EEG Microstate Characteristics of Emotional Activation in Tremor Variability in Wilson’s Disease Patients

EEG Microstate Characteristics of Emotional Activation in Tremor Variability in Wilson’s Disease Patients | 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 EEG Microstate Characteristics of Emotional Activation in Tremor Variability in Wilson’s Disease Patients Peizhu Zhang, Meng Wang, Xiao Wen, Pei Li, Ping Jin, Xin-Feng Ma, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6642480/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To investigate the neural mechanisms underlying emotion-induced tremor variability in Wilson’s disease (WD) patients via electroencephalography (EEG) microstate analysis. Methods Forty-five tremor-dominant WD patients and 20 healthy controls underwent assessments with the Body Image Disturbance Questionnaire (BIDQ), Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS), Self-Assessment Manikin (SAM) and Facial Expression Recognition (FER) tasks. Tremor kinematics were quantified via Kinovea, and EEG microstates were analyzed during emotion induction. Structural magnetic resonance imaging (MRI) was used to evaluate brain atrophy patterns. Results WD patients exhibited greater social avoidance ( Z = -5.721, p < 0.05), prolonged FER reaction times (1.78 s vs. 1.05 s, p < 0.001), and increased negative face selections (6 vs. 5, p = 0.036). Negative emotional states amplified tremor amplitude (3.79 px vs. 3.16 px , p = 0.012). EEG microstates revealed elevated microstate C (salience network) frequency (4.74/min vs. 3.41/min, p < 0.001) and coverage (16% vs. 13%, p < 0.001) during negative emotion, which was correlated with tremor severity ( ρ = 0.319, p = 0.039). Regression identified lentiform nucleus damage ( β =0.361), cerebellar atrophy ( β = 0.300) and frontal atrophy ( β = −0.386) as predictors of emotional arousal ( R² = 0.277, p = 0.042). Conclusion Emotion–tremor coupling in WD involves dysregulated salience networks and cerebellar–frontal–lentiform circuits, with EEG microstate C as a potential marker for targeted interventions. Wilson’s disease Tremor variability Embodied cognition EEG microstates Salience network Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Wilson’s disease (WD), an autosomal recessive disorder of copper metabolism ( ATP7B gene mutation), leads to pathological copper accumulation in the liver and brain, particularly within the basal ganglia, cerebellum, and cortical regions 1-2 . Neurological manifestations, such as tremor, dystonia, and dysarthria, occur in approximately 40–60% of patients and severely impair the quality of life 3 . Notably, clinical observations suggest that emotional states modulate tremor amplitude and frequency in WD patients 4-5 , however the neural mechanisms underlying this emotion–tremor coupling remain poorly understood. Current pathophysiological models of WD emphasize copper-induced neurotoxicity in the lentiform nucleus and cerebellar dentate nuclei, which disrupts cortico-striato-thalamocortical (CSTC) circuits critical for motor control 6 . However, these models largely overlook the role of emotion-processing networks in motor symptom variability. Emerging evidence indicates that WD patients exhibit deficits in facial emotion recognition and heightened social anxiety 7 , paralleling findings in functional movement disorders where negative emotions exacerbate tremor 8 . This raises a critical question — how do emotion-processing networks interact with motor circuits to drive tremor variability in WD? Dynamic systems theory posits that motor output emerges from the interaction between neural circuitry, body dynamics, and environmental constraints 9 . Within this framework, emotional states may act as "environmental constraints" that bias sensorimotor integration. Similarly, embodied cognition theory proposes that bodily states (e.g., tremor-induced proprioceptive feedback) directly shape emotional experiences, forming a closed loop between perception, emotion, and action 10 . For WD patients, chronic tremor may distort body schema representations (as measured by the Body Image Disturbance Questionnaire, BIDQ), amplifying emotional arousal and further destabilizing motor output — a hypothesis supported by recent magnetic resonance imaging (MRI) studies showing aberrant insula activation during emotional tasks in WD 11 . Electroencephalogram (EEG) microstates, which are transiently stable scalp field topographies (100–200 ms duration), provide a powerful tool to capture the rapid dynamics of large-scale brain networks 12 . Four canonical microstates (A-D) have been linked to distinct networks: auditory (A), visual (B), salience (C), and attentional (D) networks 13 . Notably, microstate C, associated with the anterior insula and anterior cingulate cortex, is modulated by emotional arousal and correlates with interoceptive awareness 14 – a process likely disrupted in WD owing to basal ganglia–cerebellar pathology. Despite these advances, critical gaps persist: (1) No studies have examined how emotional states dynamically modulate tremor physiology in WD via kinematic quantification. (2) The neurophysiological signatures of emotion-tremor interactions, particularly EEG microstate dynamics, remain unexplored. (3) The role of structural brain lesions (e.g., lentiform nucleus atrophy) in emotion–motor integration is unclear. This study addresses these gaps through a multimodal approach that integrating kinematic tremor analysis, EEG microstates, and structural MRI. We hypothesize that: negative emotions can affect the tremor amplitude and the degree of EEG microstate activity in WD patients. By testing these hypotheses, we aim to unravel the spatiotemporal mechanisms of emotion–tremor coupling in WD, offering novel targets for neuromodulation therapies. Methods 1. Participants A total of 45 tremor-dominant WD patients (28 males, 17 females; age range: 17–55 years) were recruited from the Neurology Department of Anhui University of Chinese Medicine Affiliated Hospital between August 2023 and June 2024. All patients met the following criteria: (1) confirmed WD diagnosis per the Leipzig criteria (clinical symptoms, Kayser-Fleischer rings, serum ceruloplasmin <20 mg/dL, and/or elevated 24-hour urinary copper excretion) 15 ; (2) postural/action tremor confirmed by neurologists via the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS) 16 . The exclusion criteria were as follows: comorbid neuropsychiatric disorders (e.g., Parkinson’s disease, epilepsy), severe hepatic dysfunction (Child–Pugh class C), or inability to complete assessments. Twenty age-, sex-, and education-matched healthy controls (HCs; 10 males, 10 females) were enrolled. The sample size was determined via G Power 3.1 (α = 0.05, power = 0.80, effect size = 0.6 for tremor–emotion correlations), yielding a minimum requirement of 40 WD patients and 18 HCs. 2. Clinical and Behavioral Assessments 2.1 Body Image Disturbance Body Image Disturbance Questionnaire (BIDQ-SC): A validated 7-item Chinese version 17 was used to assess distress related to tremor (e.g., "How often do you avoid social situations due to tremor?"). The items were scored from 0–8 (total range: 0–56; Cronbach’s α = 0.87). 2.2 Tremor Severity FTM-TRS 16 : This method is used to assess tremor when the patient is at rest, changing positions or performing movements. A higher score means the more severe tremor symptoms. Rated tremor amplitude (0–4), frequency (0–4), and functional impact (0–4) across 9 tasks (total range: 0–144) . 2.3 Emotional Processing Self-Assessment Manikin (SAM): Participants rated emotional valence (1=unpleasant to 9=pleasant), arousal (1=calm to 9=excited), and dominance (1=controlled to 9=in control) after each emotion induction block 18 . Facial Expression Recognition (FER) Task 19 : Using the Chinese Facial Affective Picture System (CFAPS) , 24 images (8 happy, 8 neutral, and 8 fearful; balanced for gender) were displayed on a 24-inch LCD monitor (PsychoPy v2022.2.5). The participants categorized emotions via keypress (1=positive, 2=neutral, 3=negative); reaction time (RT) and accuracy were recorded. 3. Experimental Paradigms 3.1 Emotion Induction Stimuli: Three 5-minute video blocks (positive, neutral, negative) from the SJTU Emotion EEG Dataset (SEED) 20 were used to induce the emotional experience, which was validated to induce effective valence/arousal (positive: valence=7.2±0.8, arousal=6.5±1.1; negative: valence=2.3±0.6, arousal=7.0±1.3). Procedure: After a resting-state EEG recording (eyes open, 5 min), an emotional video was presented. Each emotion block included 5 s of instruction (“Positive/Neutral/Negative video upcoming”), a 5-min video, a 45 s SAM rating and a 30 s post-video tremor recording (Kinovea motion tracking). All participants were counterbalanced across groups. 3.2 Tremor Kinematics Setup: Participants held their arms outstretched (90° shoulder abduction) with a 500 g wrist weight. A digital camera (Sony AX700, 120Hz) recorded hand motion. Analysis: Using Kinovea software for windows (version 0.9.5) 21 , the ulnar styloid process was tracked. Tremor amplitude ( px ) was calculated as the root mean square (RMS) of displacement over 30s, normalized to baseline. 4. Neurophysiological and Neuroimaging Data Acquisition 4.1 EEG Recording and Preprocessing Acquisition: EEG data were acquired via a 32-channel EEG (Brain Products ActiChamp, 500 Hz sampling, 0.1–100 Hz bandpass). A1 and A2 were used as reference electrodes. The electrode positions were placed in strict accordance with the international 10–20 system standard, and the impedance was kept below 10 kΩ. Preprocessing (EEGLAB v2024.0 22 ): Epochs are segmented into 30 s video-watching periods. preprocessed by downsampled to 256 Hz, bandpass filtering at 1-40 Hz, notch filtering at 49-51 Hz, baseline correction, electromyography and electrooculography, and independent component analysis (ICA) for ocular/motion artifact removal ( Figure 1 ). 4.2 EEG Microstate Analysis Clustering: Microstates were extracted via K-Means clustering 23 (Cartool v3.8). Four canonical microstates (A–D) were identified across all participants (global explained variance >80%). Parameters: For each microstate, frequency (number/min), duration (ms), and coverage (%) were computed ( Figure 2 A ). 4.3 Structural MRI Acquisition: 1.5T Siemens Magnetom Aera (T1: TR=2300 ms, TE=2.98 ms; T2-FLAIR: TR=9000 ms, TE=97 ms; slice thickness=5 mm). Analysis: Two neuroradiologists who were blinded to the diagnoses assessed atrophy in the lentiform nucleus, cerebellum, and frontal cortex via using visual rating scales (0=normal to 3=severe atrophy) 24 . 5. Statistical Analysis Two-sample T test and medians and interquartile ranges were used to analyze the clinical and demographic variables. Chi-square tests and Mann–Whitney U tests were employed for between-group comparisons. One-way ANOVA analyse was used to analyze tremor amplitude variability. Structural predictors of SAM arousal were assessed via stepwise regression (entry p0.10). Multiple Comparisons Benjamini-Hochberg correction (FDR=0.05) was applied for the microstate analyses. We used SPSS 25.0 for statistical analysis and values of p < 0.05 were regarded as significant. 6. Ethical Approval The study protocol was approved by the Ethics Committee of Anhui University of Chinese Medicine (Approval No. 2023-19). All participants or their legal guardians provided written informed consent. Results 1. Demographic and Clinical Characteristics Tremor-dominant WD patients (n=45) and healthy controls (HCs, n=20) were well matched for age, sex, and education level (all p > 0.05, Table 1). WD patients had a median disease duration of 6 years (IQR: 3.5–9.0) and moderate tremor severity (FTM-TRS total score: 25.6 ± 5.0; Table 1 ). Table 1 . Demographic and Clinical Features Variable WD Group (n=45) HC Group (n=20) Statistics p-Value Age (years) 33.04±7.016 31.80±5.606 t =0.699 0.487 a Sex (male/female) 45(27/18) 20(10/10) U =-0.746 0.456 b Education (years) 8.0[8.0,11.0] 8.00±3.026 U =-1.647 0.099 c Disease duration (years) 14.64±8.70 – – – FTM-TRS total score 25.6±5.0 – – – ‘- ’ indicates no score. a :Two-sample T test. b :Chi-square test. c :Nonparametric Kruskal–Wallis test. 2. Body Image Disturbance and Emotional Processing WD patients reported significantly greater body image distress (BIDQ total score: 25.6 ± 4.5 vs. 6.6 ± 2.6, t = 17.416, p < 0.001) and social avoidance (BIDQ 3, 5, 6 and 7 scores, all p < 0.05). In the FER task, WD patients exhibited longer reaction times (1.78 s [1.20, 2.35] vs. 1.05 s [0.90, 1.30], U = 89.0, p < 0.001) and selected negative faces more frequently (6.0 [5.0, 8.0] vs. 5.0 [4.0, 6.0], Z = 302.5, p = 0.036; Table 2 ). Table 2 . BIDQ and FER Task Performance Measure WD Group (n=45) HC Group (n=20) Statistics p-Value BIDQ total score 25.6±4.5 6.6±2.7 t= 17.416 <0.001 a,b BIDQ3(distress) 3.0[1.0,4.0] 0.0[0.0,0.0] Z =-5.872 <0.001 a,c BIDQ5(Social impact) 3.0[1.0,4.0] 1.0[0.0,1.75] Z =-3.716 <0.001 a,c BIDQ6(Role function) 2.0[0.0,4.0] 0.0[0.0,1.75] Z =-3.023 0.002 a,c BIDQ7(Avoidance) 5.0[3.0,6.0] 2.05±1.504 Z =-5.721 <0.001 a,c FER reaction time (s) 1.78[1.15,2.69] 1.04[0.99,1.13] Z =-4.735 <0.001 a,c Negative face choices 7.0[6.0,8.0] 6.0[5.0,7.0] Z =-2.581 <0.001 a,c a :P<0.05 indicates a statistically significant difference. b :Two-sample T test. c :Nonparametric Kruskal–Wallis test. 3. Emotion-Induced Tremor Variability One-way ANOVA revealed a significant main effect of emotional state on tremor amplitude (F (2, 132) = 4.839, p = 0.009, η² = 0.052). Post-hoc Bonferroni tests revealed greater tremor amplitude during negative emotion (3.79±1.18 px ) than during to positive emotion (3.10±1.22 px , p = 0.012) and at baseline (3.16±1.05 px , p = 0.006), but there was no difference between baseline (3.16±1.05 px ) and positive emotional states (p > 0.05, Figure 3 A‒B ). 4. EEG Microstate Dynamics 4.1 Microstate Parameters Mann–Whitney U tests revealed that microstate A duration (13.72 vs. 12.52 ms, U = 158.0, p = 0.004), microstate C frequency (4.74 vs. 3.14/min, U = 120.5, p < 0.001), coverage (16% vs. 13.22%, U = 158.0, p < 0.001), microstate D frequency (3.94 vs. 3.13/min, U = 95.0, p < 0.001) and coverage (16% vs. 13.81%, U = 62.0, p < 0.001) of negative emotions were greater than those of neutral emotions ( Figure 4 A‒C ). 4.2 Microstate-Behavior Correlations Microstate C frequency during negative emotion correlated with negative facial choices ( ρ = 0.338, p = 0.029); coverage correlated with negative tremor amplitude ( ρ = 0.319, p = 0.039). Microstate C coverage ( ρ = 0.352, p = 0.022), frequency( ρ = 0.361, p = 0.019) and microstate D frequency ( ρ = 0.353, p = 0.022) during positive emotion were correlated with positive emotion valence ( Figure 2 B ). 5. Structural MRI Predictors of Emotional Arousal Stepwise regression identified three predictors of SAM arousal scores during negative emotion ( R ² = 0.277, F (3,41) = 4.431, p = 0.042): lentiform nucleus damage: β = 0.361, t = 2.53, p = 0.016; cerebellar atrophy: β = 0.300, t = 2.11, p = 0.042. Frontal atrophy: β = -0.386, t = -2.65, p = 0.012 ( Table 3 ). Table 3 . MRI Predictors of Negative Emotional Arousal Predictor β t -Value p -Value 95% CI Lentiform nucleus 0.361 2.53 0.016 a [0.242, 2.201] Cerebellar atrophy 0.300 2.11 0.042 a [0.026, 1.315] Frontal atrophy -0.386 -2.65 0.012 a [-1.614, -0.217] a :P<0.05 indicates a statistically significant difference. 6. Correlation Between Tremor Amplitude and Clinical Variables Spearman analysis revealed a positive correlation between negative emotion tremor amplitude and BIDQ7 scores (avoidance) ( ρ = 0.34, p = 0.022) and between negative emotion tremor amplitude and FER reaction time ( ρ = 0.386, p = 0.009). Discussion This study provides the first multimodal evidence linking emotional processing abnormalities to tremor variability in WD patients through EEG microstate dynamics and structural neuroimaging. Our findings reveal three key mechanisms: (1) negative emotional bias amplifies tremor amplitude via salience network hyperactivity (microstate C); (2) lentiform nucleus‒cerebellar‒frontal circuit damage disrupts emotion‒motor integration; and (3) body image distress mediates social avoidance behaviors. Below, we contextualize these results within existing the literature and discuss their clinical implications. 1. Emotion-Tremor Coupling: A Salience Network-Driven Mechanism The heightened tremor amplitude observed during negative emotional states (3.79 px vs. 3.16 px , p = 0.012) is consistent with functional tremor models, which suggest that threat vigilance exacerbates motor symptoms 8 . The duration of microstate A is correlated with anxiety symptoms 25 . Notably, microstate C—a neurophysiological marker of the salience network, encompassing the anterior insula and anterior cingulate—exhibited an increased prevalence (16% vs. 13%, p < 0.001) and was correlated with both negative facial selections ( ρ = 0.338) and tremor severity ( ρ = 0.319). It has been demonstrated that increased activity in the anterior insula and anterior cingulate occurs during emotional stimulation 26 , potentially creating a feedback loop: tremor-related proprioceptive signals (via spinocerebellar pathways) amplify emotional arousal, which, in turn, destabilizes motor control through cortico-striatal projections 27 . Recent findings indicate that negative emotions are associated with prefrontal gamma-aminobutyric acid (GABA) concentrations 28 , which play crucial roles in the regulation of anxiety and depression. In WD, copper accumulation in the lentiform nucleus may impair GABAergic inhibition within basal ganglia-thalamocortical loops, rendering the system hypersensitive to emotional perturbations that induce more pronounced variability in tremor symptoms. 2. Structural Substrates of Emotion-Motor Dysfunction The triad of lentiform nucleus damage, cerebellar atrophy, and frontal degeneration accounted for 27.7% of the variance in emotional arousal ( p = 0.042), underscoring their synergistic roles: (1) The lentiform nucleus is the primary site of copper deposition in WD 29 , which disrupts the striatal circuits that regulate emotional salience when it is damaged. This disruption can also result in motor symptoms such as tone disorders and tremors, potentially derepressing limbic inputs to motor regions, similar to models of functional dystonia 30 . (2) Cerebellar atrophy undermines predictive motor control 31 , thereby impairing adaptive responses to emotion-induced tremors. Research has identified cerebellar‒limbic connections as crucial for emotion‒action coupling 32 , indicating that a pathway likely disrupted in WD that exacerbates emotional disturbances. (3) Frontal atrophy is negatively correlated with SAM arousal ( β = -0.386, p = 0.012), suggesting a compensatory suppression of emotional responses—an adaptive mechanism observed in Parkinson's disease patients presenting with apathy 33 . This may result in social challenges for apathetic patients due to atypical emotional expression, thereby increasing the likelihood of social avoidance. 3. Clinical Implications: From Mechanisms to Interventions The strong correlation between microstate C activity and tremor amplitude ( ρ = 0.319) positions EEG microstates as a non-invasive biomarker for emotion-tremor monitoring. This finding clinically supports the following conclusions: (1) Targeted neuromodulation. EEG microstates, a non-invasive visual indicators that easy to use clinically, can indicate the connection between human emotion and movement. Transcranial magnetic stimulation (TMS) over the right anterior insula (salience network hub) can normalize microstate C dynamics, as shown in depression trials 34 . (2) Cognitive-behavioral therapy (CBT). According to the results of this study, it is indicated that most of WD patients suffer from the negative effects of abnormal body image. Therefore, addressing body image distress (BIDQ scores) may reduce social avoidance, indirectly stabilizing tremor by lowering emotional arousal. (3) Early intervention. Patients with lentiform nuclei or cerebellar atrophy on MRI should undergo routine emotion-processing assessments and interventions to prevent tremor exacerbation, which suggests that psychological nursing should receive attention to in clinical practice. 4. Methodological Contributions and Limitations This study bridges the gap between embodied cognition theory and systems neuroscience by quantifying tremor as a dynamic phenotype of "embodied emotion". However, several limitations warrant caution: (1) The cross-sectional design includes causal inferences between emotion and tremor; therefore, longitudinal studies that track microstate changes during copper chelation therapy are necessary. (2) Sample heterogeneity was present, and subgroup analyses (e.g., hepatic versus neurological Wilson's disease subtypes) were not conducted. (3) Medication confounders, such as dopamine antagonists (e.g., trientine), may influence emotional processing but were not accounted for in this study. 5. Future Directions (1) Multimodal integration. fMRI and EEG microstates were combined to map salience network–spinal cord functional connectivity during emotion induction. (2) Digital phenotyping. Develop smartphone apps to capture real-world tremor–emotion interactions via accelerometry and ecological momentary assessment. (3) Mechanistic trials. Test whether TMS over the anterior insula reduces microstate C prevalence and tremor amplitude. Conclusion In WD, emotional activation exacerbates tremor variability through salience network hyperactivity and lentiform nucleus–cerebellar–frontal circuit disruption. EEG microstate C has emerged as a promising biomarker for personalized neuromodulation, while BIDQ scores highlight the need for integrated psychological care. By combining dynamic systems theory with clinical neurophysiology, this work advances WD management beyond copper-centric approaches to embrace emotion‒motor network plasticity. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the Affiliated Hospital of the Institute of Neurology, Anhui University of Traditional Chinese Medicine under registration number 2024-SYSFYSY-07. Written informed consent was obtained from all legal guardians. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. Competing interests The authors declare no conflicts of interest. Funding This study was supported by the Huizhou Science Research Center topic Fund of Anhui University of Traditional Chinese Medicine (2024HXYJZX03) and the Scientific Research Fund Project of Colleges and Universities in Anhui Province (2023AH050793, 2024AH050969). The funding body had no role or interference in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Findings may not generalize to asymptomatic WD or pediatric populations. Authors’ contributions Pei-Zhu Zhang, Xiao Wen, Gong-Qiang Wang, Yong-Zhu Han, Xin-Feng Ma and Guang-an Tong: conceptualization. Pei-Zhu Zhang, Pei Li, Meng Wang, Ping Jin and Kang Lin: data curation. Yong-Zhu Han: funding acquisition. Pei-Zhu Zhang, Meng Wang, Xiao Wen, Pei Li: investigation. Xin-Feng Ma, Guang-an Tong, Ping Jin and Kang Lin: methodology. Gong-Qiang Wang, Yong-Zhu Han, Xin-Feng Ma and Guang-an Tong: supervision. Pei-Zhu Zhang, Meng Wang and Xiao Wen: visualisation. Pei-Zhu Zhang, Meng Wang, Xiao Wen, Pei Li: writing – original draft. Pei-Zhu Zhang, Meng Wang, Pei Li, Xiao Wen, Ping Jin, Kang Lin, Gong-Qiang Wang, Xin-Feng Ma and Guang-an Tong: writing–review and editing. Acknowledgements The authors thank the patients and their caregivers, in addition to the co-investigators and their teams, who contributed to this study. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 07 Apr, 2026 Reviewers agreed at journal 30 May, 2025 Reviewers invited by journal 29 May, 2025 Editor assigned by journal 12 May, 2025 First submitted to journal 11 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6642480","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463678295,"identity":"1c3acd65-0f4b-49f1-8e33-49fc70486be0","order_by":0,"name":"Peizhu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACfvbmww8+/rOx42dvIFKLZM+xNMMZbGnJkj0HiNRicCNHQZqD7TDjhhsJxGthMGbgSWM2uPl44w2GGptowg478/bA4wIJGz7J22nFFgzH0nIbCGnhO56XYDzDII2Z73aOmQRjw2HCWhgO5BhI8yQcZmy4eYZILQInQFoOHGaccIOHSC3gQJ7ZAApkoF8SiPELJCobQFF5eOONDzU2RPgFCRhIJJCiHKKFVB2jYBSMglEwMgAAuHZE1SxR9EcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0000-9985-9234","institution":"Anhui University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Peizhu","middleName":"","lastName":"Zhang","suffix":""},{"id":463678296,"identity":"0b6d8711-a3f9-4bcc-a502-e66be14d72bc","order_by":1,"name":"Meng Wang","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Wang","suffix":""},{"id":463678297,"identity":"b697b88f-f700-4231-a632-8bb69626ffb8","order_by":2,"name":"Xiao Wen","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wen","suffix":""},{"id":463678298,"identity":"ec1d66cd-8c52-4dac-82a8-2b31fc4fe967","order_by":3,"name":"Pei Li","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Li","suffix":""},{"id":463678299,"identity":"021c7fc0-e137-4465-973c-c27b1f479d27","order_by":4,"name":"Ping Jin","email":"","orcid":"","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Jin","suffix":""},{"id":463678300,"identity":"e8b66f13-6e85-4721-8e06-c615a07bcc59","order_by":5,"name":"Xin-Feng Ma","email":"","orcid":"","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Xin-Feng","middleName":"","lastName":"Ma","suffix":""},{"id":463678301,"identity":"df273c80-0c8d-4aff-96d6-766e2f019e80","order_by":6,"name":"Kang Lin","email":"","orcid":"","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Lin","suffix":""},{"id":463678302,"identity":"21e549ac-524b-481d-afbb-a56eeb25208a","order_by":7,"name":"Guang-an Tong","email":"","orcid":"","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Guang-an","middleName":"","lastName":"Tong","suffix":""},{"id":463678303,"identity":"7bb8c8a9-9f0a-4789-9b8f-bd5fe9b44204","order_by":8,"name":"Gong-Qiang Wang","email":"","orcid":"https://orcid.org/0009-0000-5317-4184","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Gong-Qiang","middleName":"","lastName":"Wang","suffix":""},{"id":463678304,"identity":"cbb96a21-703c-473b-932c-fa15d282c222","order_by":9,"name":"Yong-Zhu Han","email":"","orcid":"","institution":"Affiliated Hospital of Anhui University of Traditional Chinese Medicine Institute of Neurology","correspondingAuthor":false,"prefix":"","firstName":"Yong-Zhu","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-05-12 03:31:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6642480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6642480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83883623,"identity":"8d7edc72-bf57-4c03-9ec1-9aa813868c33","added_by":"auto","created_at":"2025-06-04 06:13:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":679745,"visible":true,"origin":"","legend":"\u003cp\u003eEEG preprocessing schematic. The raw EEG data were processed in MATLAB to prevent interference from electromyographic signals, eye movement signals and other sources.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6642480/v1/80feccb8dded707ed031cb15.png"},{"id":83883877,"identity":"bf766e6c-5613-4296-ac18-f64a7b04b9a3","added_by":"auto","created_at":"2025-06-04 06:21:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":592450,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Topographic maps of microstates A–D. A: Microstate A. B: Microstate B. C: Microstate C. D: Microstate D. (B) Correlation between the frequency of Microstate C and negative facial selection. According to the Spearman analysis, the frequency of microstate C responses under negative emotions was positively correlated with the number of negative facial choices.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6642480/v1/18da739bf538be4474f25fd0.png"},{"id":83883878,"identity":"78179399-b6d7-4528-b752-83dabd97e4b0","added_by":"auto","created_at":"2025-06-04 06:21:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":214091,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comparison of tremor amplitude in different emotion states. Light green: neural emotion state. Light blue: negative emotion state. Yellow: positive emotion state. There were differences in tremor amplitude between negative emotion and neutral and positive emotion conditions according to one-way ANOVA analysis. (B) Kinematic traces of tremor displacement during negative states. Kinovea software was used to track the motion of tremor videos and generated the trajectory of ulnar styloid process movement.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6642480/v1/265dd304dc8a11794063a92e.png"},{"id":83883625,"identity":"0a49e854-f165-4ee3-a134-d954223d2922","added_by":"auto","created_at":"2025-06-04 06:13:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103271,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comparison of coverage in microstates C and D. According to the Mann–Whitney U tests, microstates C and D had higher coverage rates under negative emotions than under neutral emotions. (B) Comparison of frequency in microstates C and D. According to the Mann–Whitney U tests, microstates C and D had a faster frequency under negative emotion than under neutral emotion. (C) Comparison of duration in microstate A. According to the Mann–Whitney U tests, microstate A had a longer duration under negative emotion than under neutral emotion.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6642480/v1/a0cc56b4402fe0002738b9bf.png"},{"id":83884445,"identity":"7b1f2bbe-9e5e-4bb9-b48a-273dccf82e6a","added_by":"auto","created_at":"2025-06-04 06:29:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2884244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6642480/v1/c4317f4e-af6b-4b89-bc2c-d274e6b87789.pdf"}],"financialInterests":"","formattedTitle":"EEG Microstate Characteristics of Emotional Activation in Tremor Variability in Wilson’s Disease Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWilson\u0026rsquo;s disease (WD), an autosomal recessive disorder of copper metabolism (\u003cem\u003eATP7B\u0026nbsp;\u003c/em\u003egene mutation), leads to pathological copper accumulation in the liver and brain, particularly within the basal ganglia, cerebellum, and cortical regions \u003csup\u003e1-2\u003c/sup\u003e. Neurological manifestations, such as tremor, dystonia, and dysarthria, occur in approximately 40\u0026ndash;60% of patients and severely impair the quality of life \u003csup\u003e3\u003c/sup\u003e. Notably, clinical observations suggest that emotional states modulate tremor amplitude and frequency in WD patients \u003csup\u003e4-5\u003c/sup\u003e, however the neural mechanisms underlying this emotion\u0026ndash;tremor coupling remain poorly understood.\u003c/p\u003e\n\u003cp\u003eCurrent pathophysiological models of WD emphasize copper-induced neurotoxicity in the lentiform nucleus and cerebellar dentate nuclei, which disrupts cortico-striato-thalamocortical (CSTC) circuits critical for motor control \u003csup\u003e6\u003c/sup\u003e. However, these models largely overlook the role of emotion-processing networks in motor symptom variability. Emerging evidence indicates that WD patients exhibit deficits in facial emotion recognition and heightened social anxiety \u003csup\u003e7\u003c/sup\u003e, paralleling findings in functional movement disorders where negative emotions exacerbate tremor \u003csup\u003e8\u003c/sup\u003e. This raises a critical question\u0026nbsp;\u0026mdash;\u0026nbsp;how do emotion-processing networks interact with motor circuits to drive tremor variability in WD? Dynamic systems theory posits that motor output emerges from the interaction between neural circuitry, body dynamics, and environmental constraints \u003csup\u003e9\u003c/sup\u003e. Within this framework, emotional states may act as \u0026quot;environmental constraints\u0026quot; that bias sensorimotor integration. Similarly, embodied cognition theory proposes that bodily states (e.g., tremor-induced proprioceptive feedback) directly shape emotional experiences, forming a closed loop between perception, emotion, and action \u003csup\u003e10\u003c/sup\u003e. For WD patients, chronic tremor may distort body schema representations (as measured by the Body Image Disturbance Questionnaire, BIDQ), amplifying emotional arousal and further destabilizing motor output \u0026mdash; a hypothesis supported by recent magnetic resonance imaging (MRI) studies showing aberrant insula activation during emotional tasks in WD \u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eElectroencephalogram (EEG) microstates, which are transiently stable scalp field topographies (100\u0026ndash;200 ms duration), provide a powerful tool to capture the rapid dynamics of large-scale brain networks \u003csup\u003e12\u003c/sup\u003e. Four canonical microstates (A-D) have been linked to distinct networks: auditory (A), visual (B), salience (C), and attentional (D) networks \u003csup\u003e13\u003c/sup\u003e. Notably, microstate C, associated with the anterior insula and anterior cingulate cortex, is modulated by emotional arousal and correlates with interoceptive awareness \u003csup\u003e14\u003c/sup\u003e\u0026ndash; a process likely disrupted in WD owing to basal ganglia\u0026ndash;cerebellar pathology. Despite these\u0026nbsp;advances, critical gaps persist: (1) No studies have examined how emotional states dynamically modulate tremor physiology in WD via kinematic quantification. (2) The neurophysiological signatures of emotion-tremor interactions, particularly EEG microstate dynamics, remain unexplored. (3) The role of structural brain lesions (e.g., lentiform nucleus atrophy) in emotion\u0026ndash;motor integration is unclear.\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps through a multimodal approach that integrating kinematic tremor analysis, EEG microstates, and structural MRI. We hypothesize that: negative emotions can affect the tremor amplitude and the degree of EEG microstate activity in WD patients. By testing these hypotheses, we aim to unravel the spatiotemporal mechanisms of emotion\u0026ndash;tremor coupling in WD, offering novel targets for neuromodulation therapies.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e1. Participants\u003c/h2\u003e\n\u003cp\u003eA total of 45 tremor-dominant WD patients (28 males, 17 females; age range: 17\u0026ndash;55 years) were recruited from the Neurology Department of Anhui University of Chinese Medicine Affiliated Hospital between August 2023 and June 2024. All patients met the following criteria: (1) confirmed WD diagnosis per the Leipzig criteria (clinical symptoms, Kayser-Fleischer rings, serum ceruloplasmin \u0026lt;20 mg/dL, and/or elevated 24-hour urinary copper excretion) \u003csup\u003e15\u003c/sup\u003e; (2) postural/action tremor confirmed by neurologists via the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS) \u003csup\u003e16\u003c/sup\u003e. The exclusion criteria were as follows: comorbid neuropsychiatric disorders (e.g., Parkinson\u0026rsquo;s disease, epilepsy), severe hepatic dysfunction (Child\u0026ndash;Pugh class C), or inability to complete assessments.\u003c/p\u003e\n\u003cp\u003eTwenty age-, sex-, and education-matched healthy controls (HCs; 10 males, 10 females) were enrolled. The sample size was determined via G Power 3.1 (\u0026alpha; = 0.05, power = 0.80, effect size = 0.6 for tremor\u0026ndash;emotion correlations), yielding a minimum requirement of 40 WD patients and 18 HCs.\u003c/p\u003e\n\u003ch2\u003e2. Clinical and Behavioral Assessments\u003c/h2\u003e\n\u003ch4\u003e2.1 Body Image Disturbance\u003c/h4\u003e\n\u003cp\u003eBody Image Disturbance Questionnaire (BIDQ-SC): A validated 7-item Chinese version \u003csup\u003e17\u003c/sup\u003e was used to assess distress related to tremor (e.g., \u0026quot;How often do you avoid social situations due to tremor?\u0026quot;). The items were scored from 0\u0026ndash;8 (total range: 0\u0026ndash;56; Cronbach\u0026rsquo;s \u0026alpha; = 0.87).\u003c/p\u003e\n\u003ch4\u003e2.2 Tremor Severity\u003c/h4\u003e\n\u003cp\u003eFTM-TRS \u003csup\u003e16\u003c/sup\u003e: This method is used to assess tremor when the patient is at rest, changing positions or performing movements. A higher score means the more severe tremor symptoms. Rated tremor amplitude (0\u0026ndash;4), frequency (0\u0026ndash;4), and functional impact (0\u0026ndash;4) across 9 tasks (total range: 0\u0026ndash;144) .\u003c/p\u003e\n\u003ch4\u003e2.3 Emotional Processing\u003c/h4\u003e\n\u003cp\u003eSelf-Assessment Manikin (SAM): Participants rated emotional valence (1=unpleasant to 9=pleasant), arousal (1=calm to 9=excited), and dominance (1=controlled to 9=in control) after each emotion induction block \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFacial Expression Recognition (FER) Task \u003csup\u003e19\u003c/sup\u003e: Using the Chinese Facial Affective Picture System (CFAPS) , 24 images (8 happy, 8 neutral, and 8 fearful; balanced for gender) were displayed on a 24-inch LCD monitor (PsychoPy v2022.2.5). The participants categorized emotions via keypress (1=positive, 2=neutral, 3=negative); reaction time (RT) and accuracy were recorded.\u003c/p\u003e\n\u003ch2\u003e3. Experimental Paradigms\u003c/h2\u003e\n\u003ch4\u003e3.1 Emotion Induction\u003c/h4\u003e\n\u003cp\u003eStimuli: Three 5-minute video blocks (positive, neutral, negative) from the SJTU Emotion EEG Dataset (SEED) \u003csup\u003e20\u003c/sup\u003e were used to induce the emotional experience, which was validated to induce effective valence/arousal (positive: valence=7.2\u0026plusmn;0.8, arousal=6.5\u0026plusmn;1.1; negative: valence=2.3\u0026plusmn;0.6, arousal=7.0\u0026plusmn;1.3).\u003c/p\u003e\n\u003cp\u003eProcedure: After a resting-state EEG recording (eyes open, 5 min), an emotional video was presented. Each emotion block included 5 s of instruction (\u0026ldquo;Positive/Neutral/Negative video upcoming\u0026rdquo;), a 5-min video, a 45 s SAM rating and a 30 s post-video tremor recording (Kinovea motion tracking). All participants were counterbalanced across groups.\u003c/p\u003e\n\u003ch4\u003e3.2 Tremor Kinematics\u003c/h4\u003e\n\u003cp\u003eSetup: Participants held their arms outstretched (90\u0026deg; shoulder abduction) with a 500 g wrist weight. A digital camera (Sony AX700, 120Hz) recorded hand motion.\u003c/p\u003e\n\u003cp\u003eAnalysis: Using Kinovea software for windows (version 0.9.5) \u003csup\u003e21\u003c/sup\u003e, the ulnar styloid process was tracked. Tremor amplitude (\u003cem\u003epx\u003c/em\u003e) was calculated as the root mean square (RMS) of displacement over 30s, normalized to baseline.\u003c/p\u003e\n\u003ch2\u003e4. Neurophysiological and Neuroimaging Data Acquisition\u003c/h2\u003e\n\u003ch4\u003e4.1 EEG Recording and Preprocessing\u003c/h4\u003e\n\u003cp\u003eAcquisition: EEG data were acquired via a 32-channel EEG (Brain Products ActiChamp, 500 Hz sampling, 0.1\u0026ndash;100 Hz bandpass). A1 and A2 were used as reference electrodes. The electrode positions were placed in strict accordance with the international 10\u0026ndash;20 system standard, and the impedance was kept below 10 k\u0026Omega;.\u003c/p\u003e\n\u003cp\u003ePreprocessing (EEGLAB v2024.0 \u003csup\u003e22\u003c/sup\u003e): Epochs are segmented into 30 s video-watching periods. preprocessed by downsampled to 256 Hz, bandpass filtering at 1-40 Hz, notch filtering at 49-51 Hz, baseline correction, electromyography and electrooculography, and independent component analysis (ICA) for ocular/motion artifact removal (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003ch4\u003e4.2 EEG Microstate Analysis\u003c/h4\u003e\n\u003cp\u003eClustering: Microstates were extracted via K-Means clustering \u003csup\u003e23\u003c/sup\u003e(Cartool v3.8). Four canonical microstates (A\u0026ndash;D) were identified across all participants (global explained variance \u0026gt;80%). Parameters: For each microstate, frequency (number/min), duration (ms), and coverage (%) were computed (\u003cstrong\u003eFigure 2 A\u003c/strong\u003e).\u003c/p\u003e\n\u003ch4\u003e4.3 Structural MRI\u003c/h4\u003e\n\u003cp\u003eAcquisition: 1.5T Siemens Magnetom Aera (T1: TR=2300 ms, TE=2.98 ms; T2-FLAIR: TR=9000 ms, TE=97 ms; slice thickness=5 mm). Analysis: Two neuroradiologists who were blinded to the diagnoses assessed atrophy in the lentiform nucleus, cerebellum, and frontal cortex via using visual rating scales (0=normal to 3=severe atrophy) \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e5. Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eTwo-sample T test and medians and interquartile ranges were used to analyze the clinical and demographic variables. Chi-square tests and Mann\u0026ndash;Whitney U tests were employed for between-group comparisons. One-way ANOVA analyse was used to analyze tremor amplitude variability. Structural predictors of SAM arousal were assessed via stepwise regression (entry p\u0026lt;0.05, exit p\u0026gt;0.10). Multiple Comparisons \u0026nbsp;Benjamini-Hochberg correction (FDR=0.05) was applied for the microstate analyses. We used SPSS 25.0 for statistical analysis and values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as significant.\u003c/p\u003e\n\u003ch2\u003e6. Ethical Approval\u003c/h2\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Anhui University of Chinese Medicine (Approval No. 2023-19). All participants or their legal guardians provided written informed consent.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e1. Demographic and Clinical Characteristics\u003c/h2\u003e\n\u003cp\u003eTremor-dominant WD patients (n=45) and healthy controls (HCs, n=20) were well matched for age, sex, and education level (all p \u0026gt; 0.05, Table 1). WD patients had a median disease duration of 6 years (IQR: 3.5\u0026ndash;9.0) and moderate tremor severity (FTM-TRS total score: 25.6 \u0026plusmn; 5.0; \u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Demographic and Clinical Features\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWD Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e33.04\u0026plusmn;7.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31.80\u0026plusmn;5.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.487\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eSex (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e45(27/18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e20(10/10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.456\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8.0[8.0,11.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.00\u0026plusmn;3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.099\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14.64\u0026plusmn;8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003eFTM-TRS total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e25.6\u0026plusmn;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026lsquo;- \u0026rsquo; indicates no score.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e:Two-sample T test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e:Chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e:Nonparametric Kruskal\u0026ndash;Wallis test.\u003c/p\u003e\n\u003ch2\u003e2. Body Image Disturbance and Emotional Processing\u003c/h2\u003e\n\u003cp\u003eWD patients reported significantly greater body image distress (BIDQ total score: 25.6 \u0026plusmn; 4.5 vs. 6.6 \u0026plusmn; 2.6, t = 17.416, p \u0026lt; 0.001) and social avoidance (BIDQ 3, 5, 6 and 7 scores, all p \u0026lt; 0.05). In the FER task, WD patients exhibited longer reaction times (1.78 s [1.20, 2.35] vs. 1.05 s [0.90, 1.30], U = 89.0, p \u0026lt; 0.001) and selected negative faces more frequently (6.0 [5.0, 8.0] vs. 5.0 [4.0, 6.0], \u003cem\u003eZ\u003c/em\u003e = 302.5, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.036; \u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. BIDQ and FER Task Performance\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWD Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eBIDQ\u0026nbsp;total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e25.6\u0026plusmn;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.6\u0026plusmn;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003et=\u003c/em\u003e17.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eBIDQ3(distress)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3.0[1.0,4.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.0[0.0,0.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eBIDQ5(Social impact)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3.0[1.0,4.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.0[0.0,1.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-3.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eBIDQ6(Role function)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2.0[0.0,4.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.0[0.0,1.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-3.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.002\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eBIDQ7(Avoidance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5.0[3.0,6.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.05\u0026plusmn;1.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eFER reaction time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1.78[1.15,2.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.04[0.99,1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-4.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eNegative face choices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e7.0[6.0,8.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.0[5.0,7.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-2.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e:P\u0026lt;0.05 indicates a statistically significant difference.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e:Two-sample T test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e:Nonparametric Kruskal\u0026ndash;Wallis test.\u003c/p\u003e\n\u003ch2\u003e3. Emotion-Induced Tremor Variability\u003c/h2\u003e\n\u003cp\u003eOne-way ANOVA revealed a significant main effect of emotional state on tremor amplitude (F (2, 132) = 4.839, p = 0.009, \u0026eta;\u0026sup2; = 0.052). Post-hoc Bonferroni tests revealed greater tremor amplitude during negative emotion (3.79\u0026plusmn;1.18 \u003cem\u003epx\u003c/em\u003e) than during to positive emotion (3.10\u0026plusmn;1.22 \u003cem\u003epx\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e = 0.012) and at baseline (3.16\u0026plusmn;1.05 \u003cem\u003epx\u003c/em\u003e, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.006), but there was no difference between baseline (3.16\u0026plusmn;1.05 \u003cem\u003epx\u003c/em\u003e) and positive emotional states (p \u0026gt; 0.05, \u003cstrong\u003eFigure 3 A‒B\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e4. EEG Microstate Dynamics\u003c/h2\u003e\n\u003ch4\u003e4.1 Microstate Parameters\u003c/h4\u003e\n\u003cp\u003eMann\u0026ndash;Whitney U tests revealed that microstate A duration (13.72 vs. 12.52 ms, \u003cem\u003eU\u003c/em\u003e = 158.0, \u003cem\u003ep\u003c/em\u003e = 0.004), microstate C frequency (4.74 vs. 3.14/min, \u003cem\u003eU\u003c/em\u003e = 120.5, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), coverage (16% vs. 13.22%, \u003cem\u003eU\u003c/em\u003e = 158.0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), microstate D frequency (3.94 vs. 3.13/min, \u003cem\u003eU\u003c/em\u003e = 95.0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and coverage (16% vs. 13.81%, \u003cem\u003eU\u003c/em\u003e = 62.0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) of negative emotions were greater than those of neutral emotions (\u003cstrong\u003eFigure 4 A‒C\u003c/strong\u003e).\u003c/p\u003e\n\u003ch4\u003e4.2 Microstate-Behavior Correlations\u003c/h4\u003e\n\u003cp\u003eMicrostate C frequency during negative emotion correlated with negative facial choices (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.338, \u003cem\u003ep\u003c/em\u003e = 0.029); coverage correlated with negative tremor amplitude ( \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.319, \u003cem\u003ep\u003c/em\u003e = 0.039). Microstate C coverage ( \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.352, \u003cem\u003ep\u003c/em\u003e = 0.022), frequency( \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.361, \u003cem\u003ep\u003c/em\u003e = 0.019) and microstate D frequency ( \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.353, \u003cem\u003ep\u003c/em\u003e = 0.022) during positive emotion were correlated with positive emotion valence ( \u003cstrong\u003eFigure 2 B\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e5. Structural MRI Predictors of Emotional Arousal\u003c/h2\u003e\n\u003cp\u003eStepwise regression identified three predictors of SAM arousal scores during negative emotion (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.277, \u003cem\u003eF\u003c/em\u003e(3,41) = 4.431, \u003cem\u003ep\u003c/em\u003e = 0.042): lentiform nucleus damage: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.361, \u003cem\u003et\u003c/em\u003e = 2.53, \u003cem\u003ep\u003c/em\u003e = 0.016; cerebellar atrophy: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.300, \u003cem\u003et\u003c/em\u003e = 2.11,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.042.\u003c/p\u003e\n\u003cp\u003eFrontal atrophy: \u003cem\u003e\u0026beta;\u003c/em\u003e = -0.386, \u003cem\u003et\u003c/em\u003e = -2.65, \u003cem\u003ep\u003c/em\u003e = 0.012 (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. MRI Predictors of Negative Emotional Arousal\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eLentiform nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.016\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[0.242, 2.201]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCerebellar atrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.042\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[0.026, 1.315]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eFrontal atrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.012\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[-1.614, -0.217]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e:P\u0026lt;0.05 indicates a statistically significant difference.\u003c/p\u003e\n\u003ch2\u003e6. Correlation Between Tremor Amplitude and Clinical Variables\u003c/h2\u003e\n\u003cp\u003eSpearman analysis revealed a positive correlation between negative emotion tremor amplitude and BIDQ7 scores (avoidance) (\u003cem\u003e\u0026rho;\u0026nbsp;\u003c/em\u003e= 0.34, \u003cem\u003ep\u003c/em\u003e = 0.022) and between negative emotion tremor amplitude and FER reaction time (\u003cem\u003e\u0026rho;\u0026nbsp;\u003c/em\u003e= 0.386,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.009).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first multimodal evidence linking emotional processing abnormalities to tremor variability in WD patients through EEG microstate dynamics and structural neuroimaging. Our findings reveal three key mechanisms: (1) negative emotional bias amplifies tremor amplitude via salience network hyperactivity (microstate C); (2) lentiform nucleus‒cerebellar‒frontal circuit damage disrupts emotion‒motor integration; and (3) body image distress mediates social avoidance behaviors. Below, we contextualize these results within existing the literature and discuss their clinical implications.\u003c/p\u003e\n\u003ch2\u003e1. Emotion-Tremor Coupling: A Salience Network-Driven Mechanism\u003c/h2\u003e\n\u003cp\u003eThe heightened tremor amplitude observed during negative emotional states (3.79 \u003cem\u003epx\u003c/em\u003e vs. 3.16 \u003cem\u003epx\u003c/em\u003e, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.012) is consistent with functional tremor models, which suggest that threat vigilance exacerbates motor symptoms \u003csup\u003e8\u003c/sup\u003e. The duration of microstate A is correlated with anxiety symptoms \u003csup\u003e25\u003c/sup\u003e. Notably, microstate C\u0026mdash;a neurophysiological marker of the salience network, encompassing the anterior insula and anterior cingulate\u0026mdash;exhibited an increased prevalence (16% vs. 13%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) and was correlated with both negative facial selections (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.338) and tremor severity (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.319). It has been demonstrated that increased activity in the anterior insula and anterior cingulate occurs during emotional stimulation\u003csup\u003e\u0026nbsp;26\u003c/sup\u003e, potentially creating a feedback loop: tremor-related proprioceptive signals (via spinocerebellar pathways) amplify emotional arousal, which, in turn, destabilizes motor control through cortico-striatal projections \u003csup\u003e27\u003c/sup\u003e. Recent findings indicate that negative emotions are associated with prefrontal gamma-aminobutyric acid (GABA) concentrations \u003csup\u003e28\u003c/sup\u003e, which play crucial roles in the regulation of anxiety and depression. In WD, copper accumulation in the lentiform nucleus may impair GABAergic inhibition within basal ganglia-thalamocortical loops, rendering the system hypersensitive to emotional perturbations that induce more pronounced variability in tremor symptoms.\u003c/p\u003e\n\u003ch2\u003e2. Structural Substrates of Emotion-Motor Dysfunction\u003c/h2\u003e\n\u003cp\u003eThe triad of lentiform nucleus damage, cerebellar atrophy, and frontal degeneration accounted for 27.7% of the variance in emotional arousal (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.042), underscoring their synergistic roles: (1) The lentiform nucleus is the primary site of copper deposition in WD \u003csup\u003e29\u003c/sup\u003e, which disrupts the striatal circuits that regulate emotional salience when it is damaged. This disruption can also result in motor symptoms such as tone disorders and tremors, potentially derepressing limbic inputs to motor regions, similar to models of functional dystonia \u003csup\u003e30\u003c/sup\u003e. (2) Cerebellar atrophy undermines predictive motor control\u003csup\u003e\u0026nbsp;31\u003c/sup\u003e, thereby impairing adaptive responses to emotion-induced tremors. Research has identified cerebellar‒limbic connections as crucial for emotion‒action coupling \u003csup\u003e32\u003c/sup\u003e, indicating that a pathway likely disrupted in WD that exacerbates emotional disturbances. (3) Frontal atrophy is negatively correlated with SAM arousal (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= -0.386, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.012), suggesting a compensatory suppression of emotional responses\u0026mdash;an adaptive mechanism observed in Parkinson\u0026apos;s disease patients presenting with apathy \u003csup\u003e33\u003c/sup\u003e. This may result in social challenges for apathetic patients due to atypical emotional expression, thereby increasing the likelihood of social avoidance.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3. Clinical Implications: From Mechanisms to Interventions\u003c/h2\u003e\n\u003cp\u003eThe strong correlation between microstate C activity and tremor amplitude (\u003cem\u003e\u0026rho;\u0026nbsp;\u003c/em\u003e= 0.319) positions EEG microstates as a non-invasive biomarker for emotion-tremor monitoring. This finding clinically supports the following conclusions: (1) Targeted neuromodulation. EEG microstates, a non-invasive visual indicators that easy to use clinically, can indicate the connection between human emotion and movement. Transcranial magnetic stimulation (TMS) over the right anterior insula (salience network hub) can normalize microstate C dynamics, as shown in depression trials \u003csup\u003e34\u003c/sup\u003e. (2) Cognitive-behavioral therapy (CBT). According to the results of this study, it is indicated that most of WD patients suffer from the negative effects of abnormal body image. Therefore, addressing body image distress (BIDQ scores) may reduce social avoidance, indirectly stabilizing tremor by lowering emotional arousal. (3) Early intervention. Patients with lentiform nuclei or cerebellar atrophy on MRI should undergo routine emotion-processing assessments and interventions to prevent tremor exacerbation, which suggests that psychological nursing should receive attention to in clinical practice.\u003c/p\u003e\n\u003ch2\u003e4. Methodological Contributions and Limitations\u003c/h2\u003e\n\u003cp\u003eThis study bridges the gap between embodied cognition theory and systems neuroscience by quantifying tremor as a dynamic phenotype of \u0026quot;embodied emotion\u0026quot;. However, several limitations warrant caution: (1) The cross-sectional design includes causal inferences between emotion and tremor; therefore, longitudinal studies that track microstate changes during copper chelation therapy are necessary. (2) Sample heterogeneity was present, and subgroup analyses (e.g., hepatic versus neurological Wilson\u0026apos;s disease subtypes) were not conducted. (3) Medication confounders, such as dopamine antagonists (e.g., trientine), may influence emotional processing but were not accounted for in this study.\u003c/p\u003e\n\u003ch2\u003e5. Future Directions\u003c/h2\u003e\n\u003cp\u003e(1) Multimodal integration. fMRI and EEG microstates were combined to map salience network\u0026ndash;spinal cord functional connectivity during emotion induction. (2) Digital phenotyping. Develop smartphone apps to capture real-world tremor\u0026ndash;emotion interactions via accelerometry and ecological momentary assessment. (3) Mechanistic trials. Test whether TMS over the anterior insula reduces microstate C prevalence and tremor amplitude.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn WD, emotional activation exacerbates tremor variability through salience network hyperactivity and lentiform nucleus\u0026ndash;cerebellar\u0026ndash;frontal circuit disruption. EEG microstate C has emerged as a promising biomarker for personalized neuromodulation, while BIDQ scores highlight the need for integrated psychological care. By combining dynamic systems theory with clinical neurophysiology, this work advances WD management beyond copper-centric approaches to embrace emotion‒motor network plasticity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Affiliated Hospital of the Institute of Neurology, Anhui University of Traditional Chinese Medicine under registration number 2024-SYSFYSY-07. Written informed consent was obtained from all legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Huizhou Science Research Center topic Fund of Anhui University of Traditional Chinese Medicine (2024HXYJZX03) and the Scientific Research Fund Project of Colleges and Universities in Anhui Province (2023AH050793, 2024AH050969). The funding body had no role or interference in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Findings may not generalize to asymptomatic WD or pediatric populations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003ePei-Zhu Zhang, Xiao Wen, Gong-Qiang Wang, Yong-Zhu Han, Xin-Feng Ma and Guang-an Tong: conceptualization. Pei-Zhu Zhang, Pei Li, Meng Wang, Ping Jin and Kang Lin: data curation. Yong-Zhu Han: funding acquisition. Pei-Zhu Zhang, Meng Wang, Xiao Wen, Pei Li: investigation. Xin-Feng Ma, Guang-an Tong, Ping Jin and Kang Lin: methodology. Gong-Qiang Wang, Yong-Zhu Han, Xin-Feng Ma and Guang-an Tong: supervision. Pei-Zhu Zhang, Meng Wang and Xiao Wen: visualisation. Pei-Zhu Zhang, Meng Wang, Xiao Wen, Pei Li: writing \u0026ndash; original draft. Pei-Zhu Zhang, Meng Wang, Pei Li, Xiao Wen, Ping Jin, Kang Lin, Gong-Qiang Wang, Xin-Feng Ma and Guang-an Tong: writing\u0026ndash;review and editing.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors thank the patients and their caregivers, in addition to the co-investigators and their teams, who contributed to this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCzłonkowska A, Litwin T, Dusek P, Ferenci P, Lutsenko S, Medici V, et al. Wilson disease. Nat Rev Dis Primers. 2018;4(1):21.\u003c/li\u003e\n \u003cli\u003eRuiz-Lopez M, Moreno Est\u0026eacute;banez A, Tijero B, Fernandez T, Rebollo-Perez A, Gabilondo I, et al. Pearls \u0026amp; Oy-sters: Challenges and Controversies in Wilson Disease. Neurology. 2022;99(6):251-255.\u003c/li\u003e\n \u003cli\u003eShribman S, Bocchetta M, Sudre CH, Acosta-Cabronero J, Burrows M, Cook P, et al. 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Cerebellar contribution to emotion regulation and its association with medial frontal GABA level. Soc Cogn Affect Neurosci. 2025;20(1):nsae091.\u003c/li\u003e\n \u003cli\u003eIwański S, Seni\u0026oacute;w J, Leśniak M, Litwin T, Członkowska A. Diverse attention deficits in patients with neurologically symptomatic and asymptomatic Wilson\u0026apos;s disease. Neuropsychology. 2015;29(1):25-30.\u003c/li\u003e\n \u003cli\u003eR. Z. Adury, B. J. Wilkes, P. Girdhar, Y. Li, D. E. Vaillancourt. Altered functional brain connectivity in Dyt1 knock-in mouse models.\u0026nbsp;Dystonia. 2025;4:13874.\u003c/li\u003e\n \u003cli\u003eGrimm K, Sadeghi F, Sch\u0026ouml;n G, Okar A, Gelderblom M, Schulz R, et al. Atrophy of cerebellar lobule VI and primary motor cortex in cervical dystonia - a region of interest-based study. J Neural Transm (Vienna). 2025;132(2):257-264.\u003c/li\u003e\n \u003cli\u003eLafo JA, Mikos A, Mangal PC, Scott BM, Trifilio E, Okun MS, et al. Emotion modulation of the startle reflex in essential tremor: Blunted reactivity to unpleasant and pleasant pictures. Parkinsonism Relat Disord. 2017;34:54-58.\u003c/li\u003e\n \u003cli\u003eGe S, Liu J, Jia Y, Li Z, Wang J, Wang M. Topological alteration of the brain structural network in Parkinson\u0026apos;s disease with apathy. Brain Res Bull. 2024;208:110899.\u003c/li\u003e\n \u003cli\u003eChe Q, Xi C, Sun Y, Zhao X, Wang L, Wu K, et al. EEG microstate as a biomarker of personalized transcranial magnetic stimulation treatment on anhedonia in depression. Behav Brain Res. 2025;483:115463.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wilson’s disease, Tremor variability, Embodied cognition, EEG microstates, Salience network","lastPublishedDoi":"10.21203/rs.3.rs-6642480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6642480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e To investigate the neural mechanisms underlying emotion-induced tremor variability in Wilson’s disease (WD) patients via electroencephalography (EEG) microstate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Forty-five tremor-dominant WD patients and 20 healthy controls underwent assessments with the Body Image Disturbance Questionnaire (BIDQ), Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS), Self-Assessment Manikin (SAM) and Facial Expression Recognition (FER) tasks. Tremor kinematics were quantified via Kinovea, and EEG microstates were analyzed during emotion induction. Structural magnetic resonance imaging (MRI) was used to evaluate brain atrophy patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e WD patients exhibited greater social avoidance (\u003cem\u003eZ \u003c/em\u003e= -5.721, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05), prolonged FER reaction times (1.78 s vs. 1.05 s, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), and increased negative face selections (6 vs. 5, \u003cem\u003ep \u003c/em\u003e= 0.036). Negative emotional states amplified tremor amplitude (3.79 \u003cem\u003epx\u003c/em\u003e vs. 3.16 \u003cem\u003epx\u003c/em\u003e, \u003cem\u003ep \u003c/em\u003e= 0.012). EEG microstates revealed elevated microstate C (salience network) frequency (4.74/min vs. 3.41/min, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001) and coverage (16% vs. 13%, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001) during negative emotion, which was correlated with tremor severity (\u003cem\u003eρ \u003c/em\u003e= 0.319, \u003cem\u003ep \u003c/em\u003e= 0.039). Regression identified lentiform nucleus damage (\u003cem\u003eβ\u003c/em\u003e=0.361), cerebellar atrophy (\u003cem\u003eβ \u003c/em\u003e= 0.300) and frontal atrophy (\u003cem\u003eβ \u003c/em\u003e= −0.386) as predictors of emotional arousal (\u003cem\u003eR² \u003c/em\u003e= 0.277, \u003cem\u003ep \u003c/em\u003e= 0.042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Emotion–tremor coupling in WD involves dysregulated salience networks and cerebellar–frontal–lentiform circuits, with EEG microstate C as a potential marker for targeted interventions.\u003c/p\u003e","manuscriptTitle":"EEG Microstate Characteristics of Emotional Activation in Tremor Variability in Wilson’s Disease Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 06:13:07","doi":"10.21203/rs.3.rs-6642480/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2026-04-07T14:03:10+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-30T08:22:16+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-29T13:48:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-12T09:13:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2025-05-11T23:29:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b73aa2fe-5e78-4272-925d-155dec1cae2e","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T12:33:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 06:13:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6642480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6642480","identity":"rs-6642480","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}