Anxiety Disorders Alter Cognitive-Motor Integration During Visuomotor Adaptation and Retention

Anxiety Disorders Alter Cognitive-Motor Integration During Visuomotor Adaptation and Retention | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 January 2026 V1 Latest version Share on Anxiety Disorders Alter Cognitive-Motor Integration During Visuomotor Adaptation and Retention Authors : Leo Barzi , Matt Wilson , and Christopher M. Hill 0000-0002-0043-8753 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176831477.79379101/v1 Published Experimental Brain Research Version of record Peer review timeline 235 views 63 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Anxiety disorders are associated with prefrontal dysfunction, yet their impact on neural mechanisms underlying skilled motor learning remains poorly understood. We examined movement-readiness potentials (MRPs) using electroencephalography during a visuomotor adaptation task in 31 young adults (13 with clinically diagnosed anxiety disorders, 18 controls). MRPs were analyzed across three temporal components: early motor preparation (-1500 to -500 ms), late motor preparation (-500 to -100 ms), and movement execution (-100 to +100 ms). Individuals with anxiety disorders showed significantly reduced MRP amplitudes during late motor preparation (p = .033) and movement execution (p = .047) compared to controls, while early motor preparation remained intact. Despite these neural alterations, both groups demonstrated equivalent behavioral performance, with similar learning and retention of a visuomotor rotation task. Anxiety disorders selectively disrupt late-stage cognitive-motor integration processes during movement preparation and execution. The dissociation between impaired neural activity and preserved behavioral performance suggests compensatory mechanisms that maintain motor learning despite underlying neural inefficiencies. These findings reveal that anxiety affects integrated systems of cognition and action, providing new insights into their functional neurophysiological impact. Introduction Anxiety is a negative emotion driven by perceived uncertain or distant threats. It triggers defensive behaviors like heightened vigilance to anticipate potential dangers(Akiki et al., 2024; Grogans et al., 2023). Excessive, prolonged anxiety results in anxiety disorders where a person overestimates risks and persistently perceives threats even in non-threatening situations (Thibaut, 2017). Anxiety disorders are the most common class of psychological disorders, with a 34% lifetime prevalence in the United States (Szuhany & Simon, 2022). Beyond their high prevalence, anxiety disorders impose substantial human and economic burdens, including reduced quality of life, occupational impairment, and increased healthcare utilization (Hoffman et al., 2008). Anxiety disorders are a precursor and common comorbidity with neurological disorders and hinder subsequent treatment and rehabilitation for neurological disorders. For instance, anxiety disorders can precede Parkinson’s disease symptomology by twenty years and triple dementia risk (Burke et al., 2018; Savica et al., 2010; Shiba et al., 2000). Though anxiety disorders are typically associated with prefrontal cortex dysfunction and dysregulation of the amygdala-prefrontal circuitry (Bishop, 2007; Shin & Liberzon, 2010), recent clinical studies demonstrate cerebellar and primary motor cortex dysfunction also contribute to anxiety manifestation. The cerebellum, traditionally viewed as a motor coordinator, plays important roles in emotional processing and cognitive functions (Schmahmann, 2019), and emerging evidence implicates cerebellar circuits in fear and anxiety-related disorders (Moreno-Rius, 2018). Furthermore, transcranial magnetic stimulation studies targeting motor cortical regions have shown therapeutic effects in anxiety and trauma-related disorders (Cirillo et al., 2019). Taken together, these findings implicate a wider network of anxiety-related dysfunction that encompasses the motor system (Martins et al., 2024). Anxiety has been linked to impaired reaction time (Feldman et al., 2019), task accuracy (Lo et al., 2019), and movement coordination (Bastian, 2008). However, the current body of literature has primarily evaluated acute anxiety, and less emphasis has been placed on anxiety disorders. Chronic anxiety may result in sustained alterations to prefrontal-motor connectivity that acute laboratory stressors cannot capture. In their totality, these findings point toward anxiety creating deficits in cognitive-motor integration, a bidirectional interaction of cognitive functions and motor control systems that produce purposeful movement behaviors (Rogojin et al., 2019). Cognitive-motor integration can be indexed using movement-readiness potentials (MRPs) recorded with electroencephalography (EEG) (Alexander et al., 2016; Schurger et al., 2021; Shibasaki & Hallett, 2006). Specifically, MRPs are recorded in the contralateral motor cortices as negative deflection in neural activity prior to voluntary movement (Shibasaki & Hallett, 2006). MRPs have been associated with activation of supplementary motor area, presupplementary motor area, and primary motor cortex, but not exclusively. MRPs also reflect unconscious determinants of voluntary decisions that emerge several seconds before conscious awareness (Libet et al., 1982, 1983; Soon et al., 2008). Decrements in the MRPs have been found in patients with cerebellar degeneration (Tarkka et al., 1993; Wessel et al., 1994) and schizophrenia (Donati et al., 2021; Westphal, 2003), suggesting a wide range of cortical and subcortical inputs. Moreover, neuroeconomics studies have found changes to MRP amplitude are directly related to human decision making, showcasing MRP sensitivity to decision making processes (Maoz et al., 2019; Triggiani et al., 2023; Verbaarschot et al., 2025). Given MRPs’ sensitivity to both decisions and sensorimotor processes, this opens the possibility it can index changes associated with anxiety disorders. Visuomotor adaptation is a widely recognized skill learning assessment that utilizes motor and cognitive areas (i.e., motor cortex, cerebellum, and prefrontal cortex) (Krakauer et al., 2019; Reuter et al., 2022; Taylor & Ivry, 2011, 2014; Tsay et al., 2024). Participants adjust their reaching movement to a visual perturbation to hit a target. The gradual reduction of error over time by updating internal models (implicit) for upcoming movement and altering movement strategies (explicit) (Krakauer & Mazzoni, 2011). The cerebellum and prefrontal cortex make distinct contributions to adaptation, with the cerebellum supporting implicit adaptation and the prefrontal cortex contributing to explicit strategy development and reinforcement learning (Taylor & Ivry, 2014).The retention of the visual perturbation is often tested in conditions where visual feedback is withdrawn (i.e., no cursor or feedback) and participants are asked to move in the same way they responded to the perturbation. Encoding and recall of motor memories is attributed to activation of the motor cortex (Galea et al., 2011). Given the cortical and subcortical connections involved in visuomotor adaptation, this task is a strong model for understanding cognitive-motor integration and how it changes with various psychological disorders. For instance, patients with schizophrenia are unable to develop robust explicit strategies to counter visual perturbations and have reduced task generalization (Bansal et al., 2019). Previous studies have found interactions between prefrontal and motor cortical activation during the learning and recall of a visuomotor adaptation task (Reuter et al., 2022). For instance, increased activation of the anterior cingulate cortex decreased motor cortex output, as represented by decrements in MRP amplitude (Anguera et al., 2009; Hill et al., 2020; Hill et al., 2021). Despite evidence that anxiety affects motor cortical regions and that motor preparation can be indexed through MRPs, no studies have examined how anxiety disorders specifically alter the temporal dynamics of movement preparation during adaptive skill learning. Understanding how anxiety disorders affect adaptive skill learning and motor-related neural processes has important clinical implications (i.e treatment monitoring, identifying at risk individuals for comorbid movement disorders) and could reveal specific targets for intervention and clarify the mechanisms underlying motor difficulties in anxious populations. The purpose of this study was to examine how anxiety disorders alter the neural mechanisms of motor preparation during visuomotor adaptation and retention. We hypothesized that individuals with anxiety disorders would show: (1) reduced MRP amplitudes during motor preparation and (2) behavioral deficits in visuomotor adaptation learning or retention compared to controls. Methodology All procedures were approved by the University Institutional Review Board and were conducted according to the principles expressed in the Declaration of Helsinki. Participants were recruited from the local population of the University and the surrounding communities using word of mouth, electronic announcements, and posted flyers. All participants provided written informed consent prior to participating. Thirty-one young healthy adults participated in this study [age range: 19–27 years, mean age ± standard deviation (SD): 21.59 ± 2.02 years, Edinburgh Handedness Inventory (EHI) (Oldfield, 1971) mean handedness score ± SD: 94.54 ± 6.93 [All right handed], males: 9, females: 22)]. Participants were free of major physiological (musculoskeletal, neurological, cardiovascular) disorders. 13 participants were clinically diagnosed with an anxiety disorder [generalized anxiety disorder: 12, social anxiety disorder: 1]. Among those with an anxiety disorder, 12 were actively taking medication to manage their condition. An a priori power calculation was performed using a smallest effect size of interest (Lakens, 2022) of Cohen’s f = .25, where α = .05 and β = .2 (i.e., statistical power = 80%). Based on these criteria, an adequate sample size of 24 was calculated. Our collected sample size (n=31) is similar to a recent study (Mussini & Di Russo, 2023) that explored differences in movement preparation brain activity and trait anxiety levels (n=32). This study found differences in MRPs between high and low anxious participants with large effects (ηp² = .152) All participants completed the State-Trait Anxiety Inventory (Spielberger & Sydeman, 1994). Experimental procedures Visuomotor Adaptation Task Acquisition The visuomotor task procedures followed those outlined in previous studies (Galea et al., 2015; Song & Smiley-Oyen, 2017). The task was coded and performed in Matlab R2024b software (Mathworks, Inc.) using the Psychophysics Toolbox extensions (Brainard, 1997; Kleiner et al., 2007; Pelli, 1997). Participants were seated in front of an 83.6 cm computer monitor that was elevated to 26.5 cm using a custom-built stand and parallel to the table. A Wacom tablet was placed underneath the monitor. A Wacom pen embedded into a cone was used to perform the task. Participants were instructed to grasp the cone, similar to an air hockey paddle, and maintain the same grip throughout the experiment. Furthermore, participants were positioned close enough to the monitor so that their right hand was visually occluded. Trials were participant-initiated, by moving a cursor represented by a white dot into a small circle located in the center of the screen. Afterwards, a red target represented by a square was displayed 8 cm from the starting circle in eight different positions, pseudorandomly so that every set of eight consecutive trials would include one of each of the target positions. After a 2 second pause the target turned green and the participant was instructed to reach to the target as swiftly and accurately as possible ( Figure 1 ). Baseline 0° Endpoint 100 Adaptation 45° CCW Endpoint 200 No Vision 45° CCW No Feedback 200 Washout 0° Endpoint 100 Table 1. Description of cursor rotation, feedback type, and number trials in each task condition that was performed by the participants. CCW = counterclockwise Cursor trajectory was not provided during the reach. End point feedback, represented by a pink dot, was provided at the termination of their reach. Participants were instructed to hit target with their reach so that the pink dot would cover green target. A duration criterion of 500 ms was placed on each trial, meaning that once participants initiated the trial, they had 500 ms to move their cursor past the invisible circle boundary that passes through the target circle, which is similar to previous studies (Galea et al., 2015; Song and Smiley-Oyen, 2017). If the trial was not completed within 500 ms, an auditory message would play stating the words “too slow” and the participant would redo the trial. Participants performed a total of 400 trials consisting of four testing conditions: Baseline (100 trials), Adaptation (200 trials), Retention (200 trials), and Washout (100 trials) ( Table 1 ). After every block of 50 trials, a rest period was provided, and participants were instructed to keep their arm under the visual occlusion. During the Baseline and Washout conditions, target and cursor movement were congruent. Adaptation featured an incongruent position of the cursor and the target, with the cursor trajectory, rotated 45° counterclockwise to the target, requiring the participant to adapt their movement to hit the target. Prior to the start of Adaptation, the participants were instructed to “Adapt your movement to hit the target”. Retention still featured a 45° counterclockwise rotation, but no end point feedback. Prior to the start of the retention stage, participants were instructed to “Maintain your adaptation despite not having cursor feedback.” For Baseline and Washout, participants were instructed to “Reach towards the target.” The NASA Task Load Index assessed the subjective cognitive workload after each task condition (Baseline, Adaptation, Retention). Participants answered six questions using a 21-point graduated scale representing different workload dimensions [Mental Demand, Physical Demand, Temporal Demand, Performance Success, Effort, and Frustration] (Hart, 2006; Hart & Staveland, 1988). EEG Acquisition Surface EEG data was recorded at 1000Hz with a 32 channel actiCAP electrode system and a Brain Products LiveAmp EEG amplifier (Gilching, Germany). Electrodes were placed according to the 10-20 system at sites FZ, CZ, PZ, OZ, FP1, FP2, F3, F4, F7, F8, FT7, FT8, FC3, FC4, C3, C4, CP3, CP4, P3, P4, T3, T4, T7, T8, P7, P8, TP7, TP8, TP9, TP10, O1, and O2. A saline solution was applied with a blunt tip syringe into the individual electrodes to lower electrical signal noise. Electrical impedance for each electrode was kept below 10kΩ throughout the data collection. Visuomotor Adaptation Task Analysis Response time was defined in seconds, as the time between the target turning green and the hand’s movement 10mm from the start position. The 10mm threshold ensured that small involuntary or accidental movements noise did not prematurely trigger response time recording (Avraham et al., 2021). Using a custom Matlab script, raw hand trajectories were resampled to include only movement-related samples and converted from pixels to millimeters. From these data, we computed trial-wise hand angle relative to the target direction [Hand theta (Θ)]. Sample to sample differences in both x and y coordinates were computed, and movement onset was identified as the earliest time point at which either coordinate deviated from the previous sample. Angular deviations were normalized using circular subtraction to account for the rotated visual feedback. Learning stages were further defined over the time course of the task. Early Adaptation and Retention was defined as the average hand angle during the first 8 trials of each condition (Early Adaptation, Early Retention). Late Adaptation and Retention was defined as the average hand angle during the last 8 trials of each condition (Late Adaptation, Late Retention). NASA Task Load Index Analysis Raw NASA Task Load Index scores for each dimension [Mental Demand, Physical Demand, Temporal Demand, Performance Success, Effort, Frustration]. An overall score was calculated for each participant following the procedures outlined by (Hart & Staveland, 1988). These data were further divided into two subscales which combines scores across dimensions: Task-related [Mental, Physical, Temporal]; Behavior-related [Performance, Effort]. EEG Analysis Raw EEG files were analyzed using the EEGLAB version 2024.0 (Delorme & Makeig, 2004) in Matlab R2024b. The continuous data was preprocessed in the following steps: downsampled from 1000Hz to 250 Hz, high pass filtered at 0.1Hz, bad channels were interpolated, and channels were rereferenced to the common average. To examine movement preparation related neural activity, the data was segmented into time-locked data 2000ms epochs around the onset of movement, with a 1500 ms pre-movement baseline and a 500ms post-movement period. During epoching, baseline correction from 2000-1500ms was applied using amplitudes averaged across the premovement period. An initial visual inspection of the epochs was performed to remove trials containing excessive movement noise, electromyography, and blink artifacts. Signal decomposition was performed using independent components analysis on each participant’s data utilizing the ‘runica’ procedure in EEGLAB. Additional trials containing artifacts were identified using the IClabel tool in EEGLAB (Pion-Tonachini et al., 2019) and resultant artifact components of the signal decomposition and were removed from the analysis. Components reflecting eye blinks and electromyography activity were removed by visual inspection. Epoched EEG data for Adaptation and Retention conditions were each split into two halves with approximately 100 trials each to measure brain processes associated with the Early and Late stages of each condition, following methods used in previous studies (Anguera et al., 2009; Bracco et al., 2018; Hill et al., 2021; Labruna et al., 2019). In order to better understand how anxiety might change cognitive-motor integration, a region of interest approach was used to examine motor preparatory activity following methods from Hamel et al., (2018). Using custom EEGLAB scripts, the average movement readiness potential during each Condition (Adaptation, Retention) and Stage (Early, Late) was found for each subject across the F3, Fz, FC1, C3, and Cz electrodes. We selected this electrode cluster based on MRI studies that localized them to the midfrontal and motor regions of interest (Hamel et al., 2018; Jurcak et al., 2007; Okamoto et al., 2004). Following methods from (Jochumsen & Niazi, 2020), mean amplitudes were calculated across three-time ranges that each represent different motor preparatory processes: the motor potential, the negative slope, and the resting potential. The motor potential was calculated by taking the mean amplitude from –100 to +100 ms with 0 being the onset of movement and represents movement execution generated primarily through activity in the motor cortex (Schurger et al., 2021; Shibasaki & Hallett, 2006). The negative slope was calculated by taking the mean amplitude from –500 to –100 ms and represents the translation of movement goals into detailed motor commands and is generated primarily through activity from the premotor and motor cortices (Cunnington et al., 2003; Jahanshahi et al., 1995; Praamstra et al., 1996). The readiness potential was calculated by taking the mean amplitude from –1500 to –500 ms and represents the earliest preparation for movement generated by the supplementary motor cortex (SMA), pre-SMA, and cingulate motor area that emerges before conscious awareness of movement (Cunnington et al., 2003; Kornhuber & Deecke, 1965; Praamstra et al., 1996; Shibasaki & Hallett, 2006). Statistical Analysis Participant descriptive data [age, handedness score, state anxiety inventory score, trait anxiety inventory score] were evaluated using separate independent samples student t-tests to determine differences between the anxiety disorder and control groups. Hand angle derived from the visuomotor adaptation task was analyzed using a linear mixed-effects models to determine differences between Group (Anxiety, Control), Condition (Adaptation, Retention), and Stage (Early, Late). Hand angle was held as a dependent variable. Group, Condition, and Stage were held as fixed effects and individual subjects’ were held as random effects. Subjective cognitive workload, as measured by the NASA Task Load Index dimensions, was assessed using separate linear mixed-effects models to determine differences between groups (Anxiety, Control) and conditions (Baseline, Adaptation, Retention). Overall NASA Task Load Index score and subscales scores [Task-Related, Behavior-Related] were held as dependent variables. Group and Condition were held as fixed effects and individual subjects’ were held as random effects. Response time (seconds) after the target turned green was task was analyzed using a linear mixed-effects model to determine differences between Group (Anxiety, Control), Condition (Adaptation, Retention), and Stage (Early, Late). Response time was held as a dependent variable. Group, Condition, and Stage were held as fixed effects and individual subjects’ were held as random effects. To examine the effects of Group (Anxiety, Control), Condition (Adaptation, Retention), and Stage (Early, Late) on movement readiness potentials amplitudes during the visuomotor adaptation task, three separate linear mixed-effects models were conducted on each component of the movement readiness potential (Boisgontier & Cheval, 2016; Giboin et al., 2020; Kumari et al., 2020). ERP amplitudes were held as the dependent variables. Group, Condition, and Stage were held as fixed effects and individual subjects’ were held as random effects. For all linear mixed model analysis, Satterthwaite approximation was used for degrees of freedom calculations to appropriately handle the mixed design structure. Individual slopes were not included due our small sample size and avoid convergence issues with an overly complex model (Burnham & Anderson, 2004; Matuschek et al., 2017; Meteyard & Davies, 2020). The advantages associated with linear mixed models, as opposed to conventional statistical methodologies, encompass the capability to account for measurements nested within individual subjects, the accommodation of missing and unbalanced data, prevention of information loss attributable to data averaging, and the facilitation of enhanced parameter estimation through the implementation of a partial pooling strategy (Boisgontier & Cheval, 2016; Giboin et al., 2020; Kumari et al., 2020). Post-hoc pairwise comparisons were planned using estimated marginal means with Sidak adjustment to control for Type I error across multiple comparisons. All analyses were conducted using IBM SPSS Statistics (version 29.0). Results The raw behavior and EEG data are available at https://osf.io/a65wk. n 13 18 - - - Age (years) 22.3 ± 2.5 21.4 ± 1.6 1.1(29) .24 .42 Handedness score (EDI) 93.1 ± 7.5 94.6 ± 6.8 .5(29) .57 .21 State Anxiety (STAI-S) 34.7 ± 7.9 20.5 ± 8.3 4.7(29) <.001* 1.7 Trait Anxiety (STAI-T) 44.2 ± 10.9 28.2 ± 11.4 3.9(29) <.001* 1.4 Participant descriptive Table 2. Participant Descriptive Data. Presented as mean ± standard deviation. * represents a significant difference between anxiety and control. To ensure similarity between groups and to confirm successful randomization we analyzed the demographic variables (Age, Handedness). Both groups were similar in age (t(29)=1.180, p=.248, Cohen’s d ( d) =.429, mean differences (MD)=0.863) and handedness scores (t(29)=.563, p=.578, d =.205, MD=1.455). STAI is commonly used to index anxiety levels and has been associated with event related potentials in previous studies (Du et al., 2022; Mussini & Di Russo, 2023; Xia et al., 2017; Zheng et al., 2024). As expected, we found that those with anxiety disorders had higher state (t(29)=4.791, p<.001, d =1.744, MD=14.190) and trait anxiety inventory (t(29)=3.926, p<.001, d =1.429, MD=16.010) scores compared to controls. Response Time Response time decreased from Early to Late stages and from Adaptation to Retention conditions across all participants. We found a significant main effect for Stage (F(1,87.000) = 27.726, p < .001, d = 0.62). Response time was faster during Late compared to Early [MD: 0.073, p < .001, 95% CIs = 0.045–0.101]. A significant main effect for Condition was also observed (F(1,87.000) = 26.696, p < .001, d = 0.52). Response time was faster during Retention compared to Adaptation [MD: 0.072, p < .001, 95% CIs = 0.044–0.099]. No significant main effect was found for Group (F(1,29) = 0.068, p = .796, d = 0.10). Similarly, no significant interactions emerged: Group × Stage (F(1,87.000) = 0.358, p = .551, d = 0.14), Group × Condition (F(1,87.000) = 1.294, p = .258, d = 0.24), Stage × Condition (F(1,87.000) = 0.066, p = .798, d = 0.06), or Group × Stage × Condition (F(1,87.000) = 0.000, p = .994, d =0.09). These findings suggest that participants became faster and more efficient in their responses as they progressed through practice (Late stage) and after learning was consolidated (Retention), with these improvements occurring similarly across both anxiety and control groups. NASA Task Workload Subjective cognitive workload changed across the motor adaptation conditions but not between groups. A significant main effect for Condition for overall cognitive workload (F(1,58.005) = 14.185, p < .001, d = 0.69). Overall cognitive workload increased in Adaptation compared to Baseline [MD: 11.230, p <.001, 95% CIs = 4.770–17.689, d = 0.69] and Retention [MD: 12.845, p < .001, 95% CIs = 6.385–19.304, d = 0.84]. Overall cognitive workload did not differ between Baseline and Retention [MD: 1.615, p = .903, 95% CIs = -4.844–8.074, d = 0.16] (Figure 2) . No significant main effect was found for Group (F(1,29.001) = 1.214, p = .280, d = 0.50). No significant Group × Condition interaction emerged (F(1,58.005) = .117, p = .890, d = 0.26). Task-Related cognitive workload demonstrated a similar pattern, where the introduction of the visuomotor rotation increased workload compared to other conditions. A significant main effect for Condition for overall cognitive workload (F(1,58.005) = 14.550, p < .001, d = 0.67). Task-Related cognitive workload increased in Adaptation compared to Baseline [MD: 11.829, p <.001, 95% CIs = 6.089–17.569, d = 0.68] and Retention [MD: 9.657, p < .001, 95% CIs = 3.917–15.397, d = 0.67]. Task-Related cognitive workload did not differ between Baseline and Retention [MD: 2.172, p = .733, 95% CIs = -7.912–3.568, d = 0.01]. No significant main effect was found for Group (F(1,29.001) = 1.433, p = .241, d = 0.50). No significant Group × Condition interaction emerged (F(1,58.005) = .283, p = .755, d = 0.26). This trend continued with Behavior-Related cognitive workload. A significant main effect for Condition for overall cognitive workload (F(1,58.005) = 5.034, p = .010, d = 0.52). Behavior-Related cognitive workload increased in Adaptation compared to Baseline [MD: 9.548, p =.029, 95% CIs = .737–18.330, d = 0.52] and Retention [MD: 10.061, p = .020, 95% CIs = 1.280–18.843, d = 0.53]. Behavior-Related cognitive workload did not differ between Baseline and Retention [MD: .513, p = .999, 95% CIs = -8.269–9.294, d = 0.01]. No significant main effect was found for Group (F(1,29.001) = .351, p = .558, d = 0.50). No significant Group × Condition interaction emerged (F(1,58.005) = 1.111, p = .336, d = 0.35). Visuomotor Task Performance All groups adapted to the 45-degree rotation during Adaptation and retained it during Retention (Figure 3A) . We found a significant Stage x Condition interaction (F(1,87.005) = 102.576, p < .001, d = 1.10). Hand angle increased from Early Adaptation to Late Adaptation [MD: 34.139, p < .001, 95% CIs = 29.555–38.722, d = 2.46]. However, hand angle was similar during Early and Late Retention [MD: 1.107, p = .632, 95% CIs = -3.476–5.691, d = 0.13]. Early Retention had a greater hand angle compared to Early Adaptation [MD: 34.3411, p < .001, 95% CIs = 29.727–38.894, d = 2.46]. Hand angle was similar between Late Adaptation and Late Retention [MD: 1.279, p = .581, 95% CIs = -3.305–5.863, d = 0.19] (Figure 3B) . No significant main effects were found for Group (F(1,29.022) = 0.334, p = .568, d = 0.21). Similarly, no other significant interactions emerged: Group × Condition (F(1,87.005) = 0.232, p = .631, d = 0.26), Group × Stage (F(1,87.005) = 0.300, p = .585, d = 0.20), or Group × Condition × Stage (F(1,87.005) = 1.079, p = .302, d = 0.02). Movement Readiness Potentials Prior to data synthesis, epoched EEG data was cleaned through trial and ICA removal. The average number of rejected trials (out of 200 total per participant) for the Adaptation condition was 18.74 ± 14.60 (mean ± standard deviation) resulting in 90.06% trials being retained. The number of ICA components removed for this group ranged from 0 to 10 with an average of 4.51± 1.14. The average number of rejected trials (out of 200 total per participant) for the Retention condition was 23.90 ± 20.90 resulting in 88.05% trials being retained. The number of ICA components removed for this group ranged from 0 to 3 with an average of 2.17±0.85. Movement readiness potentials for Adaptation and Retention are depicted on Figure 4 . Readiness Potential To investigate early preparatory motor activity during the period from -1500 to -500 ms before movement onset, we analyzed readiness potential. Readiness potential represents early preparatory activity prior to movement and is generated primarily by the supplementary motor area (SMA), the pre-supplementary motor area (pre-SMA), and cingulate motor area (Cunnington et al., 2003; Kornhuber & Deecke, 1965; Praamstra et al., 1996; Shibasaki & Hallett, 2006). The analysis revealed no significant differences between Groups, Conditions and Stages. No significant main effects were found for Group (F(1,29.001) = 0.285, p = .598, d = 0.10), Condition (F(1,87.002) = 0.620, p = .433, d = 0.20), or Stage (F(1,87.002) = 2.706, p = .104, d = 0.34). Similarly, no significant interactions emerged: Group × Condition (F(1,87.002) = 2.239, p = .138, d = 0.76), Group × Stage (F(1,87.002) = 0.705, p = .403, d= 0.13), Condition × Stage (F(1,87.002) = 1.080, p = .302, d= 0.61), or Group × Condition × Stage (F(1,87.002) = 0.115, p = .735, d= 0.23 ). These findings suggest that early preparatory motor activity remains stable across groups and experimental manipulations. Negative Slope Negative slope represents reflects the translation of movement goals into detailed motor commands (Shibasaki & Hallett, 2006; Praamstra et al., 1996; Colebatch, 2007). The anxiety group exhibited reduced preparatory motor activity compared to controls during the late preparatory phase (-500 to -100 ms before movement onset) (Figure 5) . Significant main effects were found for Group (F(1,29.002) = 5.010, p= .033, d= 0.83) and Stage (F(1,87.000) = 4.697, p = .033, d= 0.31). The anxiety group demonstrated a less negative amplitude compared to the control group [MD: 0.883, p = .033, 95% CIs = 0.076–1.690, d= 0.83]. Early stages showed less negative slopes compared to later stages [MD: 0.423, p = .033, 95% CIs = 0.037–0.809, d= 0.31]. No significant main effect was found for Condition (F(1,87.000) = 0.072, p = .789, d= 0.05). No significant interactions were observed: Group × Condition (F(1,87.000) = 2.563, p = .113, d= 0.30), Group × Stage (F(1,87.000) = 1.478, p = .227, d= 0.39), Condition × Stage (F(1,87.000) = 0.006, p = .940, d= 0.09), or Group × Condition × Stage (F(1,87.000) = 0.145, p = .704, d= 0.25). These findings indicate that anxiety specifically attenuates late-stage motor preparation, with this effect intensifying as movement onset approaches. Motor Potential The anxiety group demonstrated reduced motor cortical activation during the movement execution period (-100 to 100 ms around movement onset) compared to controls, with adaptation phases showing enhanced motor activity overall (Figure 6). Significant main effects were found for Group (F(1,29.000) = 4.293, p = .047, d= 0.76) and Condition (F(1,87.021) = 6.684, p = .011, d= 0.43). The anxiety group exhibited more positive peak amplitudes compared to the control group [MD: 1.289, p = .047, 95% CIs = 0.018–2.560]. Adaptation phases elicited more negative peak amplitudes compared to retention phases [MD: 0.598, p = .011, 95% CIs = 0.143–1.053]. No significant main effect was found for Stage (F(1,87.021) = 0.785, p = .378, d= 0.05). No significant interactions emerged: Group × Condition (F(1,87.021) = 1.973, p = .164, d= 0.06), Group × Stage (F(1,87.021) = 0.360, p = .550, d= 0.31), Condition × Stage (F(1,87.021) = 0.054, p = .817, d= 0.39), or Group × Condition × Stage (F(1,87.021) = 0.103, p = .749, d= 0.24). These amplitude differences suggest that anxiety attenuates motor cortical engagement during voluntary movement execution, and adaptation demands requiring greater motor processing resources compared to retention. Discussion The purpose of this study was to evaluate how anxiety disorders change the neural correlates of cognitive-motor integration. We took the novel approach of examining how anxiety disorders modulate movement readiness potentials (MRPs) during during visuomotor adaptation and retention. We found significant group differences in the MRPs components, with anxious individuals exhibiting reduced amplitudes during late motor preparation (-500 to -100 ms) and movement execution (-100 to 100 ms) compared to controls. Early motor preparation (-1500 to -500 ms) remained intact, indicating a stage-specific disruption in the motor preparation cascade. Despite these neural differences, both groups demonstrated equivalent learning and retention of the visuomotor adaptation task, with similar error reduction across adaptation blocks and comparable performance during retention testing. These findings reveal that anxiety disorders selectively disrupt cognitive-motor integration processes, and that compensatory mechanisms might preserve skilled learning performance despite these neural alterations. Anxiety Disorders Alter Late Cognitive-Motor Integration The movement readiness potential represents cortical activity associated with the preparation of a motor action that occurs prior to the onset of movement. The late preparatory component of the movement readiness potential referred to as the negative slope occurs -500 to -100 ms before movement onset and reflects the translation of movement goals into detailed motor commands (Shibasaki & Hallett, 2006; Praamstra et al., 1996; Colebatch, 2007). Neurophysiological studies have established that the negative slope specifically indexes motor specification processes in premotor and primary motor cortices (Cunnington et al., 2002; Jahanshahi et al., 1995; Praamstra et al., 1996) In other words, when movement intentions are converted into motor commands specifying the precise muscle activations, force profiles, and movement kinematics needed to achieve the goal (Haggard, 2008; Praamstra et al., 1996). We found a significant main effect of Group for the negative slope component, with the anxiety group exhibiting lower amplitudes compared to controls. Previous studies have found similar decrements when evaluating the relationship between anxiety and movement related brain activity. Specifically, Mussini & Di Russo (2023), demonstrated that high-trait anxiety reduced anticipatory brain activity in frontal regions during a go no-go task. They attributed these results to a reduced preparatory engagement in anxious individuals. That is, they may adopt less anticipatory (proactive) control because of heightened uncertainty about their responses or outcomes. Movement readiness potentials, including late preparatory components, are strongly modulated by frontal and supplementary motor networks and are critical for efficient action selection (Brunia et al., 2004; Di Russo et al., 2017; López-Larraz et al., 2014). The reduction in negative slope amplitude suggests that this integration process is less efficient in anxiety disorders, requiring the motor system to work with a weaker preparatory signal suggesting that anxiety disorders specifically disrupts the refinement and specification of motor plans. Our results are in line with previous work showing reduced frontal activity in highly anxious individuals (Ansari & Derakshan, 2011; Bishop, 2009). Anxiety disorders are also associated with disrupted prefrontal network connectivity more broadly. Resting-state studies show reduced connectivity between the anterior cingulate cortex (ACC) and the dorsomedial prefrontal cortex in panic disorder patients (Langhammer et al., 2025), indicating widespread prefrontal dysfunction that may extend to prefrontal-motor pathways involved in movement preparation. These structural and functional alterations weaken top-down regulation and may specifically disrupt the prefrontal cortex ’s contribution to late motor preparation. A concurrent fMRI-EEG study demonstrated that effective connectivity between the supplementary motor area (SMA) and cingulate cortex was associated with sustained motor preparatory activity during self-paced movements(Nguyen et al., 2014), suggesting that the reduced negative slope we observed may reflect disrupted communication within this network. This is consistent with the broader literature showing that anxiety disrupts prefrontal regions (Kenwood et al., 2021; Roberts & Mulvihill, 2024). The prefrontal cortex connects to the SMA primarily through the pre-SMA, which plays a crucial role in cognitive functions, motor selection, and planning (Nachev et al., 2008; Tanji, 1994) meaning that this disruption to the prefrontal region could be causing decrements in the transition from motor planning to motor execution. Our findings show that anxiety disorders reduces the negative slope during visuomotor adaptation, demonstrating these anxiety related frontal disruptions to the neural activity measured during movement preparation. Anxiety Disorders Reduce Cortical Activity at Movement Onset We found a significant main effect of Group for the motor potential (-100 to 100 ms around movement onset), with the anxiety group showing lower mean amplitudes compared to controls. This component, occurring from 100 ms before to 100 ms after movement onset, reflects the final activation of the primary motor cortex immediately before and during movement execution and its direct activation of the spinal motoneurons (Schurger et al., 2021; Shibasaki & Hallett, 2006). The motor cortex receives convergent input from premotor areas, the SMA, and the cerebellum, and relies on this preparatory activity to generate appropriately scaled motor commands (Thach et al., 1992; Tzvi et al., 2020). When preparatory signals are diminished, as we observed in the negative slope component, the motor cortex operates with less robust input, potentially explaining the reduced motor potential amplitude. The attenuated motor potential we observed in anxious individuals is enhanced with recent evidence from oscillatory brain dynamics. Cheng et al. (2025) found that trait anxiety negatively modulates the coupling between motor event-related desynchronization (ERD) and event-related synchronization (ERS) during a go no-go task. ERD reflects cortical activation during motor preparation and execution, while ERS represents post-movement cortical inhibition. The disrupted coupling they observed suggests that anxiety affects not only the magnitude of motor cortical engagement (as reflected in our motor potential findings) but also the coordination between activation and deactivation phases of motor control. The attenuation of motor potential in the anxiety group suggests that the dampened preparatory drive from upstream areas results in less robust activation of the motor cortex during movement execution. The observation of reduced MRPs in anxious individuals is consistent with findings in other psychiatric disorders, suggesting that altered motor preparation may be a transdiagnostic marker of frontal network dysfunction. For instance, Donati et al. (2021) showed reduced readiness potentials and reduced post-movement beta synchronization in patients with early-course schizophrenia. Similarly, Vöckel et al. (2023), found that the amplitude of the movement readiness potential was reduced in individuals with schizophrenia and that lower amplitudes were associated with more severe negative symptoms further highlighting the sensitivity of preparatory cortical activity to frontal network alterations. Although the phenomenology of anxiety differs substantially from schizophrenia, both conditions appear to share alterations in frontal motor network dynamics that manifest as attenuated preparatory signals. In schizophrenia, prefrontal dysfunction is associated with cognitive deficits and disorganized behavior (Luvsannyam et al., 2022). In anxiety, prefrontal dysfunction manifests as impaired cognitive control, heightened threat processing, and difficulty with emotion regulation (Kenwood et al., 2021). Our findings suggest that despite these different clinical presentations, both disorders affect the prefrontal cortex’s role in preparing voluntary actions. This may reflect a fundamental vulnerability of the prefrontal-motor connectivity that is disrupted across multiple forms of psychopathology. Future research directly comparing MRP components across diagnostic groups could reveal whether different disorders show distinct temporal profiles of motor preparation deficits, potentially providing neurophysiological markers for differential diagnosis or illness progression. Anxiety Disorders Do Not Change Early Movement Preparation We did not find significant group differences in the readiness potential (-1500 to -500 ms prior to movement onset). This early component primarily represents non-lateralized movement preparation generated by the SMA, pre-SMA, and cingulate motor area (Cunnington et al., 2005; Nguyen et al., 2014; Praamstra et al., 1996; Schurger et al., 2021; Shibasaki & Hallett, 2006). The lack of group differences in this early stage suggests that the initial preparation for movement is intact in anxiety disorders. Because this component emerges well before conscious awareness of movement (Cunnington et al., 2005; Libet et al., 1983), it reflects a generalized state of motor readiness or excitability rather than the specification of precise movement parameters (Schurger et al., 2021). This early preparatory activity depends less on prefrontal executive networks that anxiety typically disrupts (Ansari & Derakshan, 2011; Bishop, 2007, 2009; Eysenck et al., 2023) which indicates that brain remains relatively resilient to anxiety-related dysfunctions that primarily affect top-down cognitive regulation. Visuomotor Task Progression Reduces Cortical Preparatory Activity We also found a significant main effect of Stage, with early stages showing smaller amplitudes compared to late stages. This finding is consistent with the literature which suggests that the movement readiness potential becomes larger at the end of a single-training session of a self-paced motor task, potentially due to increases in cortical excitability (Jochumsen et al., 2017; Smith & Staines, 2012; Staines et al., 2002; M. J. Taylor, 1978). While, for multiple motor training sessions, a decrease in amplitude would be expected related to improved neural efficiency where less cortical and attentional demand is needed to perform a task effectively (Jochumsen et al., 2017; Smith & Staines, 2012). The absence of a Group × Stage interaction indicates that anxious individuals show similar learning-related improvements in preparatory activity as controls, despite maintaining consistently lower amplitude throughout. This suggests that while the absolute level of preparatory activity is reduced in anxiety disorders, the capacity to optimize motor preparation with practice remains intact. The parallel improvement across groups indicates that the neural mechanisms supporting learning and retention are preserved, even though the baseline efficiency of motor preparation is compromised. Adaptation demonstrated a greater motor potential compared to Retention indicating increased motor cortical engagement when learning a new visuomotor rotation compared to retaining a previously learned one. This is likely explained by a lack of end-point visual feedback during the retention stage. Previous studies suggest that predictable sensory feedback during a motor task, whether visual or auditory, produces greater movement related cortical activity compared to just having the somatosensory feedback inherent to the task (Niederberger & Gerber, 2006; Reznik et al., 2018; Vercillo et al., 2018; Wen et al., 2018). Importantly, both groups showed this adaptation-retention difference, indicating that anxious individuals retain the capacity to modulate motor cortical activity based on task demands, even though their overall amplitude is reduced . Another possibility for the increase in MRP during adaptation is increased task effort. Our NASA TLX results showcase an overall greater workload while learning to adapt to the 45-degree counterclockwise rotation. Previous studies have noted that high effort under conditions such as fatigue or exertion increases MRP amplitudes (de Morree et al., 2012, 2014; Kristeva et al., 1990; Oda et al., 1996). Thus the increases in cognitive effort to learn and performed the task increases MRP amplitude, showcasing the integration of cognitive and motor processing during visuomotor adaptation. Anxiety Disorders Alter Neural Correlates Without Behavioral Impairment Despite the clear neural differences in motor preparation and execution, both the anxiety and control groups demonstrated similar behavioral performance throughout the visuomotor adaptation task. Learning curves showed comparable error reduction during initial adaptation blocks. Retention performance did not differ between groups, indicating that both the acquisition and recall of the motor skill were unaffected by anxiety disorder status. Furthermore, anxiety did not alter response times (the duration from stimulus presentation to movement initiation), which decreased similarly in both groups. The dissociation between reduced MRP amplitudes and intact behavioral performance points to the engagement of compensatory mechanisms that maintain motor learning despite underlying neural inefficiencies (Basten et al., 2011; Berggren & Derakshan, 2013; Eysenck et al., 2007). To interpret this pattern, it is useful to consider theoretical models that describe how anxiety influences cognitive and motor functioning. One of the most influential frameworks is Attentional Control Theory, developed by Eysenck and colleagues (2007), which explains how anxiety alters the balance between goal directed and stimulus driven attentional systems. According to attentional control theory, anxiety impairs the efficient functioning of the goal-directed attentional system by increasing the influence of the stimulus-driven system and reducing available attentional resources (Eysenck et al., 2007). The theory also proposes that when compensatory strategies are employed such as increased effort, cognitive monitoring, or alternative processing routes task performance may remain unaffected despite underlying neural inefficiencies (Ansari & Derakshan, 2011; Eysenck et al., 2007). This compensatory account aligns with perceptual and motor models of anxiety as well. Nieuwenhuys & Oudejans, (2011) argue that anxiety compromises automatic sensorimotor integration and action preparation, which forces individuals to rely on more effortful and consciously controlled processes in order to maintain performance. Our findings are consistent with both perspectives, because although anxiety reduced the efficiency of motor preparation, as reflected in diminished MRP amplitudes, performance on the visuomotor adaptation task remained comparable across groups. This pattern strongly suggests that compensatory processes were successfully recruited to support motor learning. Limitations Although this work provides critical insight into the neural correlates of anxiety disorders, it is not without limitation. We did not test for explicit strategy usage during the task. Previous experiments have showcased the role of explicit aiming strategies to achieve the task goal (Avraham et al., 2021; Hegele & Heuer, 2010; Jahani et al., 2020) . Another limitation of the study is that the anxiety disorders group were actively taking psychiatric medication to manage their condition. Medication may modulate neural activity or behavioral performance (Volpato et al., 2016; Willerslev-Olsen et al., 2011). Importantly, no participant was taking medication that demonstrates active interference with motor cortex functions such as anti-psychotics, anti-epileptics, or barbiturates (Daskalakis et al., 2003; Ziemann et al., 2015) . Though our study is comparable to a recent study in this domain, our findings would have benefited from a larger sample size. Conclusion In conclusion, our study provides novel electrophysiological evidence that anxiety disorders alter the brain’s preparation for voluntary movement. Despite these neural differences, behavioral performance remained intact, suggesting that compensatory mechanisms may help preserve motor output in individuals with anxiety. These findings highlight that anxiety disorders impact integrated systems of cognition and action, offering a new perspective on their functional impact. Future research should investigate how acute stress interacts with chronic anxiety to influence motor preparation and performance, as well as explore whether specific anxiety disorder subtypes exhibit distinct neurobehavioral signatures. Given the prefrontal changes seen in this study, future work should use dual-site TMS to directly test prefrontal -motor cortical interactions during movement preparation in individuals with chronic anxiety. Figure legends Figure 1: Single trial time course. Movement initiation was cued by the target. No cursor feedback will be provided during the reach (dashed line). A “WAIT” message will appear instructing the participant to pause until the target turns green, cueing them begin their reach. Endpoint feedback (pink dot) was provided at trial completion. Figure 2: Total raw score of the NASA-TLX across conditions between anxiety and control groups. Error bars show standard error. # represents a significant difference from Baseline and Retention. Figure 3: A . Relative hand angle across epochs of eight trials all conditions between anxiety and control groups. 1 cycle represents the presentation of each target position (8 total). Represented as mean ± standard error. B. Boxplot for the average hand angle across the Adaptation and Retention conditions for the Anxiety and Control groups during the Early and Late stages. Box ends represent the interquartile range (IQR) [25th percentile (Q1) -75th percentile (Q3)]. Whiskers represent minimum (Q1-1.5*IQR) and maximum (Q3 – 1.5*IQR). Median is represented by horizontal line inside of the box and dots represent individual data. ^ represents a significant difference compared to Early Adaptation. Figure 3: A . Mean movement readiness potential averaged across the mid-frontal region of interest (electrodes F3, Fz, FC1, C3, and Cz) during Adaptation. B. Mean movement readiness potential averaged across the mid-frontal region of interest during Retention. Shaded error-bars indicate standard error. Black line at 0 ms represents the onset of movement. Voltage (µV) is plotted for Anxiety Early (orange), Control Early (light blue), Anxiety Late (brown), and Control Late (dark blue). Plots were created in R using ggplot2 package. Figure aliasing was completed using the geom_smooth defaults. Figure 5: Boxplot for the negative slope component during Early and Late stages across Anxiety and Control groups. Box ends represent the interquartile range (IQR) [25th percentile (Q1) -75th percentile (Q3)]. Whiskers represent minimum (Q1-1.5*IQR) and maximum (Q3 – 1.5*IQR). Median is represented by horizontal line inside of the box and dots represent individual data. ^ represents a significant difference from Early. * represents a significant difference from Control. Figure 6: Boxplot for the motor potential component during Adaptation and Retention across Anxiety and Control groups. Box ends represent the interquartile range (IQR) [25th percentile (Q1) -75th percentile (Q3)]. Whiskers represent minimum (Q1-1.5*IQR) and maximum (Q3 – 1.5*IQR). Median is represented by horizontal line inside of the box and dots represent individual data. # represents a significant difference from Retention. * represents a significant difference from Control. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. References Akiki, T. J., Jubeir, J., Bertrand, C., Tozzi, L., & Williams, L. M. (2024). Neural circuit basis of pathological anxiety. 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