IntroductionFunctional neurological disorders (FND) are characterized by involuntary sensory, motor, and/or other seemingly neurological symptoms lacking expected medical explanation. Once diagnosed, patients often are referred to mental health providers for psychotherapy, which is effective in treating many FND symptom presentations either alone or as part of an integrative approach. Mental health providers may feel reluctant or unprepared to accept these referrals, however.MethodsWe conducted a U.S.-based online survey of 286 practicing psychological service providers (psychologists, psychiatrists, counselors, social workers in private practice, neuropsychologists) to examine knowledge of, attitudes toward, and preparedness to treat FND.ResultsFindings revealed that most of our sample had limited exposure to FND in routine practice. Confidence in treating FND was modest, with neuropsychologists reporting more knowledge of and more confidence in treating FND than other psychologists/mental health providers. Greater perceived adequacy of psychotherapy training for, and more experience working with, FND each were associated with more positive FND attitudes, including greater confidence and enjoyment in treating FND and greater belief that symptoms would improve.DiscussionThis study is novel in its examination of knowledge and attitudes among psychological service providers, who are pivotal in furnishing effective treatment for FND patients but may not be well-versed in their needs. In contrast to existing studies of medical providers that revealed stigma as a major barrier to providing adequate care for FND, our findings suggest lack of knowledge and lack of access to FND patients may limit the ability to gain more training and expertise. Targeted training and consultation for mental health providers may improve confidence and motivation and expand the workforce capable of delivering effective treatment for FND.
Depression is a globally prevalent mental disorder; however, current pharmacological treatments are limited by a delayed onset of action and significant adverse effects. As a safe and effective non-pharmacological therapy, acupuncture demonstrates unique advantages in treating mild to moderate depression. Its antidepressant mechanisms are multifaceted. Primarily, acupuncture mitigates neuroinflammation by inhibiting classical signaling pathways (e.g., nuclear factor kappa B/NOD-like receptor family pyrin domain containing 3), modulating microglial polarization, and restoring cytokine homeostasis. Furthermore, it enhances neuroplasticity by upregulating brain-derived neurotrophic factor expression, improving synaptic plasticity, and promoting adult neurogenesis. At the cellular level, acupuncture exerts neuroprotective effects by suppressing apoptotic cascades associated with mitochondrial dysfunction and endoplasmic reticulum stress, regulating autophagic balance, and restoring neuronal excitability. Systemically, it ameliorates metabolic dysregulation by optimizing cerebral energy metabolism, modulating gut microbiota composition, and reinforcing intestinal barrier integrity. Additionally, acupuncture effectively alleviates comorbid pain by modulating peripheral nociceptive signaling and central pain-emotion feedback loops. These synergistic effects across multiple systems and targets may together ameliorate the pathophysiological alterations associated with depression. Despite these advances, current research faces challenges, including the translational gap between animal models and clinical pathology, as well as an insufficient investigation of multi-mechanism interactions. Future efforts should focus on developing clinically relevant models, integrating multi-omics technologies, conducting high-quality clinical trials, and exploring biomarker-guided personalized treatments to advance the scientific and standardized application of acupuncture for depression.
ObjectiveComorbid anxiety disorder is common in children and adolescents with functional abdominal pain disorders, but clinically usable risk-stratification tools integrating abdominal pain, psychosocial, school-related, and family-level information remain limited.MethodsIn this observational predictive modeling study, children and adolescents aged 7–18 years with Rome IV-defined functional abdominal pain disorders were evaluated between December 2021 and December 2025. Participants were randomly allocated at a 7:3 ratio into training and internal validation cohorts using stratified sampling by anxiety status. Candidate predictors included demographic, abdominal pain-related, somatic, psychosocial, school-related, and family-level variables. After LASSO-based feature selection, four prespecified base learners—explainable boosting machine, calibrated support vector machine, random forest, and CatBoost—were combined using an L2-penalized logistic regression stacking meta-learner. Performance was assessed using discrimination, threshold-dependent metrics, calibration, decision curve analysis, learner diversity, and SHAP-based interpretability.ResultsAmong 629 eligible patients, 210 (33.4%) had comorbid anxiety disorder. The training cohort included 440 patients, and the validation cohort included 189 patients, with similar anxiety prevalence in both cohorts. Clinically relevant correlates of anxiety included older age, longer symptom duration, higher pain frequency, night-time awakening, pain temporally associated with stressful events, higher SDSC and CSI-24 scores, higher parental GAD-7 score, and greater school absence. In out-of-fold training estimates, the stacking model achieved an AUC of 0.831 and a Brier score of 0.151. In the validation cohort, it showed the highest numerical performance among candidate models, with an AUC of 0.846 (95% CI, 0.789–0.894), accuracy of 0.788, sensitivity of 0.762, specificity of 0.802, F1 score of 0.706, and Brier score of 0.146. Decision curve analysis suggested potential clinical utility across selected threshold ranges. SHAP analysis identified school absence, parental GAD-7 score, CSI-24 score, pain-stress association, symptom duration, pain frequency, SDSC score, age, and night-time awakening as leading contributors to the final model output.ConclusionA stacking machine learning model integrating clinical, psychosocial, school-related, and family-level variables showed acceptable internal-validation performance for identifying comorbid anxiety disorder in children and adolescents with functional abdominal pain disorders. External validation is needed before clinical implementation.
BackgroundConversational artificial intelligence (AI) is increasingly used for emotional support, reflection, and consequential decision-making. Although conversational continuity may improve responsiveness, sustained interaction also exposes AI systems to their own prior relational language, potentially shaping how they respond when users become increasingly dependent or surrender personal agency. We examined whether model-generated conversational history alters relational-safety behavior as human–AI interactions escalate from ordinary support-seeking toward emotional dependence, social displacement, and decision delegation.MethodsWe conducted a standardized multi-turn benchmarking study of ChatGPT, Gemini, and Doubao. Twelve four-turn trajectories represented two agency-related domains: emotional dependency and displacement of human support, and decision delegation and surrender of personal agency. Each trajectory was evaluated under two matched context architectures: reconstructed cumulative user-only context, which retained all prior user disclosures but excluded previous AI responses, and native continuous conversation, which retained the complete interaction history. Across 72 model-condition-trajectory sequences comprising 288 turn-level responses, relational safety was independently evaluated for dependency reinforcement, agency preservation, and human-support preservation using predefined ordinal criteria. Context effects and their interaction with escalating relational risk were examined using cumulative-link mixed-effects models.ResultsRelational-safety differences between context architectures increased across successive turns of the standardized trajectories. Overall, explicit dependency reinforcement increased from 9.7% in cumulative user-only contexts to 18.1% in continuous conversations, while strong agency preservation declined from 75.7% to 65.3% and strong human-support preservation from 73.6% to 63.2%. Outcome-specific analyses showed condition-by-turn interactions for dependency reinforcement (cumulative odds ratio [cOR], 1.26; 95% CI, 1.03–1.55; unadjusted P = .026), agency preservation (cOR, 0.82; 95% CI, 0.69–0.98; unadjusted P = .031), and human-support preservation (cOR, 0.78; 95% CI, 0.65–0.94; unadjusted P = .009), indicating progressively greater divergence between context conditions across successive turns. After Holm adjustment across the three interaction tests, only the human-support-preservation interaction remained below the.05 threshold (adjusted P = .027).The overall pattern was observed across systems and was directionally consistent in predefined binary sensitivity analyses.ConclusionsAcross this benchmark, between-condition differences generally widened across successive turns, although the present design cannot determine whether this pattern reflects increasing relational-risk intensity, increasing accumulated model-generated history, or their combination. Only the human-support-preservation interaction remained below the.05 threshold after Holm adjustment, whereas the dependency-reinforcement and agency-preservation interactions were directionally similar but less robust after multiplicity adjustment. The observed pattern is compatible with a post-hoc interpretive framework of relational inertia but does not establish this process as a causal mechanism or demonstrate downstream psychological effects in human users.
IntroductionDrawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this longitudinal study examined the prospective associations between gaming motives and Gaming Disorder (GD) among adolescents. Specifically, we investigated whether gratification-oriented motives (Fantasy, Identity) predict subsequent GD symptoms and whether compensation-oriented motives (Coping, Escape, Introjected Regulation) emerge as consequences of GD.MethodsData were collected from 120 secondary-school gamers at two time points separated by a three-month interval. Cross-lagged panel models were estimated separately for each gaming motive–GD pair to examine the directionality of associations over time.ResultsThe findings supported the gratification hypothesis, as higher baseline Fantasy and Identity motives predicted increased GD symptoms at follow-up. A bidirectional association emerged between Fantasy and GD, whereas higher initial GD symptoms predicted a subsequent decline in Identity motives. The compensation hypothesis received partial support: GD prospectively predicted increases in Coping and Introjected Regulation, while no significant cross-lagged associations were observed for Escape.DiscussionThese findings suggest that gratification-oriented motives may contribute to the early development of GD, whereas increasing GD symptoms may reinforce specific compensation-oriented motives. The results highlight distinct, motive-specific dynamics in adolescent gaming and underscore the importance of distinguishing between gratification and compensation processes. From a practical perspective, assessing Fantasy and Identity motives as potential risk indicators and addressing emerging Coping and Introjected Regulation motives may inform more targeted school-based prevention and intervention strategies.
