Computational psychiatry (Cambridge, Mass.)最新文献

{"title":"Neural Bases of Affect-Based Impulsivity: A Decision Neuroscience Account.","authors":"Alison M Schreiber, Michael N Hallquist","doi":"10.5334/cpsy.159","DOIUrl":"https://doi.org/10.5334/cpsy.159","url":null,"abstract":"<p><p>Affect-based impulsivity describes the tendency to behave impulsively while experiencing negative or positive affective states. In the context of psychiatric disorders, the consequences of affect-based impulsivity can be dire, including suicidal behavior and harmful substance use. Here, we provide a narrative review and articulate a decision neuroscience account of affect-based impulsivity. We focus specifically on how negative emotions alter the balance of Pavlovian and goal-directed decision systems. We consider how negative affect influences <i>whether</i> to act, <i>what</i> actions to consider, <i>which</i> action to select, and <i>how</i> vigorously to engage in a selected action. Further, we describe the neural and neuroendocrine bases of these computations. We propose that modulation of norepinephrine and glucocorticoids during negative affective states enhances the pursuit of rewards by reducing goal-directed computations and increasing appetitive Pavlovian computations.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"85-103"},"PeriodicalIF":0.0,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anhedonic Traits Do Not Impair Performance in a 3-Arm Bandit Task.","authors":"Arjun Ramaswamy, Yumeya Yamamori, Umesh Vivekananda, Vladimir Litvak, Jonathan P Roiser","doi":"10.5334/cpsy.135","DOIUrl":"10.5334/cpsy.135","url":null,"abstract":"<p><p>Anhedonia, a transdiagnostic symptom marked by diminished reward sensitivity, is often linked to impairments in reinforcement learning (RL). Standard tasks (e.g., the 4-arm bandit) can place substantial demands on participants and may blur valuation with other processes. We therefore adapted a three-arm bandit (3AB) task from Seymour et al. (2012), incorporating design features intended to lessen task demands (fewer options; denser feedback) while enabling separate estimation of reward and punishment learning rates and sensitivities. In an online sample pre-screened for anhedonia (N = 206; 111 anhedonic, 95 non-anhedonic), hierarchical Bayesian modelling using a four-parameter specification showed no credible group differences in reward learning rate, punishment learning rate, reward sensitivity, or punishment sensitivity; Bayes factors favoured the null (BF₀₁ = 3.36-5.96). Model-agnostic win-stay/lose-shift strategies likewise showed no group differences (Welch's tests, all p > .05). Posterior predictive checks indicated above-chance choice prediction: the model's highest-probability action matched participants' actual choices on 59.6% of trials (chance = 33%). Parameter recovery was excellent for valuation parameters (r = 0.96-0.97) and acceptable for learning rates (r = 0.67-0.85). Simulations generated from fitted parameters preserved individual-difference structure, with high correlations between observed and simulated win-stay (r = 0.89 anhedonic; 0.86 non-anhedonic) and moderate correlations for lose-shift (r = 0.62; 0.67), alongside small systematic mean-level biases (simulated win-stay lower by 3.5-4.9 percentage points; simulated lose-shift higher by 12.8-13.2 points). Model comparison showed that lapse-augmented variants achieved marginally better predictive fit, but group comparisons under both lapse models yielded overlapping posteriors with 95% HDIs including zero for all learning, sensitivity, and lapse parameters, indicating that the null findings were robust to inclusion of lapse terms. Non-anhedonic participants also responded more slowly on average than anhedonic participants, which we treat as exploratory. Together, these results suggest that in this 3AB task, anhedonia is not reliably associated with differences in core RL parameters or simple choice strategies, while providing a detailed characterisation of model performance and limitations in an online setting.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"58-84"},"PeriodicalIF":0.0,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Alexithymia in Social Learning and Feedback-Driven Social Inferences.","authors":"Mina Hosseinnezhad, Soroosh Golbabaei, Mohammad Reza Bigham, Khatereh Borhani","doi":"10.5334/cpsy.153","DOIUrl":"https://doi.org/10.5334/cpsy.153","url":null,"abstract":"<p><p>Dynamic social interactions and feedback are crucial for understanding others' emotions, particularly when confronted with contradictory emotional cues. Alexithymia, a condition that co-occurs with many psychiatric disorders, is characterized by impairment in emotional processing. However, computational mechanisms by which it alters social inferences based on feedback cues remain unexplored. To examine this, 60 participants with low and high levels of alexithymia completed an emotional learning task involving contradictory social (verbal and visual) cues to infer targets' emotions. Computational analyses, including bin-based, reinforcement learning, and drift-diffusion modeling, revealed how alexithymia alters latent parameters that govern value updating and choice. Individuals with high alexithymia demonstrated lower accuracy in learning from social feedback, and learning rate for verbal cues was negatively associated with difficulties in identifying and describing feelings. Drift diffusion analysis revealed a perceptual bias toward the visual cue, with higher drift rates and bias in the visual-correct condition, and a general requirement for greater evidence accumulation to infer others' emotions. These findings suggest that individuals with high alexithymia exhibit impaired social learning and difficulty with decision-making in situations with conflicting social information, with computational modeling quantifying the latent processes involved and advancing mechanistic targets for computational psychiatry.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"35-57"},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Reflection Protects Behavior from Volatile Beliefs Linked to Paranoia.","authors":"Praveen Suthaharan, Santiago Castiello, Yuen-Siang Ang, Phil Corlett","doi":"10.5334/cpsy.150","DOIUrl":"https://doi.org/10.5334/cpsy.150","url":null,"abstract":"<p><p>Processing uncertainty may be pathognomonic (characteristic of a disease) for some psychiatric conditions. Some people expect the world to change, even when it doesn't. This tendency is central to paranoia, where individuals often anticipate threat or change without clear evidence. But what determines whether these beliefs translate into behavior? One possibility is that metacognitive structure - the coherence and depth with which one articulates their own thinking - acts as a buffer. An agent may endorse a belief but have sufficient accessory hypotheses to insulate it from action. To test this, we used metacognitive prompting in GPT-4 to score individual reflections on open-ended questions (e.g., <i>did you use any particular strategy?</i>) after completing a probabilistic reversal learning task. Individuals with higher paranoia demonstrate lower metacognitive structure (<i>t</i> = 5.98, <i>p</i> < 0.001), with metacognition attenuating the relationship between volatility belief and switching behavior (Δ = -15 pp, <i>p</i> < 0.001) even after controlling for reflection verbosity and general cognitive ability. These findings suggest that metacognition protects against uncertainty-driven instability, pointing to a key mechanism by which reflection protects against cognition under change. This work provides a novel framework to measure metacognition from behavioral task debrief questions.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"18-34"},"PeriodicalIF":0.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12922661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Modelling Reveals Slower Safety Learning and Threat Extinction are Associated With Higher Anxiety Severity in Remote Fear Conditioning.","authors":"Tim Kerr, Kirstin Purves, Thomas McGregor, Michelle G Craske, Tom Barry, Kathryn J Lester, Elena Constantinou, Michael Sun, Oliver J Robinson, Thalia C Eley","doi":"10.5334/cpsy.138","DOIUrl":"10.5334/cpsy.138","url":null,"abstract":"<p><p>Anxiety disorders are chronic, pervasive, and debilitating; characterised by a persistent or exaggerated response to distal or abstract threats. Impaired threat discrimination (distinguishing safe from threatening stimuli) and impaired threat extinction (learning a once threatening stimulus is now safe), are known risk factors in the development and persistence of anxiety disorders. These effects can be experimentally elicited through fear conditioning. First, repeated trials of paired aversive and neutral stimuli are delivered during a fear acquisition phase, followed by repeated trials with no aversive stimuli in a fear extinction phase. The effects are typically measured through comparison of end-phase data points, or simple descriptive or statistical models. Computational modelling, by contrast, can offer a hypothesis-driven, trial-by-trial mechanistic account of fear conditioning. This unmasks within subject task variance by estimating the rate of threat learning, safety learning, and threat extinction, examining individual differences in the cognitive mechanisms behind anxiety. A normative sample (n = 145) underwent a differential fear conditioning task on a bespoke smartphone app, in addition to completing an anxiety severity measure (GAD-7). Computational models fitted to task data estimated learning rates. Whilst the threat learning rate showed no association, the threat extinction and safety learning rates showed small negative associations with anxiety severity (ρ = -0.22, p = 0.01 & ρ = -0.21, p = 0.01 respectively). These findings are in keeping with prior studies using traditional analytical approaches, and indicate that anxious individuals are not quicker to develop fear of a stimulus, but take more time than their non-anxious counterparts to learn that a stimulus is safe. This study strengthens the evidence for impairments in fear extinction in those with anxiety, and the importance of learning rates as an index of anxiety severity, a previously hidden cognitive mechanism underlying anxiety persistence.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"18-35"},"PeriodicalIF":0.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.","authors":"Brian Zaboski, Sarah Fineberg, Patrick Skosnik, Stephen Kichuk, Madison Fitzpatrick, Christopher Pittenger","doi":"10.5334/cpsy.149","DOIUrl":"10.5334/cpsy.149","url":null,"abstract":"<p><strong>Objective: </strong>Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but identifying predictive signals in EEG data has met with little success, even with the application of traditional machine learning methods. We explored whether convolutional neural networks (CNNs) applied to EEG time-frequency representations can distinguish individuals with OCD from healthy controls.</p><p><strong>Method: </strong>We collected resting-state EEG data from 20 unmedicated participants (10 with OCD, 10 healthy controls). Four-second EEG segments were transformed into time-frequency representations. We then trained a 2D CNN using a leave-one-subject-out cross-validation framework to perform subject-level classification and compared its performance to a more traditional support vector machine (SVM) approach. Next, using multimodal fusion, we examined whether adding clinical and demographic information improved classification.</p><p><strong>Results: </strong>The CNN classifier achieved high subject-level performance, distinguishing individuals with an accuracy of 85.0% and an area under the curve (AUC) of 0.88. This significantly outperformed the SVM baseline, which performed no better than chance (45.0% accuracy, AUC: 0.47). A subsequent multimodal analysis revealed that clinical and demographic variables did not contribute any additional independent information.</p><p><strong>Conclusion: </strong>CNNs applied to resting-state EEG show promise for identifying OCD, outperforming traditional machine learning methods. These findings highlight the potential of deep learning to uncover complex, diagnostically relevant patterns in neural data. While limited by sample size, this work supports further investigation into multimodal models for psychiatric classification, warranting replication in larger, more diverse samples.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illness Severity in Psychotic Disorders Amplifies Anterior Insula's Sensitivity to Unreciprocated Smiles.","authors":"Jayson Jeganathan, Megan E J Campbell, Renate Thienel, Nikitas C Koussis, Bryan Paton, Katharina V Wellstein, Michael Breakspear","doi":"10.5334/cpsy.142","DOIUrl":"10.5334/cpsy.142","url":null,"abstract":"<p><p>When we smile, we expect that others will smile back. When one's smile is not reciprocated, these expectations are violated, producing prediction error signals in the brain. Prediction error signals may be experienced as aversive, disincentivizing smiling. Social smiling is impaired in psychotic disorders suggesting increased sensitivity to unreciprocated smiles. We developed the Incongruent Facial Emotion task to probe responses to unreciprocated smiles. Healthy controls and persons with schizophrenia or schizoaffective disorder voluntarily smiled, after which they viewed a stimulus face with a happy or angry expression. Brain activations were quantified with functional magnetic resonance imaging. Greater illness severity was associated with reduced smile amplitude. Across both groups, viewing an incongruent stimulus after initiating a smile activated the bilateral anterior insulae and right supplementary motor cortex. Brain activations in the left middle occipital and left superior frontal gyri were greater in the clinical group. The anterior insula response to incongruent facial reactions was significantly greater in more severely ill clinical participants. Dynamic causal modelling suggests that incongruent stimuli reduce tonic self-inhibition in the anterior insula, and that this disinhibition is enhanced by illness severity. The results suggest that the anterior insula processes affective prediction errors and sends feedback to supplementary motor areas to alter behavioural responses. The underlying brain circuits are enhanced in clinical participants with severe illness, suggesting new avenues to understand affective blunting in psychotic disorders.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"253-267"},"PeriodicalIF":0.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning and Decision Making in Depression in Adolescents and Young Adults: Insights from a New Model of the Probabilistic Reward Task.","authors":"Ziwei Cheng, Amelia D Moser, Jenna Jones, Christopher D Schneck, David J Miklowitz, Daniel G Dillon, Roselinde H Kaiser","doi":"10.5334/cpsy.147","DOIUrl":"10.5334/cpsy.147","url":null,"abstract":"<p><p>Depression is a prevalent psychiatric condition that commonly emerges in adolescence and young adulthood and is associated with reward processing abnormalities. The Probabilistic Reward Task (PRT) is widely used to investigate the impact of depression on reward processing, but prior studies have not comprehensively addressed the reinforcement learning and decision-making mechanisms involved in the task. In 726 adolescents and young adults with varying levels of depression, we collected PRT data and applied a novel computational model with response-outcome learning and evidence accumulation processes to provide new insights into the cognitive processes implicated in depression. Compared to participants with no history of psychopathology, those with depressive disorders showed reduced impact of learned response values on decision bias toward the more frequently rewarded action. In addition, higher levels of anhedonia were associated with slower evidence accumulation during decision-making. Together, these findings improved our understanding of the reinforcement learning and decision-making mechanisms assessed by the PRT and their associations with depression.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"268-283"},"PeriodicalIF":0.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research.","authors":"Samuel Zorowitz, Gili Karni, Natalie Paredes, Nathaniel Daw, Yael Niv","doi":"10.5334/cpsy.127","DOIUrl":"10.5334/cpsy.127","url":null,"abstract":"<p><strong>Background: </strong>The Pavlovian go/no-go task is commonly used to measure individual differences in Pavlovian biases and their interaction with instrumental learning. The task has also been widely used in computational psychiatry research, to correlate Pavlovian biases with mental health symptoms. However, prior research has reported unacceptable reliability for computational model-based performance measures for this task, limiting its usefulness in individual-differences research. Here, we apply several strategies previously shown to enhance task-measure reliability (e.g., task gamification, hierarchical Bayesian modeling for model estimation) to the Pavlovian go/no-go task, to improve the reliability of the task as a tool for future research.</p><p><strong>Methods: </strong>In two experiments, two independent samples of adult participants (N = 103, N = 110) completed a novel, gamified version of the Pavlovian go/no-go task multiple times over several weeks. We used hierarchical Bayesian modeling to derive reinforcement learning model-based indices of participants' task performance, and to estimate the reliability of these measures.</p><p><strong>Results: </strong>In Experiment 1, we observed considerable practice effects, with most participants reaching near-ceiling levels of performance with repeat testing. Consequently, the test-retest reliability of some model parameters was unacceptable (as low as 0.379). In Experiment 2, participants completed a modified version of the task designed to lessen these practice effects. We observed greatly reduced practice effects and improved estimates of the test-retest reliability (range: 0.696-0.989).</p><p><strong>Conclusion: </strong>The results demonstrate that model-based measures of performance on our modified Pavlovian go/no-go task can reach levels of reliability sufficient for use in individual-differences research. We therefore provide the task code for use by the computational psychiatry community (as well as other researchers). Additional investigation is necessary to validate the modified version of the task in other populations and settings.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"231-252"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12716264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposing Intolerance of Uncertainty: No Association With Affective Decision Making in a Community Sample.","authors":"Yannik Paul, Anya Pedersen, Kamil Fuławka","doi":"10.5334/cpsy.140","DOIUrl":"10.5334/cpsy.140","url":null,"abstract":"<p><p>Intolerance of Uncertainty (IU) is a transdiagnostic factor in psychological disorders, yet its underlying psychological mechanisms remain unclear. To close this gap, we first identify three potential mechanisms from existing definitions of IU: (1) negativity overweighting, (2) probability distortion, and (3) information deficit aversion. Second, we demonstrate how these mechanisms map onto well-established preference patterns in decision making under uncertainty as captured by Cumulative Prospect Theory: (1) loss aversion, (2) nonlinear probability weighting, and (3) the description-experience (DE) gap. Third, we conduct an affective decision-making experiment to investigate the relationship between self-reported IU and these preference patterns, as measured with individually estimated parameters of cumulative prospect theory. In the study, 100 participants made 120 choices between hypothetical painkillers with different probabilistic side effects. Half of the choices were made in a description condition, where all information was provided upfront; the other half in an experience condition, where participants acquired information through sampling. Trait IU was measured with a questionnaire. Participants overweighed side effects relative to treatment benefits (loss aversion), overestimated the probability of unlikely negative outcomes (increased nonlinear probability weighting), and their probability weighting patterns differed between the experimental conditions (DE gap). However, their preference patterns did not correlate with IU scores. Possible explanations are that the task did not effectively establish an affective context with real consequences for behavior, or that disorder-specific processes were not captured in our community sample. These findings highlight the need for a precise definition of IU and suggest avenues for designing tasks that enable a better understanding of IU.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"210-230"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}