images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) according to probability density; (3) normalized images to MNI space with image resampling 1.5×1.5×1.5mm 3 ; (4) estimated total intracranial volume (TIV) as a covariate for modulated data in VBM analysis to correct for different brain sizes and volumes; (5) smoothed GM images with Gaussian kernel of 8 mm full width at half maximum (FWHM) to obtain higher signal to noise ratio (SNR) and reduce individual differences. From the CAT12 manual, TIV of active and sham TMS group didn't correlate too much with visual selective attention behavior (active TMS group_GMV and ACC: r = −0.042, p = 0.82; active TMS group_GMV and RT: r = 0.039, p = 0.832; sham TMS group_GMV and ACC: r = 0.336, p = 0.06; sham TMS group_GMV and RT: r = 0.009, p = 0.962) (Supplementary Fig. 1). We used multiple regression analysis and an uncorrected threshold of 𝑃 < 0.001 with a cluster size of at least 40 voxels. The nuisance variables were gender, age, and TIV. Voxels with absolute values < 0.2 in the GM images were excluded to eliminate boundary effects between GM and WM. To obtain the r-value, the partial correlation analysis was used to analyze the correlation between GMV and visual selective attention behavior (ACC and RT) by SPSS 20.0. The p < 0.05 indicated that a linear relationship between two variables is significant. 3 Results 3.1 Behavioral results A three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) to analyze the ACC for the pop-out task and the search task, respectively. For the pop-out task, results showed a significant main effect of TMS times ( F (1, 62) = 16.235, p < 0.001, \({\eta }_{p}^{2}\) = 0.208). When rSPL of the active TMS group was delivered stimulation, ACC of late TMS was significantly higher than early TMS ( t (31) = 2.476, p = 0.019, Cohen’s d = 0.438) ( Fig. 2 a ) . There were no other main effect and interaction. For the search task, results showed a significant main effect of TMS times ( F (1, 62) = 9.977, p = 0.002, \({\eta }_{p}^{2}\) = 0.139), an interaction between the TMS group and TMS sites ( F (1, 62) = 7.988, p = 0.006, \({\eta }_{p}^{2}\) = 0.114), and an interaction between the TMS group, TMS sites, and TMS times ( F (1, 62) =12.097, p < 0.001, \({\eta }_{p}^{2}\) =0.163). The other main effect and interaction were not significant. Further analysis showed that ACC of late TMS over rSPL was significantly higher than early TMS for the active TMS group ( t (31) = 4.539, p < 0.001, Cohen’s d = 0.802), the difference between late TMS of rDLPFC and early TMS of rDLPFC ( t (31) = 2.99, p = 0.005, Cohen’s d = 0.529) and late TMS of rSPL( t (31) = 4.131, p < 0.001, Cohen’s d = 0.73) were also significant in the sham TMS group ( Fig. 2 b ). This result suggested that late TMS over rSPL improved the ACC of top-down attention. In addition, when late TMS stimulated rDLPFC, there was a significant difference between the active TMS and sham TMS group ( t (62) = 2.4, p = 0.019, Cohen’s d = 0.6). Late TMS over rDLPFC reduced the ACC of top-down attention in the active TMS group compared to the sham TMS group. To explore the TMS site's effect on ACC, two-way repeated measures ANOVA (TMS group and TMS sites) were used to analyze ACC of early and late TMS times, respectively (Supplementary Fig. 2) . The late TMS over rDLPFC decreased ACC to eliminate ACC difference between rDLPFC and rSPL for top-down attention. For the RT of the pop-out task and search task, we also used three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) to analyze, respectively. For RT of the pop-out task, results showed that a significant main effect of TMS times ( F (1, 62) = 185.272, p < 0.001, \({\eta }_{p}^{2}\) = 0.749) and interaction between the TMS group, TMS sites, and TMS times ( F (1, 62) = 5.761, p = 0.376, \({\eta }_{p}^{2}\) = 0.013). Further paired t-test showed that difference between early TMS and late TMS was significant for each TMS group and TMS sites (active TMS over rDLPFC: t (31) = 10.172, p < 0.001, Cohen’s d = 1.798; active TMS over rSPL: t (31) =10.392, p < 0.001, Cohen’s d = 1.837; sham TMS over rDLPFC: t (31) = 6.61, p < 0.001, Cohen’s d = 1.168; sham TMS over rSPL: t (31) = 9.194, p < 0.001, Cohen’s d = 1.625) ( Fig. 2 c ) . There was no other significant main effect and interaction. For RT of the search task, there was a significant main effect of TMS sites ( F (1, 62) = 5.369, p = 0.024, \({\eta }_{p}^{2}\) = 0.08), a significant main effect of TMS times ( F (1, 62) = 29.576, p < 0.001, \({\eta }_{p}^{2}\) = 0.323), and a significant interaction between the TMS group and TMS times ( F (1, 62) = 7.547, p = 0.008, \({\eta }_{p}^{2}\) = 0.109). The paired t-test showed that search RT for late TMS was significantly longer than early TMS in active TMS over rDLPFC ( t (31) =5.15, p < 0.001, Cohen’s d = 0.91) and rSPL( t (31) = 4.13, p < 0.001, Cohen’s d = 0.73) ( Fig. 2 d ) . There was no other significant main effect and interaction. Compared with the search task, the pop-out task had lower ACC and longer RT, so the ACC and RT difference values between pop-out and search tasks were used as dependent variables to analyze ΔACC and ΔRT (ΔACC = ACC pop−out − ACC search ; ΔRT = RT search − RT pop−out ) under different TMS conditions. For ΔACC, a three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) showed an interaction between the TMS group and TMS sites ( F (1, 62) = 6.861, p = 0.011, \({\eta }_{p}^{2}\) = 0.1) and interaction between the TMS group, TMS sites, and TMS times ( F (1, 62) = 8.357, p = 0.005, \({\eta }_{p}^{2}\) = 0.119). There was no other main effect and interaction. For active TMS group, a paired t-test showed that ΔACC of early TMS over rSPL was significantly higher than that of late TMS ( t (31) = 3.013, p = 0.005, Cohen’s d = 0.533) ( Fig. 2 e ) . For sham TMS group, ΔACC of early TMS over rDLPFC was significantly higher than that of late TMS ( t (31) = 2.209, p = 0.035, Cohen’s d = 0.391), the ΔACC of late TMS over rSPL was significantly higher than that of rDPLFC ( t (31) = 4.443, p < 0.001, Cohen’s d = 0.785). A two independent-sample t-test showed that the ΔACC of the active TMS group was significantly higher than that of the sham TMS group ( t (62) = 2.239, p = 0.029, Cohen’s d = 0.56) when late TMS stimulated rDLPFC ( Fig. 2 e ) . This result suggested that late TMS over rDLPFC increased cognitive load differences between top-down and bottom-up attention. For ΔRT, a three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) only showed a significant main effect of TMS times ( F (1, 62) = 28.32, p < 0.001, \({\eta }_{p}^{2}\) = 0.314). The paired t-test showed that there was a significant difference between early and late TMS in sham TMS over rSPL ( t (31) = 4.79, p = 0.001, Cohen’s d = 0.847) ( Fig. 2 f ) . There were no other main effects and interaction. To explore the TMS site's effect on ΔACC, we used two-way repeated measures ANOVA (TMS group and TMS sites) to analyze ΔACC for early and late TMS times, respectively (Supplementary Fig. 3) . The late TMS over rDLPFC increased the cognitive load difference between top-down and bottom-up attention to eliminating the difference of two TMS sites in active TMS group. In addition, TMS times consist of eight points (early TMS time points: 33ms, 50ms, 66ms, 83ms; late TMS time points: 216ms, 233ms, 250ms, 266ms), therefore we viewed ACC in each TMS time point. Due to the small number of trials (less than 16 trials) at TMS time points, the ACC at each TMS time point was not statistically analyzed. The ACC for pop-out task at eight TMS time points didn't show regularity ( Fig. 3 a ) . The ACC of search task at eight TMS time points showed regularity, specifically, active TMS group and sham TMS group intersected at 83ms (early TMS) and 216ms (late TMS) connecting lines when TMS stimulated rSPL ( Fig. 3 b ) . When early TMS stimulated rSPL, search ACC in active TMS group was always lower than that of sham TMS group, and as late TMS time pints increased, search ACC in active TMS group gradually increased and was always higher than that of sham TMS group. This result confirmed that late TMS over rSPL improved the ACC of top-down attention. The RT for pop-out and search tasks at eight TMS time points showed regularity. For each TMS group and TMS sites, the RT of the pop-out task was longer for the four late TMS time points than for four early TMS time points ( Fig. 3 c ) , whereas the RT of the search task gradually increased with early TMS time points and decreased with late TMS time points ( Fig. 3 d ) . 3.2 VBM results VBM analysis revealed the correlation between the behavior index (ACC and RT) of visual selection attention task and gray matter volume of brain regions in active TMS and sham TMS groups, brain regions of the active TMS group were more distributed in the fronto-parietal cortex ( Table 1 )( Fig. 4 ) . Specifically, for active TMS group, our results suggested a positive correlation between ACC and bilateral superior frontal gyrus and right precentral gyrus, and a negative correlation between ACC and left supramarginal gyrus. RT of the active TMS group was significantly positively correlated with the right inferior frontal gyrus (orbital part) and left superior frontal gyrus (medial), and negatively correlated with the calcarine sulcus. For the sham TMS group, there was a positive correlation between ACC and right postcentral gyrus and right inferior frontal gyrus (triangular part), and a positive correlation between RT and right pallidum and left thalamus. Based on the above behavior-brain correlations, the frontal-parietal cortex contains bilateral superior frontal gyrus, right precentral gyrus, left supramarginal gyrus, right inferior frontal gyrus (orbital part), and left superior frontal gyrus (medial) in the active TMS group, while right postcentral gyrus and right inferior frontal gyrus (triangular part) belong to the frontal-parietal cortex in the sham TMS group. Table 1 Correlation between attention behavior and GMV of the brain in the active and sham TMS group Brain regions H Cluster (voxels) Peak MNI coordinates T r x y z active TMS_ACC Superior frontal gyrus L 77 −14 15 59 5.14 0.65*** Precentral gyrus R 46 47 −6 45 4.11 0.612*** Superior frontal gyrus L 97 −23 3 65 4.02 0.637*** Superior frontal gyrus R 46 18 20 59 3.71 0.58*** Supramarginal gyrus L 68 −57 −26 38 4.3 −0.613*** active TMS_RT Inferior frontal gyrus, orbital part R 56 32 33 23 4.27 0.603*** Superior frontal gyrus, medial L 109 −9 63 27 4.11 0.608*** Calcarine sulcus L 497 −2 −86 −3 5.87 −0.747*** sham TMS_ACC Postcentral gyrus R 80 21 −35 69 4.46 −0.644*** Inferior frontal gyrus, triangular part R 100 44 38 6 4.42 −0.632*** sham TMS_RT Pallidum R 45 20 2 0 4.17 0.6*** Thalamus L 62 −17 −23 3 4.16 0.6*** Note. The attention behavior combined with the pop-out and search tasks. The minimum cluster is 40 voxels. p < 0.001, uncorrected; *** p < 0.001. Abbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute. We assessed the correlation between the behavioral index (ACC and RT) of the pop-out and search task and the gray matter volume of brain regions in the active TMS group, respectively ( Table 2 and Fig. 5 ). For the pop-out task, ACC was negatively correlated with right parahippocampal gyrus, right angular gyrus, right middle frontal gyrus (orbital part), left postcentral gyrus, and right inferior temporal gyrus. RT of the pop-out task was positively correlated with the left superior frontal gyrus (medial orbital), right middle frontal gyrus, and left angular gyrus, and negatively correlated with left calcarine sulcus. For the search task of the active TMS group, there was a positive correlation between ACC and left superior frontal gyrus, right precentral gyrus, and left superior frontal gyrus, and a negative correlation between ACC and right cerebellum. RT of the search task was significantly positively correlated with the left superior frontal gyrus (medial), the right Inferior frontal gyrus (orbital part), and negatively correlated with the left calcarine sulcus, right inferior temporal gyrus, and right postcentral gyrus. Behavior-brain correlation regions of the active TMS group were widely distributed in the frontal-parietal cortex, including the right middle frontal gyrus (orbital part), left postcentral gyrus, left superior frontal gyrus (medial orbital), right middle frontal gyrus, and left angular gyrus, right precentral gyrus, right Inferior frontal gyrus (orbital part), and right postcentral gyrus. Table 2 Correlation between attention behavior and GMV of the brain in the active TMS group Brain regions H Cluster (voxels) Peak MNI coordinates T r x y z pop-out_ACC Parahippocampal gyrus R 137 35 −15 −24 4.99 −0.666*** Angular gyrus R 153 50 −57 24 4.78 −0.682*** Middle frontal gyrus, orbital part R 44 30 56 −17 4.48 −0.633** Postcentral gyrus L 132 −65 −8 24 4.16 −0.621** Inferior temporal gyrus R 107 51 −23 −33 4.12 −0.604** pop-out_RT Superior frontal gyrus, medial orbital L 73 −2 60 −12 4.18 0.614*** Middle frontal gyrus R 102 36 54 11 4.14 0.622*** Angular gyrus L 134 −54 −51 35 4.02 0.638*** Calcarine sulcus L 364 −2 −86 −5 5.31 −0.703*** search_ACC Superior frontal gyrus L 42 −14 15 59 4.55 0.627*** Precentral gyrus R 62 45 −5 44 4.22 0.612*** Superior frontal gyrus L 115 −21 3 63 4.09 0.623*** Cerebellum R 40 17 −66 −21 3.84 −0.584*** search_RT Superior frontal gyrus, medial L 105 −9 63 27 4.26 0.613*** Inferior frontal gyrus, orbital part R 44 32 33 −23 4.06 0.594*** Calcarine sulcus L 494 −3 −87 −5 5.6 −0.75*** Inferior temporal gyrus R 63 53 −33 −20 4.42 −0.643*** Postcentral gyrus R 156 51 −20 42 4.01 −0.601*** Note. The minimum cluster is 40 voxels. p < 0.001, uncorrected. *** p < 0.001 Abbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute. Behavior-brain correlations of pop-out and search tasks were also assessed in the sham TMS group, respectively. For the pop-out task of the sham TMS group, there was a positive correlation between ACC and left superior parietal lobule, right parahippocampal gyrus, and right thalamus, a positive correlation between RT and left inferior temporal gyrus and right fusiform gyrus, and a negative correlation between RT and left superior temporal gyrus. For the search task of the sham TMS group, there was a negative correlation between ACC and right inferior frontal gyrus (triangular part), right postcentral gyrus, right superior frontal gyrus (medial orbital), and right precentral gyrus, a positive correlation between ACC and left thalamus and right pallidum ( Table 3 and Fig. 6 ). Table 3 Correlation between attention behavior and GMV of the brain in the sham TMS group Brain regions H Cluster (voxels) Peak MNI coordinates T r x y z pop-out_ACC superior parietal lobule L 225 −21 −66 41 4.94 0.688*** Parahippocampal gyrus R 225 27 −32 −11 4.18 0.627*** Thalamus R 56 8 −17 0 4.12 0.611*** pop-out_RT Inferior temporal gyrus L 42 −47 −27 −26 3.99 0.599*** Fusiform gyrus R 78 24 −2 −41 3.95 0.592*** Superior temporal gyrus L 55 −60 −30 23 4.07 −0.602*** search_ACC Inferior frontal gyrus, triangular part R 84 42 38 5 4.33 −0.621*** Postcentral gyrus R 64 21 −35 69 4.15 −0.632*** Superior frontal gyrus, medial orbital R 41 32 56 −8 3.86 −0.597*** Precentral gyrus R 92 38 −15 42 3.76 −0.589*** search_RT Thalamus L 202 −17 −23 3 4.58 0.61*** Pallidum R 47 21 0 −2 4.21 0.603*** Note. The minimum cluster is 40 voxels. p < 0.001(uncorrected). Abbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute. 4 Discussions We focused on behavior and structural brain functions of visual selective attention after single-pulse TMS. Our findings reveal the temporal difference of TMS modulation effect on visual selective attention behavior and the relationship between GMV in the fronto-parietal cortex and visual selective attention behavior. 4.1 The regulation effect of different time TMS on selective attention As hypothesized, different time TMS over the FPN nodes modulated visual selective attention. Compared with early TMS over the rSPL, late TMS increased ACC and RT of top-down attention. Prior studies showed timing effect on RT of attention was reflected in the earlier TMS time and increased RT. The single-pulse TMS over rPPC was applied 100ms after stimuli onset, prolonging RT of visual attention [ 46 ]. The interaction between the initial state of the cortex and cognitive tasks led to an inhibition effect of single-pulse TMS [ 30 , 47 ]. Some studies applied single-pulse TMS early after the stimulus displayed onset, such as 50ms [ 48 ], 100ms [ 49 , 50 ], and 150ms[ 51 ]. The pre-TMS applied to FPN can also change the attention behavior of target detection [ 52 , 53 ] and event-related potentials (ERP) components [ 54 ]. Inconsistent with the hypothesis, results showed that TMS over FPN had no modulation effect on top-down attention. The pop-out task showed a ceiling effect because of a quite salient target resulting in no significant difference in bottom-up attention across TMS conditions. 4.2 Different cognitive loads of top-down and bottom-up attention Compared with the search task, the pop-out task had higher ACC and shorter RT. The pop-out task required more color coding and the salient feature improved target detection (i.e. increased ACC and decreased RT)[ 55 , 56 ]. The different goal features of the pop-out task and the search task resulted in different cognitive loads of bottom-up and top-down attention. Low cognitive load acted more on bottom-up attention, while high cognitive load involved more top-down attention and was associated with longer RT and synergistic interaction effect [ 57 , 58 ]. The rDLPFC was associated with an increased cognitive load between the pop-out and search tasks [ 40 ], our studies also had consistent results that late TMS over rDLPFC increased the cognitive load difference between top-down and bottom-up attention. It also suggested the TMS time effect and the role of the rDLPFC in visual selection attention. For the rSPL, late TMS decreased the cognitive load difference between top-down and bottom-up attention. Prior findings revealed right parietal cortex with TMS holds a sensitive role in the interaction between visual attention and working memory load [ 59 , 60 ], high cognitive load increased the activity of the SPL [ 61 , 62 ]. 4.3 The correlation between GMV of fronto-parietal cortex and visual selection attention Previous studies focused on activity regions of pop-put and search tasks, our study combined structural MRI images and behavior to focus on GM regions associated with selective attention. Compared with the sham TMS group, regional GMV correlated with visual selective attention was distributed in the fronto-parietal cortex in the active TMS group, which also demonstrated that TMS effectively modulated attention behavior and the relationship between the fronto-parietal cortex and visual selective attention. The GMV in the middle frontal gyrus was involved in attentional control processing in the young and old [ 63 ], and the lateral frontal and orbital frontal regions significantly predicted attention [ 64 ]. For attention impairment disorders, gray matter volume in the prefrontal cotes was smaller in ADHD patients [ 65 , 66 ]. Reduced effective connectivity of fronto-parietal circuits in early-stage Alzheimer's disease (AD) correlated with gray matter volume in fronto-parietal regions, leading to impaired top-down attention[ 67 ]. 4.4 Limitation It is important to mention limitations. First, the same coordinates of the TMS sites for all participants may not be the most significant activation cluster. Although we used a precise localization navigation system, individuals have differences in activation coordinates in the rDLPFC and rSPL. In the future, individualization of TMS sites can increase the reliability and precision of the TMS effect [ 68 – 70 ]. Second, this study only explored the causal relationship between the FPN and visual selective attention behavior. Future studies could use congruent TMS-fMRI to explore the TMS effect on brain activity [ 71 – 73 ]. 5 Conclusions To conclude, different times of TMS over the fronto-parietal network to modulate visual selective attentional behavior, especially the late TMS over rSPL improved top-down attention and decreased cognitive load difference between top-down and bottom-up attention. Active TMS induced more regions of fronto-parietal cortex correlated with visual selection attention. These findings demonstrate the cause role of the fronto-parietal network on visual selective attention behavior and the contribution of fronto-parietal cortex to visual selective attention. Declarations Funding This work was supported by the National Natural Science Foundation of China [grant number 62176045], by Sichuan Science and Technology Program [grant number 2023YFS0191], 111 project [grant number B12027], and the Fundamental Research Funds for the Central Universities [grant number ZYGX2020FRJH014]. Author Contribution Qiuzhu Zhang: Formal analysis, Data curation, Methodology, Investigation, Writing–original draft. Danmei Zhang: Data curation, Methodology. Gulibaier Alimu: Data curation, Investigation. Guragai Bishal: Writing-Reviewing and Editing. Wenjun Li: Visualization, Writing-Reviewing and Editing. Junjun Zhang: Supervision, Writing-Reviewing and Editing. Zhenlan Jin: Supervision, Writing-Reviewing and Editing. Ling Li: Methodology, Supervision, Writing-Reviewing and Editing. Data availability Data or materials for the experiments are available upon reasonable request. Ethics approval The Experiment procedure received ethical approval from the committee for approval of the University of Electronic Science and Technology of China (UESTC) and was conducted in accordance with the Declaration of Helsinki. Conflict of interest The authors have no conflicts of interest to declare. References Murphy G, Groeger JA, Greene CM. Twenty years of load theory-Where are we now, and where should we go next? Psychonomic Bulletin & Review 2016;23(5):1316-40. Ibos G, Duhamel J-R, Ben Hamed S. A functional hierarchy within the parietofrontal network in stimulus selection and attention control. J Neurosci 2013;33(19):8359-69. Buschman TJ, Miller EK. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 2007;315(5820):1860-2. Li L, Gratton C, Yao D, Knight RT. Role of frontal and parietal cortices in the control of bottom-up and top-down attention in humans. Brain Res 2010;1344:173-84. Li L, Gratton C, Fabiani M, Knight RT. Age-related frontoparietal changes during the control of bottom-up and top-down attention: an ERP study. Neurobiol Aging 2013;34(2):477-88. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002;3(3):201-15. Katsuki F, Constantinidis C. Bottom-up and top-down attention: different processes and overlapping neural systems. Neuroscientist 2014;20(5):509-21. Giesbrecht B, Woldorff MG, Song AW, Mangun GR. Neural mechanisms of top-down control during spatial and feature attention. Neuroimage 2003;19(3):496-512. Ptak R. The Frontoparietal attention network of the human brain: action, a aliency, and a priority map of the environment. Neuroscientist 2012;18(5):502-15. Spreng RN, Sepulcre J, Turner GR, Stevens WD, Schacter DL. Intrinsic architecture underlying the relations among the default, dorsal Attention, and frontoparietal control networks of the human brain. Journal of Cognitive Neuroscience 2013;25(1):74-86. Xu GQ, Lan Y, Huang DF, Chen SZ, Chen L, Zeng JS, et al. The study on the frontoparietal networks by continuous theta burst stimulation in healthy human subjects. Behavioural Brain Research 2013;240:60-8. Cieslik EC, Zilles K, Kurth F, Eickhoff SB. Dissociating bottom-up and top-down processes in a manual stimulus-response compatibility task. Journal of Neurophysiology 2010;104(3):1472-83. Pahi MR, Cavalli J, Nees F, Flor H, Andoh J. Disruption of the prefrontal cortex improves implicit contextual memory-guided attention: combined behavioral and electrophysiological evidence. Cerebral Cortex 2020;30(1):20-30. Wu Y, Wang JJ, Zhang Y, Zheng DC, Zhang JF, Rong ML, et al. The neuroanatomical basis for posterior superior parietal lobule control lateralization of visuospatial attention. Frontiers in Neuroanatomy 2016;10:1-9. Banaszkiewicz A, Bola L, Matuszewski J, Szczepanik M, Kossowski B, Mostowski P, et al. The role of the superior parietal lobule in lexical processing of sign language: Insights from fMRI and TMS. Cortex 2021;135:240-54. Du XM, Chen L, Zhou K. The role of the left posterior parietal lobule in top-down modulation on space-based attention: A transcranial magnetic stimulation study. Human Brain Mapping 2012;33(10):2477-86. Bonnard M, Chen S, Gaychet J, Carrere M, Woodman M, Giusiano B, et al. Resting state brain dynamics and its transients: a combined TMS-EEG study. Scientific Reports 2016;6:1-9. Zeng H, Weidner R, Fink GR, Chen Q. Neural correlates underlying the attentional spotlight in human parietal cortex independent of task difficulty. Human Brain Mapping 2017;38(10):4996-5018. Shomstein S. Cognitive functions of the posterior parietal cortex: top-down and bottom-up attentional control. Frontiers in integrative neuroscience 2012;6:1-7. Spadone S, Wyczesany M, Della Penna S, Corbetta M, Capotosto P. Directed flow of beta band communication during reorienting of attention within the dorsal attention network. Brain Connectivity 2021;11(9):717-24. Shomstein S, Lee J, Behrmann M. Top-down and bottom-up attentional guidance: investigating the role of the dorsal and ventral parietal cortices. Experimental Brain Research 2010;206(2):197-208. Ashburner J, Friston KJ. Voxel-based morphometry - The methods. Neuroimage 2000;11(6):805-21. Mechelli A, Price CJ, Friston KJ, Ashburner J. Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging 2005;1(2):105-13. Bonath B, Tegelbeckers J, Wilke M, Flechtner HH, Krauel K. Regional gray matter volume differences between adolescents with ADHD and typically developing controls: Further evidence for anterior cingulate involvement. Journal of Attention Disorders 2018;22(7):627-38. Carlson JM, Fang L. Attentional bias to threat and gray matter volume morphology in high anxious individuals. Cognitive Affective & Behavioral Neuroscience 2022;22(3):600-9. Lean RE, Melzer TR, Bora S, Watts R, Woodward LJ. Attention and regional gray matter development in very preterm children at age 12 years. Journal of the International Neuropsychological Society 2017;23(7):539-50. Sassa Y, Taki Y, Takeuchi H, Hashizume H, Asano M, Asano K, et al. The correlation between brain gray matter volume and empathizing and systemizing quotients in healthy children. Neuroimage 2012;60(4):2035-41. Klomjai W, Katz R, Lackmy-Vallee A. Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS). Annals of physical and rehabilitation medicine 2015;58(4):208-13. Farzan F, Vernet M, Shafi MMD, Rotenberg A, Daskalakis ZJ, Pascual-Leone A. Characterizing and modulating brain circuitry through transcranial magnetic stimulation combined with electroencephalography. Frontiers in neural circuits 2016;10:1-24. Parkin BL, Ekhtiari H, Walsh VF. Non-invasive human brain atimulation in cognitive neuroscience: A primer. Neuron 2015;87(5):932-45. Hallett M. Transcranial magnetic stimulation: A primer. Neuron 2007;55(2):187-99. Ziemann U. Thirty years of transcranial magnetic stimulation: where do we stand? Experimental Brain Research 2017;235(4):973-84. Song PH, Lin H, Liu CY, Jiang YL, Lin YC, Xue Q, et al. Transcranial magnetic stimulation to the middle frontal gyrus during attention modes induced dynamic module reconfiguration in brain networks. Frontiers in Neuroinformatics 2019;13:1-9. Jin ZL, Xie K, Ni XJ, Jin DG, Zhang JJ, Li L. Transcranial magnetic stimulation over the right dorsolateral prefrontal cortex modulates visuospatial distractor suppression. European Journal of Neuroscience 2021;53(10):3394-403. Jigo M, Gong MY, Liu TS. Neural determinants of task performance during feature-based attention in human cortex. Eneuro 2018;5(1):1-15. Ashbridge E, Walsh V, Cowey A. Temporal aspects of visual search studied by transcranial magnetic stimulation. Neuropsychologia 1997;35(8):1121-31. Kehrer S, Kraft A, Koch SP, Kathmann N, Irlbacher K, Brandt SA. Timing of spatial priming within the fronto-parietal attention network: A TMS study. Neuropsychologia 2015;74:30-6. Zanona AD, da Silva ACR, Maciel ABD, do Nascimento LSG, da Silva AB, Bolognini N, et al. Somatosensory cortex repetitive transcranial magnetic stimulation and associative sensory stimulation of peripheral nerves could assist motor and sensory recovery after stroke. Frontiers in Human Neuroscience 2022;16:1-14. Balslev D, Braet W, McAllister C, Miall RC. Inter-individual variability in optimal current direction for transcranial magnetic stimulation of the motor cortex. Journal of Neuroscience Methods 2007;162(1-2):309-13. Wang M, Yu B, Luo C, Fogelson N, Zhang J, Jin Z, et al. Evaluating the causal contribution of fronto-parietal cortices to the control of the bottom-up and top-down visual attention using fMRI-guided TMS. Cortex 2020;126:200-12. Rossi S, Antal A, Bestmann S, Bikson M, Brewer C, Brockmoller J, et al. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2021;132(1):269-306. Rossi S, Hallett M, Rossini PM, Pascual-Leone A, Safety of TMSCG. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2009;120(12):2008-39. Wassermann EM. Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the international workshop on the safety of repetitive transcranial magnetic stimulation, June 5-7, 1996. Evoked Potentials-Electroencephalography and Clinical Neurophysiology 1998;108(1):1-16. Armstrong RA. When to use the Bonferroni correction. Ophthalmic and Physiological Optics 2014;34(5):502-8. Lu Y-T, Chang W-N, Chang C-C, Lu C-H, Chen N-C, Huang C-W, et al. Insula volume and salience network are associated with memory decline in parkinson disease: complementary analyses of voxel-based morphometry versus volume of interest. Parkinsons Disease 2016;2016:1-8. Fuggetta G, Pavone EF, Walsh V, Kiss M, Eimer M. Cortico-cortical interactions in spatial attention: A combined ERP/TMS study. Journal of Neurophysiology 2006;95(5):3277-80. Waldvogel D, van Gelderen P, Muellbacher W, Ziemann U, Immisch I, Hallett M. The relative metabolic demand of inhibition and excitation. Nature 2000;406(6799):995-8. Koivisto M, Grassini S, Hurme M, Salminen-Vaparanta N, Railo H, Vorobyev V, et al. TMS-EEG reveals hemispheric asymmetries in top-down influences of posterior intraparietal cortex on behavior and visual event-related potentials. Neuropsychologia 2017;107:94-101. Mattavelli G, Rosanova M, Casali AG, Papagno C, Lauro LJR. Timing of emotion representation in right and left occipital region: Evidence from combined TMS-EEG. Brain and Cognition 2016;106:13-22. Hartwigsen G, Price CJ, Baumgaertner A, Geiss G, Koehnke M, Ulmer S, et al. The right posterior inferior frontal gyrus contributes to phonological word decisions in the healthy brain: Evidence from dual-site TMS. Neuropsychologia 2010;48(10):3155-63. Ricci R, Salatino A, Li XB, Funk AP, Logan SL, Mu QW, et al. Imaging the neural mechanisms of TMS neglect-like bias in healthy volunteers with the interleaved TMS/fMRI technique: preliminary evidence. Frontiers in Human Neuroscience 2012;6:1-13. Duecker F, Sack AT. Pre-stimulus sham TMS facilitates target detection. Plos One 2013;8(3):1-6. Jacobs C, Goebel R, Sack AT. Visual awareness suppression by pre-stimulus brain stimulation; a neural effect. Neuroimage 2012;59(1):616-24. Taylor PCJ, Thut G. Brain activity underlying visual perception and attention as inferred from TMS-EEG: A review. Brain Stimulation 2012;5(2):124-9. Becker SI, Valuch C, Ansorge U. Color priming in pop-out search depends on the relative color of the target. Frontiers in Psychology 2014;5:1-11. Zhuang X, Papathomas TV. Cue relevance effects in conjunctive visual search: Cueing for location, color, and orientation. Journal of Vision 2011;11(7):1-13. Wu Q, Chang CF, Xi SS, Huang IW, Liu ZX, Juan CH, et al. A critical role of temporoparietal junction in the integration of top-down and bottom-up attentional control. Human Brain Mapping 2015;36(11):4317-33. Uher R, Brooks SJ, Bartholdy S, Tchanturia K, Campbell IC. Increasing cognitive load reduces interference from masked appetitive and aversive but not neutral stimuli. Plos One 2014;9(4):1-8. Kiyonaga A, Powers JP, Chiu YC, Egner T. Hemisphere-specific parietal contributions to the interplay between working memory and attention. Journal of Cognitive Neuroscience 2021;33(8):1428-41. Yamanaka K, Yamagata B, Tomioka H, Kawasaki S, Mimura M. Transcranial magnetic stimulation of the parietal cortex facilitates spatial working memory: Near-infrared apectroscopy study. Cerebral Cortex 2010;20(5):1037-45. Gennari SP, Millman RE, Hymers M, Mattys SL. Anterior paracingulate and cingulate cortex mediates the effects of cognitive load on speech sound discrimination. Neuroimage 2018;178:735-43. Secen J, Culham J, Ho C, Giaschi D. Neural correlates of the multiple-object tracking deficit in amblyopia. Vision Research 2011;51(23-24):2517-27. Thomsen T, Specht K, Hammar Å, Nyttingnes J, Ersland L, Hugdahl K. Brain localization of attentional control in different age groups by combining functional and structural MRI. Neuroimage 2004;22(2):912-9. Zimmerman ME, Brickman AM, Pau RH, Grieve SM, Tate DF, Gunstad J, et al. The relationship between frontal gray matter volume and cognition varies across the healthy adult lifespan. American Journal of Geriatric Psychiatry 2006;14(10):823-33. Seidman LJ, Valera EM, Makris N, Monuteaux MC, Boriel DL, Kelkar K, et al. Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biological Psychiatry 2006;60(10):1071-80. Mostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE. Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biological Psychiatry 2002;52(8):785-94. Neufang S, Akhrif A, Riedl V, Förstl H, Kurz A, Zimmer C, et al. Disconnection of frontal and parietal areas contributes to impaired attention in very early Alzheimer's disease. Journal of Alzheimers Disease 2011;25(2):309-21. Gallotto S, Schuhmann T, Duecker F, Middag-van Spanje M, de Graaf TA, Sack AT. Concurrent frontal and parietal network TMS for modulating attention. Iscience 2022;25(3):1-14. Menardi A, Ozdemir RA, Momi D, Tadayon E, Boucher P, Vallesi A, et al. Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS. Neuroimage 2022;264:1-13. Sack AT, Kadosh RC, Schuhmann T, Moerel M, Walsh V, Goebel R. Optimizing functional accuracy of TMS in cognitive atudies: A comparison of methods. Journal of Cognitive Neuroscience 2009;21(2):207-21. Bergmann TO, Varatheeswaran R, Hanlon CA, Madsen KH, Thielscher A, Siebner HR. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 2021;237:3-29. Webler RD, Fox J, McTeague LM, Burton PC, Dowdle L, Short EB, et al. DLPFC stimulation alters working memory related activations and performance: An interleaved TMS-fMRI study. Brain Stimulation 2022;15(3):823-32. Bestmann S, Feredoes E. Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future. In: Miller MB, Kingstone A, editors. Year in Cognitive Neuroscience; 2013, p. 11-30. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4237359","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289573359,"identity":"b4c507fc-d01b-4735-8a65-72dacd3dd63d","order_by":0,"name":"Qiuzhu Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Qiuzhu","middleName":"","lastName":"Zhang","suffix":""},{"id":289573360,"identity":"43525fc4-a8af-4076-8c56-0efe89987c2a","order_by":1,"name":"Danmei Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Danmei","middleName":"","lastName":"Zhang","suffix":""},{"id":289573361,"identity":"6bbd189f-2fbc-41a0-9bc1-39bf9d3c3d65","order_by":2,"name":"Gulibaier Alimu","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Gulibaier","middleName":"","lastName":"Alimu","suffix":""},{"id":289573362,"identity":"992ab0fe-51e7-4e6e-be2a-ca30f299fb6e","order_by":3,"name":"Guragai Bishal","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Guragai","middleName":"","lastName":"Bishal","suffix":""},{"id":289573364,"identity":"9fb8e0dc-397e-4e2d-a903-c8e66ec39bb4","order_by":4,"name":"WenJuan Li","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"WenJuan","middleName":"","lastName":"Li","suffix":""},{"id":289573365,"identity":"1abb7bbf-61d7-41f8-8b5e-14e2adbb46e1","order_by":5,"name":"Junjun Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Zhang","suffix":""},{"id":289573366,"identity":"c1f03e17-eb63-4fbd-97ca-93c1d652b8c7","order_by":6,"name":"Zhenlan Jin","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Zhenlan","middleName":"","lastName":"Jin","suffix":""},{"id":289573367,"identity":"4d241192-2c38-4fc1-9a54-2db21affa0fd","order_by":7,"name":"Ling Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACPmbmhgMMDDYwPjNhLWzMjCAtaTDVxGhhYGwAUodJ0cLO2Hi44Nf5xH7+8wc/MFRYJzawnz1A0GGHZ/bdTpw5I5lZguFMemIDT14CYS28PbcTN9xgBjqy7XBigwSPATFaziXuP38YqOUfsVp4fhxI3MCQDAoKom1pSDaecSPZWCLhWLpxG08Ofi38/IcPf+b5Yyfb33/w4YcPNday/exn8GsBA8Y2KCMBZC9h9SDwhzhlo2AUjIJRMEIBAOpkQhdh4zP7AAAAAElFTkSuQmCC","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-08 15:01:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4237359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4237359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54928696,"identity":"4b0f009b-9dda-4bf1-9472-2b302434b206","added_by":"auto","created_at":"2024-04-18 17:39:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1136682,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental procedure. (a) The process of pop-out and search tasks in a mini-block. In the pop-out task, the target and distractors had different orientations and colors. In the search task, the target and distractors had different orientations and the same color. Each mini block began with a target for 1500ms, then a \"+\" fixation lasted for 500ms, followed by stimulus (pop-out or search) for 500ms. The single TMS stimulated rDLPFC and rSPL at early times (33ms, 50ms, 66ms, and 83ms) and late times (216ms, 233ms, 250ms, and 266ms) after pop-out and search stimuli displayed onset. Participants judged the target to be on the left or right side and pressed \"f\"(left) or \"j\"(right). ITI lasted for 1500ms. One trial contains a fixation, a stimulus, and an ITI. A mini-block consisted of a target and four trials. (b) The single TMS sites at the Montreal Neurological Institute (MNI) coordinates of rDLPFC (x = 39, y = 39, z = 30) and rSPL (x = 27, y = -66, z = 51). Abbreviations: ITI, intertrial interval; rDLPFC, right dorsolateral prefrontal cortex; rSPL, right superior parietal lobule.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/7f1646a2f52fe676e8f508bc.png"},{"id":54928694,"identity":"e9ef4d67-b98b-4629-9832-f5b2aa957970","added_by":"auto","created_at":"2024-04-18 17:39:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2368329,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The ACC of different TMS groups, TMS sites, and TMS times in the pop-out task. (b) The ACC of different TMS groups, TMS sites, and TMS times in the search task. (c) The RT of different TMS groups, TMS sites, and TMS times in the pop-out task. (d) The RT of different TMS groups, TMS sites, and TMS times in the search task. (e) The difference ACC between the pop-out and search tasks in different TMS groups, TMS sites, and TMS times. (f) The difference RT between pop-out and search tasks in different TMS groups, TMS sites, and TMS times. The error bar represents the standard error of the mean (SEM). Each dot represents each participant. Abbreviations: ACC, accuracy; RT, reaction time. ΔACC = ACC \u003csub\u003epop-out \u003c/sub\u003e− ACC \u003csub\u003esearch.\u003c/sub\u003e; ΔRT = RT \u003csub\u003esearch \u003c/sub\u003e− RT \u003csub\u003epop-out. \u003c/sub\u003e*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/31ed486f29544330f9f70c5f.png"},{"id":54928695,"identity":"ecd71bdb-1c41-42b6-9265-59b9471b70fa","added_by":"auto","created_at":"2024-04-18 17:39:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1592990,"visible":true,"origin":"","legend":"\u003cp\u003e(a) For the pop-out task, the ACC of different TMS groups and TMS sites in eight TMS time points. (b) For the search task, the ACC of different TMS groups and TMS sites in eight TMS time points. A drawing of partial enlargement is shown in the upper-left. (c) For the pop-out task, the RT of different TMS groups and TMS sites in eight TMS time points. (d) For the search task, the RT of different TMS groups and TMS sites in eight TMS time points. The error bar represents the standard error of the mean (SEM).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/33b50582baedc53d0590f8c0.png"},{"id":54928697,"identity":"eace5986-937e-4676-8e35-d74c7d99a1ca","added_by":"auto","created_at":"2024-04-18 17:39:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3079770,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots and sagittal views of the brain in correlation with attention behavior (combined with the pop-out and search tasks) and gray matter volume of the brain. (a) Correlation between ACC and gray matter volume of brain regions in the active TMS group; (b) Correlation between RT and gray matter volume of brain regions in the active TMS group; (c) Correlation between ACC and gray matter volume of brain regions in the sham TMS group; (d) Correlation between RT and gray matter volume of brain regions in the sham TMS group. The shadow is a 95% confidence interval. Color scale indicates t values.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/f1548ff18d7b74b1f576d1e0.png"},{"id":54928699,"identity":"79428880-22af-4f2b-b45b-6b53d014c103","added_by":"auto","created_at":"2024-04-18 17:39:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4299320,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots and sagittal views of the brain in correlation with attention behavior and gray matter volume of brain in the active TMS group. (a) Correlation between ACC of the pop-out task and gray matter volume of brain regions; (b) Correlation between RT of the pop-out task and gray matter volume of brain regions; (c) Correlation between ACC of the search task and gray matter volume of brain regions; (d) Correlation between RT of the search task and gray matter volume of brain regions. The shadow is a 95% confidence interval. Color scale indicates t values.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/7f9e2634e25c9e646feca3f1.png"},{"id":54929281,"identity":"56527111-6ca6-4b11-a670-30753b036a25","added_by":"auto","created_at":"2024-04-18 17:47:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2811375,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots and sagittal views of the brain in correlation with attention behavior and gray matter volume of the brain in the sham TMS group. (a) Correlation between ACC of the pop-out task and gray matter volume of brain regions; (b) Correlation between RT of the pop-out task and gray matter volume of brain regions; (c) Correlation between ACC of the search task and gray matter volume of brain regions; (d) Correlation between RT of the search task and gray matter volume of brain regions. The shadow is a 95% confidence interval. Color scale indicates t values.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/499aa04d45a05ae59552c2a6.png"},{"id":58758455,"identity":"d4939ea6-75b2-435b-a22f-9979f3aac4a4","added_by":"auto","created_at":"2024-06-20 18:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19697709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/5e7c489a-5f4a-40cf-a13a-006b5f75d4c9.pdf"},{"id":54928700,"identity":"45d79d86-65ea-474c-b266-b5b23aa57368","added_by":"auto","created_at":"2024-04-18 17:39:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4135648,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4237359/v1/5de40f825314ccbb9863cc13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Different times TMS over fronto-parietal network regulates visual selective attention","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSelective attention helps individuals process information from different sources separately, select meaningful and behaviorally relevant information, and filter meaningless and behaviorally irrelevant information [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Top-down attention is goal-driven and requires intentional arousal and maintenance, while bottom-up attention is stimulus-driven and automatically captures attention. Bottom-up attention control signals were dominant in the parietal cortex, while top-down attention control signals occurred earlier in the frontal cortex [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe fronto-parietal network (FPN) is involved in information selection and visual selective attention detection of target [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The DLPFC and superior parietal lobule (SPL) were key nodes of the FPN. The DLPFC was associated with execution control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], activity reflected top-down control mediating task response demands and interfered with contextual memory-guided attention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Many studies of the parietal cortex's contribution to attention have focused on the SPL, the major subregion of the PPC [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The SPL was considered a key brain region in the dorsal attention network [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and involved in top-down attentional orientations [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough research into the neural mechanisms of visual selective attention is abundant, the relationship between brain structure and visual selective attention behavior has been understudied. Voxel-based morphometry (VBM) was used to assess the relationship between gray matter volumes (GMV) of structural MRI and cognition behavior [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To the best of our knowledge, reduced gray matter volume in the anterior cingulate cortex (ACC) was associated with selective inattention scores in ADHD patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], increased attentional bias to threat in anxious individuals was associated with greater GMV in the middle frontal gyrus and superior frontal gyrus [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], poor executive shifting attention in the very preterm born children was associated with reduced GM in the right superior temporal cortex and bilateral thalami[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], systemizing quotient (SQ) questionnaire in the 5\u0026ndash;15 years healthy children showed a significant negative correlation with the regional GMV of the left posterior parietal cortex [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, these studies do not a direct relationship between brain structure and visual selective attention behavior in healthy adults.\u003c/p\u003e \u003cp\u003eTranscranial magnetic stimulation (TMS) was applied to cognitive neuroscience to explore the causal relationship between the brain and behavior [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Different patterns of TMS of fronto-parietal cortex could modulate visual attention behavior [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The five-pulses repetitive TMS (rTMS) over the SPL interfered with the processing of arousal associated with the informative peripheral cueing paradigm [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The single-pulse TMS was applied 100ms after the probe onset over the DLPFC or the PPC, changing the negative and positive priming effect, which reflects the top-down mechanism in the special TMS time applied to the right FPN [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although previous studies have confirmed that TMS can modulate attention behavior, the modulation effect of TMS on visual selective attention at different attention times is unclear.\u003c/p\u003e \u003cp\u003eThe aim is to explore how different times TMS affects visual selective attention by stimulating the FPN. Furthermore, this study was also designed to assess the relationship between brain structure and visual selective attention behavior. Therefore, we used single-pulse TMS to stimulate the right DLPFC (rDLPFC) and right SPL (rSPL) at early and late times after pop-out and search stimuli displayed onset. we hypothesized that different times of single-pulse TMS over FPN can effectively regulate visual selection attention behavior, and TMS delivered to rDLPFC and rSPL improved top-down and bottom-up attention.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eSixty-six (thirty-six males, age range: 18\u0026ndash;26 years, mean age: 22.18 \u0026plusmn; 2.27 years) healthy and right-handed volunteers from the University of Electronic Science and Technology of China (UESTC) were recruited. Participants had normal or corrected vision, no color blindness or color weakness, and no history of neurological or psychiatric disorders. Since the behavioral accuracy (ACC) of two females was lower than three standard deviations, sixty-four participants were included in the analysis (active TMS group: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32; sham TMS group: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32). The sample size was calculated a priori using G*Power 3.1.9.7 software, and the sample size was at least \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54 at a significance level α\u0026thinsp;=\u0026thinsp;0.05, a medium effect (f\u0026thinsp;=\u0026thinsp;0.25), and 95% statistical power. The study was approved by the ethics and human protection committee of the UESTC and conducted in line with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Stimuli and procedure\u003c/h2\u003e \u003cp\u003eThe pop-out and search tasks were designed based on a previous study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the pop-out task, the target and distractors had distinct colors and orientations, whereas in the search task, the target and distractors had different orientations but the same color. Isosceles triangle consisted of two colors (red and green) with eight orientations (clockwise 0\u0026deg;, 45\u0026deg;, 90\u0026deg;, 135\u0026deg;, 180\u0026deg;, 225\u0026deg;, 270\u0026deg;, and 315\u0026deg;). Each mini block began with the target for 1500ms, followed by a \"+\" fixation for 500ms, and then the stimulus (pop-out or search) lasted for 500ms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Then, intertrial interval (ITI) was presented for 1500ms. After pop-out or search stimuli were displayed, single-pulse TMS stimulated the rDLPFC and rSPL. The formal trials were divided into four sessions, each with thirty-two mini blocks containing four trials, for a total of 512 trials. The E-prime 3.0 (Psychology program Tools, Pittsburgh, USA) was used to present stimuli and record behavioral data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Transcranial magnetic stimulation protocol\u003c/h2\u003e \u003cp\u003eA 70 mm figure-of-eight coil connected Magstim Rapid\u003csup\u003e2\u003c/sup\u003e stimulator (Magstim Company Limited, Whiteland, United Kingdom) to deliver a single TMS pulse. A stereotactic navigation system (BrainSight Frameless, Rogue Research, Montreal, Canada) was used to coil the localization of the target sites. The anatomical T1 image of each participant was obtained in the rest state before TMS. A head model was constructed based on the T1 images, and then the constructed model was matched with a head based on the brow center, eyes, and ears as a reference. The TMS location was marked in Brainsight with T1 images of each participant, and the coil was adjusted to the center of the target location to apply the TMS.\u003c/p\u003e \u003cp\u003eThe single-pulse TMS intensity was set at 100% of the resting motor threshold (RMT)(71.25 \u0026plusmn; 5.56% of the maximum stimulator output), which was defined as the lowest stimulus intensity that single-pulse TMS over M1 (C3 of the 10\u0026ndash;20 EEG system) induced visual muscles twitches of the contralateral hand resting in five out of ten consecutive trials at least [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ten consecutive pulses were delivered for each intensity, starting at 50% of the maximum stimulator output. The stimulation target locations were centered on Montreal Neurological Institute (MNI) coordinates for rDLPFC (x\u0026thinsp;=\u0026thinsp;39, y\u0026thinsp;=\u0026thinsp;39, z\u0026thinsp;=\u0026thinsp;30) and rSPL (x\u0026thinsp;=\u0026thinsp;27, y\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;66, z\u0026thinsp;=\u0026thinsp;51) from activation regions of pop-out and search tasks [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e Based on the reaction time (RT) of the saccade (visual pop-out: 233\u0026thinsp;\u0026plusmn;\u0026thinsp;33ms; visual search: 272\u0026thinsp;\u0026plusmn;\u0026thinsp;43ms) and times of fronto-parietal neurons first began during the pop-out and search tasks (LIP neuron began 170ms before pop-out target saccade; LPFC neuron began 40ms before search target saccade)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], we determined the early time (30-90ms) and late time (200-260ms). Because it took the TMS stimulator 16-17ms to receive the stimulated order from E-Prime, we jittered the onset of TMS relative to the stimuli around early time (33ms, 50ms, 66ms, and 83ms) and late time (216ms, 233ms, 250ms, and 266ms). We used single-pulse TMS to stimulate rDLPFC and rSPL at early and late time points (random TMS time points) after pop-out and search stimuli onset. The interval of two TMS sites was fifteen minutes, and each target site received stimulation after completing 256 trials, so each participant completed 512 trials in total. To reduce the practice effect and order effect, the order of TMS sites was counterbalanced with the ABBA design. The operations process of TMS strictly enforced in accordance with safety manual [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 structural MRI data collection\u003c/h2\u003e \u003cp\u003eThe 5T GE scanner with an eight-channel head coil (General Electric, Milwaukee, WI, USA) was used to obtain anatomical MRI images to scan the brains of participants within the rest state. T1-weighted sequences were collected with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;5.96ms, echo time (TE)\u0026thinsp;=\u0026thinsp;1.96ms, voxel size\u0026thinsp;=\u0026thinsp;1\u0026times;1\u0026times;1mm\u003csup\u003e3\u003c/sup\u003e, flip angle (FA)\u0026thinsp;=\u0026thinsp;9\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;256\u0026times;256mm\u003csup\u003e2\u003c/sup\u003e, 152 slices, slice thickness\u0026thinsp;=\u0026thinsp;1mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Behavioral data\u003c/h2\u003e \u003cp\u003eThe design was a 2 (TMS group: active TMS and sham TMS) \u0026times; 2 (TMS sites: rDLPFC and rSPL) \u0026times; 2 (TMS times: early and late) \u0026times; 2 (tasks: pop-out and search) mixed design. The between-subject factor was TMS group, and the within-subject factors were TMS sites, TMS times, and tasks. The ACC and reaction time (RT) were analyzed on repeated measurement analysis of variance (ANOVA) and t-test. To explore the TMS effect, we removed trials that RT less than TMS times. ACC was the mean of the number of correct trials and RT was the mean of correct and 200-1200ms (including 200ms and 1200ms) trials. The ΔACC and ΔRT were the differences between pop-out and search tasks (i.e.ΔACC\u0026thinsp;=\u0026thinsp;ACC\u003csub\u003epop\u0026minus;out\u003c/sub\u003e\u0026minus;ACC\u003csub\u003esearch\u003c/sub\u003e;ΔRT\u0026thinsp;=\u0026thinsp;RT\u003csub\u003esearch\u003c/sub\u003e\u0026minus;RT\u003csub\u003epop\u0026minus;out\u003c/sub\u003e). The SPSS 20.0 (IBM Corp, Armonk, NY) performed statistical analyses. Post-test using two-tailed t-tests to compare conditions differences and Bonferroni correction to correct the experiment-wise error rate (type I error) when using multiple t-tests [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 shows that the difference between different conditions is significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Structural MRI data\u003c/h2\u003e \u003cp\u003eThe VBM analysis was used to assess the correlation between the GMV and visual selective attention behavior indexes (ACC and RT)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The CAT12 Toolbox runs within SPM12 (Statistical Parametric Mapping; Wellcome Trust Centre for Neuroimaging, London, UK) and was used to pre-process the T1 image based on the MATLAB R2013a (MathWorks, Sherborn, MA, USA). The pre-process included five steps: (1) check for orientation artifacts of raw images; (2) T1 images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) according to probability density; (3) normalized images to MNI space with image resampling 1.5\u0026times;1.5\u0026times;1.5mm\u003csup\u003e3\u003c/sup\u003e; (4) estimated total intracranial volume (TIV) as a covariate for modulated data in VBM analysis to correct for different brain sizes and volumes; (5) smoothed GM images with Gaussian kernel of 8 mm full width at half maximum (FWHM) to obtain higher signal to noise ratio (SNR) and reduce individual differences. From the CAT12 manual, TIV of active and sham TMS group didn't correlate too much with visual selective attention behavior (active TMS group_GMV and ACC: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82; active TMS group_GMV and RT: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.832; sham TMS group_GMV and ACC: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06; sham TMS group_GMV and RT: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.962)\u003cb\u003e(Supplementary Fig.\u0026nbsp;1).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used multiple regression analysis and an uncorrected threshold of \u0026#119875; \u0026lt; 0.001 with a cluster size of at least 40 voxels. The nuisance variables were gender, age, and TIV. Voxels with absolute values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in the GM images were excluded to eliminate boundary effects between GM and WM. To obtain the r-value, the partial correlation analysis was used to analyze the correlation between GMV and visual selective attention behavior (ACC and RT) by SPSS 20.0. The \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that a linear relationship between two variables is significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Behavioral results\u003c/h2\u003e\n\u003cp\u003eA three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) to analyze the ACC for the pop-out task and the search task, respectively. For the pop-out task, results showed a significant main effect of TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;16.235, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.208). When rSPL of the active TMS group was delivered stimulation, ACC of late TMS was significantly higher than early TMS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 2.476, \u003cem\u003ep\u003c/em\u003e = 0.019, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.438)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. There were no other main effect and interaction. For the search task, results showed a significant main effect of TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 9.977, \u003cem\u003ep\u003c/em\u003e = 0.002, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.139), an interaction between the TMS group and TMS sites (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 7.988, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.114), and an interaction between the TMS group, TMS sites, and TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e=12.097, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.163). The other main effect and interaction were not significant. Further analysis showed that ACC of late TMS over rSPL was significantly higher than early TMS for the active TMS group (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 4.539, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.802), the difference between late TMS of rDLPFC and early TMS of rDLPFC (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 2.99, \u003cem\u003ep\u003c/em\u003e = 0.005, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.529) and late TMS of rSPL(\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 4.131, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.73) were also significant in the sham TMS group \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cstrong\u003e).\u003c/strong\u003e This result suggested that late TMS over rSPL improved the ACC of top-down attention. In addition, when late TMS stimulated rDLPFC, there was a significant difference between the active TMS and sham TMS group (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(62)\u003c/sub\u003e= 2.4, \u003cem\u003ep\u003c/em\u003e = 0.019, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.6). Late TMS over rDLPFC reduced the ACC of top-down attention in the active TMS group compared to the sham TMS group. To explore the TMS site's effect on ACC, two-way repeated measures ANOVA (TMS group and TMS sites) were used to analyze ACC of early and late TMS times, respectively \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/strong\u003e. The late TMS over rDLPFC decreased ACC to eliminate ACC difference between rDLPFC and rSPL for top-down attention.\u003c/p\u003e\n\u003cp\u003eFor the RT of the pop-out task and search task, we also used three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) to analyze, respectively. For RT of the pop-out task, results showed that a significant main effect of TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;185.272, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.749) and interaction between the TMS group, TMS sites, and TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 5.761, \u003cem\u003ep\u003c/em\u003e = 0.376, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.013). Further paired t-test showed that difference between early TMS and late TMS was significant for each TMS group and TMS sites (active TMS over rDLPFC: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 10.172, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 1.798; active TMS over rSPL: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e=10.392, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.837; sham TMS over rDLPFC: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 6.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.168; sham TMS over rSPL: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 9.194, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.625)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec\u003cstrong\u003e)\u003c/strong\u003e. There was no other significant main effect and interaction. For RT of the search task, there was a significant main effect of TMS sites (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 5.369, \u003cem\u003ep\u003c/em\u003e = 0.024, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.08), a significant main effect of TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 29.576, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.323), and a significant interaction between the TMS group and TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 7.547, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.109). The paired t-test showed that search RT for late TMS was significantly longer than early TMS in active TMS over rDLPFC (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e=5.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.91) and rSPL(\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 4.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed\u003cstrong\u003e)\u003c/strong\u003e. There was no other significant main effect and interaction.\u003c/p\u003e\n\u003cp\u003eCompared with the search task, the pop-out task had lower ACC and longer RT, so the ACC and RT difference values between pop-out and search tasks were used as dependent variables to analyze \u0026Delta;ACC and \u0026Delta;RT (\u0026Delta;ACC\u0026thinsp;=\u0026thinsp;ACC \u003csub\u003epop\u0026minus;out\u003c/sub\u003e \u0026minus; ACC \u003csub\u003esearch\u003c/sub\u003e; \u0026Delta;RT\u0026thinsp;=\u0026thinsp;RT \u003csub\u003esearch\u003c/sub\u003e \u0026minus; RT \u003csub\u003epop\u0026minus;out\u003c/sub\u003e) under different TMS conditions. For \u0026Delta;ACC, a three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) showed an interaction between the TMS group and TMS sites (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6.861, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.1) and interaction between the TMS group, TMS sites, and TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 8.357, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.119). There was no other main effect and interaction. For active TMS group, a paired t-test showed that \u0026Delta;ACC of early TMS over rSPL was significantly higher than that of late TMS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e = 3.013, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.533)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee\u003cstrong\u003e)\u003c/strong\u003e. For sham TMS group, \u0026Delta;ACC of early TMS over rDLPFC was significantly higher than that of late TMS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e = 2.209, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.391), the \u0026Delta;ACC of late TMS over rSPL was significantly higher than that of rDPLFC (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e = 4.443, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.785). A two independent-sample t-test showed that the \u0026Delta;ACC of the active TMS group was significantly higher than that of the sham TMS group (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(62)\u003c/sub\u003e= 2.239, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56) when late TMS stimulated rDLPFC \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee\u003cstrong\u003e)\u003c/strong\u003e. This result suggested that late TMS over rDLPFC increased cognitive load differences between top-down and bottom-up attention. For \u0026Delta;RT, a three-way repeated measures ANOVA (TMS group, TMS sites, and TMS times) only showed a significant main effect of TMS times (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 62)\u003c/sub\u003e= 28.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 0.314). The paired t-test showed that there was a significant difference between early and late TMS in sham TMS over rSPL (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(31)\u003c/sub\u003e= 4.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.847)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef\u003cstrong\u003e)\u003c/strong\u003e. There were no other main effects and interaction. To explore the TMS site's effect on \u0026Delta;ACC, we used two-way repeated measures ANOVA (TMS group and TMS sites) to analyze \u0026Delta;ACC for early and late TMS times, respectively \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;3)\u003c/strong\u003e. The late TMS over rDLPFC increased the cognitive load difference between top-down and bottom-up attention to eliminating the difference of two TMS sites in active TMS group.\u003c/p\u003e\n\u003cp\u003eIn addition, TMS times consist of eight points (early TMS time points: 33ms, 50ms, 66ms, 83ms; late TMS time points: 216ms, 233ms, 250ms, 266ms), therefore we viewed ACC in each TMS time point. Due to the small number of trials (less than 16 trials) at TMS time points, the ACC at each TMS time point was not statistically analyzed. The ACC for pop-out task at eight TMS time points didn't show regularity \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. The ACC of search task at eight TMS time points showed regularity, specifically, active TMS group and sham TMS group intersected at 83ms (early TMS) and 216ms (late TMS) connecting lines when TMS stimulated rSPL \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e. When early TMS stimulated rSPL, search ACC in active TMS group was always lower than that of sham TMS group, and as late TMS time pints increased, search ACC in active TMS group gradually increased and was always higher than that of sham TMS group. This result confirmed that late TMS over rSPL improved the ACC of top-down attention. The RT for pop-out and search tasks at eight TMS time points showed regularity. For each TMS group and TMS sites, the RT of the pop-out task was longer for the four late TMS time points than for four early TMS time points \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cstrong\u003e)\u003c/strong\u003e, whereas the RT of the search task gradually increased with early TMS time points and decreased with late TMS time points \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 VBM results\u003c/h2\u003e\n\u003cp\u003eVBM analysis revealed the correlation between the behavior index (ACC and RT) of visual selection attention task and gray matter volume of brain regions in active TMS and sham TMS groups, brain regions of the active TMS group were more distributed in the fronto-parietal cortex \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. Specifically, for active TMS group, our results suggested a positive correlation between ACC and bilateral superior frontal gyrus and right precentral gyrus, and a negative correlation between ACC and left supramarginal gyrus. RT of the active TMS group was significantly positively correlated with the right inferior frontal gyrus (orbital part) and left superior frontal gyrus (medial), and negatively correlated with the calcarine sulcus. For the sham TMS group, there was a positive correlation between ACC and right postcentral gyrus and right inferior frontal gyrus (triangular part), and a positive correlation between RT and right pallidum and left thalamus. Based on the above behavior-brain correlations, the frontal-parietal cortex contains bilateral superior frontal gyrus, right precentral gyrus, left supramarginal gyrus, right inferior frontal gyrus (orbital part), and left superior frontal gyrus (medial) in the active TMS group, while right postcentral gyrus and right inferior frontal gyrus (triangular part) belong to the frontal-parietal cortex in the sham TMS group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation between attention behavior and GMV of the brain in the active and sham TMS group\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBrain regions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCluster\u003c/p\u003e\n\u003cp\u003e(voxels)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePeak MNI coordinates\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ex\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ey\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eactive TMS_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.612***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.637***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSupramarginal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.613***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eactive TMS_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior frontal gyrus, orbital part\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.603***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus, medial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.608***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcarine sulcus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.747***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esham TMS_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostcentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.644***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior frontal gyrus, triangular part\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.632***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esham TMS_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePallidum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThalamus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eThe attention behavior combined with the pop-out and search tasks.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eThe minimum cluster is 40 voxels. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, uncorrected; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eAbbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe assessed the correlation between the behavioral index (ACC and RT) of the pop-out and search task and the gray matter volume of brain regions in the active TMS group, respectively \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e For the pop-out task, ACC was negatively correlated with right parahippocampal gyrus, right angular gyrus, right middle frontal gyrus (orbital part), left postcentral gyrus, and right inferior temporal gyrus. RT of the pop-out task was positively correlated with the left superior frontal gyrus (medial orbital), right middle frontal gyrus, and left angular gyrus, and negatively correlated with left calcarine sulcus. For the search task of the active TMS group, there was a positive correlation between ACC and left superior frontal gyrus, right precentral gyrus, and left superior frontal gyrus, and a negative correlation between ACC and right cerebellum. RT of the search task was significantly positively correlated with the left superior frontal gyrus (medial), the right Inferior frontal gyrus (orbital part), and negatively correlated with the left calcarine sulcus, right inferior temporal gyrus, and right postcentral gyrus. Behavior-brain correlation regions of the active TMS group were widely distributed in the frontal-parietal cortex, including the right middle frontal gyrus (orbital part), left postcentral gyrus, left superior frontal gyrus (medial orbital), right middle frontal gyrus, and left angular gyrus, right precentral gyrus, right Inferior frontal gyrus (orbital part), and right postcentral gyrus.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation between attention behavior and GMV of the brain in the active TMS group\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBrain regions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCluster\u003c/p\u003e\n\u003cp\u003e(voxels)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePeak MNI coordinates\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ex\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ey\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epop-out_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParahippocampal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.666***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAngular gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.682***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle frontal gyrus, orbital part\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.633**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostcentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.621**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior temporal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.604**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epop-out_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus, medial orbital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.614***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.622***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAngular gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.638***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcarine sulcus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.703***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esearch_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.627***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.612***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.623***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCerebellum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.584***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esearch_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus, medial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.613***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior frontal gyrus, orbital part\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.594***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcarine sulcus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e494\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.75***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior temporal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.643***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostcentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.601***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eThe minimum cluster is 40 voxels. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, uncorrected. ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eAbbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBehavior-brain correlations of pop-out and search tasks were also assessed in the sham TMS group, respectively. For the pop-out task of the sham TMS group, there was a positive correlation between ACC and left superior parietal lobule, right parahippocampal gyrus, and right thalamus, a positive correlation between RT and left inferior temporal gyrus and right fusiform gyrus, and a negative correlation between RT and left superior temporal gyrus. For the search task of the sham TMS group, there was a negative correlation between ACC and right inferior frontal gyrus (triangular part), right postcentral gyrus, right superior frontal gyrus (medial orbital), and right precentral gyrus, a positive correlation between ACC and left thalamus and right pallidum \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation between attention behavior and GMV of the brain in the sham TMS group\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBrain regions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCluster\u003c/p\u003e\n\u003cp\u003e(voxels)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePeak MNI coordinates\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ex\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ey\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epop-out_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esuperior parietal lobule\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.688***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParahippocampal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.627***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThalamus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.611***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epop-out_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior temporal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.599***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFusiform gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.592***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior temporal gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.602***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esearch_ACC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInferior frontal gyrus, triangular part\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.621***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePostcentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.632***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuperior frontal gyrus, medial orbital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.597***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecentral gyrus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.589***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003esearch_RT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThalamus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePallidum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.603***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eThe minimum cluster is 40 voxels. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001(uncorrected).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eAbbreviations: GMV, Gray Matter Volume; ACC, Accuracy; RT, Reaction Time; H, Hemisphere; L, Left; R: Right; MNI, Montreal Neurological Institute.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cp\u003eWe focused on behavior and structural brain functions of visual selective attention after single-pulse TMS. Our findings reveal the temporal difference of TMS modulation effect on visual selective attention behavior and the relationship between GMV in the fronto-parietal cortex and visual selective attention behavior.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The regulation effect of different time TMS on selective attention\u003c/h2\u003e \u003cp\u003eAs hypothesized, different time TMS over the FPN nodes modulated visual selective attention. Compared with early TMS over the rSPL, late TMS increased ACC and RT of top-down attention. Prior studies showed timing effect on RT of attention was reflected in the earlier TMS time and increased RT. The single-pulse TMS over rPPC was applied 100ms after stimuli onset, prolonging RT of visual attention [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The interaction between the initial state of the cortex and cognitive tasks led to an inhibition effect of single-pulse TMS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome studies applied single-pulse TMS early after the stimulus displayed onset, such as 50ms [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], 100ms [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and 150ms[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The pre-TMS applied to FPN can also change the attention behavior of target detection [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and event-related potentials (ERP) components [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Inconsistent with the hypothesis, results showed that TMS over FPN had no modulation effect on top-down attention. The pop-out task showed a ceiling effect because of a quite salient target resulting in no significant difference in bottom-up attention across TMS conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Different cognitive loads of top-down and bottom-up attention\u003c/h2\u003e \u003cp\u003eCompared with the search task, the pop-out task had higher ACC and shorter RT. The pop-out task required more color coding and the salient feature improved target detection (i.e. increased ACC and decreased RT)[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The different goal features of the pop-out task and the search task resulted in different cognitive loads of bottom-up and top-down attention. Low cognitive load acted more on bottom-up attention, while high cognitive load involved more top-down attention and was associated with longer RT and synergistic interaction effect [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rDLPFC was associated with an increased cognitive load between the pop-out and search tasks [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], our studies also had consistent results that late TMS over rDLPFC increased the cognitive load difference between top-down and bottom-up attention. It also suggested the TMS time effect and the role of the rDLPFC in visual selection attention. For the rSPL, late TMS decreased the cognitive load difference between top-down and bottom-up attention. Prior findings revealed right parietal cortex with TMS holds a sensitive role in the interaction between visual attention and working memory load [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], high cognitive load increased the activity of the SPL [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The correlation between GMV of fronto-parietal cortex and visual selection attention\u003c/h2\u003e \u003cp\u003ePrevious studies focused on activity regions of pop-put and search tasks, our study combined structural MRI images and behavior to focus on GM regions associated with selective attention. Compared with the sham TMS group, regional GMV correlated with visual selective attention was distributed in the fronto-parietal cortex in the active TMS group, which also demonstrated that TMS effectively modulated attention behavior and the relationship between the fronto-parietal cortex and visual selective attention. The GMV in the middle frontal gyrus was involved in attentional control processing in the young and old [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and the lateral frontal and orbital frontal regions significantly predicted attention [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. For attention impairment disorders, gray matter volume in the prefrontal cotes was smaller in ADHD patients [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Reduced effective connectivity of fronto-parietal circuits in early-stage Alzheimer's disease (AD) correlated with gray matter volume in fronto-parietal regions, leading to impaired top-down attention[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitation\u003c/h2\u003e \u003cp\u003eIt is important to mention limitations. First, the same coordinates of the TMS sites for all participants may not be the most significant activation cluster. Although we used a precise localization navigation system, individuals have differences in activation coordinates in the rDLPFC and rSPL. In the future, individualization of TMS sites can increase the reliability and precision of the TMS effect [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Second, this study only explored the causal relationship between the FPN and visual selective attention behavior. Future studies could use congruent TMS-fMRI to explore the TMS effect on brain activity [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eTo conclude, different times of TMS over the fronto-parietal network to modulate visual selective attentional behavior, especially the late TMS over rSPL improved top-down attention and decreased cognitive load difference between top-down and bottom-up attention. Active TMS induced more regions of fronto-parietal cortex correlated with visual selection attention. These findings demonstrate the cause role of the fronto-parietal network on visual selective attention behavior and the contribution of fronto-parietal cortex to visual selective attention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant number 62176045], by Sichuan Science and Technology Program [grant number 2023YFS0191], 111 project [grant number B12027], and the Fundamental Research Funds for the Central Universities [grant number ZYGX2020FRJH014].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQiuzhu Zhang:\u003c/strong\u003e Formal analysis, Data curation, Methodology, Investigation, Writing\u0026ndash;original draft. \u003cstrong\u003eDanmei Zhang:\u003c/strong\u003e Data curation, Methodology. \u003cstrong\u003eGulibaier Alimu:\u003c/strong\u003e Data curation, Investigation. \u003cstrong\u003eGuragai Bishal:\u0026nbsp;\u003c/strong\u003eWriting-Reviewing and Editing. \u003cstrong\u003eWenjun Li:\u0026nbsp;\u003c/strong\u003eVisualization, Writing-Reviewing and Editing.\u003cstrong\u003e\u0026nbsp;Junjun Zhang:\u003c/strong\u003e Supervision, Writing-Reviewing and Editing. \u003cstrong\u003eZhenlan Jin:\u0026nbsp;\u003c/strong\u003eSupervision, Writing-Reviewing and Editing.\u003cstrong\u003e\u0026nbsp;Ling Li:\u003c/strong\u003e Methodology, Supervision, Writing-Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData or materials for the experiments are available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Experiment procedure received ethical approval from the committee for approval of the University of Electronic Science and Technology of China (UESTC) and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurphy G, Groeger JA, Greene CM. Twenty years of load theory-Where are we now, and where should we go next? Psychonomic Bulletin \u0026amp; Review 2016;23(5):1316-40.\u003c/li\u003e\n\u003cli\u003eIbos G, Duhamel J-R, Ben Hamed S. A functional hierarchy within the parietofrontal network in stimulus selection and attention control. J Neurosci 2013;33(19):8359-69.\u003c/li\u003e\n\u003cli\u003eBuschman TJ, Miller EK. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 2007;315(5820):1860-2.\u003c/li\u003e\n\u003cli\u003eLi L, Gratton C, Yao D, Knight RT. Role of frontal and parietal cortices in the control of bottom-up and top-down attention in humans. Brain Res 2010;1344:173-84.\u003c/li\u003e\n\u003cli\u003eLi L, Gratton C, Fabiani M, Knight RT. Age-related frontoparietal changes during the control of bottom-up and top-down attention: an ERP study. Neurobiol Aging 2013;34(2):477-88.\u003c/li\u003e\n\u003cli\u003eCorbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002;3(3):201-15.\u003c/li\u003e\n\u003cli\u003eKatsuki F, Constantinidis C. Bottom-up and top-down attention: different processes and overlapping neural systems. Neuroscientist 2014;20(5):509-21.\u003c/li\u003e\n\u003cli\u003eGiesbrecht B, Woldorff MG, Song AW, Mangun GR. Neural mechanisms of top-down control during spatial and feature attention. Neuroimage 2003;19(3):496-512.\u003c/li\u003e\n\u003cli\u003ePtak R. The Frontoparietal attention network of the human brain: action, a aliency, and a priority map of the environment. Neuroscientist 2012;18(5):502-15.\u003c/li\u003e\n\u003cli\u003eSpreng RN, Sepulcre J, Turner GR, Stevens WD, Schacter DL. Intrinsic architecture underlying the relations among the default, dorsal Attention, and frontoparietal control networks of the human brain. Journal of Cognitive Neuroscience 2013;25(1):74-86.\u003c/li\u003e\n\u003cli\u003eXu GQ, Lan Y, Huang DF, Chen SZ, Chen L, Zeng JS, et al. The study on the frontoparietal networks by continuous theta burst stimulation in healthy human subjects. Behavioural Brain Research 2013;240:60-8.\u003c/li\u003e\n\u003cli\u003eCieslik EC, Zilles K, Kurth F, Eickhoff SB. Dissociating bottom-up and top-down processes in a manual stimulus-response compatibility task. Journal of Neurophysiology 2010;104(3):1472-83.\u003c/li\u003e\n\u003cli\u003ePahi MR, Cavalli J, Nees F, Flor H, Andoh J. Disruption of the prefrontal cortex improves implicit contextual memory-guided attention: combined behavioral and electrophysiological evidence. Cerebral Cortex 2020;30(1):20-30.\u003c/li\u003e\n\u003cli\u003eWu Y, Wang JJ, Zhang Y, Zheng DC, Zhang JF, Rong ML, et al. The neuroanatomical basis for posterior superior parietal lobule control lateralization of visuospatial attention. Frontiers in Neuroanatomy 2016;10:1-9.\u003c/li\u003e\n\u003cli\u003eBanaszkiewicz A, Bola L, Matuszewski J, Szczepanik M, Kossowski B, Mostowski P, et al. The role of the superior parietal lobule in lexical processing of sign language: Insights from fMRI and TMS. Cortex 2021;135:240-54.\u003c/li\u003e\n\u003cli\u003eDu XM, Chen L, Zhou K. The role of the left posterior parietal lobule in top-down modulation on space-based attention: A transcranial magnetic stimulation study. Human Brain Mapping 2012;33(10):2477-86.\u003c/li\u003e\n\u003cli\u003eBonnard M, Chen S, Gaychet J, Carrere M, Woodman M, Giusiano B, et al. Resting state brain dynamics and its transients: a combined TMS-EEG study. Scientific Reports 2016;6:1-9.\u003c/li\u003e\n\u003cli\u003eZeng H, Weidner R, Fink GR, Chen Q. Neural correlates underlying the attentional spotlight in human parietal cortex independent of task difficulty. Human Brain Mapping 2017;38(10):4996-5018.\u003c/li\u003e\n\u003cli\u003eShomstein S. Cognitive functions of the posterior parietal cortex: top-down and bottom-up attentional control. Frontiers in integrative neuroscience 2012;6:1-7.\u003c/li\u003e\n\u003cli\u003eSpadone S, Wyczesany M, Della Penna S, Corbetta M, Capotosto P. Directed flow of beta band communication during reorienting of attention within the dorsal attention network. Brain Connectivity 2021;11(9):717-24.\u003c/li\u003e\n\u003cli\u003eShomstein S, Lee J, Behrmann M. Top-down and bottom-up attentional guidance: investigating the role of the dorsal and ventral parietal cortices. Experimental Brain Research 2010;206(2):197-208.\u003c/li\u003e\n\u003cli\u003eAshburner J, Friston KJ. Voxel-based morphometry - The methods. Neuroimage 2000;11(6):805-21.\u003c/li\u003e\n\u003cli\u003eMechelli A, Price CJ, Friston KJ, Ashburner J. Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging 2005;1(2):105-13.\u003c/li\u003e\n\u003cli\u003eBonath B, Tegelbeckers J, Wilke M, Flechtner HH, Krauel K. Regional gray matter volume differences between adolescents with ADHD and typically developing controls: Further evidence for anterior cingulate involvement. Journal of Attention Disorders 2018;22(7):627-38.\u003c/li\u003e\n\u003cli\u003eCarlson JM, Fang L. Attentional bias to threat and gray matter volume morphology in high anxious individuals. Cognitive Affective \u0026amp; Behavioral Neuroscience 2022;22(3):600-9.\u003c/li\u003e\n\u003cli\u003eLean RE, Melzer TR, Bora S, Watts R, Woodward LJ. Attention and regional gray matter development in very preterm children at age 12 years. Journal of the International Neuropsychological Society 2017;23(7):539-50.\u003c/li\u003e\n\u003cli\u003eSassa Y, Taki Y, Takeuchi H, Hashizume H, Asano M, Asano K, et al. The correlation between brain gray matter volume and empathizing and systemizing quotients in healthy children. Neuroimage 2012;60(4):2035-41.\u003c/li\u003e\n\u003cli\u003eKlomjai W, Katz R, Lackmy-Vallee A. Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS). Annals of physical and rehabilitation medicine 2015;58(4):208-13.\u003c/li\u003e\n\u003cli\u003eFarzan F, Vernet M, Shafi MMD, Rotenberg A, Daskalakis ZJ, Pascual-Leone A. Characterizing and modulating brain circuitry through transcranial magnetic stimulation combined with electroencephalography. Frontiers in neural circuits 2016;10:1-24.\u003c/li\u003e\n\u003cli\u003eParkin BL, Ekhtiari H, Walsh VF. Non-invasive human brain atimulation in cognitive neuroscience: A primer. Neuron 2015;87(5):932-45.\u003c/li\u003e\n\u003cli\u003eHallett M. Transcranial magnetic stimulation: A primer. Neuron 2007;55(2):187-99.\u003c/li\u003e\n\u003cli\u003eZiemann U. Thirty years of transcranial magnetic stimulation: where do we stand? Experimental Brain Research 2017;235(4):973-84.\u003c/li\u003e\n\u003cli\u003eSong PH, Lin H, Liu CY, Jiang YL, Lin YC, Xue Q, et al. Transcranial magnetic stimulation to the middle frontal gyrus during attention modes induced dynamic module reconfiguration in brain networks. Frontiers in Neuroinformatics 2019;13:1-9.\u003c/li\u003e\n\u003cli\u003eJin ZL, Xie K, Ni XJ, Jin DG, Zhang JJ, Li L. Transcranial magnetic stimulation over the right dorsolateral prefrontal cortex modulates visuospatial distractor suppression. European Journal of Neuroscience 2021;53(10):3394-403.\u003c/li\u003e\n\u003cli\u003eJigo M, Gong MY, Liu TS. Neural determinants of task performance during feature-based attention in human cortex. Eneuro 2018;5(1):1-15.\u003c/li\u003e\n\u003cli\u003eAshbridge E, Walsh V, Cowey A. Temporal aspects of visual search studied by transcranial magnetic stimulation. Neuropsychologia 1997;35(8):1121-31.\u003c/li\u003e\n\u003cli\u003eKehrer S, Kraft A, Koch SP, Kathmann N, Irlbacher K, Brandt SA. Timing of spatial priming within the fronto-parietal attention network: A TMS study. Neuropsychologia 2015;74:30-6.\u003c/li\u003e\n\u003cli\u003eZanona AD, da Silva ACR, Maciel ABD, do Nascimento LSG, da Silva AB, Bolognini N, et al. Somatosensory cortex repetitive transcranial magnetic stimulation and associative sensory stimulation of peripheral nerves could assist motor and sensory recovery after stroke. Frontiers in Human Neuroscience 2022;16:1-14.\u003c/li\u003e\n\u003cli\u003eBalslev D, Braet W, McAllister C, Miall RC. Inter-individual variability in optimal current direction for transcranial magnetic stimulation of the motor cortex. Journal of Neuroscience Methods 2007;162(1-2):309-13.\u003c/li\u003e\n\u003cli\u003eWang M, Yu B, Luo C, Fogelson N, Zhang J, Jin Z, et al. Evaluating the causal contribution of fronto-parietal cortices to the control of the bottom-up and top-down visual attention using fMRI-guided TMS. Cortex 2020;126:200-12.\u003c/li\u003e\n\u003cli\u003eRossi S, Antal A, Bestmann S, Bikson M, Brewer C, Brockmoller J, et al. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2021;132(1):269-306.\u003c/li\u003e\n\u003cli\u003eRossi S, Hallett M, Rossini PM, Pascual-Leone A, Safety of TMSCG. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2009;120(12):2008-39.\u003c/li\u003e\n\u003cli\u003eWassermann EM. Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the international workshop on the safety of repetitive transcranial magnetic stimulation, June 5-7, 1996. Evoked Potentials-Electroencephalography and Clinical Neurophysiology 1998;108(1):1-16.\u003c/li\u003e\n\u003cli\u003eArmstrong RA. When to use the Bonferroni correction. Ophthalmic and Physiological Optics 2014;34(5):502-8.\u003c/li\u003e\n\u003cli\u003eLu Y-T, Chang W-N, Chang C-C, Lu C-H, Chen N-C, Huang C-W, et al. Insula volume and salience network are associated with memory decline in parkinson disease: complementary analyses of voxel-based morphometry versus volume of interest. Parkinsons Disease 2016;2016:1-8.\u003c/li\u003e\n\u003cli\u003eFuggetta G, Pavone EF, Walsh V, Kiss M, Eimer M. Cortico-cortical interactions in spatial attention: A combined ERP/TMS study. Journal of Neurophysiology 2006;95(5):3277-80.\u003c/li\u003e\n\u003cli\u003eWaldvogel D, van Gelderen P, Muellbacher W, Ziemann U, Immisch I, Hallett M. The relative metabolic demand of inhibition and excitation. Nature 2000;406(6799):995-8.\u003c/li\u003e\n\u003cli\u003eKoivisto M, Grassini S, Hurme M, Salminen-Vaparanta N, Railo H, Vorobyev V, et al. TMS-EEG reveals hemispheric asymmetries in top-down influences of posterior intraparietal cortex on behavior and visual event-related potentials. Neuropsychologia 2017;107:94-101.\u003c/li\u003e\n\u003cli\u003eMattavelli G, Rosanova M, Casali AG, Papagno C, Lauro LJR. Timing of emotion representation in right and left occipital region: Evidence from combined TMS-EEG. Brain and Cognition 2016;106:13-22.\u003c/li\u003e\n\u003cli\u003eHartwigsen G, Price CJ, Baumgaertner A, Geiss G, Koehnke M, Ulmer S, et al. The right posterior inferior frontal gyrus contributes to phonological word decisions in the healthy brain: Evidence from dual-site TMS. Neuropsychologia 2010;48(10):3155-63.\u003c/li\u003e\n\u003cli\u003eRicci R, Salatino A, Li XB, Funk AP, Logan SL, Mu QW, et al. Imaging the neural mechanisms of TMS neglect-like bias in healthy volunteers with the interleaved TMS/fMRI technique: preliminary evidence. Frontiers in Human Neuroscience 2012;6:1-13.\u003c/li\u003e\n\u003cli\u003eDuecker F, Sack AT. Pre-stimulus sham TMS facilitates target detection. Plos One 2013;8(3):1-6.\u003c/li\u003e\n\u003cli\u003eJacobs C, Goebel R, Sack AT. Visual awareness suppression by pre-stimulus brain stimulation; a neural effect. Neuroimage 2012;59(1):616-24.\u003c/li\u003e\n\u003cli\u003eTaylor PCJ, Thut G. Brain activity underlying visual perception and attention as inferred from TMS-EEG: A review. Brain Stimulation 2012;5(2):124-9.\u003c/li\u003e\n\u003cli\u003eBecker SI, Valuch C, Ansorge U. Color priming in pop-out search depends on the relative color of the target. Frontiers in Psychology 2014;5:1-11.\u003c/li\u003e\n\u003cli\u003eZhuang X, Papathomas TV. Cue relevance effects in conjunctive visual search: Cueing for location, color, and orientation. Journal of Vision 2011;11(7):1-13.\u003c/li\u003e\n\u003cli\u003eWu Q, Chang CF, Xi SS, Huang IW, Liu ZX, Juan CH, et al. A critical role of temporoparietal junction in the integration of top-down and bottom-up attentional control. Human Brain Mapping 2015;36(11):4317-33.\u003c/li\u003e\n\u003cli\u003eUher R, Brooks SJ, Bartholdy S, Tchanturia K, Campbell IC. Increasing cognitive load reduces interference from masked appetitive and aversive but not neutral stimuli. Plos One 2014;9(4):1-8.\u003c/li\u003e\n\u003cli\u003eKiyonaga A, Powers JP, Chiu YC, Egner T. Hemisphere-specific parietal contributions to the interplay between working memory and attention. Journal of Cognitive Neuroscience 2021;33(8):1428-41.\u003c/li\u003e\n\u003cli\u003eYamanaka K, Yamagata B, Tomioka H, Kawasaki S, Mimura M. Transcranial magnetic stimulation of the parietal cortex facilitates spatial working memory: Near-infrared apectroscopy study. Cerebral Cortex 2010;20(5):1037-45.\u003c/li\u003e\n\u003cli\u003eGennari SP, Millman RE, Hymers M, Mattys SL. Anterior paracingulate and cingulate cortex mediates the effects of cognitive load on speech sound discrimination. Neuroimage 2018;178:735-43.\u003c/li\u003e\n\u003cli\u003eSecen J, Culham J, Ho C, Giaschi D. Neural correlates of the multiple-object tracking deficit in amblyopia. Vision Research 2011;51(23-24):2517-27.\u003c/li\u003e\n\u003cli\u003eThomsen T, Specht K, Hammar \u0026Aring;, Nyttingnes J, Ersland L, Hugdahl K. Brain localization of attentional control in different age groups by combining functional and structural MRI. Neuroimage 2004;22(2):912-9.\u003c/li\u003e\n\u003cli\u003eZimmerman ME, Brickman AM, Pau RH, Grieve SM, Tate DF, Gunstad J, et al. The relationship between frontal gray matter volume and cognition varies across the healthy adult lifespan. American Journal of Geriatric Psychiatry 2006;14(10):823-33.\u003c/li\u003e\n\u003cli\u003eSeidman LJ, Valera EM, Makris N, Monuteaux MC, Boriel DL, Kelkar K, et al. Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biological Psychiatry 2006;60(10):1071-80.\u003c/li\u003e\n\u003cli\u003eMostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE. Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biological Psychiatry 2002;52(8):785-94.\u003c/li\u003e\n\u003cli\u003eNeufang S, Akhrif A, Riedl V, F\u0026ouml;rstl H, Kurz A, Zimmer C, et al. Disconnection of frontal and parietal areas contributes to impaired attention in very early Alzheimer\u0026apos;s disease. Journal of Alzheimers Disease 2011;25(2):309-21.\u003c/li\u003e\n\u003cli\u003eGallotto S, Schuhmann T, Duecker F, Middag-van Spanje M, de Graaf TA, Sack AT. Concurrent frontal and parietal network TMS for modulating attention. Iscience 2022;25(3):1-14.\u003c/li\u003e\n\u003cli\u003eMenardi A, Ozdemir RA, Momi D, Tadayon E, Boucher P, Vallesi A, et al. Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS. Neuroimage 2022;264:1-13.\u003c/li\u003e\n\u003cli\u003eSack AT, Kadosh RC, Schuhmann T, Moerel M, Walsh V, Goebel R. Optimizing functional accuracy of TMS in cognitive atudies: A comparison of methods. Journal of Cognitive Neuroscience 2009;21(2):207-21.\u003c/li\u003e\n\u003cli\u003eBergmann TO, Varatheeswaran R, Hanlon CA, Madsen KH, Thielscher A, Siebner HR. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 2021;237:3-29.\u003c/li\u003e\n\u003cli\u003eWebler RD, Fox J, McTeague LM, Burton PC, Dowdle L, Short EB, et al. DLPFC stimulation alters working memory related activations and performance: An interleaved TMS-fMRI study. Brain Stimulation 2022;15(3):823-32.\u003c/li\u003e\n\u003cli\u003eBestmann S, Feredoes E. Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future. In: Miller MB, Kingstone A, editors. Year in Cognitive Neuroscience; 2013, p. 11-30.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"right dorsolateral prefrontal cortex (rDLPFC), right superior parietal lobule (rSPL), single-pulse transcranial magnetic stimulation (single-pulse TMS), visual selective attention","lastPublishedDoi":"10.21203/rs.3.rs-4237359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4237359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIndividuals pay attention to meaningful information by using visual selective attention. Top-down attention is goal-driven and requires cognitive effort to guide attention. Bottom-up attention is stimuli-driven and automatically attracted by salient stimuli. The fronto-parietal network (FPN) is involved in visual selective attention, and top-down and bottom-up attention from neuron activation in the FPN at different times. To explore how different times of transcranial magnetic stimulation (TMS) over the nodes of FPN modulate visual selective attention behavior.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe single-pulse TMS was applied to stimulate the right dorsolateral prefrontal cortex (rDLPFC) and right superior parietal lobule (rSPL) of two groups (active TMS and sham TMS group) at early times (33ms, 50ms, 66ms, and 83ms) and late times (216ms, 233ms, 250ms, and 266ms) after the pop-out and search stimulus displayed onset.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe behavior results showed late TMS over rDLPFC decreased ACC of top-down attention. Late TMS over rSPL improved ACC of top-down attention and decreased cognitive load difference between top-down and bottom-up attention. Voxel-based morphometry (VBM) results of T1 images showed that gray matter volumes (GMV) in fronto-parietal cortex correlated with visual selective attention behavior, including bilateral superior frontal gyrus, right precentral gyrus, left supramarginal gyrus, right inferior frontal gyrus (orbital part), and left superior frontal gyrus (medial), especially in the active TMS group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings reveal the cause role of the FPN on visual selective attention behavior and the relationship between GMV in the fronto-parietal cortex and visual selective attention.\u003c/p\u003e","manuscriptTitle":"Different times TMS over fronto-parietal network regulates visual selective attention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 17:39:47","doi":"10.21203/rs.3.rs-4237359/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c74e276b-8e36-46d8-bf85-6a380a269bfd","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T18:00:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 17:39:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4237359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4237359","identity":"rs-4237359","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}