Goal Narrowing Drives Compulsive Decision Making in Patients with Methamphetamine Use Disorder: A Cross-Sectional Study of Neural and Behavioral Correlates

Goal Narrowing Drives Compulsive Decision Making in Patients with Methamphetamine Use Disorder: A Cross-Sectional Study of Neural and Behavioral Correlates | 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 Goal Narrowing Drives Compulsive Decision Making in Patients with Methamphetamine Use Disorder: A Cross-Sectional Study of Neural and Behavioral Correlates Shiyan Qu, Lei Guo, Gang Wang, Hang Su, Xiandong Lin, Wei Shao, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8549153/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Methamphetamine use disorder (MUD) is characterized by compulsive drug use, which has been attributed to compulsive decision-making related to drug-use events, with either exaggerated habit formation or excessive goal-directed selection contributing to it. However, the specific behavioral mechanism in MUD patients and their underlying neurophysiological mechanisms remain unclear. This study tried to fill this gap. Methods This cross-sectional study enrolled 54 MUD patients and 27 healthy volunteers. Patients were divided into Mild (N = 27) and Moderate-Severe (N = 27) groups according to DSM-5 criteria. Participants completed a monetary probability reward task with electroencephalography recording. Reinforcement learning model was used to address behavioral deficits, and event-related potentials (ERPs), time-frequency analysis and source localization were applied to identify differential electrophysiological signals and brain regions. Results Compared with healthy participants, Moderate-Severe MUD patients exhibited inflexible reward-seeking behaviors correlated with greater loss of control over drug use. ERP analysis revealed an abnormal component in the left frontal lobe within the early 200ms overlapping in time with feedback-related negativity, and the theta power was greater in incongruent than in congruent conditions, driven by hyperactivation in caudate nucleus correlated with inflexible reward-seeking and loss of control over drug use. Conclusions This study demonstrates that MUD patients, depending on the severity of their condition, exhibit distinct behavioral and neurophysiological patterns in processing reward and punishment signals. Goal Narrowing may underlie the compulsive decision-making, manifesting as extreme reward pursuit and hyperactivation of caudate nucleus. Developing targeted interventions for abnormal behavioral and electrophysiological signals may be beneficial in future research. reinforcement learning reward processing electroencephalograph methamphetamine use disorder Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Amphetamine-type stimulants are the most widely used illicit drug globally after cannabis[ 1 ] and the most commonly abused drugs in China, causing huge public health consequences. Methamphetamine (MA) is a highly potent amphetamine derivative that affects the central nervous system[ 2 , 3 ], and prolonged exposure to MA results in profound neural alterations[ 4 , 5 ], progressively, impairing reward and punishment processing, cognitive control and behavioral inflexibility[ 6 ]. Over time, such impairment leads to a compulsive drug use[ 7 ], manifesting as persistent drug use despite serious negative consequences[ 8 ]. As MUD progresses, compulsive drug use becomes its core clinical feature[ 9 ], often attributed to compulsive decision-making related to drug-use events[ 10 , 11 ]. Previous research primarily attributed this maladaptive decision-making pattern to excessive habit formation[ 12 ]. Repeated experience of drug reward progressively strengthens the association between drug stimuli and the drug-related responses, leading to the triggering of drug-related choices by drug stimuli without value-based evaluation in decision-making[ 13 ]. As a result, when the drug loses its value and even causes negative consequences, drug-related cues can still elicit habitual choices, driving compulsive drug use[ 14 , 15 ]. However, recent studies suggest that excessive goal-directed selection can also contribute to compulsive decision-making[ 16 – 18 ]. When the anticipated value of drug-related goals is excessively amplified, decision-making rigidly narrows down to a single goal, neglecting alternative choices and long-term consequences[ 19 ]. As a result, exaggerated drug-related goals dominate the decision-making process, making it difficult for individuals to adjust their strategies despite recognizing potential adverse consequences, ultimately driving compulsive drug use. And studies have also identified distinct neural mechanisms, with habitual behavior linked to the posterior putamen, supplementary motor area, and premotor and motor cortices, while goal-directed behavior involves the ventromedial prefrontal cortex, anterior cingulate cortex (ACC), and caudate nucleus[ 19 ]. Evidences suggest that MUD patients exhibit an inflexible tendency toward value-seeking[ 20 ] and are less likely to engage in habitual choices[ 21 , 22 ]. However, no definitive conclusion has been made on the mechanisms underlying the compulsive decision-making pattern in MUD patients, creating barriers for developing effective interventions. Reinforcement learning (RL) theory provides a computational framework for modelling such decision-making patterns[ 23 ]. RL models the decision-making process by simulating interactions between agents and their environments, where learning is guided by feedback from rewards and punishments, guiding behavioral adjustments. The models summarize behavioral strategies using a few parameters[ 24 ], with models optimized to predict actual behavior[ 25 ]. The parameters of reinforcement learning models may serve as potential markers of compulsive decision-making patterns in MUD patients[ 26 ]. Electroencephalography (EEG) is a valuable tool for investigating neural processes with its superior temporal resolution, enabling the detection of event-related potential (ERP) components and frequency characteristics. By characterizing the neural representation of feedback signals, we can gain insights into the mechanisms underlying RL processes[ 27 ]. This study examines the decision-making patterns of MUD patients within the RL framework, and employs EEG to explore the underlying neural mechanisms, providing insights into their tendency of goal-directed and habitual control. We hypothesize that as MUD progressing to advanced stages, compulsive decision-making related to drug use is driven by goal narrowing, manifesting as abnormalities in reinforcement learning parameters and hyperactivation of brain regions associated with goal-directed behavior in EEG during the decision-making task. 2. Methods and Materials 2.1 Participants A total of fifty-four individuals with MUD were recruited from the Wuhan Drug Rehabilitation Centre, China to participate in our assessment (Fig. 1 A). Inclusion criteria were:(1) meeting DSM-5 criteria for MUD; (2) > 9 years of education and ability to complete assessments; (3) aged 18–49, with normal vision and hearing, and no medication use; (4) methamphetamine use for ≥ 1 year, at least weekly, with ≤ 6 months of abstinence. Twenty-seven healthy participants, matched for age and education, were recruited from the community of Wuhan. Exclusion criteria included:(1) serious physical or neurological disorders; (2) drug use history; (3) severe head injury; (4) psychiatric disorders; and (5) family history of psychiatric disorders. This study was approved by the ethics committee of Shanghai Mental Health Center, with all participants providing written informed consent. 2.2 Clinical measurement Demographic information was collected for all participants. For MUD patients, drug-use history included age of first use, duration of use, and abstinence length. Loss of control was assessed using the number of DSM-5 criteria met, excluding tolerance and withdrawal [ 28 ]. Patients meeting 2–3 criteria were classified as Mild, and those meeting ≥ 4 criteria as Moderate–Severe. 2.3 Behavioral Task Participants performed a monetary reinforcement learning task[ 29 ] programmed in PsychoPy during EEG recording (Fig. 1 B). The task comprised two sessions of 60 trials each. Each trial included fixation (500ms), stimulus presentation (4000ms), response indication (300ms), a jittered interval (500–1500ms), and feedback (1500ms). Stimulus pairs were associated with reward or punishment outcomes (± $ 10 or $ 0) with reciprocal probabilities (80/20%). Reward and punishment trials were randomly interleaved. Participants learned stimulus–outcome associations through trial and error to maximize payoffs. 2.4 Reinforcement learning model Eight reinforcement learning models were initially tested, and the Q-learning model was selected as the most appropriate (Table 1 in Supplement Material ). For each participant, the simple Q-learning algorithm calculates the expected reward value (Q) for an action (a) based on their choice and feedback history. The expected values are initialized to zero before learning, and the expected reward value for action a after trial t is, $$\:Qa\left(t+1\right)=Qa\left(t\right)+\alpha\:*d\left(t\right)$$ a is the learning rate ranging between 0 and 1 and d(t) is the difference between the actual and expected outcome, $$\:d\left(t\right)=R\left(t\right)-Qa\left(t\right)$$ The simple Q-learning model assumes that the subject learns the value of each action (a1 or a2) at the same learning rate. The probability of choosing each option in a trial is computed using the following softmax rule, 𝑠𝑜𝑓𝑡𝑚𝑎𝑥_𝑎1=𝑒𝑥𝑝(𝛽∗𝑄𝑎1(𝑡))/{𝑒𝑥𝑝[𝛽∗𝑄𝑎1(𝑡)]+𝑒𝑥𝑝[𝛽∗𝑄𝑎2(𝑡)]} Here, the parameter β represents the inverse temperature during the choice process. Lower β values suggest greater randomness in the choice process and lesser sensitivity to expected reward values. Conversely, higher β values suggest a higher propensity to choose the stimulus with a larger expected reward. The optimization algorithm for the parametric model was the L-BFGS-B algorithm[ 30 – 32 ]. Model selection was based on a combination of the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). 2.5 EEG Recording and Analysis EEG data were recorded in a quiet room using a 64-channel Ag/AgCl system with a NeuSen W32 amplifier (Neuracle, Changzhou, China), arranged according to the international 10–20 system (ground: AFz; reference: CPz). Signals were sampled at 1000 Hz with electrode impedance maintained below 5 kΩ. Preprocessing was performed in EEGLAB (MATLAB 2022b). Data were bandpass filtered (0.5–120 Hz), and line noise (50/100 Hz) was removed using Cleanline. Bad channels were identified using RANSAC and interpolated. Data were average-referenced, spike noise was attenuated with ASR, and artefacts were removed using ICALabel. Epochs were extracted from − 1000 to 2000 ms relative to feedback onset following optimal choices. Four feedback conditions were defined: congruent reward (80%), incongruent no-reward (20%) in reward trials, and congruent no-punishment (80%), incongruent punishment (20%) in punishment trials. ERP data were filtered at 1–30 Hz and baseline-corrected using a − 200 to 0 ms window.Time–frequency representations were computed using complex Morlet wavelets (1–50 Hz; 3–7 cycles; 50 ms step), with power baseline-corrected to the pre-feedback interval. Beamformer source analysis was conducted using a standard T1 template. Regions of interest were defined based on significant TFR effects, with cross-spectral density computed at 7 ± 3 Hz within 150–450 ms post-feedback. 2.6 Statistical analysis Statistical analyses were conducted using R (v4.4.1) and JASP (v0.19.1). Group differences in demographic and clinical variables were assessed using one-way ANOVA or Kruskal–Wallis tests with post hoc Dunn tests, as appropriate. Spearman correlations examined associations between variables. ERP and TFR analyses focused on the 0–800 ms post-feedback window, comparing incongruent versus congruent feedback separately for reward and punishment conditions. Group differences were assessed using independent-samples t-tests with cluster-based permutation correction (5000 permutations; cluster threshold p < 0.001; two-tailed p < 0.05). Mean values from significant clusters were extracted and compared across groups using ANOVA (p < 0.05). 3. Results 3.1 Demographic and Clinical evaluation A total of 81 right-handed male participants was involved in the study including a Healthy group (n = 27) and groups of MUD patients including a Mild group (n = 27) and a Moderate-Severe group (n = 27). No significant demographic differences were observed among the three groups ( Table 2 in Supplement Material ). 3.2 Choice behavior No significant differences were observed in the proportion of optimal choices across groups in either reward (p = 0.374) or punishment conditions (p = 0.966; Fig. 2 A), nor were there group differences in parameter α (p = 0.131). Given the skewed distribution of β, logarithmic transformation (lgβ) was applied. In reward conditions, the Moderate–Severe group showed significantly higher lgβ than the other groups (H = 6.796, p = 0.0334), with post hoc analysis revealing a higher lgβ compared with the Healthy group (Z = 2.442, p = 0.0292). No differences were found for the Mild group or in punishment conditions (Fig. 2 B). Spearman analysis revealed a significant positive correlation between β and loss of control in the Moderate–Severe group (r = 0.485, p = 0.0102; Fig. 2 C). 3.3 EEG ERP voltages were compared between incongruent and congruent feedback. Cluster-based permutation tests identified two significant clusters in early 200ms after feedback in reward conditions when comparing the Moderate-Severe group with the Healthy group (Fig. 3 A). A positive cluster of enhanced amplitudes was observed at four electrodes over the prefrontal and frontal electrodes (AF7, F7, FC5, FT7, cluster t = 329.340, p = 0.0196) ranging from 197-228ms, corresponding to the time range typically associated with the feedback-related negativity (FRN). And a negative cluster of reduced amplitudes was observed at four electrodes over the occipital and parietal electrodes (POz, PO3, PO4, Oz, cluster t = -561.361, p = 0.00280) ranging from 186-259ms (Fig. 3 B), in response to incongruent versus congruent feedback. No significant differences were observed between the Mild group and the Healthy group. Given that significant time points across individual electrodes ranged from 186 to 259ms, topographical maps of scalp voltage distributions were plotted for the 200–250ms in both congruent and incongruent conditions across all three groups (Fig. 3 C). Average voltage for each cluster across groups were extracted, with one-way ANOVA revealing significant differences between the Moderate-Severe group and the Healthy group for both the positive cluster (Mean Diff = 3.682, 95% CI [2.071, 5.292], p < 0.001) and the negative cluster (Mean Diff = -2.468, 95% CI [-3.947, -0.9891], p < 0.001) (Fig. 3 D). No difference was found in the punishment conditions. Time-frequency analysis was conducted to examine power differences between incongruent and congruent feedback across frequency bands. In reward conditions, cluster-based permutation testing identified three significant clusters showing increased power within the theta band (4–8 Hz) between 100–500ms after feedback in the Moderate-Severe group compared with the Healthy group (Fig. 4 A). The first cluster was localized in the occipital region (Cluster t = 403.166, p < 0.001), the second cluster was in the frontal and central region (Cluster t = 118.442, p = 0.0072), and the third in the frontal region (Cluster t = 68.594, p = 0.0236) (Fig. 4 B). Then we extracted the average power spectral density from each cluster for all participants (Fig. 4 C) and found the Moderate-Severe group exhibited significantly higher power compared with the Healthy group in all three clusters (Cluster1: Mean rank diff = 38.30, p < 0.0001; Cluster2: Mean rank difference = 29.96, p < 0.0001; Cluster3: Mean rank diff = 26.3, p < 0.0001). No significant difference was found between the Healthy group and the Mild group. No difference was observed in the punishment conditions. Source analysis showed that compared with the Healthy group, the increased frontal theta power in the Moderate-Severe group primarily originated from the enhanced activity (Cluster t = 18.061; p = 0.0026) of left caudate nucleus (MNI coordinates of peak voxels: x = -14, y = 5, z = 20 mm) and extending to surrounding regions in dorsal striatum, as well as the ACC, insula and thalamus ( Fig. 5A , Table 1 ). The power in the incongruent conditions within this cluster exhibited a significant positive correlation with both the β value (r = 0.330, p = 0.0464) and the loss of control over drug use (r = 0.353, p = 0.0356) in the Moderate-Severe group ( Fig. 5B ). Table 1 Source analysis of each significant cluster AAL Anatomic region Full name Fraction of non-zero voxels Insula_L 3001 Cingulum_Ant_L 4001 Cingulum_Ant_R 4002 Cingulum_Mid_L 4011 Cingulum_Mid_R 4012 Caudate_L 7001 Putamen_L 7011 Pallidum_L 7021 Thalamus_L 7101 Left Insula Left Anterior Cingulate Cortex Right Anterior Cingulate Cortex Left Middle Cingulate Cortex Right Middle Cingulate Cortex Left Caudate Nucleus Left Putamen Left Globus Pallidus Left Thalamus 0.006 0.091 < 0.001 0.138 0.002 0.522 0.257 0.143 0.024 Table 1 Source analysis of significant cluster related with behavior in TFR revealed differences between incongruent and congruent feedback in reward trials for the Moderate-Severe group when compared to the healthy group. 4. Discussion This study employed a monetary reinforcement learning task with concurrent EEG recording to investigate compulsive decision-making in MUD patients across different severity levels. Behavioural data were modelled using Q-learning, and neural responses were analyzed via ERP, TFR, and source localization. Moderate–Severe MUD patients exhibited a heightened drive to maximize reward, which positively correlated with loss of control over drug use. Abnormal FRN responses in the left frontal region and increased theta activity originating from the caudate nucleus may underlie these cognitive alterations. Our results reflected compulsive decision-making patterns to maximize reward in MUD patients and demonstrate correlations with drug-use events. Specifically, increased β was observed only in Moderate–Severe MUD patients, with no significant changes in α or task performance. This indicates preserved value learning but an exaggerated exploitative tendency during reward-based decisions, likely due to reduced decision noise or indeterminacy, resulting in rigid reward-guided behaviour. Importantly, greater β values were associated with more severe loss of control, linking compulsive decision-making to compulsive drug use. Consistent with prior evidence showing that neuromodulation-induced reductions in β alleviate compulsive symptoms without affecting α [ 33 ], our findings suggest that elevated β reflects a pathological bias towards reward maximization in MUD. Our whole-brain corrected ERP analysis revealed robust differences in the left frontal lobe within the first 200 ms following feedback, corresponding to the typical time window of the feedback-related negativity (FRN). The FRN reflects the difference between unexpected and expected outcomes(Holroyd and Krigolson, 2007) and is critical for reinforcement learning and behavioural adjustment [ 34 ]. The absence of this response in the Moderate–Severe MUD group suggests impaired reward processing and disrupted prediction error computation, leading to inefficient behavioural adjustment and persistent exploitation of reward contingencies. Consistent with this, abnormal FRN responses have also been reported in other substance use disorders, including heroin[ 35 ] and cocaine use disorders[ 36 ], indicating a shared neural mechanism across SUDs[ 37 ]. Our study also identified abnormalities in theta-band activity at frontal electrodes. Frontal theta activity has been thought to reflect sensitivity to events incongruent with expectations signaling the need for behavioral adjustment, and is linked to cognitive control[ 38 – 41 ]. Normal cognitive control plays a crucial role in maintaining the balance between the goal-directed and habitual systems in the brain[ 42 , 43 ], and deficits in cognitive control may result in the relative dominance of either system over behavior[ 6 , 19 ]. And evidence has suggested that elevated midfrontal theta activity may indicate enhanced goal-directed control over behavior[ 44 ]. Moreover, our source analysis revealed that the increased frontal theta power primarily originated from the caudate nucleus, with greater activation associated with more severe loss of control over drug use and a stronger exploitative tendency in behavior. The caudate nucleus, a component of the goal-directed system, contributes to behavior through the excitation of correct action schemas and the selection of appropriate sub-goals based on an evaluation of action-outcomes[ 45 ]. fMRI studies have linked increased caudate activation to goal-directed action control[ 46 ], and caudate nucleus inactivation has been shown to impair the ability to judge contingency changes in goal-directed behavior[ 47 ]. In our findings, the increased theta power originating from the caudate nucleus may be associated with hyperactivation in the goal-directed system, resulting in excessive goal-directed control over behavior. This may lead to extreme goal pursuit through goal narrowing via excessive ‘attractor network activity’[ 48 ], causing individuals to allocate excessive time and resources to obtain and use MA while neglecting other essential life goals. Therefore, the narrowing of goal options resulting from the hyperactivation of the goal-directed system may underlie the compulsive decision-making observed in MUD patients, associated with excessive reward-seeking behavior and the loss of control over drug use. Additionally, we observed altered activation in the ACC and insula, both of which belong to the interoception system. Disfunctions in this system could accelerate the imbalance between goal-directed and habit systems[ 19 ] and thus contribute to compulsive behavior[ 42 ]. Furthermore, previous research has identified EEG beta connectivity patterns as craving biomarkers in MUD[ 49 ], suggesting that abnormal neural signals observed in our study during decision-making may provide a reference for future diagnosis and interventions[ 50 ]. We also observed abnormal activity at occipital electrodes. Moderate-Severe patients exhibited reduced amplitudes in occipital electrodes in 200ms after feedback compared with the Healthy group. Occipital N2 has been associated with visual processing and attention[ 51 ]. The prefrontal cortex is known to exert top-down modulation on visual processing in posterior parieto-occipital regions[ 52 , 53 ], and early visual circuits may be shaped by cognitive control strategies through prefrontal-occipital connectivity[ 54 ]. Studies have shown that visual cortex activity is modulated by the value of stimuli in decision making[ 55 ], and learned stimulus-reward associations can involuntarily drive attention allocation[ 56 , 57 ]. Therefore, the abnormal occipital N2 in MUD may reflect maladaptive attentional biases resulting from excessive interference of reward stimuli with normal cognitive processes. However, we cannot exclude the possibility that the N2 in our study may represent a novel ERP component related to decision-making processes, warranting further investigation in future studies. Additionally, we observed abnormal theta activity in occipital electrodes in MUD patients. Previous studies have reported visuospatial processing deficits associated with abnormal theta activity in occipital electrodes among cannabis and alcohol use disorder patients[ 58 – 60 ]. These findings suggest that attentional dysfunction may be a common feature across SUDs. Furthermore, our results mirror the opposite effects of sulpiride, a selective dopamine antagonist[ 61 ] that reduce exploitation by random choice in reward conditions. Previous studies have suggested that reinforcement learning dysfunction may associated with dopamine-dependent modulation of striatal activity[ 62 – 64 ], and increased dopamine levels promote goal-directed choice[ 65 ]. MA promotes dopamine release while inhibiting reuptake, leading to excessive dopamine activity, whereas sulpiride blocks D2 receptors, reducing dopaminergic signaling[ 66 , 67 ]. These opposing behavioural effects likely reflect their distinct influences on mesolimbic dopamine function, providing novel insights into the neurobiochemical mechanisms underlying compulsive decision-making in MUD. We observed group differences only in the Moderate–Severe MUD group under reward conditions. No impairments were detected in the Mild MUD group, whose behavioural and electrophysiological measures lay between those of the Healthy and Moderate–Severe groups. This pattern is consistent with the view that addictive behaviours exist along a continuum[ 68 ] and that compulsive drug use emerges following prolonged substance exposure[ 69 ]. As reward-processing impairments may not arise until later stages of MUD, early intervention for Mild MUD patients may be particularly important. Differences were observed exclusively under reward conditions, suggesting altered sensitivity to reward rather than punishment in MUD. This aligns with evidence that methamphetamine primarily affects the mesolimbic dopamine system, which preferentially encodes rewarding outcomes[ 70 ], although asymmetric dopaminergic signalling may also influence cortical sensitivity to reward and punishment[ 71 , 72 ]. Several limitations should be noted. All patients were assessed after 3–6 months of abstinence, yet deficits persisted, consistent with animal evidence of long-lasting reward dysfunction[ 73 ]. Second, as our study focused exclusively on MUD, findings may vary across other types of SUDs, warranting further comparative research. For instance, in patients with alcohol use disorder, they exhibit overreliance on habit learning and brain regions associated with habit learning show increased engagement[ 74 ]. In summary, compulsive decision-making in Moderate–Severe MUD appears to be driven by goal narrowing, reflected as excessive reward pursuit and as hyperactivation of the caudate nucleus. These findings advance understanding of reward-processing dysfunction in MUD and future research should explore more refined intervention strategies, considering the severity of the disorder and individual differences in reward and punishment sensitivity. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and all procedures involving human subjects were approved by the ethics committee of Shanghai Mental Health Center (SMHC2023-27). Consent for publication Informed consent was obtained from all the subjects involved in the study. Funding This work was supported by STI2030-Major Projects(2021ZD0202105), Shanghai Oriental Talent Program - Elite Project (BJWS2024016), the National Natural Science Foundation of China (82130041, 82171483, 82201650, 82471510, 82130041, 32441102), National Key R&D Program of China (2024YFF0507604), Shanghai Shenkang Hospital Development Center (SHDC2020CR3045B), Shanghai Rising-star Cultivation Program (22YF1439200), Capability Promotion Project for Research-oriented Doctor at SMHC (2021-YJXYS-01), Research Project of National Center for Mental Health of China (XS24B072), Shanghai Municipal Health Commission Talent Project (2022YQ048), Shanghai Mental Health Center Flying Talent Project (2022-FX-01), and Shanghai Municipal Education Commission (2024AIZD014). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contribution Shiyan Qu and Lei Guo contributed to the conceptualisation and drafting of the manuscript. Gang Wang, Xiandong Lin, and Wei Shao were responsible for patient recruitment. Ru-yuan Zhang and Zeming Fang assisted in computational modelling methodology. Li Liu, Chuanning Huang, Yue Wang, Zijing Wang, and Jing Tian conducted the experimental trials and EEG data acquisition. Tianzhen Chen and Hang Su contributed to methodological development. Min Zhao, Trevor W. Robbins, and Haifeng Jiang provided supervision, critical review, and manuscript editing. All authors reviewed and approved the final manuscript for submission. Acknowledgements Thank all the participants for their contribution in this study Clinical trial number Not applicable. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Stoneberg DM, Shukla RK, Magness MB. 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08:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8549153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8549153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101273326,"identity":"b953041f-afed-4113-9cc8-269ab3fd8d83","added_by":"auto","created_at":"2026-01-28 03:04:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155390,"visible":true,"origin":"","legend":"\u003cp\u003eMonetary reinforcement learning task and task performance.\u003c/p\u003e\n\u003cp\u003eA. Participants chose one of two abstract visual stimuli and later observed the outcome. In reward trials, one stimulus was associated with an 80% probability of reward 10$ and a 20% probability of no reward, and the other stimulus had the opposite probability structure. In punishment trials, one stimulus was associated with an 80% probability of loss 10$ and a 20% probability of no loss, and the other stimulus had the opposite probability structure.\u003c/p\u003e\n\u003cp\u003eB. There was no significant difference in the proportion of optimal choices between groups in the reward session (\u003cem\u003ep\u003c/em\u003e = 0.374) or punishment trials (\u003cem\u003ep\u003c/em\u003e= 0.966, plotted in reversed orientation).\u003c/p\u003e\n\u003cp\u003eC. The Moderate-Severe group showed a significant difference in lgβ compared with the other two groups (H = 6.796, \u003cem\u003ep\u003c/em\u003e = 0.0334), with lgβ values being higher in the Moderate-Severe group than in the healthy group (Z = 2.442, \u003cem\u003ep\u003c/em\u003e = 0.0292). No significant difference in lgβwas observed in the punishment session (\u003cem\u003ep\u003c/em\u003e = 0.696).\u003c/p\u003e\n\u003cp\u003eD. Spearman correlation analysis in the Moderate-Severe group revealed a positive correlation between the βvalue in the reward session and the loss of control over drug use (measured by the number of DSM-5 symptoms excluding tolerance and withdrawal; r = 0.485, \u003cem\u003ep\u003c/em\u003e= 0.0103, n = 27).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/714264612a04984be68e1058.png"},{"id":101297629,"identity":"715dacc9-407d-4cd8-a3c2-fdeff91845be","added_by":"auto","created_at":"2026-01-28 09:28:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156330,"visible":true,"origin":"","legend":"\u003cp\u003eCluster-based statistical analysis of ERP.\u003c/p\u003e\n\u003cp\u003eA. There were two significant cluster in early 200ms after feedback in reward session between Moderate-Severe MA group and HCs\u003c/p\u003e\n\u003cp\u003eB. There was a positive cluster in the left frontal region (i.e., AF7, F7, FT7, FC5. cluster t=329.340, \u003cem\u003ep\u003c/em\u003e = 0.0196) and a negative cluster in the occipital region (i.e., PO3, PO4, POZ, OZ. cluster t= -561.361, \u003cem\u003ep\u003c/em\u003e = 0.0028).\u003c/p\u003e\n\u003cp\u003eC. The topographic maps of ERP activity in the 200–250ms window for all three groups under congruent and incongruent conditions.\u003c/p\u003e\n\u003cp\u003eD. The average values of the difference between of ERP voltage congruent and incongruent conditions in Moderate-Severe MA group and HCs were extracted and performed one-way ANOVA. The results showed that there was significant difference between moderate-severe MA group and HCs in positive (Mean Diff=3.682, with 95% IC [2.071, 5.292], \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and negative cluster (Mean Diff=-2.468, with 95% IC [-3.947, -0.9891], \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001), and no difference between mild MA group and HCs.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/359ecbbeffeb7a4592a85482.png"},{"id":101273330,"identity":"4bdf966d-f9c7-45fe-a5c2-3b0adca97a3e","added_by":"auto","created_at":"2026-01-28 03:04:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280211,"visible":true,"origin":"","legend":"\u003cp\u003eCluster-based statistical analysis of TFR.\u003c/p\u003e\n\u003cp\u003eA. There were three positive clusters of increased power within the theta band (4–8 Hz) between 100–500ms after feedback onset in Moderate-Severe patients compared to HCs in the occipital and frontal areas under the incongruent condition in the reward session.\u003c/p\u003e\n\u003cp\u003eB. The first cluster was localized in the occipital region (Cluster t = 403.166, p \u0026lt; 0.001), the second in the frontal-central region (Cluster t = 118.442, p = 0.0072), and the third in the frontal region (Cluster t = 68.594, p = 0.0236).\u003c/p\u003e\n\u003cp\u003eC. Mean power spectral density within significant range was higher in the Moderate-Severe group compared to the HC group (Cluster1: Mean rank diff =38.30, p \u0026lt; 0.0001; Cluster2: Mean rank diff =29.96, p \u0026lt; 0.0001; Cluster3: Mean rank diff =26.3, p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/2d2e43e3411a0eec414b4246.png"},{"id":101273327,"identity":"d4abb166-ce8d-436c-b6bb-cbeeda351e29","added_by":"auto","created_at":"2026-01-28 03:04:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":246553,"visible":true,"origin":"","legend":"\u003cp\u003eSource analysis and the correlation with behavior.\u003c/p\u003e\n\u003cp\u003eA. A significant positive cluster correlated with behavior was identified, with peak activation at x = -14, y = 5, z = 20 mm. The Moderate-Severe group exhibited greater activation than the HC group in this cluster (Cluster t = 18.061; p = 0.0026). The cluster was centered in the left caudate nucleus and extended to other regions of the dorsal striatum, as well as the anterior cingulate cortex (ACC) and insula.\u003c/p\u003e\n\u003cp\u003eB. Correlation analysis revealed a significant positive correlation between the activation degree of this cluster in the incongruent condition and the \u003cem\u003eβ\u003c/em\u003e value (r=0.330, p=0.0464) as well as the loss of control (r=0.353, p=0.0356) in the Moderate-Severe group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/0df1f0aab21bc50ec1080670.png"},{"id":101297918,"identity":"a20ab6f6-b602-4d10-b2a9-78015789e4f8","added_by":"auto","created_at":"2026-01-28 09:29:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":249319,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/6e38c3651d5a728dd5008e20.png"},{"id":101299410,"identity":"5af36fb6-1b3d-4caa-b045-69045d488e5e","added_by":"auto","created_at":"2026-01-28 09:41:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1818399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/93418aff-ad78-415f-b315-30100fdeba7e.pdf"},{"id":101273329,"identity":"c3a49121-6ffc-43b6-901f-0c39c85bc9fe","added_by":"auto","created_at":"2026-01-28 03:04:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26479,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8549153/v1/9656cb93b0495dd58536ba05.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Goal Narrowing Drives Compulsive Decision Making in Patients with Methamphetamine Use Disorder: A Cross-Sectional Study of Neural and Behavioral Correlates","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmphetamine-type stimulants are the most widely used illicit drug globally after cannabis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and the most commonly abused drugs in China, causing huge public health consequences. Methamphetamine (MA) is a highly potent amphetamine derivative that affects the central nervous system[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and prolonged exposure to MA results in profound neural alterations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], progressively, impairing reward and punishment processing, cognitive control and behavioral inflexibility[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Over time, such impairment leads to a compulsive drug use[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], manifesting as persistent drug use despite serious negative consequences[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs MUD progresses, compulsive drug use becomes its core clinical feature[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], often attributed to compulsive decision-making related to drug-use events[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous research primarily attributed this maladaptive decision-making pattern to excessive habit formation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Repeated experience of drug reward progressively strengthens the association between drug stimuli and the drug-related responses, leading to the triggering of drug-related choices by drug stimuli without value-based evaluation in decision-making[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a result, when the drug loses its value and even causes negative consequences, drug-related cues can still elicit habitual choices, driving compulsive drug use[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, recent studies suggest that excessive goal-directed selection can also contribute to compulsive decision-making[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. When the anticipated value of drug-related goals is excessively amplified, decision-making rigidly narrows down to a single goal, neglecting alternative choices and long-term consequences[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As a result, exaggerated drug-related goals dominate the decision-making process, making it difficult for individuals to adjust their strategies despite recognizing potential adverse consequences, ultimately driving compulsive drug use. And studies have also identified distinct neural mechanisms, with habitual behavior linked to the posterior putamen, supplementary motor area, and premotor and motor cortices, while goal-directed behavior involves the ventromedial prefrontal cortex, anterior cingulate cortex (ACC), and caudate nucleus[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Evidences suggest that MUD patients exhibit an inflexible tendency toward value-seeking[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and are less likely to engage in habitual choices[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, no definitive conclusion has been made on the mechanisms underlying the compulsive decision-making pattern in MUD patients, creating barriers for developing effective interventions.\u003c/p\u003e \u003cp\u003eReinforcement learning (RL) theory provides a computational framework for modelling such decision-making patterns[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. RL models the decision-making process by simulating interactions between agents and their environments, where learning is guided by feedback from rewards and punishments, guiding behavioral adjustments. The models summarize behavioral strategies using a few parameters[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with models optimized to predict actual behavior[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The parameters of reinforcement learning models may serve as potential markers of compulsive decision-making patterns in MUD patients[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Electroencephalography (EEG) is a valuable tool for investigating neural processes with its superior temporal resolution, enabling the detection of event-related potential (ERP) components and frequency characteristics. By characterizing the neural representation of feedback signals, we can gain insights into the mechanisms underlying RL processes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examines the decision-making patterns of MUD patients within the RL framework, and employs EEG to explore the underlying neural mechanisms, providing insights into their tendency of goal-directed and habitual control. We hypothesize that as MUD progressing to advanced stages, compulsive decision-making related to drug use is driven by goal narrowing, manifesting as abnormalities in reinforcement learning parameters and hyperactivation of brain regions associated with goal-directed behavior in EEG during the decision-making task.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003e A total of fifty-four individuals with MUD were recruited from the Wuhan Drug Rehabilitation Centre, China to participate in our assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Inclusion criteria were:(1) meeting DSM-5 criteria for MUD; (2)\u0026thinsp;\u0026gt;\u0026thinsp;9 years of education and ability to complete assessments; (3) aged 18\u0026ndash;49, with normal vision and hearing, and no medication use; (4) methamphetamine use for \u0026ge;\u0026thinsp;1 year, at least weekly, with \u0026le;\u0026thinsp;6 months of abstinence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwenty-seven healthy participants, matched for age and education, were recruited from the community of Wuhan. Exclusion criteria included:(1) serious physical or neurological disorders; (2) drug use history; (3) severe head injury; (4) psychiatric disorders; and (5) family history of psychiatric disorders.\u003c/p\u003e \u003cp\u003e This study was approved by the ethics committee of Shanghai Mental Health Center, with all participants providing written informed consent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical measurement\u003c/h2\u003e \u003cp\u003eDemographic information was collected for all participants. For MUD patients, drug-use history included age of first use, duration of use, and abstinence length. Loss of control was assessed using the number of DSM-5 criteria met, excluding tolerance and withdrawal [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Patients meeting 2\u0026ndash;3 criteria were classified as Mild, and those meeting\u0026thinsp;\u0026ge;\u0026thinsp;4 criteria as Moderate\u0026ndash;Severe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Behavioral Task\u003c/h2\u003e \u003cp\u003eParticipants performed a monetary reinforcement learning task[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] programmed in PsychoPy during EEG recording (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The task comprised two sessions of 60 trials each. Each trial included fixation (500ms), stimulus presentation (4000ms), response indication (300ms), a jittered interval (500\u0026ndash;1500ms), and feedback (1500ms). Stimulus pairs were associated with reward or punishment outcomes (\u0026plusmn;\u003cspan\u003e$\u003c/span\u003e10 or \u003cspan\u003e$\u003c/span\u003e0) with reciprocal probabilities (80/20%). Reward and punishment trials were randomly interleaved. Participants learned stimulus\u0026ndash;outcome associations through trial and error to maximize payoffs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Reinforcement learning model\u003c/h2\u003e \u003cp\u003eEight reinforcement learning models were initially tested, and the Q-learning model was selected as the most appropriate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ein Supplement Material\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor each participant, the simple Q-learning algorithm calculates the expected reward value (Q) for an action (a) based on their choice and feedback history. The expected values are initialized to zero before learning, and the expected reward value for action \u003cem\u003ea\u003c/em\u003e after trial t is,\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Qa\\left(t+1\\right)=Qa\\left(t\\right)+\\alpha\\:*d\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ea\u003c/em\u003e is the learning rate ranging between 0 and 1 and d(t) is the difference between the actual and expected outcome,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:d\\left(t\\right)=R\\left(t\\right)-Qa\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe simple Q-learning model assumes that the subject learns the value of each action (a1 or a2) at the same learning rate. The probability of choosing each option in a trial is computed using the following softmax rule,\u003c/p\u003e \u003cp\u003e\u0026#119904;\u0026#119900;\u0026#119891;\u0026#119905;\u0026#119898;\u0026#119886;\u0026#119909;_\u0026#119886;1=\u0026#119890;\u0026#119909;\u0026#119901;(\u0026#120573;\u0026lowast;\u0026#119876;\u0026#119886;1(\u0026#119905;))/{\u0026#119890;\u0026#119909;\u0026#119901;[\u0026#120573;\u0026lowast;\u0026#119876;\u0026#119886;1(\u0026#119905;)]+\u0026#119890;\u0026#119909;\u0026#119901;[\u0026#120573;\u0026lowast;\u0026#119876;\u0026#119886;2(\u0026#119905;)]}\u003c/p\u003e \u003cp\u003eHere, the parameter \u003cem\u003eβ\u003c/em\u003e represents the inverse temperature during the choice process. Lower \u003cem\u003eβ\u003c/em\u003e values suggest greater randomness in the choice process and lesser sensitivity to expected reward values. Conversely, higher \u003cem\u003eβ\u003c/em\u003e values suggest a higher propensity to choose the stimulus with a larger expected reward.\u003c/p\u003e \u003cp\u003eThe optimization algorithm for the parametric model was the L-BFGS-B algorithm[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Model selection was based on a combination of the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 EEG Recording and Analysis\u003c/h2\u003e \u003cp\u003eEEG data were recorded in a quiet room using a 64-channel Ag/AgCl system with a NeuSen W32 amplifier (Neuracle, Changzhou, China), arranged according to the international 10\u0026ndash;20 system (ground: AFz; reference: CPz). Signals were sampled at 1000 Hz with electrode impedance maintained below 5 kΩ.\u003c/p\u003e \u003cp\u003ePreprocessing was performed in EEGLAB (MATLAB 2022b). Data were bandpass filtered (0.5\u0026ndash;120 Hz), and line noise (50/100 Hz) was removed using Cleanline. Bad channels were identified using RANSAC and interpolated. Data were average-referenced, spike noise was attenuated with ASR, and artefacts were removed using ICALabel.\u003c/p\u003e \u003cp\u003eEpochs were extracted from \u0026minus;\u0026thinsp;1000 to 2000 ms relative to feedback onset following optimal choices. Four feedback conditions were defined: congruent reward (80%), incongruent no-reward (20%) in reward trials, and congruent no-punishment (80%), incongruent punishment (20%) in punishment trials.\u003c/p\u003e \u003cp\u003eERP data were filtered at 1\u0026ndash;30 Hz and baseline-corrected using a \u0026minus;\u0026thinsp;200 to 0 ms window.Time\u0026ndash;frequency representations were computed using complex Morlet wavelets (1\u0026ndash;50 Hz; 3\u0026ndash;7 cycles; 50 ms step), with power baseline-corrected to the pre-feedback interval. Beamformer source analysis was conducted using a standard T1 template. Regions of interest were defined based on significant TFR effects, with cross-spectral density computed at 7\u0026thinsp;\u0026plusmn;\u0026thinsp;3 Hz within 150\u0026ndash;450 ms post-feedback.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R (v4.4.1) and JASP (v0.19.1). Group differences in demographic and clinical variables were assessed using one-way ANOVA or Kruskal\u0026ndash;Wallis tests with post hoc Dunn tests, as appropriate. Spearman correlations examined associations between variables.\u003c/p\u003e \u003cp\u003eERP and TFR analyses focused on the 0\u0026ndash;800 ms post-feedback window, comparing incongruent versus congruent feedback separately for reward and punishment conditions. Group differences were assessed using independent-samples t-tests with cluster-based permutation correction (5000 permutations; cluster threshold p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mean values from significant clusters were extracted and compared across groups using ANOVA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and Clinical evaluation\u003c/h2\u003e \u003cp\u003eA total of 81 right-handed male participants was involved in the study including a Healthy group (n\u0026thinsp;=\u0026thinsp;27) and groups of MUD patients including a Mild group (n\u0026thinsp;=\u0026thinsp;27) and a Moderate-Severe group (n\u0026thinsp;=\u0026thinsp;27). No significant demographic differences were observed among the three groups (\u003cb\u003eTable\u0026nbsp;2 in Supplement Material\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Choice behavior\u003c/h2\u003e \u003cp\u003eNo significant differences were observed in the proportion of optimal choices across groups in either reward (p\u0026thinsp;=\u0026thinsp;0.374) or punishment conditions (p\u0026thinsp;=\u0026thinsp;0.966; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), nor were there group differences in parameter α (p\u0026thinsp;=\u0026thinsp;0.131). Given the skewed distribution of β, logarithmic transformation (lgβ) was applied. In reward conditions, the Moderate\u0026ndash;Severe group showed significantly higher lgβ than the other groups (H\u0026thinsp;=\u0026thinsp;6.796, p\u0026thinsp;=\u0026thinsp;0.0334), with post hoc analysis revealing a higher lgβ compared with the Healthy group (Z\u0026thinsp;=\u0026thinsp;2.442, p\u0026thinsp;=\u0026thinsp;0.0292). No differences were found for the Mild group or in punishment conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman analysis revealed a significant positive correlation between β and loss of control in the Moderate\u0026ndash;Severe group (r\u0026thinsp;=\u0026thinsp;0.485, p\u0026thinsp;=\u0026thinsp;0.0102; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 EEG\u003c/h2\u003e \u003cp\u003eERP voltages were compared between incongruent and congruent feedback. Cluster-based permutation tests identified two significant clusters in early 200ms after feedback in reward conditions when comparing the Moderate-Severe group with the Healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A positive cluster of enhanced amplitudes was observed at four electrodes over the prefrontal and frontal electrodes (AF7, F7, FC5, FT7, cluster t\u0026thinsp;=\u0026thinsp;329.340, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0196) ranging from 197-228ms, corresponding to the time range typically associated with the feedback-related negativity (FRN). And a negative cluster of reduced amplitudes was observed at four electrodes over the occipital and parietal electrodes (POz, PO3, PO4, Oz, cluster t = -561.361, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00280) ranging from 186-259ms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), in response to incongruent versus congruent feedback. No significant differences were observed between the Mild group and the Healthy group. Given that significant time points across individual electrodes ranged from 186 to 259ms, topographical maps of scalp voltage distributions were plotted for the 200\u0026ndash;250ms in both congruent and incongruent conditions across all three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Average voltage for each cluster across groups were extracted, with one-way ANOVA revealing significant differences between the Moderate-Severe group and the Healthy group for both the positive cluster (Mean Diff\u0026thinsp;=\u0026thinsp;3.682, 95% CI [2.071, 5.292], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the negative cluster (Mean Diff = -2.468, 95% CI [-3.947, -0.9891], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). No difference was found in the punishment conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTime-frequency analysis was conducted to examine power differences between incongruent and congruent feedback across frequency bands. In reward conditions, cluster-based permutation testing identified three significant clusters showing increased power within the theta band (4\u0026ndash;8 Hz) between 100\u0026ndash;500ms after feedback in the Moderate-Severe group compared with the Healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The first cluster was localized in the occipital region (Cluster t\u0026thinsp;=\u0026thinsp;403.166, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the second cluster was in the frontal and central region (Cluster t\u0026thinsp;=\u0026thinsp;118.442, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0072), and the third in the frontal region (Cluster t\u0026thinsp;=\u0026thinsp;68.594, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0236) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Then we extracted the average power spectral density from each cluster for all participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and found the Moderate-Severe group exhibited significantly higher power compared with the Healthy group in all three clusters (Cluster1: Mean rank diff\u0026thinsp;=\u0026thinsp;38.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Cluster2: Mean rank difference\u0026thinsp;=\u0026thinsp;29.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Cluster3: Mean rank diff\u0026thinsp;=\u0026thinsp;26.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). No significant difference was found between the Healthy group and the Mild group. No difference was observed in the punishment conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource analysis showed that compared with the Healthy group, the increased frontal theta power in the Moderate-Severe group primarily originated from the enhanced activity (Cluster t\u0026thinsp;=\u0026thinsp;18.061; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0026) of left caudate nucleus (MNI coordinates of peak voxels: x = -14, y\u0026thinsp;=\u0026thinsp;5, z\u0026thinsp;=\u0026thinsp;20 mm) and extending to surrounding regions in dorsal striatum, as well as the ACC, insula and thalamus (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The power in the incongruent conditions within this cluster exhibited a significant positive correlation with both the \u003cem\u003eβ\u003c/em\u003e value (r\u0026thinsp;=\u0026thinsp;0.330, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0464) and the loss of control over drug use (r\u0026thinsp;=\u0026thinsp;0.353, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0356) in the Moderate-Severe group (\u003cb\u003eFig.\u0026nbsp;5B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSource analysis of each significant cluster\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAL\u003c/p\u003e \u003cp\u003eAnatomic region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFraction of\u003c/p\u003e \u003cp\u003enon-zero voxels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsula_L 3001\u003c/p\u003e \u003cp\u003eCingulum_Ant_L 4001\u003c/p\u003e \u003cp\u003eCingulum_Ant_R 4002\u003c/p\u003e \u003cp\u003eCingulum_Mid_L 4011\u003c/p\u003e \u003cp\u003eCingulum_Mid_R 4012\u003c/p\u003e \u003cp\u003eCaudate_L 7001\u003c/p\u003e \u003cp\u003ePutamen_L 7011\u003c/p\u003e \u003cp\u003ePallidum_L 7021\u003c/p\u003e \u003cp\u003eThalamus_L 7101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft Insula\u003c/p\u003e \u003cp\u003eLeft Anterior Cingulate Cortex\u003c/p\u003e \u003cp\u003eRight Anterior Cingulate Cortex\u003c/p\u003e \u003cp\u003eLeft Middle Cingulate Cortex\u003c/p\u003e \u003cp\u003eRight Middle Cingulate Cortex\u003c/p\u003e \u003cp\u003eLeft Caudate Nucleus\u003c/p\u003e \u003cp\u003eLeft Putamen\u003c/p\u003e \u003cp\u003eLeft Globus Pallidus\u003c/p\u003e \u003cp\u003eLeft Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003cp\u003e0.091\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.138\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e0.522\u003c/p\u003e \u003cp\u003e0.257\u003c/p\u003e \u003cp\u003e0.143\u003c/p\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eTable 1 Source analysis of significant cluster related with behavior in TFR revealed differences between incongruent and congruent feedback in reward trials for the Moderate-Severe group when compared to the healthy group.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed a monetary reinforcement learning task with concurrent EEG recording to investigate compulsive decision-making in MUD patients across different severity levels. Behavioural data were modelled using Q-learning, and neural responses were analyzed via ERP, TFR, and source localization. Moderate\u0026ndash;Severe MUD patients exhibited a heightened drive to maximize reward, which positively correlated with loss of control over drug use. Abnormal FRN responses in the left frontal region and increased theta activity originating from the caudate nucleus may underlie these cognitive alterations.\u003c/p\u003e \u003cp\u003eOur results reflected compulsive decision-making patterns to maximize reward in MUD patients and demonstrate correlations with drug-use events. Specifically, increased β was observed only in Moderate\u0026ndash;Severe MUD patients, with no significant changes in α or task performance. This indicates preserved value learning but an exaggerated exploitative tendency during reward-based decisions, likely due to reduced decision noise or indeterminacy, resulting in rigid reward-guided behaviour. Importantly, greater β values were associated with more severe loss of control, linking compulsive decision-making to compulsive drug use. Consistent with prior evidence showing that neuromodulation-induced reductions in β alleviate compulsive symptoms without affecting α [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], our findings suggest that elevated β reflects a pathological bias towards reward maximization in MUD.\u003c/p\u003e \u003cp\u003eOur whole-brain corrected ERP analysis revealed robust differences in the left frontal lobe within the first 200 ms following feedback, corresponding to the typical time window of the feedback-related negativity (FRN). The FRN reflects the difference between unexpected and expected outcomes(Holroyd and Krigolson, 2007) and is critical for reinforcement learning and behavioural adjustment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The absence of this response in the Moderate\u0026ndash;Severe MUD group suggests impaired reward processing and disrupted prediction error computation, leading to inefficient behavioural adjustment and persistent exploitation of reward contingencies. Consistent with this, abnormal FRN responses have also been reported in other substance use disorders, including heroin[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and cocaine use disorders[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], indicating a shared neural mechanism across SUDs[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also identified abnormalities in theta-band activity at frontal electrodes. Frontal theta activity has been thought to reflect sensitivity to events incongruent with expectations signaling the need for behavioral adjustment, and is linked to cognitive control[\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Normal cognitive control plays a crucial role in maintaining the balance between the goal-directed and habitual systems in the brain[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and deficits in cognitive control may result in the relative dominance of either system over behavior[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. And evidence has suggested that elevated midfrontal theta activity may indicate enhanced goal-directed control over behavior[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, our source analysis revealed that the increased frontal theta power primarily originated from the caudate nucleus, with greater activation associated with more severe loss of control over drug use and a stronger exploitative tendency in behavior. The caudate nucleus, a component of the goal-directed system, contributes to behavior through the excitation of correct action schemas and the selection of appropriate sub-goals based on an evaluation of action-outcomes[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. fMRI studies have linked increased caudate activation to goal-directed action control[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and caudate nucleus inactivation has been shown to impair the ability to judge contingency changes in goal-directed behavior[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In our findings, the increased theta power originating from the caudate nucleus may be associated with hyperactivation in the goal-directed system, resulting in excessive goal-directed control over behavior. This may lead to extreme goal pursuit through goal narrowing via excessive \u0026lsquo;attractor network activity\u0026rsquo;[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], causing individuals to allocate excessive time and resources to obtain and use MA while neglecting other essential life goals. Therefore, the narrowing of goal options resulting from the hyperactivation of the goal-directed system may underlie the compulsive decision-making observed in MUD patients, associated with excessive reward-seeking behavior and the loss of control over drug use. Additionally, we observed altered activation in the ACC and insula, both of which belong to the interoception system. Disfunctions in this system could accelerate the imbalance between goal-directed and habit systems[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and thus contribute to compulsive behavior[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, previous research has identified EEG beta connectivity patterns as craving biomarkers in MUD[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], suggesting that abnormal neural signals observed in our study during decision-making may provide a reference for future diagnosis and interventions[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also observed abnormal activity at occipital electrodes. Moderate-Severe patients exhibited reduced amplitudes in occipital electrodes in 200ms after feedback compared with the Healthy group. Occipital N2 has been associated with visual processing and attention[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The prefrontal cortex is known to exert top-down modulation on visual processing in posterior parieto-occipital regions[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and early visual circuits may be shaped by cognitive control strategies through prefrontal-occipital connectivity[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Studies have shown that visual cortex activity is modulated by the value of stimuli in decision making[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and learned stimulus-reward associations can involuntarily drive attention allocation[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Therefore, the abnormal occipital N2 in MUD may reflect maladaptive attentional biases resulting from excessive interference of reward stimuli with normal cognitive processes. However, we cannot exclude the possibility that the N2 in our study may represent a novel ERP component related to decision-making processes, warranting further investigation in future studies. Additionally, we observed abnormal theta activity in occipital electrodes in MUD patients. Previous studies have reported visuospatial processing deficits associated with abnormal theta activity in occipital electrodes among cannabis and alcohol use disorder patients[\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These findings suggest that attentional dysfunction may be a common feature across SUDs.\u003c/p\u003e \u003cp\u003eFurthermore, our results mirror the opposite effects of sulpiride, a selective dopamine antagonist[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] that reduce exploitation by random choice in reward conditions. Previous studies have suggested that reinforcement learning dysfunction may associated with dopamine-dependent modulation of striatal activity[\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and increased dopamine levels promote goal-directed choice[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. MA promotes dopamine release while inhibiting reuptake, leading to excessive dopamine activity, whereas sulpiride blocks D2 receptors, reducing dopaminergic signaling[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. These opposing behavioural effects likely reflect their distinct influences on mesolimbic dopamine function, providing novel insights into the neurobiochemical mechanisms underlying compulsive decision-making in MUD.\u003c/p\u003e \u003cp\u003eWe observed group differences only in the Moderate\u0026ndash;Severe MUD group under reward conditions. No impairments were detected in the Mild MUD group, whose behavioural and electrophysiological measures lay between those of the Healthy and Moderate\u0026ndash;Severe groups. This pattern is consistent with the view that addictive behaviours exist along a continuum[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and that compulsive drug use emerges following prolonged substance exposure[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. As reward-processing impairments may not arise until later stages of MUD, early intervention for Mild MUD patients may be particularly important. Differences were observed exclusively under reward conditions, suggesting altered sensitivity to reward rather than punishment in MUD. This aligns with evidence that methamphetamine primarily affects the mesolimbic dopamine system, which preferentially encodes rewarding outcomes[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], although asymmetric dopaminergic signalling may also influence cortical sensitivity to reward and punishment[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. All patients were assessed after 3\u0026ndash;6 months of abstinence, yet deficits persisted, consistent with animal evidence of long-lasting reward dysfunction[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Second, as our study focused exclusively on MUD, findings may vary across other types of SUDs, warranting further comparative research. For instance, in patients with alcohol use disorder, they exhibit overreliance on habit learning and brain regions associated with habit learning show increased engagement[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, compulsive decision-making in Moderate\u0026ndash;Severe MUD appears to be driven by goal narrowing, reflected as excessive reward pursuit and as hyperactivation of the caudate nucleus. These findings advance understanding of reward-processing dysfunction in MUD and future research should explore more refined intervention strategies, considering the severity of the disorder and individual differences in reward and punishment sensitivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and all procedures involving human subjects were approved by the ethics committee of Shanghai Mental Health Center (SMHC2023-27).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all the subjects involved in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by STI2030-Major Projects(2021ZD0202105), Shanghai Oriental Talent Program - Elite Project (BJWS2024016), the National Natural Science Foundation of China (82130041, 82171483, 82201650, 82471510, 82130041, 32441102), National Key R\u0026amp;D Program of China (2024YFF0507604), Shanghai Shenkang Hospital Development Center (SHDC2020CR3045B), Shanghai Rising-star Cultivation Program (22YF1439200), Capability Promotion Project for Research-oriented Doctor at SMHC (2021-YJXYS-01), Research Project of National Center for Mental Health of China (XS24B072), Shanghai Municipal Health Commission Talent Project (2022YQ048), Shanghai Mental Health Center Flying Talent Project (2022-FX-01), and Shanghai Municipal Education Commission (2024AIZD014). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShiyan Qu and Lei Guo contributed to the conceptualisation and drafting of the manuscript. Gang Wang, Xiandong Lin, and Wei Shao were responsible for patient recruitment. Ru-yuan Zhang and Zeming Fang assisted in computational modelling methodology. Li Liu, Chuanning Huang, Yue Wang, Zijing Wang, and Jing Tian conducted the experimental trials and EEG data acquisition. Tianzhen Chen and Hang Su contributed to methodological development. Min Zhao, Trevor W. Robbins, and Haifeng Jiang provided supervision, critical review, and manuscript editing. All authors reviewed and approved the final manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThank all the participants for their contribution in this study\u003c/p\u003e \u003cp\u003eClinical trial number\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStoneberg DM, Shukla RK, Magness MB. Global Methamphetamine Trends: An Evolving Problem. 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Transl Psychiatry. 2013;3:e337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/tp.2013.107\u003c/span\u003e\u003cspan address=\"10.1038/tp.2013.107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"reinforcement learning, reward processing, electroencephalograph, methamphetamine use disorder","lastPublishedDoi":"10.21203/rs.3.rs-8549153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8549153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMethamphetamine use disorder (MUD) is characterized by compulsive drug use, which has been attributed to compulsive decision-making related to drug-use events, with either exaggerated habit formation or excessive goal-directed selection contributing to it. However, the specific behavioral mechanism in MUD patients and their underlying neurophysiological mechanisms remain unclear. This study tried to fill this gap.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled 54 MUD patients and 27 healthy volunteers. Patients were divided into Mild (N\u0026thinsp;=\u0026thinsp;27) and Moderate-Severe (N\u0026thinsp;=\u0026thinsp;27) groups according to DSM-5 criteria. Participants completed a monetary probability reward task with electroencephalography recording. Reinforcement learning model was used to address behavioral deficits, and event-related potentials (ERPs), time-frequency analysis and source localization were applied to identify differential electrophysiological signals and brain regions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e Compared with healthy participants, Moderate-Severe MUD patients exhibited inflexible reward-seeking behaviors correlated with greater loss of control over drug use. ERP analysis revealed an abnormal component in the left frontal lobe within the early 200ms overlapping in time with feedback-related negativity, and the theta power was greater in incongruent than in congruent conditions, driven by hyperactivation in caudate nucleus correlated with inflexible reward-seeking and loss of control over drug use.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that MUD patients, depending on the severity of their condition, exhibit distinct behavioral and neurophysiological patterns in processing reward and punishment signals. Goal Narrowing may underlie the compulsive decision-making, manifesting as extreme reward pursuit and hyperactivation of caudate nucleus. Developing targeted interventions for abnormal behavioral and electrophysiological signals may be beneficial in future research.\u003c/p\u003e","manuscriptTitle":"Goal Narrowing Drives Compulsive Decision Making in Patients with Methamphetamine Use Disorder: A Cross-Sectional Study of Neural and Behavioral Correlates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:04:32","doi":"10.21203/rs.3.rs-8549153/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T22:18:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169601093297981415683765650505037401203","date":"2026-05-15T11:24:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82516161614760524970820981987556102147","date":"2026-05-08T14:26:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T10:04:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T09:21:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T06:39:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T06:35:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-01-08T08:40:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f45d89f-e1b8-4881-b9b6-48031fb0fe08","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T22:18:39+00:00","index":88,"fulltext":""},{"type":"reviewerAgreed","content":"169601093297981415683765650505037401203","date":"2026-05-15T11:24:50+00:00","index":86,"fulltext":""},{"type":"reviewerAgreed","content":"82516161614760524970820981987556102147","date":"2026-05-08T14:26:01+00:00","index":65,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T03:04:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 03:04:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8549153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8549153","identity":"rs-8549153","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}