Cognitive processes in goal-directed attentional system dysfunction of generalized anxiety disorder

Cognitive processes in goal-directed attentional system dysfunction of generalized anxiety disorder | 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 Cognitive processes in goal-directed attentional system dysfunction of generalized anxiety disorder Xinyu Hao, Xiaoya Liu, Danfeng Yuan, Chunyu Liang, Xiangyun Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6320865/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 Attentional control theory proposes that anxiety impairs the goal-directed attentional system, which has been well-documented in subclinical populations. However, the underlying cognitive mechanisms of generalized anxiety disorder (GAD) remain poorly understood. A thorough investigation of the goal-directed attentional system in GAD may clarify its etiological and pathophysiological roles and offer insights for developing targeted interventions. Method This study investigated two key subcomponents of the goal-directed attentional system in GAD: inhibition and shifting functions. Twenty-four GAD patients and twenty-eight healthy controls (HC) were recruited and completed the Attentional Control Scale. Behavioral performance and 64-channel EEG data were collected during the Go/No-Go and more-odd shifting tasks. Results Self-reported questionnaire and behavioral performance indicated that GAD patients had significantly lower total and subscale scores ( p < 0.001) and higher inverse efficiency ( p < 0.01), reflecting subjective attentional control deficits and reduced processing efficiency. EEG results revealed reduced NoGo-N2 ( p < 0.01) and NoGo-P3 ( p < 0.001) peak amplitudes in GAD, indicating impaired subprocesses of response inhibition. Furthermore, GAD patients exhibited decreased theta power ( p < 0.05) and theta-phase/gamma-amplitude coupling (TGC, p < 0.05) during the more-odd shifting task, suggesting deficits in attentional processing and impaired neural communication related to cognitive flexibility. HAMA scores were significantly correlated with behavioral performance, NoGo-N2 and NoGo-P3 amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task (all p < 0.01). These results suggest that higher levels of anxiety are associated with deficits in inhibitory control, cognitive resources, and neural oscillatory dysfunction. Conclusion These findings provide neurophysiological insights into attentional deficits in GAD, highlighting impairments in inhibitory control and cognitive flexibility. Understanding these deficits can offer guidance for developing targeted interventions to enhance cognitive control in GAD. Trial registration: The trial is registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024). EEG Generalized anxiety disorder Attention control Inhibition function Shifting function Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Attentional control theory suggests that anxiety disrupts the balance between the stimulus-driven and goal-directed attentional systems, leading to attentional control deficits [1, 2]. Specifically, anxiety enhances stimulus-driven processing while weakening goal-directed control, making individuals more susceptible to salient and distracting stimuli. Understanding this disruption requires an in-depth examination of these two attentional systems. The stimulus-driven attentional system automatically captures attention in response to salient or novel stimuli, independent of an individual's intentions. It comprises three key processes: (1) attentional facilitation (attentional vigilance), characterized by a heightened sensitivity to negative stimuli; (2) attentional disengagement (attention fixation), referring to the difficulty in shifting attention away from negative stimuli; and (3) attentional avoidance, where attention is redirected away from a threatening stimulus [3]. In contrast, the goal-directed attentional system enables deliberate focus on specific objects, events, or thoughts despite distractions and facilitates flexible shifts between stimuli. It plays a crucial role in regulating cognitive control and adaptive behavior, supporting two core functions: inhibition and shifting [4–6]. The inhibition function involves suppressing internal impulses or external distractions to maintain goal-directed behavior and execute appropriate actions [7, 8]. The shifting function refers to the ability to flexibly redirect attention between tasks or mental sets in response to a changing environment [9, 10]. Disruptions in these attentional systems may contribute to the cognitive and emotional deficits commonly associated with anxiety disorders. A comprehensive understanding of these systems provides a framework for investigating how anxiety affects cognitive processes. The two attentional systems have been well-documented in subclinical high trait anxiety (HTA) populations. Research on the stimulus-driven system has shown that HTA individuals have difficulty disengaging from negative stimuli, such as angry and fearful facial expressions [11–13]. Studies on the goal-directed system have revealed abnormal neural responses in HTA individuals, including altered frontal NoGo-N2 and parietal NoGo-P3 amplitudes during inhibition tasks, along with compensatory neural strategies during task switching [9, 14, 15]. However, emerging evidence suggests that generalized anxiety disorder (GAD) patients disengage from negative stimuli faster than expected, challenging the generalizability of HTA-based findings to clinical populations [11]. Despite these insights, the cognitive and neural mechanisms of the goal-directed attentional system in GAD remain poorly understood. Given the potential distinctions between HTA and GAD, a systematic investigation of the goal-directed attentional system in GAD has both theoretical and clinical significance. Understanding these mechanisms could clarify the neurocognitive basis of GAD, refine ACT’s applicability to clinical populations, and inform targeted interventions for attentional deficits in GAD. This study aims to examine goal-directed attentional deficits in GAD by comparing inhibition and shifting functions between GAD patients and healthy controls (HC) at both behavioral and electrophysiological levels. By examining differences in self-report scales, behavioral performance, and EEG features, this study seeks to provide a comprehensive understanding of goal-directed attentional deficits in GAD. The findings may offer novel insights into the pathological mechanisms underlying these impairments and contribute to the development of targeted interventions for cognitive dysfunction in GAD. 2. Methods 2.1 Subjects Participants with GAD (n = 24) were recruited from the outpatient clinics of Beijing Anding Hospital. Inclusion criteria were (1) 18–60 years of age and right-handed; (2) diagnosed with GAD according to DSM-5 criteria; (3) completed junior high school or higher education. Exclusion criteria were as follows: (1) Hamilton Anxiety Rating Scale (HAMA) [16] score ≥ 14 and the Hamilton Depression Rating Scale (HAMD) [17] score ≤ 17 [18–21]; (2) history of mental illness other than major depressive disorder or GAD (e.g., bipolar disorder) or history of brain injury; (3) history of alcohol or substance abuse; (4) current serious suicidal risk; and (5) serious somatic illness that might preclude study participation. HC (n = 28) were volunteers recruited via WeChat. Given the established associations between GAD and factors such as age [22], gender [23], and education level [24], HC were matched on these variables. None of the HC met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for any lifetime mood, anxiety, psychotic, or substance use disorders. Diagnostic interviews were conducted by trained diagnosticians with post-baccalaureate and master's level diagnosticians. Written informed consent was obtained from all participants. The study was approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University and was registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024), in accordance with the guidelines of the Declaration of Helsinki. 2.2 Procedure Participants signed an informed consent form and completed the Attentional Control Scale (ACS) [25], a 20-item self-report questionnaire designed to assess an individual's ability to manage inhibitory interference and to flexibly regulate attention. The ACS comprises two subscales: focusing and shifting [26]. Participants then performed two tasks: the Go/No-Go task, which measures inhibitory control, and the more-odd shifting task, which assesses cognitive flexibility. 2.2.1 Task design 1) Go/No-Go task The Go/No-Go task is a widely used paradigm for studying response inhibition [27, 28]. Participants respond to frequent "Go" targets while withholding responses to infrequent "No-Go" stimuli [15, 29]. The inhibitory function is measured by the successful and timely suppression of responses when a low-probability "No-Go" stimulus appears [30, 31]. The entire procedure took about 20 minutes and required participants to respond to stimuli according to different color cues. The Go/No-Go task consisted of two phases: a preliminary block of 8 trials to eliminate the learning effect, followed by six main blocks of 600 trials. The main blocks were divided into two levels: an easy level with two blocks (60% Go stimuli and 40% No-Go stimuli per block) [32] and a hard level with four blocks (91% Go stimuli and 9% No-Go stimuli per block) [33]. The high proportion of Go stimuli created a prepotent response, making it challenging to inhibit responses to No-Go stimuli [30, 31]. Stimuli were presented on a black background, beginning with a fixation point ("+") displayed for 500 to 800 ms, followed by the Go stimuli (blue, green, or white squares) or the No-Go stimuli (red square), each presented for 1000 ms. Three Go stimuli appeared in a randomized order approximately the same number of times across the six blocks. Participants viewed sequentially presented colored squares (120 × 120 pixels) and were instructed to press the "J" key with their right hand as quickly as possible for all squares except the red square, which appeared less frequently. If participants did not respond, the target would disappear after 1000 ms. The Go and No-Go stimuli were presented randomly (see Fig. 1 ). Figure 1 about here 2) More-odd shifting task The more-odd shifting task, adapted from Salthouse et al. (1998) [34], is a well-established computerized assessment that measures the shifting function [8]. In this task, participants respond differently based on specific cues. Using this task-switching paradigm, researchers can evaluate individuals' cognitive flexibility in adapting to changing task demands. The entire procedure took approximately 15 minutes and required participants to respond to stimuli based on different shape cues. The task began with three preliminary blocks of 30 trials each, followed by three main blocks of 96 trials each. The preliminary blocks, which lasted approximately 2 minutes, included feedback on response accuracy. The main block commenced only after participants achieved an accuracy rate above 80%. During the main blocks, participants received no feedback and were instructed to respond as quickly and accurately as possible. Each trial began with a fixation point ("+") displayed for 500 ms, followed by the stimulus, which was presented for 2500 ms or until a response was made. The stimuli consisted of a gray square or diamond (250 × 250 pixels) on a black background, with a silver number (120 × 120 pixels, size 56, Times New Roman font) in the center. Numbers ranged from 1 to 9, with the exception of 5. For trials displaying a diamond shape, participants were instructed to press the "F" key with their left index finger if the number was odd; otherwise, they pressed the "J" key with their right index finger. For trials displaying a square, participants judged whether the number was greater than 5, pressing "F" for numbers less than 5 and "J" for numbers greater than 5. Response rules were counterbalanced across participants. If the response rules of the current trial matched those of the previous trial, the trial was defined as a non-switch condition (congruent); if they differed, the trial was defined as a switch condition (incongruent). The first trial of each block contained no repetitions or switches, with 89 trials in the congruent condition and 88 trials in the incongruent condition, ensuring that congruent and incongruent trials did not occur more than three times consecutively (see Figure. 2). Figure 2 about here 2.3 Data acquisition Participants were instructed not to smoke or consume coffee before the experiment. All tasks were presented at the center of a 19-inch Lenovo computer screen with a resolution of 1024 × 768 pixels. Participants were tested individually in a quiet room while seated approximately 60 cm from the screen at a comfortable viewing distance. 2.3.1 Behavioral acquisition Both tasks were programmed and administered using E-Prime® 2.0 software (Psychology Software Tools, Inc., Pittsburgh, PA, USA), which automatically recorded participants’ reaction times (RTs) and accuracy (ACC). RTs were captured via the E-Prime serial response box. 2.3.2 EEG Recording EEG signals were recorded using a 64-channel system with standard Ag/AgCl electrodes placed on the scalp according to the international 10–20 system (Brain Products, Munich, Germany). Data were sampled at 1 kHz with a common reference electrode, and electrode impedances were maintained below 10 kΩ. 2.4 Data analysis 2.4.1 Behavioral performance analysis An inverse efficiency (IE) measure, calculated by dividing the mean RTs of correct trials by the ACC, was used to correct for speed-accuracy trade-off effects in both tasks [35–37]. A higher IE indicates lower processing efficiency [9]. Besides, the switching cost index, which measures the cost of switching between tasks, was calculated using the following formula: Switching Cost = IE switch condition – IE non−switch condition . 2.4.1 EEG analysis Offline processing of the EEG data was conducted using MATLAB with the EEGLAB toolbox [38], running in the MATLAB environment (version R2017a). The data were downsampled from 1000 Hz to 200 Hz and digitally filtered with a 0.1 Hz–50 Hz bandpass filter. The EEG signals were then referenced to the average of the two mastoid electrodes. Independent component analysis (ICA) was applied to remove artifacts associated with saccadic eye movements and eye blinks. This process involved identifying and manually removing components associated with blinks, eye movements, and muscle artifacts based on visual inspection of the component activations and maps [9]. 1) Go/No-Go task Since this study focused on the inhibition function, only the No-Go stimuli were analyzed [15, 39, 40]. The NoGo-N2 and NoGo-P3 components represent distinct subprocesses of response inhibition [15]. The NoGo-N2 component reflects the inhibition or revision of a motor plan before execution, while the NoGo-P3 component is linked to implicit attention orientation and motor inhibition [39, 41–43]. Due to its longer latency, the NoGo-P3 may also reflect the evaluation of response inhibition [44, 45]. These components are modulated by distinct neurobiological systems, indicating separate sub-processes in response inhibition [46, 47]. For data processing, all epochs were baseline-corrected using the mean voltage during the 200 ms before the onset of the color cues. The corrected data were segmented into 1000 ms epochs, starting 200 ms before cue onset, and then averaged for the No-Go trials. Peak amplitudes and latencies of the frontal-midline N2 and parietal P3 were analyzed, with averages calculated from waveforms at selected electrodes based on grand-mean ERP topographies and relevant literature [44, 48, 49]. The NoGo-N2 wave was defined as the most negative deflection at frontal electrodes within a 200–350 ms post-stimulus interval, and the NoGo-P3 wave was defined as the most positive deflection at parietal electrodes within a 300–450 ms interval. The Fz electrode was selected for NoGo-N2 and the Pz electrode for NoGo-P3. 2) More-odd shifting task Power spectrum analysis EEG power spectrum analysis is an essential tool in neuroscience research, providing insight into the relationship between brain activity across various frequency bands and specific cognitive and behavioral functions. Different brain regions oscillate at characteristic frequency bands that are associated with distinct functions [50]. In this study, the following frequency bands were focused: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), sensorimotor rhythm (SMR, 12–15 Hz), beta (15–30 Hz), and gamma (30–50 Hz) [51]. For data processing, epochs were baseline-corrected using the mean voltage during the 500 ms pre-stimulus period and then averaged across trials. The corrected data were segmented into 2500 ms epochs, starting 500 ms before stimulus onset. Power spectrum estimation was performed using the Welch method, with a 2-second Hamming window, 25% overlap, and zero-padding to 256 points [52, 53]. Theta-gamma coupling Theta-phase/gamma-amplitude coupling (TGC) is essential for cognitive functions such as learning and memory representation and has received considerable research attention [54–56]. In this study, TGC has been computed using the Kullback-Leibler modulation index method [57], selected for its stability across different data lengths and sampling rates [58]. This method measures the Kullback-Leibler distance between observed amplitude distributions. For TGC estimation, phase and amplitude signals were extracted from the filtered EEG time series. The phase was estimated using a bandpass filter (4–8 Hz) with 2 Hz steps and a 2 Hz bandwidth, while amplitude was estimated in the 30–50 Hz range, yielding narrow-band phase and broad-band amplitude signals. The Hilbert transform was applied to extract phase data from the low-frequency components and amplitude data from the high-frequency components. This enabled the construction of a composite time series representing the amplitudes of high-frequency oscillations at each low-frequency phase. Phase values were binned into 18 intervals (20° per bin) for analysis, following the method outlined by Zhang et al. [59]. 2.5 Statistical analysis Statistical analyses were performed using SPSS (version 20.0, IBM Corp., Armonk, NY, 2011). The Shapiro-Wilk test was used to assess normality. Differences in age and education level between the GAD and HC groups were assessed using the Mann-Whitney U test, while gender differences were examined using the chi-squared test. Group comparisons for ACS scores were performed using independent two-sample t-tests (two-tailed) with statistical significance set at α = 0.05. Mauchly's test for sphericity was applied with more than two levels of the within-subject factors. If the sphericity assumption was violated, Greenhouse-Geisser corrections were applied to the repeated-measures analysis of variance (ANOVA), with uncorrected degrees of freedom and corrected p-values reported. Bonferroni correction was used for multiple comparisons, with adjusted p-values reported. Effect sizes are reported as partial eta-squared ( ηp ² ) for F tests. 2.5.1 Behavioral performance statistical A 2 × 2 Mixed-design ANOVA was performed to analyze the IE for the Go/No-Go task, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. Similarly, a two-way Mixed-design ANOVA was performed on the IE in the more-odd shifting task, with Group (GAD vs. HC) and Condition (congruent vs. incongruent) as factors. To further assess the shifting function, a two-sample independent t-test was used to compare the switching cost between the two groups. Spearman correlation analyses were performed to examine the relationship between IE and scale scores. The false discovery rate (FDR) was used to adjust for all comparisons [60]. Statistical significance was set at p < 0.05. 2.5.2 EEG features statistical A 2 × 2 Mixed-design ANOVA was conducted to examine the peak amplitudes and latencies of NoGo-N2 and NoGo-P3, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. A 2 × 2 × 5 Mixed-design ANOVA was also conducted to analyze the power spectrum of six frequency bands (delta, theta, alpha, SMR, beta, and gamma), with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) and Brain Region (frontal, central, parietal, temporal, occipital) as the within-subjects factors. A 2 × 2 Mixed-design ANOVA was performed on the TGC in the more-odd shifting task with Group (GAD vs. HC) and Condition (congruent vs. incongruent) as factors. Spearman correlation analyses were performed to explore the relationships between significant EEG features and scale scores. The FDR method was applied to adjust for multiple comparisons, with statistical significance set at p < 0.05. 3. Results 3.1 Clinical and demographic variables Table 1 presents the clinical and demographic characteristics of the two groups, along with statistical comparisons. Chi-squared analysis showed no significant difference in gender between the groups (χ 2 (1) = − 1.223, p = 0.221). The Mann-Whitney U test showed no significant difference in education level between the GAD and HC groups (Z = − 0.522, p = 0.602). Age did not differ significantly between the groups (Z = 0.468, p = 0.494). A detailed description of the participants is provided in the Supplementary Information . Table 1 Clinical and demographic variables of the participants Variables HC group GAD group Z/χ2 p mean (SD) (n = 28) (n = 24) Age (years) 29.96 (10.43) 29.79 (6.69) 0.468 0.494 Gender (Female/Male) 20/8 15/9 –1.223 0.221 Education (years) 16.57 (3.09) 17.25 (2.27) –0.522 0.602 HAMA score - 18.32 (4.42) - - HAMD score - 11.11 (4.16) - - Note: SD, standard deviation; GAD, generalized anxiety disorder; HC, healthy controls; HAMA, Hamilton anxiety scale; HAMD Hamilton depression rating scale 3.2 ACS Score ACS scores were calculated and analyzed to assess the capacity of the goal-directed attentional system. Figure 3 (A-C) shows the ACS total score and its two subscales for the GAD and HC groups. GAD participants exhibited significantly lower scores on the ACS total score (t(50) = -6.678, p < 0.001) and its subscales: focus subscale (t(50) = -5.960, p < 0.001) and shifting subscale (t(50) = -4.393, p < 0.001). Based on the self-report questionnaire, the results indicated that the GAD group struggled with managing inhibitory interference and to flexibly regulating attention, indicating impairments in the goal-directed attentional system. Figure 3 about here 3.3 Behavioral Performance 3.3.1 Go/No-Go task The IE was calculated for each level, followed by statistical analyses comparing the two groups. Figure 4 A illustrates the mean and standard error of the IE for both groups. In terms of IE, GAD exhibited higher values on both levels, with the hard level showing higher values for both groups. A 2 × 2 Mixed-design ANOVA was performed, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. The analysis revealed significant main effects for both Group and Level (Group: F(1, 37) = 60.577, p < 0.001, partial ηp ² = 0.621) and Level (F(1, 37) = 58.328, p < 0.001, partial ηp ² = 0.612). Additionally, a significant interaction between Group and Level was observed (F(1, 37) = 11.600, p = 0.002, partial ηp ² = 0.239). A simple effects analysis indicated that the GAD group had significantly increased IE values at the hard level (Hard Level: F(1, 37) = 11.114, p = 0.002, partial ηp ² = 0.221; Easy Level: F(1, 37) = 3.243, p = 0.080, partial ηp ² = 0.081). Furthermore, the analysis showed that IE was higher on the hard level for both groups (GAD: F(1, 37) = 59.452, p = 0.000, partial ηp ² = 0.616; HC: F(1, 37) = 9.188, p = 0.004, partial ηp ² = 0.199). The results indicate that participants with GAD performed lower processing efficiency than HC, especially on the hard level during the Go/No-Go task. 3.3.2 More-odd shifting task The IE was calculated for each condition and statistical analyses were performed. Figure 4 B shows the mean and standard error of the IE for both groups. In terms of IE, GAD showed higher values across both conditions, with the incongruent condition yielding higher values in each group. A 2 × 2 Mixed-design ANOVA was performed with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) as the within-subjects factor. Although there was no significant interaction between Group and Condition (F(1, 37) = 1.790, p = 0.189, partial ηp ² = 0.046), significant main effects were observed for both Group and Condition (Group: F(1, 37) = 35.579, p < 0.01, partial ηp ² = 0.490; Condition: F(1, 37) = 73.426, p < 0.001, partial ηp ² = 0.665). The GAD group showed an overall higher IE, especially in the incongruent condition. Furthermore, there was no significant difference in switching costs between the two groups (t(37) = 1.338, p = 0.189). These results indicate that participants with GAD exhibited lower processing efficiency during the more-odd shifting task, regardless of whether the condition was congruent or incongruent. Figure 4 about here 3.4 EEG feature 3.4.1 Go/No-Go task Four EEG features—the peak amplitudes and latencies of NoGo-N2 and NoGo-P3—were calculated for each task level, followed by statistical comparisons between the GAD and HC groups. NoGo-N2 Figure 5 A displays the mean and standard error of the NoGo-N2 peak amplitude at the FZ electrode site for both groups. The GAD group exhibited lower peak amplitudes on both levels, with a more pronounced reduction on the hard level, while the HC group showed higher amplitudes on the hard level. A 2 × 2 Mixed-design ANOVA was computed, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. The main effect of the Group was significant (F(1, 37) = 6.888, p = 0.013, partial ηp ² = 0.157), while the effect of Level was not significant (F(1, 37) = 0.014, p = 0.908, partial ηp ² = 0.000). A significant Group × Level interaction was observed (F(1, 37) = 10.857, p = 0.002, partial ηp ² = 0.227). A simple effects analysis revealed that the NoGo-N2 peak amplitude on the hard level was significantly lower in the GAD group (F(1, 37) = 5.675, p = 0.022, partial ηp ² = 0.133), whereas it was significantly higher in the HC group (F(1, 37) = 5.183, p = 0.029, partial ηp ² = 0.123). Further analysis showed a significantly lower NoGo-N2 peak amplitude on the hard level for the GAD group (F(1, 37) = 12.632, p = 0.001, partial ηp ² = 0.255), while no significant difference was observed on the easy level (F(1, 37) = 1.875, p = 0.179, partial ηp ² = 0.048). Figure 5 B shows the ERP waveforms at the FZ electrode site on the hard level between the GAD and HC groups. Figure 5 C presents the mean and standard error of NoGo-N2 latency at electrode site FZ for both groups. In terms of latency, the GAD group exhibited higher values on both levels, with the hard level showing higher values in both groups. A 2 × 2 Mixed-design ANOVA was calculated, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. No significant interaction between Group and Level was found (F(1, 37) = 0.003, p = 0.960, partial ηp ² = 0.000), and neither the main effect of Group (F(1, 37) = 0.760, p = 0.389, partial ηp ² = 0.020) nor that of Level (F(1, 37) = 1.196, p = 0.281, partial ηp ² = 0.031) reached significance. These results suggest that participants with GAD may lack inhibition or revision of a motor plan prior execution to execution, particularly on the hard level. NoGo-P3 Figure 5 D illustrates the mean and standard error of the NoGo-P3 peak amplitude at the PZ electrode site for both groups. The GAD group had lower peak amplitudes on both levels, with a notably larger reduction on the hard level, whereas the HC group showed higher amplitudes on the hard level. A 2 × 2 Mixed-design ANOVA was computed with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. Significant main effects were observed for both Group and Level (Group: F(1, 37) = 17.537, p = 0.000, partial ηp ² = 0.322; Level: F(1, 37) = 12.769, p = 0.001, partial ηp ² = 0.257). Furthermore, the interaction between Group and Level was significant (F(1, 37) = 26.994, p = 0.000, partial ηp ² = 0.422). In the HC group, peak amplitude was significantly lower on the hard level (F(1, 37) = 39.459, p = 0.000, partial ηp ² = 0.516), whereas the GAD group showed no significant difference between levels (F(1, 37) = 1.283, p = 0.265, partial ηp ² = 0.034). Moreover, a simple effects analysis indicated that the peak amplitude on the hard level was significantly lower in the GAD group (F(1, 37) = 31.084, p = 0.000, partial ηp ² = 0.457). No significant difference between the two groups was observed on the easy level (F(1, 37) = 1.544, p = 0.222, partial ηp ² = 0.040). Figure 5 E illustrates the ERP waveforms at the PZ electrode site between the two groups on the hard level. Figure 5 F shows the mean and standard error of the latency of NoGo-P3 at electrode site PZ for both groups. For latency, the GAD group had lower values on the hard level but higher values on the easy level, while the latency on the hard level was higher in both groups. A 2 × 2 Mixed-design ANOVA was computed with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. There was no significant interaction between Group and Level (F(1, 37) = 0.067, p = 0.797, partial ηp ² = 0.002). The main effects of Group and Level were also nonsignificant (Group: F(1, 37) = 0.415, p = 0.523, partial ηp ² = 0.011; Level: F(1, 37) = 2.078, p = 0.158, partial ηp ² = 0.053). These results suggest that participants with GAD have deficits in implicit attentional orientation, motor inhibition, and response inhibition evaluations, especially on the hard level. Figure 5 about here 3.4.2 More-odd shifting task The power spectrum of six bands (delta, theta, alpha, SMR, beta, and gamma) was computed for each condition, followed by statistical analyses between the two groups. A 2 × 2 × 5 Mixed-design ANOVA was performed, with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) and Brain Region (frontal, central, parietal, temporal, occipital) as the within-subjects factors. A significant difference between the two groups was observed only in the theta band, with no significant group differences in the other bands ( Supplementary Information ). A significant Group × Region interaction effect was found (F(4, 34) = 3.126, p = 0.027, partial ηp ² = 0.269). A simple effects analysis revealed significantly lower theta power in the GAD group in the temporal and occipital regions (Temporal: F(1, 37) = 5.176, p = 0.029, partial ηp ² = 0.123; Occipital: F(1, 37) = 10.022, p = 0.003, partial ηp ² = 0.213). Figures 6 A and 6 B show comparisons of theta power between the two groups in the temporal and occipital regions, showing that theta power was lower in the GAD group in both conditions, with higher theta values observed in the incongruent condition for both groups. Figure 6 C displays the five regions of interest, and Fig. 6 D presents the topographic distribution of theta power differences between groups in the temporal and occipital regions. These results suggest that participants with GAD exhibit significantly reduced theta power in the temporal and occipital regions. These reductions in theta power may reflect disruptions in attentional processing and neural efficiency within these brain regions, which are critical for cognitive control. Such deficits may contribute to the difficulties in shifting attention that are commonly observed in GAD. Further, the mean TGC in the temporal and occipital regions was calculated and statistically analyzed based on the significant theta-band differences between the two groups. Figure 6 E displays the TGC comparisons between the groups, showing that the GAD group had lower values in both conditions and TGC was higher in the congruent condition for both groups. A two-way mixed-design ANOVA was performed with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) as the within-subjects factor. Both main effects were significant (Group: F(1, 37) = 7.888, p = 0.008, partial ηp ² = 0.176; Condition: F(1, 37) = 20.445, p = 0.000, partial ηp ² = 0.356), and there was a significant Group × Condition interaction effect (F(1, 37) = 4.155, p = 0.049, partial ηp ² = 0.101). A simple effects analysis showed that the GAD group exhibited lower TGC under both conditions (Congruent: F(1, 37) = 4.174, p = 0.048, partial ηp ² = 0.101; Incongruent: F(1, 37) = 11.135, p = 0.002, partial ηp ² = 0.231). Additionally, the GAD group showed a significant decrease in TGC, whereas the HC group showed no significant change (GAD: F(1, 37) = 20.979, p = 0.000, partial ηp ² = 0.362; HC: F(1, 37) = 3.164, p = 0.083, partial ηp ² = 0.079). Figure 6 F shows the differences in TGC comodulograms between the groups in both conditions. These results suggest that participants with GAD exhibited reduced TGC, which is typically associated with coordination of brain regions and large-scale network integration. Deficits in TGC may indicate reduced interregional communication efficiency and potential cognitive impairments in shifting attention. Figure 6 about here 3.5 Correlation 3.5.1 Go/No-Go task Spearman correlation analyses were performed between three scales (HAMA, HAMD, and ACS) and three behavioral/ERP indexes with significant group differences: No-Go IE and the peak amplitudes of NoGo-N2 and NoGo-P3 on the hard level. After FDR correction for multiple comparisons, eight significant correlations were identified. Figure 7 shows the correlation between the three scales (HAMA, HAMD, and ACS) and the three behavioral/ERP indexes. Figure 7 A shows that the No-Go IE was positively correlated with both HAMA (r = 0.510, p < 0.01) and HAMD (r = 0.513, p < 0.01) scores, suggesting that higher anxiety and depression scores are associated with lower processing efficiency. For ERP components, Fig. 7 B shows that NoGo-N2 peak amplitude correlated positively with HAMA (r = 0.542, p < 0.01) and HAMD (r = 0.562, p < 0.01), and negatively with ACS scores (r = -0.499, p < 0.01). As NoGo-N2 is a negative component, this positive correlation reflects greater original values, not absolute values. These results suggest that greater anxiety and depression symptoms are associated with lower NoGo-N2 peak amplitude, whereas higher ACS scores are associated with higher NoGo-N2 peak amplitude. Figure 7 C shows an opposite correlation pattern for NoGo-P3 amplitude, where HAMA (r = -0.667, p < 0.01) and HAMD (r = -0.624, p < 0.01) scores correlated negatively, while ACS scores correlated positively (r = 0.525, p < 0.01). These results suggest that higher anxiety and depression are associated with lower NoGo-P3 amplitude, whereas better attentional control is associated with increased NoGo-P3 amplitude. Taken together, these findings underscore the role of NoGo-N2 and NoGo-P3 as core components of inhibition function, highlighting their relevance for understanding inhibitory control in GAD. 3.5.2 More-odd shifting task Spearman correlation analyses were performed between three scales (HAMA, HAMD, and ACS) and four EEG features that showed significant group differences: theta power in the temporal and occipital regions, and TGC in the congruent and incongruent conditions. After FDR correction for multiple comparisons, only one significant correlation was found. Figure 7 D illustrates that TGC in the incongruent condition exhibited a significant negative correlation with HAMA scores (r = -0.456, p < 0.01). These results suggest that higher TGC may be associated with lower anxiety levels in GAD. This may indicate that participants with GAD experience impaired neural communication and integration between brain regions during the more-odd shifting task. A stronger TGC may reflect more efficient information processing or attentional control, both of which are likely to be impaired in GAD. Figure 7 about here 4. Discussion This study provides novel insights into the cognitive processes underlying the goal-directed attentional system in GAD, focusing on two key subcomponents: inhibition and shifting functions. To explore these processes, ACS scores were assessed and compared between GAD and HC, along with behavioral performance and EEG features collected during the Go/No-Go and more-odd shifting tasks. Self-report results indicated that GAD participants had significantly lower ACS total scores and subscale scores, suggesting perceived deficits in attentional control. Consistent with these findings, GAD participants exhibited higher IE during both tasks, indicating reduced processing efficiency. Electrophysiological analysis further showed that the GAD group had lower peak amplitudes of NoGo-N2 and NoGo-P3, reflecting deficits in pre-execution inhibitory processes and response inhibition. In the more-odd shifting task, the GAD group demonstrated decreased theta power, and TGC, suggesting impaired attentional processing and disrupted interregional communication, which likely contribute to cognitive deficits in attentional shifting. Finally, correlation analysis revealed significant correlations between HAMA scores and multiple measures: behavioral performance, NoGo-N2, and NoGo-P3 peak amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task. Taken together, these findings elucidate the cognitive processes underlying the goal-directed attentional system in GAD, highlighting deficits in inhibitory control and attentional flexibility. The ACT posits that anxiety impairs inhibitory control [1, 10, 61]. To compensate for this deficit, HTA often employ compensatory strategies, allocating more top-down attentional control resources [1]. This aligns with the findings of this study, which showed no significant interaction effect in behavioral performance on the easy level of the Go/No-Go task. Furthermore, the behavioral performance results of this study did not reveal a significant Group × Condition interaction effect in the more-odd shifting task, contrary to some previous findings [62, 63]. This suggests that while GAD may impair shifting function, it does not necessarily lead to significant behavioral deficits, as reflected by the absence of significant differences in switching costs between the two groups. Regarding the Go/No-Go task, several studies have shown that anxious individuals may experience disrupted inhibitory control, as reflected at the electrophysiological level [5, 15, 64]. Consistent with these findings, electrophysiological data revealed that the GAD group had lower peak amplitudes of NoGo-N2 and NoGo-P3 on the hard level of the Go/No-Go task. This suggests that anxiety impairs inhibitory control, hindering the efficient evaluation and monitoring of incorrect responses. Such impairment may result in reduced cognitive control effort or a diminished allocation of additional processing resources in anxious individuals. Response inhibition and cognitive control are primarily associated with activity in the anterior cingulate cortex (ACC) and other frontal brain regions [65–67]. The ACC plays a critical role in integrating cognitive and emotional processes [68], is implicated in the pathophysiology of psychiatric disorders [69], and serves as a key component of the anxiety circuitry [70]. Given these findings, participants with GAD may exhibit neurocognitive deficits in inhibitory processing and response monitoring. Regarding the more-odd shifting, previous cross-sectional studies have documented a decline in theta power associated with a gradual reduction in cognitive function across different age groups, including healthy young and older adults [71, 72]. This is consistent with the literature linking theta oscillations to effective cognitive function, particularly memory, and tasks requiring sustained attention [73]. Consistent with these findings, the power spectrum analysis suggests that the reduced theta power may result from impaired cognitive control in GAD. Furthermore, extensive research has demonstrated that higher TGC values are associated with various cognitive functions, including learning [74], memory formation [75, 76], and decision-making [77]. TGC plays a critical role in facilitating information processing and integrating neural activity across brain regions [78]. Thus, the attenuated TGC in GAD may reflect disrupted neural communication and impaired redistribution of cognitive resources across brain regions, further reinforcing the notion of cognitive dysfunction in GAD during the more-odd shifting task. Given that the effective use of emotion regulation strategies serves as a resilience factor for mental health [79] and is positively correlated with better health outcomes [80], a deeper understanding of the neural mechanisms underlying impaired emotion regulation in clinical populations is essential for advancing therapeutic interventions. The findings of this study have significant clinical implications, particularly in the development of targeted treatments for GAD. Specifically, our results suggest that strengthening cognitive control may be an effective strategy for improving emotion regulation in GAD. Furthermore, identifying disorder-specific mechanisms underlying deficits in emotion regulation could facilitate the development of novel therapeutic targets and validation indicators for cognitive-behavioral interventions or neuromodulation approaches. 5. Conclusion This study provides novel evidence on the cognitive processes underlying the goal-directed attentional system in GAD, specifically focusing on inhibition and shifting functions. By integrating self-reported attentional control, behavioral performance, and electrophysiological level, this study offers a comprehensive perspective on cognitive dysfunction in GAD. Results from the ACS questionnaire indicated that participants with GAD reported significantly lower total and subscale scores, reflecting subjective impairments in attentional control. Consistent with these findings, GAD participants demonstrated higher IE scores in both tasks, suggesting reduced processing efficiency. Electrophysiological analyses further revealed that the GAD group exhibited lower NoGo-N2 and NoGo-P3 peak amplitudes during the Go/No-Go task, indicating deficits in pre-execution inhibitory processes and response inhibition. In the more-odd shifting task, GAD participants showed reduced theta power and TGC, reflecting impairments in attentional processing and interregional neural communication, which may underlie deficits in cognitive flexibility. Moreover, correlation analyses revealed significant associations between HAMA scores and multiple measures, including behavioral performance, NoGo-N2 and NoGo-P3 amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task. These findings provide neurophysiological insights into the mechanisms of GAD, highlighting impairments in inhibitory control and attentional flexibility. Understanding these deficits can inform the development of targeted interventions aimed at improving cognitive control in individuals with GAD. 6. Limitation Several limitations of this study should be acknowledged. First, the sample was recruited solely from Beijing, which may limit the generalizability of the findings due to potential geographic and ethnic influences. Future multi-center studies with more diverse populations are necessary to enhance external validity. Second, the relatively small sample size may have reduced the statistical power of the study, potentially obscuring certain effects of anxiety on cognitive and neural processes. Replication with a larger cohort is essential to confirm the robustness of these findings. Third, the restricted age range of participants further constrains the applicability of the results. Despite these limitations, this study provides valuable insights into the cognitive and neurophysiological mechanisms of GAD, underscoring the need for further research with larger, more representative samples. 7. Abbreviations GAD generalized anxiety disorder HC healthy controls EEG electroencephalograms ERPs event-related potentials HTA high trait anxiety LTA low trait anxiety HAMA Hamilton anxiety rating scale HAMD Hamilton depression rating scale ACS attentional control scale ACT attentional control theory MDD major depressive disorder RT reaction times ACC accuracy IE inverse efficiency ICA independent component analysis ACC the anterior cingulate cortex TGC theta-phase/gamma-amplitude coupling FDR false discovery rate ANOVA analysis of variance Declarations Author contributions Xinyu Hao: Writing– review & editing, Writing– original draft, Formal analysis, Data curation. Xiaoya Liu: Writing– review & editing. Danfeng Yuan : Data curation. Chunyu Liang: Formal analysis. Xiangyun Yang: Project administration. Bo Zhang: Formal analysis. Shuang Liu: Writing– review & editing, Project administration. Zhanjiang Li: Project administration. Dong Ming: Project administration. All authors contributed to manuscript revision and read and approved the submitted version. Funding This work was funded by the STI2030-Major Projects (No. 2021ZD0202000), the National Key Research and Development Program of China (No. 2023YFF1203700), the National Natural Science Foundation of China (No. 62376187) and the National Natural Science Foundation of China (No. 81925020). Ethical approval The study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki, approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University, and registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024). Written informed consent was obtained from all participants. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 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Prog Neurobiol. 2008;86(3):156–85. https://doi.org/10.1016/j.pneurobio.2008.09.005. Nakazono T, Takahashi S, Sakurai Y. Enhanced Theta and High-Gamma Coupling during Late Stage of Rule Switching Task in Rat Hippocampus. Neuroscience. 2019;412:216–32. https://doi.org/10.1016/j.neuroscience.2019.05.053. Tamura M, Spellman TJ, Rosen AM, et al. Hippocampal-prefrontal theta-gamma coupling during performance of a spatial working memory task. Nat Commun. 2017;8(1):2182. https://doi.org/10.1038/s41467-017-02108-9. Sauseng P, Peylo C, Biel AL, et al. Does cross-frequency phase coupling of oscillatory brain activity contribute to a better understanding of visual working memory? Br J Psychol. 2019;110(2):245–55. https://doi.org/10.1111/bjop.12340. Tort ABL, Kramer MA, Thorn C, et al. Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task. PNAS. 2008;105(51):20517–22. https://doi.org/10.1073/pnas.0810524105. Wang W. Brain network features based on theta-gamma cross-frequency coupling connections in EEG for emotion recognition. Neurosci Lett. 2021;761:136106. https://doi.org/10.1016/j.neulet.2021.136106. Min JA, Yu JJ, Lee CU, et al. Cognitive emotion regulation strategies contributing to resilience in patients with depression and/or anxiety disorders. Compr Psychiatry. 2013;54(8):1190–7. https://doi.org/10.1016/j.comppsych.2013.05.008. Hu T, Zhang D, Wang J, et al. Relation between emotion regulation and mental health: a meta-analysis review. Psychol Rep. 2014;114(2):341–62. https://doi.org/10.2466/03.20.PR0.114k22w4. Additional Declarations No competing interests reported. Supplementary Files SupplementaryinformationSI.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. 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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-6320865","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456342168,"identity":"da631920-0518-4799-adb5-fbab1f48cc75","order_by":0,"name":"Xinyu Hao","email":"","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Hao","suffix":""},{"id":456342169,"identity":"3b0f0d78-57a7-4717-9bab-d8aa410ee4f4","order_by":1,"name":"Xiaoya Liu","email":"","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"Liu","suffix":""},{"id":456342170,"identity":"a5414cec-7740-4b5f-b8c9-ce07c6d06171","order_by":2,"name":"Danfeng Yuan","email":"","orcid":"","institution":"Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danfeng","middleName":"","lastName":"Yuan","suffix":""},{"id":456342171,"identity":"fab65003-2b6c-417a-a203-ff1b41a371ec","order_by":3,"name":"Chunyu Liang","email":"","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Liang","suffix":""},{"id":456342172,"identity":"ce275dec-038f-4831-8313-73ae2cff74a6","order_by":4,"name":"Xiangyun Yang","email":"","orcid":"","institution":"Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyun","middleName":"","lastName":"Yang","suffix":""},{"id":456342173,"identity":"d701fd91-0c42-41d1-9733-bbbb9bd9e283","order_by":5,"name":"Bo Zhang","email":"","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":456342174,"identity":"87ff08e7-c8dd-4cdc-8f0e-952e1a1297bb","order_by":6,"name":"Shuang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACCTBpwcPA3sBgAGQxNhCpRYKHgecwiVqAKBnMIqxFfnaP4eeCXxIyBjffHyjmYbCR3XCA+dkDfFoM7pwxlp7ZJ8FjcDuZwZiHIc14wwE2cwO8WiRyDKR5e+BaDiduOMDDJoHXYTNyjH+Dtdw8DNLyn7AWhhs5ZtI8P4BabjCDtBwgrMXgRlqZNW+DBI/kmWQDwzkGycYzD7OZEXBY8ubbPH9s7PmOH3xm8KbCTrbvePMz/A4DAcY2MMVmAI5MZoLqQeAPmGR+QJTiUTAKRsEoGHEAAKaVQaQuDTB2AAAAAElFTkSuQmCC","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Liu","suffix":""},{"id":456342175,"identity":"c2fd61de-94bc-4d61-8c6c-8e6607576138","order_by":7,"name":"Zhanjiang Li","email":"","orcid":"","institution":"Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhanjiang","middleName":"","lastName":"Li","suffix":""},{"id":456342180,"identity":"f732eec3-6c7c-452b-8592-ab4006f06ffc","order_by":8,"name":"Dong Ming","email":"","orcid":"","institution":"Academy of Medical Engineering and Translational Medicine, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Ming","suffix":""}],"badges":[],"createdAt":"2025-03-27 13:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6320865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6320865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82887616,"identity":"bdebb7ec-d2d1-4cc2-b6d5-193ad7b27d8c","added_by":"auto","created_at":"2025-05-16 11:57:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88952,"visible":true,"origin":"","legend":"\u003cp\u003eThe procedure of the Go/No-Go task\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/614a2040f34eb3c01e9c7fef.png"},{"id":82889234,"identity":"ec48e4c3-6a9c-4dec-8247-cbbbff9c8b61","added_by":"auto","created_at":"2025-05-16 12:05:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61490,"visible":true,"origin":"","legend":"\u003cp\u003eThe procedure of the more-odd shifting task\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/91611955dfcd6441ae9232d7.png"},{"id":82887614,"identity":"ce7a4951-93b6-4ba0-ab31-c90d2e7816ef","added_by":"auto","created_at":"2025-05-16 11:57:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73773,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart of the ACS score in the GAD group (purple) and the HC group (orange). ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. Error bars indicate SD. Dark gray dots represent each participant’s value. \u003cstrong\u003eA\u003c/strong\u003e. ACS total score; \u003cstrong\u003eB\u003c/strong\u003e. Focus subscale score; \u003cstrong\u003eC\u003c/strong\u003e. Shifting subscale score\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/9d635899bb07033908d916b3.png"},{"id":82889236,"identity":"573afaa7-3b3c-49dd-8c14-3c46c7af1bf8","added_by":"auto","created_at":"2025-05-16 12:05:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115942,"visible":true,"origin":"","legend":"\u003cp\u003eThe inverse efficiency for the GAD group (purple) and the HC group (orange). **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. Error bars indicate SD. Dark gray dots represent each participant’s value. \u003cstrong\u003eA\u003c/strong\u003e. The No-Go IE; \u003cstrong\u003eB\u003c/strong\u003e. The more-odd shifting task IE\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/01b0144093226f42d1b2b049.png"},{"id":82887622,"identity":"2070e6bb-2aae-47ac-9b59-171e0cde5233","added_by":"auto","created_at":"2025-05-16 11:57:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":185374,"visible":true,"origin":"","legend":"\u003cp\u003eThe amplitude, latency, and waveform of the NoGo-N2 at electrode site Fz and the NoGo-P3 at electrode site Pz for the GAD group (purple) and the HC group (orange). *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. Error bars indicate SD. Dark gray dots represent each participant’s value. \u003cstrong\u003eA\u003c/strong\u003e. Bar chart of the peak amplitude of NoGo-N2 at electrode site FZ; \u003cstrong\u003eB\u003c/strong\u003e. The waveform of NoGo-N2 at electrode site FZ on the hard level; \u003cstrong\u003eC\u003c/strong\u003e. Bar chart of NoGo-N2 latency at electrode site FZ; \u003cstrong\u003eD.\u003c/strong\u003e Bar chart of the peak amplitude of NoGo-P3 at electrode site PZ;\u003cstrong\u003eE\u003c/strong\u003e. The waveform of NoGo-P3 at electrode site PZ on the hard level; \u003cstrong\u003eF\u003c/strong\u003e. Bar chart of the latency of NoGo-P3 at electrode site PZ\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/e95bfa389ae5a8dfe78d8862.png"},{"id":82889235,"identity":"43219f14-ba84-439b-bebd-877eafc1a4c9","added_by":"auto","created_at":"2025-05-16 12:05:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":212927,"visible":true,"origin":"","legend":"\u003cp\u003eTheta power and TGC in two groups. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. \u003cstrong\u003eA\u003c/strong\u003e. Theta power in the temporal region of both groups (GAD: purple; HC: orange); \u003cstrong\u003eB\u003c/strong\u003e. Theta power in the occipital region of both groups (GAD: purple; HC: orange). Dark gray dots represent the value of each participant. Error bars indicate SD; \u003cstrong\u003eC\u003c/strong\u003e. Five regions of interest (ROI); \u003cstrong\u003eD\u003c/strong\u003e. Topographic distribution of significant differences in theta power in temporal and occipital regions for two conditions of the two groups (congruent: green; incongruent: orange). Each subplot represents the theta power values (GAD minus HC, and each row represents one condition); \u003cstrong\u003eE\u003c/strong\u003e. The average TGC in the temporal and occipital regions of the two groups (GAD: purple; HC: orange). Dark gray dots represent the value of each participant; \u003cstrong\u003eF\u003c/strong\u003e. The averaged group difference of TGC comodulograms for two conditions (GAD minus HC)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/99d4d0d0fd39f8a52be1c168.png"},{"id":82887620,"identity":"7b0971bb-3c35-4156-9f81-88d90a6e3113","added_by":"auto","created_at":"2025-05-16 11:57:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":230855,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between three scales (HAMA, HAMD, and ACS) and behavioral/EEG features. The shaded area shows the 95 % confidence interval. r denotes the correlation coefficient. The dots represent each participant’s value\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/ba0d0690963e6d63f0fe31eb.png"},{"id":95196135,"identity":"7bd4266a-6474-4ce1-ac93-723c6d35f657","added_by":"auto","created_at":"2025-11-05 11:24:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1959494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/7615b621-5d1f-47a7-8ab0-8db9d67e2afc.pdf"},{"id":82887610,"identity":"a12ecb22-dd0b-4697-b936-1f42d09c5e93","added_by":"auto","created_at":"2025-05-16 11:57:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37408,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-6320865/v1/2462863f965f947a0e01e403.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive processes in goal-directed attentional system dysfunction of generalized anxiety disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAttentional control theory suggests that anxiety disrupts the balance between the stimulus-driven and goal-directed attentional systems, leading to attentional control deficits [1, 2]. Specifically, anxiety enhances stimulus-driven processing while weakening goal-directed control, making individuals more susceptible to salient and distracting stimuli. Understanding this disruption requires an in-depth examination of these two attentional systems.\u003c/p\u003e \u003cp\u003eThe stimulus-driven attentional system automatically captures attention in response to salient or novel stimuli, independent of an individual's intentions. It comprises three key processes: (1) attentional facilitation (attentional vigilance), characterized by a heightened sensitivity to negative stimuli; (2) attentional disengagement (attention fixation), referring to the difficulty in shifting attention away from negative stimuli; and (3) attentional avoidance, where attention is redirected away from a threatening stimulus [3]. In contrast, the goal-directed attentional system enables deliberate focus on specific objects, events, or thoughts despite distractions and facilitates flexible shifts between stimuli. It plays a crucial role in regulating cognitive control and adaptive behavior, supporting two core functions: inhibition and shifting [4\u0026ndash;6]. The inhibition function involves suppressing internal impulses or external distractions to maintain goal-directed behavior and execute appropriate actions [7, 8]. The shifting function refers to the ability to flexibly redirect attention between tasks or mental sets in response to a changing environment [9, 10]. Disruptions in these attentional systems may contribute to the cognitive and emotional deficits commonly associated with anxiety disorders. A comprehensive understanding of these systems provides a framework for investigating how anxiety affects cognitive processes.\u003c/p\u003e \u003cp\u003eThe two attentional systems have been well-documented in subclinical high trait anxiety (HTA) populations. Research on the stimulus-driven system has shown that HTA individuals have difficulty disengaging from negative stimuli, such as angry and fearful facial expressions [11\u0026ndash;13]. Studies on the goal-directed system have revealed abnormal neural responses in HTA individuals, including altered frontal NoGo-N2 and parietal NoGo-P3 amplitudes during inhibition tasks, along with compensatory neural strategies during task switching [9, 14, 15]. However, emerging evidence suggests that generalized anxiety disorder (GAD) patients disengage from negative stimuli faster than expected, challenging the generalizability of HTA-based findings to clinical populations [11]. Despite these insights, the cognitive and neural mechanisms of the goal-directed attentional system in GAD remain poorly understood. Given the potential distinctions between HTA and GAD, a systematic investigation of the goal-directed attentional system in GAD has both theoretical and clinical significance. Understanding these mechanisms could clarify the neurocognitive basis of GAD, refine ACT\u0026rsquo;s applicability to clinical populations, and inform targeted interventions for attentional deficits in GAD.\u003c/p\u003e \u003cp\u003eThis study aims to examine goal-directed attentional deficits in GAD by comparing inhibition and shifting functions between GAD patients and healthy controls (HC) at both behavioral and electrophysiological levels. By examining differences in self-report scales, behavioral performance, and EEG features, this study seeks to provide a comprehensive understanding of goal-directed attentional deficits in GAD. The findings may offer novel insights into the pathological mechanisms underlying these impairments and contribute to the development of targeted interventions for cognitive dysfunction in GAD.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003e Participants with GAD (n\u0026thinsp;=\u0026thinsp;24) were recruited from the outpatient clinics of Beijing Anding Hospital. Inclusion criteria were (1) 18\u0026ndash;60 years of age and right-handed; (2) diagnosed with GAD according to DSM-5 criteria; (3) completed junior high school or higher education. Exclusion criteria were as follows: (1) Hamilton Anxiety Rating Scale (HAMA) [16] score\u0026thinsp;\u0026ge;\u0026thinsp;14 and the Hamilton Depression Rating Scale (HAMD) [17] score\u0026thinsp;\u0026le;\u0026thinsp;17 [18\u0026ndash;21]; (2) history of mental illness other than major depressive disorder or GAD (e.g., bipolar disorder) or history of brain injury; (3) history of alcohol or substance abuse; (4) current serious suicidal risk; and (5) serious somatic illness that might preclude study participation. HC (n\u0026thinsp;=\u0026thinsp;28) were volunteers recruited via WeChat. Given the established associations between GAD and factors such as age [22], gender [23], and education level [24], HC were matched on these variables. None of the HC met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for any lifetime mood, anxiety, psychotic, or substance use disorders. Diagnostic interviews were conducted by trained diagnosticians with post-baccalaureate and master's level diagnosticians. Written informed consent was obtained from all participants. The study was approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University and was registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024), in accordance with the guidelines of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedure\u003c/h2\u003e \u003cp\u003eParticipants signed an informed consent form and completed the Attentional Control Scale (ACS) [25], a 20-item self-report questionnaire designed to assess an individual's ability to manage inhibitory interference and to flexibly regulate attention. The ACS comprises two subscales: focusing and shifting [26]. Participants then performed two tasks: the Go/No-Go task, which measures inhibitory control, and the more-odd shifting task, which assesses cognitive flexibility.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Task design\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1) Go/No-Go task \u003c/h3\u003e\n\u003cp\u003eThe Go/No-Go task is a widely used paradigm for studying response inhibition [27, 28]. Participants respond to frequent \"Go\" targets while withholding responses to infrequent \"No-Go\" stimuli [15, 29]. The inhibitory function is measured by the successful and timely suppression of responses when a low-probability \"No-Go\" stimulus appears [30, 31].\u003c/p\u003e \u003cp\u003eThe entire procedure took about 20 minutes and required participants to respond to stimuli according to different color cues. The Go/No-Go task consisted of two phases: a preliminary block of 8 trials to eliminate the learning effect, followed by six main blocks of 600 trials. The main blocks were divided into two levels: an easy level with two blocks (60% Go stimuli and 40% No-Go stimuli per block) [32] and a hard level with four blocks (91% Go stimuli and 9% No-Go stimuli per block) [33]. The high proportion of Go stimuli created a prepotent response, making it challenging to inhibit responses to No-Go stimuli [30, 31]. Stimuli were presented on a black background, beginning with a fixation point (\"+\") displayed for 500 to 800 ms, followed by the Go stimuli (blue, green, or white squares) or the No-Go stimuli (red square), each presented for 1000 ms. Three Go stimuli appeared in a randomized order approximately the same number of times across the six blocks. Participants viewed sequentially presented colored squares (120 \u0026times; 120 pixels) and were instructed to press the \"J\" key with their right hand as quickly as possible for all squares except the red square, which appeared less frequently. If participants did not respond, the target would disappear after 1000 ms. The Go and No-Go stimuli were presented randomly (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e2) More-odd shifting task\u003c/h3\u003e\n\u003cp\u003eThe more-odd shifting task, adapted from Salthouse et al. (1998) [34], is a well-established computerized assessment that measures the shifting function [8]. In this task, participants respond differently based on specific cues. Using this task-switching paradigm, researchers can evaluate individuals' cognitive flexibility in adapting to changing task demands.\u003c/p\u003e \u003cp\u003e The entire procedure took approximately 15 minutes and required participants to respond to stimuli based on different shape cues. The task began with three preliminary blocks of 30 trials each, followed by three main blocks of 96 trials each. The preliminary blocks, which lasted approximately 2 minutes, included feedback on response accuracy. The main block commenced only after participants achieved an accuracy rate above 80%. During the main blocks, participants received no feedback and were instructed to respond as quickly and accurately as possible. Each trial began with a fixation point (\"+\") displayed for 500 ms, followed by the stimulus, which was presented for 2500 ms or until a response was made. The stimuli consisted of a gray square or diamond (250 \u0026times; 250 pixels) on a black background, with a silver number (120 \u0026times; 120 pixels, size 56, Times New Roman font) in the center. Numbers ranged from 1 to 9, with the exception of 5. For trials displaying a diamond shape, participants were instructed to press the \"F\" key with their left index finger if the number was odd; otherwise, they pressed the \"J\" key with their right index finger. For trials displaying a square, participants judged whether the number was greater than 5, pressing \"F\" for numbers less than 5 and \"J\" for numbers greater than 5. Response rules were counterbalanced across participants. If the response rules of the current trial matched those of the previous trial, the trial was defined as a non-switch condition (congruent); if they differed, the trial was defined as a switch condition (incongruent). The first trial of each block contained no repetitions or switches, with 89 trials in the congruent condition and 88 trials in the incongruent condition, ensuring that congruent and incongruent trials did not occur more than three times consecutively (see Figure. 2).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data acquisition\u003c/h2\u003e \u003cp\u003eParticipants were instructed not to smoke or consume coffee before the experiment. All tasks were presented at the center of a 19-inch Lenovo computer screen with a resolution of 1024 \u0026times; 768 pixels. Participants were tested individually in a quiet room while seated approximately 60 cm from the screen at a comfortable viewing distance.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Behavioral acquisition\u003c/h2\u003e \u003cp\u003eBoth tasks were programmed and administered using E-Prime\u0026reg; 2.0 software (Psychology Software Tools, Inc., Pittsburgh, PA, USA), which automatically recorded participants\u0026rsquo; reaction times (RTs) and accuracy (ACC). RTs were captured via the E-Prime serial response box.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 EEG Recording\u003c/h2\u003e \u003cp\u003eEEG signals were recorded using a 64-channel system with standard Ag/AgCl electrodes placed on the scalp according to the international 10\u0026ndash;20 system (Brain Products, Munich, Germany). Data were sampled at 1 kHz with a common reference electrode, and electrode impedances were maintained below 10 kΩ.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Behavioral performance analysis\u003c/h2\u003e \u003cp\u003eAn inverse efficiency (IE) measure, calculated by dividing the mean RTs of correct trials by the ACC, was used to correct for speed-accuracy trade-off effects in both tasks [35\u0026ndash;37]. A higher IE indicates lower processing efficiency [9]. Besides, the switching cost index, which measures the cost of switching between tasks, was calculated using the following formula: Switching Cost\u0026thinsp;=\u0026thinsp;IE \u003csub\u003eswitch condition\u003c/sub\u003e \u0026ndash; IE \u003csub\u003enon\u0026minus;switch condition\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 EEG analysis\u003c/h2\u003e \u003cp\u003eOffline processing of the EEG data was conducted using MATLAB with the EEGLAB toolbox [38], running in the MATLAB environment (version R2017a). The data were downsampled from 1000 Hz to 200 Hz and digitally filtered with a 0.1 Hz\u0026ndash;50 Hz bandpass filter. The EEG signals were then referenced to the average of the two mastoid electrodes. Independent component analysis (ICA) was applied to remove artifacts associated with saccadic eye movements and eye blinks. This process involved identifying and manually removing components associated with blinks, eye movements, and muscle artifacts based on visual inspection of the component activations and maps [9].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1) Go/No-Go task\u003c/h3\u003e\n\u003cp\u003eSince this study focused on the inhibition function, only the No-Go stimuli were analyzed [15, 39, 40]. The NoGo-N2 and NoGo-P3 components represent distinct subprocesses of response inhibition [15]. The NoGo-N2 component reflects the inhibition or revision of a motor plan before execution, while the NoGo-P3 component is linked to implicit attention orientation and motor inhibition [39, 41\u0026ndash;43]. Due to its longer latency, the NoGo-P3 may also reflect the evaluation of response inhibition [44, 45]. These components are modulated by distinct neurobiological systems, indicating separate sub-processes in response inhibition [46, 47].\u003c/p\u003e \u003cp\u003eFor data processing, all epochs were baseline-corrected using the mean voltage during the 200 ms before the onset of the color cues. The corrected data were segmented into 1000 ms epochs, starting 200 ms before cue onset, and then averaged for the No-Go trials. Peak amplitudes and latencies of the frontal-midline N2 and parietal P3 were analyzed, with averages calculated from waveforms at selected electrodes based on grand-mean ERP topographies and relevant literature [44, 48, 49]. The NoGo-N2 wave was defined as the most negative deflection at frontal electrodes within a 200\u0026ndash;350 ms post-stimulus interval, and the NoGo-P3 wave was defined as the most positive deflection at parietal electrodes within a 300\u0026ndash;450 ms interval. The Fz electrode was selected for NoGo-N2 and the Pz electrode for NoGo-P3.\u003c/p\u003e\n\u003ch3\u003e2) More-odd shifting task\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePower spectrum analysis\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEEG power spectrum analysis is an essential tool in neuroscience research, providing insight into the relationship between brain activity across various frequency bands and specific cognitive and behavioral functions. Different brain regions oscillate at characteristic frequency bands that are associated with distinct functions [50]. In this study, the following frequency bands were focused: delta (1\u0026ndash;4 Hz), theta (4\u0026ndash;8 Hz), alpha (8\u0026ndash;12 Hz), sensorimotor rhythm (SMR, 12\u0026ndash;15 Hz), beta (15\u0026ndash;30 Hz), and gamma (30\u0026ndash;50 Hz) [51].\u003c/p\u003e \u003cp\u003eFor data processing, epochs were baseline-corrected using the mean voltage during the 500 ms pre-stimulus period and then averaged across trials. The corrected data were segmented into 2500 ms epochs, starting 500 ms before stimulus onset. Power spectrum estimation was performed using the Welch method, with a 2-second Hamming window, 25% overlap, and zero-padding to 256 points [52, 53].\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTheta-gamma coupling\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTheta-phase/gamma-amplitude coupling (TGC) is essential for cognitive functions such as learning and memory representation and has received considerable research attention [54\u0026ndash;56]. In this study, TGC has been computed using the Kullback-Leibler modulation index method [57], selected for its stability across different data lengths and sampling rates [58]. This method measures the Kullback-Leibler distance between observed amplitude distributions.\u003c/p\u003e \u003cp\u003eFor TGC estimation, phase and amplitude signals were extracted from the filtered EEG time series. The phase was estimated using a bandpass filter (4\u0026ndash;8 Hz) with 2 Hz steps and a 2 Hz bandwidth, while amplitude was estimated in the 30\u0026ndash;50 Hz range, yielding narrow-band phase and broad-band amplitude signals. The Hilbert transform was applied to extract phase data from the low-frequency components and amplitude data from the high-frequency components. This enabled the construction of a composite time series representing the amplitudes of high-frequency oscillations at each low-frequency phase. Phase values were binned into 18 intervals (20\u0026deg; per bin) for analysis, following the method outlined by Zhang et al. [59].\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS (version 20.0, IBM Corp., Armonk, NY, 2011). The Shapiro-Wilk test was used to assess normality. Differences in age and education level between the GAD and HC groups were assessed using the Mann-Whitney U test, while gender differences were examined using the chi-squared test. Group comparisons for ACS scores were performed using independent two-sample t-tests (two-tailed) with statistical significance set at α\u0026thinsp;=\u0026thinsp;0.05. Mauchly's test for sphericity was applied with more than two levels of the within-subject factors. If the sphericity assumption was violated, Greenhouse-Geisser corrections were applied to the repeated-measures analysis of variance (ANOVA), with uncorrected degrees of freedom and corrected p-values reported. Bonferroni correction was used for multiple comparisons, with adjusted p-values reported. Effect sizes are reported as partial eta-squared (\u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e) for F tests.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Behavioral performance statistical\u003c/h2\u003e \u003cp\u003eA 2 \u0026times; 2 Mixed-design ANOVA was performed to analyze the IE for the Go/No-Go task, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. Similarly, a two-way Mixed-design ANOVA was performed on the IE in the more-odd shifting task, with Group (GAD vs. HC) and Condition (congruent vs. incongruent) as factors. To further assess the shifting function, a two-sample independent t-test was used to compare the switching cost between the two groups. Spearman correlation analyses were performed to examine the relationship between IE and scale scores. The false discovery rate (FDR) was used to adjust for all comparisons [60]. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 EEG features statistical\u003c/h2\u003e \u003cp\u003eA 2 \u0026times; 2 Mixed-design ANOVA was conducted to examine the peak amplitudes and latencies of NoGo-N2 and NoGo-P3, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. A 2 \u0026times; 2 \u0026times; 5 Mixed-design ANOVA was also conducted to analyze the power spectrum of six frequency bands (delta, theta, alpha, SMR, beta, and gamma), with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) and Brain Region (frontal, central, parietal, temporal, occipital) as the within-subjects factors. A 2 \u0026times; 2 Mixed-design ANOVA was performed on the TGC in the more-odd shifting task with Group (GAD vs. HC) and Condition (congruent vs. incongruent) as factors. Spearman correlation analyses were performed to explore the relationships between significant EEG features and scale scores. The FDR method was applied to adjust for multiple comparisons, with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical and demographic variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the clinical and demographic characteristics of the two groups, along with statistical comparisons. Chi-squared analysis showed no significant difference in gender between the groups (χ\u003csup\u003e2\u003c/sup\u003e (1) = \u0026minus;\u0026thinsp;1.223, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.221). The Mann-Whitney U test showed no significant difference in education level between the GAD and HC groups (Z = \u0026minus;\u0026thinsp;0.522, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.602). Age did not differ significantly between the groups (Z\u0026thinsp;=\u0026thinsp;0.468, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.494). A detailed description of the participants is provided in the \u003cb\u003eSupplementary Information\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and demographic variables of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAD group\u003c/p\u003e \u003c/th\u003e\u003cth\u003eZ/χ2\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.96 (10.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.79 (6.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Female/Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.57 (3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.25 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.32 (4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.11 (4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: SD, standard deviation; GAD, generalized anxiety disorder; HC, healthy controls; HAMA, Hamilton anxiety scale; HAMD Hamilton depression rating scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ACS Score\u003c/h2\u003e \u003cp\u003eACS scores were calculated and analyzed to assess the capacity of the goal-directed attentional system. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (A-C) shows the ACS total score and its two subscales for the GAD and HC groups. GAD participants exhibited significantly lower scores on the ACS total score (t(50) = -6.678, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and its subscales: focus subscale (t(50) = -5.960, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and shifting subscale (t(50) = -4.393, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on the self-report questionnaire, the results indicated that the GAD group struggled with managing inhibitory interference and to flexibly regulating attention, indicating impairments in the goal-directed attentional system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Behavioral Performance\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Go/No-Go task\u003c/h2\u003e \u003cp\u003eThe IE was calculated for each level, followed by statistical analyses comparing the two groups. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA illustrates the mean and standard error of the IE for both groups. In terms of IE, GAD exhibited higher values on both levels, with the hard level showing higher values for both groups. A 2 \u0026times; 2 Mixed-design ANOVA was performed, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. The analysis revealed significant main effects for both Group and Level (Group: F(1, 37)\u0026thinsp;=\u0026thinsp;60.577, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.621) and Level (F(1, 37)\u0026thinsp;=\u0026thinsp;58.328, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.612). Additionally, a significant interaction between Group and Level was observed (F(1, 37)\u0026thinsp;=\u0026thinsp;11.600, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.239). A simple effects analysis indicated that the GAD group had significantly increased IE values at the hard level (Hard Level: F(1, 37)\u0026thinsp;=\u0026thinsp;11.114, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.221; Easy Level: F(1, 37)\u0026thinsp;=\u0026thinsp;3.243, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.081). Furthermore, the analysis showed that IE was higher on the hard level for both groups (GAD: F(1, 37)\u0026thinsp;=\u0026thinsp;59.452, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.616; HC: F(1, 37)\u0026thinsp;=\u0026thinsp;9.188, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.199). The results indicate that participants with GAD performed lower processing efficiency than HC, especially on the hard level during the Go/No-Go task.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 More-odd shifting task\u003c/h2\u003e \u003cp\u003eThe IE was calculated for each condition and statistical analyses were performed. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows the mean and standard error of the IE for both groups. In terms of IE, GAD showed higher values across both conditions, with the incongruent condition yielding higher values in each group. A 2 \u0026times; 2 Mixed-design ANOVA was performed with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) as the within-subjects factor. Although there was no significant interaction between Group and Condition (F(1, 37)\u0026thinsp;=\u0026thinsp;1.790, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.189, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.046), significant main effects were observed for both Group and Condition (Group: F(1, 37)\u0026thinsp;=\u0026thinsp;35.579, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.490; Condition: F(1, 37)\u0026thinsp;=\u0026thinsp;73.426, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.665). The GAD group showed an overall higher IE, especially in the incongruent condition. Furthermore, there was no significant difference in switching costs between the two groups (t(37)\u0026thinsp;=\u0026thinsp;1.338, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.189). These results indicate that participants with GAD exhibited lower processing efficiency during the more-odd shifting task, regardless of whether the condition was congruent or incongruent.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 EEG feature\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Go/No-Go task\u003c/h2\u003e \u003cp\u003eFour EEG features\u0026mdash;the peak amplitudes and latencies of NoGo-N2 and NoGo-P3\u0026mdash;were calculated for each task level, followed by statistical comparisons between the GAD and HC groups.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNoGo-N2\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA displays the mean and standard error of the NoGo-N2 peak amplitude at the FZ electrode site for both groups. The GAD group exhibited lower peak amplitudes on both levels, with a more pronounced reduction on the hard level, while the HC group showed higher amplitudes on the hard level. A 2 \u0026times; 2 Mixed-design ANOVA was computed, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. The main effect of the Group was significant (F(1, 37)\u0026thinsp;=\u0026thinsp;6.888, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.157), while the effect of Level was not significant (F(1, 37)\u0026thinsp;=\u0026thinsp;0.014, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.908, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.000). A significant Group \u0026times; Level interaction was observed (F(1, 37)\u0026thinsp;=\u0026thinsp;10.857, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.227). A simple effects analysis revealed that the NoGo-N2 peak amplitude on the hard level was significantly lower in the GAD group (F(1, 37)\u0026thinsp;=\u0026thinsp;5.675, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.133), whereas it was significantly higher in the HC group (F(1, 37)\u0026thinsp;=\u0026thinsp;5.183, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.123). Further analysis showed a significantly lower NoGo-N2 peak amplitude on the hard level for the GAD group (F(1, 37)\u0026thinsp;=\u0026thinsp;12.632, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.255), while no significant difference was observed on the easy level (F(1, 37)\u0026thinsp;=\u0026thinsp;1.875, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.179, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.048). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB shows the ERP waveforms at the FZ electrode site on the hard level between the GAD and HC groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC presents the mean and standard error of NoGo-N2 latency at electrode site FZ for both groups. In terms of latency, the GAD group exhibited higher values on both levels, with the hard level showing higher values in both groups. A 2 \u0026times; 2 Mixed-design ANOVA was calculated, with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. No significant interaction between Group and Level was found (F(1, 37)\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.960, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.000), and neither the main effect of Group (F(1, 37)\u0026thinsp;=\u0026thinsp;0.760, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.389, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.020) nor that of Level (F(1, 37)\u0026thinsp;=\u0026thinsp;1.196, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.281, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.031) reached significance.\u003c/p\u003e \u003cp\u003eThese results suggest that participants with GAD may lack inhibition or revision of a motor plan prior execution to execution, particularly on the hard level.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNoGo-P3\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD illustrates the mean and standard error of the NoGo-P3 peak amplitude at the PZ electrode site for both groups. The GAD group had lower peak amplitudes on both levels, with a notably larger reduction on the hard level, whereas the HC group showed higher amplitudes on the hard level. A 2 \u0026times; 2 Mixed-design ANOVA was computed with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. Significant main effects were observed for both Group and Level (Group: F(1, 37)\u0026thinsp;=\u0026thinsp;17.537, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e= 0.322; Level: F(1, 37)\u0026thinsp;=\u0026thinsp;12.769, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.257). Furthermore, the interaction between Group and Level was significant (F(1, 37)\u0026thinsp;=\u0026thinsp;26.994, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.422). In the HC group, peak amplitude was significantly lower on the hard level (F(1, 37)\u0026thinsp;=\u0026thinsp;39.459, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.516), whereas the GAD group showed no significant difference between levels (F(1, 37)\u0026thinsp;=\u0026thinsp;1.283, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.265, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.034). Moreover, a simple effects analysis indicated that the peak amplitude on the hard level was significantly lower in the GAD group (F(1, 37)\u0026thinsp;=\u0026thinsp;31.084, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.457). No significant difference between the two groups was observed on the easy level (F(1, 37)\u0026thinsp;=\u0026thinsp;1.544, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.222, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.040). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE illustrates the ERP waveforms at the PZ electrode site between the two groups on the hard level.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF shows the mean and standard error of the latency of NoGo-P3 at electrode site PZ for both groups. For latency, the GAD group had lower values on the hard level but higher values on the easy level, while the latency on the hard level was higher in both groups. A 2 \u0026times; 2 Mixed-design ANOVA was computed with Group (GAD vs. HC) as the between-subjects factor and Level (hard vs. easy) as the within-subjects factor. There was no significant interaction between Group and Level (F(1, 37)\u0026thinsp;=\u0026thinsp;0.067, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.797, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.002). The main effects of Group and Level were also nonsignificant (Group: F(1, 37)\u0026thinsp;=\u0026thinsp;0.415, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.523, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.011; Level: F(1, 37)\u0026thinsp;=\u0026thinsp;2.078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.158, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.053).\u003c/p\u003e \u003cp\u003eThese results suggest that participants with GAD have deficits in implicit attentional orientation, motor inhibition, and response inhibition evaluations, especially on the hard level.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 More-odd shifting task\u003c/h2\u003e \u003cp\u003eThe power spectrum of six bands (delta, theta, alpha, SMR, beta, and gamma) was computed for each condition, followed by statistical analyses between the two groups. A 2 \u0026times; 2 \u0026times; 5 Mixed-design ANOVA was performed, with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) and Brain Region (frontal, central, parietal, temporal, occipital) as the within-subjects factors. A significant difference between the two groups was observed only in the theta band, with no significant group differences in the other bands (\u003cb\u003eSupplementary Information\u003c/b\u003e). A significant Group \u0026times; Region interaction effect was found (F(4, 34)\u0026thinsp;=\u0026thinsp;3.126, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.269). A simple effects analysis revealed significantly lower theta power in the GAD group in the temporal and occipital regions (Temporal: F(1, 37)\u0026thinsp;=\u0026thinsp;5.176, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.123; Occipital: F(1, 37)\u0026thinsp;=\u0026thinsp;10.022, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.213). Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB show comparisons of theta power between the two groups in the temporal and occipital regions, showing that theta power was lower in the GAD group in both conditions, with higher theta values observed in the incongruent condition for both groups. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC displays the five regions of interest, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD presents the topographic distribution of theta power differences between groups in the temporal and occipital regions. These results suggest that participants with GAD exhibit significantly reduced theta power in the temporal and occipital regions. These reductions in theta power may reflect disruptions in attentional processing and neural efficiency within these brain regions, which are critical for cognitive control. Such deficits may contribute to the difficulties in shifting attention that are commonly observed in GAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther, the mean TGC in the temporal and occipital regions was calculated and statistically analyzed based on the significant theta-band differences between the two groups. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE displays the TGC comparisons between the groups, showing that the GAD group had lower values in both conditions and TGC was higher in the congruent condition for both groups. A two-way mixed-design ANOVA was performed with Group (GAD vs. HC) as the between-subjects factor and Condition (congruent vs. incongruent) as the within-subjects factor. Both main effects were significant (Group: F(1, 37)\u0026thinsp;=\u0026thinsp;7.888, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.176; Condition: F(1, 37)\u0026thinsp;=\u0026thinsp;20.445, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.356), and there was a significant Group \u0026times; Condition interaction effect (F(1, 37)\u0026thinsp;=\u0026thinsp;4.155, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.101). A simple effects analysis showed that the GAD group exhibited lower TGC under both conditions (Congruent: F(1, 37)\u0026thinsp;=\u0026thinsp;4.174, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.101; Incongruent: F(1, 37)\u0026thinsp;=\u0026thinsp;11.135, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.231). Additionally, the GAD group showed a significant decrease in TGC, whereas the HC group showed no significant change (GAD: F(1, 37)\u0026thinsp;=\u0026thinsp;20.979, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.362; HC: F(1, 37)\u0026thinsp;=\u0026thinsp;3.164, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083, partial \u003cem\u003eηp\u003c/em\u003e\u003csup\u003e\u0026sup2;\u003c/sup\u003e = 0.079). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF shows the differences in TGC comodulograms between the groups in both conditions. These results suggest that participants with GAD exhibited reduced TGC, which is typically associated with coordination of brain regions and large-scale network integration. Deficits in TGC may indicate reduced interregional communication efficiency and potential cognitive impairments in shifting attention.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Go/No-Go task\u003c/h2\u003e \u003cp\u003eSpearman correlation analyses were performed between three scales (HAMA, HAMD, and ACS) and three behavioral/ERP indexes with significant group differences: No-Go IE and the peak amplitudes of NoGo-N2 and NoGo-P3 on the hard level. After FDR correction for multiple comparisons, eight significant correlations were identified. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the correlation between the three scales (HAMA, HAMD, and ACS) and the three behavioral/ERP indexes. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA shows that the No-Go IE was positively correlated with both HAMA (r\u0026thinsp;=\u0026thinsp;0.510, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and HAMD (r\u0026thinsp;=\u0026thinsp;0.513, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) scores, suggesting that higher anxiety and depression scores are associated with lower processing efficiency. For ERP components, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB shows that NoGo-N2 peak amplitude correlated positively with HAMA (r\u0026thinsp;=\u0026thinsp;0.542, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and HAMD (r\u0026thinsp;=\u0026thinsp;0.562, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and negatively with ACS scores (r = -0.499, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). As NoGo-N2 is a negative component, this positive correlation reflects greater original values, not absolute values. These results suggest that greater anxiety and depression symptoms are associated with lower NoGo-N2 peak amplitude, whereas higher ACS scores are associated with higher NoGo-N2 peak amplitude. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC shows an opposite correlation pattern for NoGo-P3 amplitude, where HAMA (r = -0.667, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and HAMD (r = -0.624, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) scores correlated negatively, while ACS scores correlated positively (r\u0026thinsp;=\u0026thinsp;0.525, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that higher anxiety and depression are associated with lower NoGo-P3 amplitude, whereas better attentional control is associated with increased NoGo-P3 amplitude. Taken together, these findings underscore the role of NoGo-N2 and NoGo-P3 as core components of inhibition function, highlighting their relevance for understanding inhibitory control in GAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 More-odd shifting task\u003c/h2\u003e \u003cp\u003eSpearman correlation analyses were performed between three scales (HAMA, HAMD, and ACS) and four EEG features that showed significant group differences: theta power in the temporal and occipital regions, and TGC in the congruent and incongruent conditions. After FDR correction for multiple comparisons, only one significant correlation was found. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD illustrates that TGC in the incongruent condition exhibited a significant negative correlation with HAMA scores (r = -0.456, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that higher TGC may be associated with lower anxiety levels in GAD. This may indicate that participants with GAD experience impaired neural communication and integration between brain regions during the more-odd shifting task. A stronger TGC may reflect more efficient information processing or attentional control, both of which are likely to be impaired in GAD.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides novel insights into the cognitive processes underlying the goal-directed attentional system in GAD, focusing on two key subcomponents: inhibition and shifting functions. To explore these processes, ACS scores were assessed and compared between GAD and HC, along with behavioral performance and EEG features collected during the Go/No-Go and more-odd shifting tasks. Self-report results indicated that GAD participants had significantly lower ACS total scores and subscale scores, suggesting perceived deficits in attentional control. Consistent with these findings, GAD participants exhibited higher IE during both tasks, indicating reduced processing efficiency. Electrophysiological analysis further showed that the GAD group had lower peak amplitudes of NoGo-N2 and NoGo-P3, reflecting deficits in pre-execution inhibitory processes and response inhibition. In the more-odd shifting task, the GAD group demonstrated decreased theta power, and TGC, suggesting impaired attentional processing and disrupted interregional communication, which likely contribute to cognitive deficits in attentional shifting. Finally, correlation analysis revealed significant correlations between HAMA scores and multiple measures: behavioral performance, NoGo-N2, and NoGo-P3 peak amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task. Taken together, these findings elucidate the cognitive processes underlying the goal-directed attentional system in GAD, highlighting deficits in inhibitory control and attentional flexibility.\u003c/p\u003e \u003cp\u003eThe ACT posits that anxiety impairs inhibitory control [1, 10, 61]. To compensate for this deficit, HTA often employ compensatory strategies, allocating more top-down attentional control resources [1]. This aligns with the findings of this study, which showed no significant interaction effect in behavioral performance on the easy level of the Go/No-Go task. Furthermore, the behavioral performance results of this study did not reveal a significant Group \u0026times; Condition interaction effect in the more-odd shifting task, contrary to some previous findings [62, 63]. This suggests that while GAD may impair shifting function, it does not necessarily lead to significant behavioral deficits, as reflected by the absence of significant differences in switching costs between the two groups.\u003c/p\u003e \u003cp\u003eRegarding the Go/No-Go task, several studies have shown that anxious individuals may experience disrupted inhibitory control, as reflected at the electrophysiological level [5, 15, 64]. Consistent with these findings, electrophysiological data revealed that the GAD group had lower peak amplitudes of NoGo-N2 and NoGo-P3 on the hard level of the Go/No-Go task. This suggests that anxiety impairs inhibitory control, hindering the efficient evaluation and monitoring of incorrect responses. Such impairment may result in reduced cognitive control effort or a diminished allocation of additional processing resources in anxious individuals. Response inhibition and cognitive control are primarily associated with activity in the anterior cingulate cortex (ACC) and other frontal brain regions [65\u0026ndash;67]. The ACC plays a critical role in integrating cognitive and emotional processes [68], is implicated in the pathophysiology of psychiatric disorders [69], and serves as a key component of the anxiety circuitry [70]. Given these findings, participants with GAD may exhibit neurocognitive deficits in inhibitory processing and response monitoring.\u003c/p\u003e \u003cp\u003eRegarding the more-odd shifting, previous cross-sectional studies have documented a decline in theta power associated with a gradual reduction in cognitive function across different age groups, including healthy young and older adults [71, 72]. This is consistent with the literature linking theta oscillations to effective cognitive function, particularly memory, and tasks requiring sustained attention [73]. Consistent with these findings, the power spectrum analysis suggests that the reduced theta power may result from impaired cognitive control in GAD. Furthermore, extensive research has demonstrated that higher TGC values are associated with various cognitive functions, including learning [74], memory formation [75, 76], and decision-making [77]. TGC plays a critical role in facilitating information processing and integrating neural activity across brain regions [78]. Thus, the attenuated TGC in GAD may reflect disrupted neural communication and impaired redistribution of cognitive resources across brain regions, further reinforcing the notion of cognitive dysfunction in GAD during the more-odd shifting task.\u003c/p\u003e \u003cp\u003eGiven that the effective use of emotion regulation strategies serves as a resilience factor for mental health [79] and is positively correlated with better health outcomes [80], a deeper understanding of the neural mechanisms underlying impaired emotion regulation in clinical populations is essential for advancing therapeutic interventions. The findings of this study have significant clinical implications, particularly in the development of targeted treatments for GAD. Specifically, our results suggest that strengthening cognitive control may be an effective strategy for improving emotion regulation in GAD. Furthermore, identifying disorder-specific mechanisms underlying deficits in emotion regulation could facilitate the development of novel therapeutic targets and validation indicators for cognitive-behavioral interventions or neuromodulation approaches.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides novel evidence on the cognitive processes underlying the goal-directed attentional system in GAD, specifically focusing on inhibition and shifting functions. By integrating self-reported attentional control, behavioral performance, and electrophysiological level, this study offers a comprehensive perspective on cognitive dysfunction in GAD. Results from the ACS questionnaire indicated that participants with GAD reported significantly lower total and subscale scores, reflecting subjective impairments in attentional control. Consistent with these findings, GAD participants demonstrated higher IE scores in both tasks, suggesting reduced processing efficiency. Electrophysiological analyses further revealed that the GAD group exhibited lower NoGo-N2 and NoGo-P3 peak amplitudes during the Go/No-Go task, indicating deficits in pre-execution inhibitory processes and response inhibition. In the more-odd shifting task, GAD participants showed reduced theta power and TGC, reflecting impairments in attentional processing and interregional neural communication, which may underlie deficits in cognitive flexibility. Moreover, correlation analyses revealed significant associations between HAMA scores and multiple measures, including behavioral performance, NoGo-N2 and NoGo-P3 amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task. These findings provide neurophysiological insights into the mechanisms of GAD, highlighting impairments in inhibitory control and attentional flexibility. Understanding these deficits can inform the development of targeted interventions aimed at improving cognitive control in individuals with GAD.\u003c/p\u003e"},{"header":"6. Limitation","content":"\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the sample was recruited solely from Beijing, which may limit the generalizability of the findings due to potential geographic and ethnic influences. Future multi-center studies with more diverse populations are necessary to enhance external validity. Second, the relatively small sample size may have reduced the statistical power of the study, potentially obscuring certain effects of anxiety on cognitive and neural processes. Replication with a larger cohort is essential to confirm the robustness of these findings. Third, the restricted age range of participants further constrains the applicability of the results. Despite these limitations, this study provides valuable insights into the cognitive and neurophysiological mechanisms of GAD, underscoring the need for further research with larger, more representative samples.\u003c/p\u003e"},{"header":"7.\tAbbreviations ","content":"\u003cp\u003eGAD\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003egeneralized anxiety disorder\u003c/p\u003e\n\u003cp\u003eHC healthy controls\u003c/p\u003e\n\u003cp\u003eEEG electroencephalograms\u003c/p\u003e\n\u003cp\u003eERPs event-related potentials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHTA high trait anxiety\u003c/p\u003e\n\u003cp\u003eLTA low trait anxiety\u003c/p\u003e\n\u003cp\u003eHAMA Hamilton anxiety rating scale\u003c/p\u003e\n\u003cp\u003eHAMD Hamilton depression rating scale\u003c/p\u003e\n\u003cp\u003eACS attentional control scale\u003c/p\u003e\n\u003cp\u003eACT attentional control theory\u003c/p\u003e\n\u003cp\u003eMDD major depressive disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRT reaction times\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACC accuracy\u003c/p\u003e\n\u003cp\u003eIE inverse efficiency\u003c/p\u003e\n\u003cp\u003eICA independent component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACC the anterior cingulate cortex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTGC theta-phase/gamma-amplitude coupling\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR false discovery rate\u003c/p\u003e\n\u003cp\u003eANOVA analysis of variance\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXinyu Hao:\u003c/strong\u003e Writing\u0026ndash; review \u0026amp; editing, Writing\u0026ndash; original draft, Formal analysis, Data curation. \u003cstrong\u003eXiaoya Liu:\u003c/strong\u003e Writing\u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eDanfeng Yuan\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eData curation. \u003cstrong\u003eChunyu Liang:\u0026nbsp;\u003c/strong\u003eFormal analysis.\u0026nbsp;\u003cstrong\u003eXiangyun Yang:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eProject administration. \u003cstrong\u003eBo Zhang:\u003c/strong\u003e Formal analysis. \u003cstrong\u003eShuang Liu:\u003c/strong\u003e Writing\u0026ndash; review \u0026amp; editing, Project administration.\u0026nbsp;\u003cstrong\u003eZhanjiang Li:\u003c/strong\u003e Project administration. \u003cstrong\u003eDong Ming:\u0026nbsp;\u003c/strong\u003eProject administration. All authors contributed to manuscript revision and read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was funded by the STI2030-Major Projects (No. 2021ZD0202000), the National Key Research and Development Program of China (No. 2023YFF1203700), the National Natural Science Foundation of China (No. 62376187) and the National Natural Science Foundation of China (No. 81925020).\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThe study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki, approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University, and registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEysenck MW, Derakshan N, Santos R, et al. Anxiety and cognitive performance: The attentional control theory. Emotion. 2007;7(2):336\u0026ndash;53. https://doi.org/10.1037/1528-3542.7.2.336.\u003c/li\u003e\n\u003cli\u003eCorbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. 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Psychol Rep. 2014;114(2):341\u0026ndash;62. https://doi.org/10.2466/03.20.PR0.114k22w4.\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":"EEG, Generalized anxiety disorder, Attention control, Inhibition function, Shifting function","lastPublishedDoi":"10.21203/rs.3.rs-6320865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6320865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAttentional control theory proposes that anxiety impairs the goal-directed attentional system, which has been well-documented in subclinical populations. However, the underlying cognitive mechanisms of generalized anxiety disorder (GAD) remain poorly understood. A thorough investigation of the goal-directed attentional system in GAD may clarify its etiological and pathophysiological roles and offer insights for developing targeted interventions.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study investigated two key subcomponents of the goal-directed attentional system in GAD: inhibition and shifting functions. Twenty-four GAD patients and twenty-eight healthy controls (HC) were recruited and completed the Attentional Control Scale. Behavioral performance and 64-channel EEG data were collected during the Go/No-Go and more-odd shifting tasks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSelf-reported questionnaire and behavioral performance indicated that GAD patients had significantly lower total and subscale scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher inverse efficiency (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reflecting subjective attentional control deficits and reduced processing efficiency. EEG results revealed reduced NoGo-N2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and NoGo-P3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) peak amplitudes in GAD, indicating impaired subprocesses of response inhibition. Furthermore, GAD patients exhibited decreased theta power (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and theta-phase/gamma-amplitude coupling (TGC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) during the more-odd shifting task, suggesting deficits in attentional processing and impaired neural communication related to cognitive flexibility. HAMA scores were significantly correlated with behavioral performance, NoGo-N2 and NoGo-P3 amplitudes in the Go/No-Go task, and TGC in the more-odd shifting task (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that higher levels of anxiety are associated with deficits in inhibitory control, cognitive resources, and neural oscillatory dysfunction.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings provide neurophysiological insights into attentional deficits in GAD, highlighting impairments in inhibitory control and cognitive flexibility. Understanding these deficits can offer guidance for developing targeted interventions to enhance cognitive control in GAD.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eThe trial is registered on ClinicalTrials.gov (ChiCTR2400079676, January 9, 2024).\u003c/p\u003e","manuscriptTitle":"Cognitive processes in goal-directed attentional system dysfunction of generalized anxiety disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:57:27","doi":"10.21203/rs.3.rs-6320865/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":"5015629e-7af0-4b51-b12c-963bef625ab0","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T11:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 11:57:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6320865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6320865","identity":"rs-6320865","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}