Search Results (140)

Background: Mental illness constitutes one of the greatest worldwide health burdens. The use of artificial intelligence (AI) and virtual reality (VR) is becoming increasingly relevant in mental health. Nevertheless, evidence regarding their integrated application remains sparse. This scoping review identified existing evidence on AI-integrated VR in mental health care, including clinical applications, reported outcomes, and future research directions. Methods: The Population, Concept, and Context framework was used as the eligibility criteria. The mental health-related studies considered were original studies that addressed explicit AI integration using VR systems or workflows and had at least one outcome or clinical or implementation finding. PubMed, Scopus, Web of Science, and PsycINFO were searched to retrieve English-language studies published between January 2020 and February 2026. Results: The available evidence is heterogeneous, generally small, and primarily focused on feasibility or predictive modeling. The focus of applications is on the assessment or prediction of anxiety spectrum conditions, trauma and post-traumatic stress disorders, stress, and panic disorder/agoraphobia. Most of the research examines immersive VR with multimodal inputs and machine-learning-based prediction models. However, the field remains largely in an early stage, with a lack of standardization, implementation readiness, safety reporting, and real-world validation. Conclusions: AI-integrated VR can be considered as a promising but emerging field, and further development requires stricter, more clinically based, and implementation-focused studies that can help establish safe, effective, and scalable implementation in mental health care. Furthermore, pragmatic, multicenter research directly investigates whether AI-integrated VR has additional clinical value compared to regular VR or regular care in mental health care. Full article

Heart rate variability (HRV) refers to variations in the time intervals between consecutive heart beats. Changes in HRV reflect changes in either sympathetic or decreased parasympathetic tone that can originate in the brain. This brain–heart connection has led to the proposal that HRV may have utility in the diagnosis of psychiatric conditions and/or be a predictor of the response to psychiatric medications. There have been attempts to improve the correlation between HRV and psychological and psychiatric conditions by using artificial intelligence or specific machine learning algorithms. The objective of this review is to synthesize data on the use of machine learning to improve accuracy in differentiating psychological conditions such as mental stress, as well as distinguishing persons with anxiety disorders, panic disorders, major depression disorders and schizophrenia from health subjects. Reported accuracies for the identification of mental stress vary from 42 to 94%, while accuracies for anxiety vary from 67 to 98%, panic disorders from 71 to 93% and depression from 71 to 95%. The ability of HRV to differentiate different psychological or psychiatric conditions from each other requires more investigation. The ‘best’ machine learning algorithm varied between studies, with some reporting the k-nearest neighbor algorithm, support vector machine, random forest, or neural networks to be the best. A number of studies combined HRV with other variables such as respiration, EEG, or electromyography to obtain a composite index, but in doing so obscured the independent contribution of HRV. In summary, HRV has shown promise in detecting abnormalities in a range of psychological and psychiatric conditions. The use of machine learning algorithms improves diagnostic accuracy. Full article

Background and Objectives: Cyberchondria (CYB) has been associated with health anxiety and anxiety sensitivity (AS); however, its role in panic disorder (PD) remains unclear. This study aimed to compare CYB and AS levels between patients with PD and healthy controls and to examine their associations with PD severity. Materials and Methods: This cross-sectional case–control study included 71 patients with PD and 69 age- and sex-matched healthy controls. Participants completed the Cyberchondria Severity Scale (CSS), Anxiety Sensitivity Index-3 (ASI-3), and Beck Anxiety Inventory (BAI). PD severity was assessed using the Panic Disorder Severity Scale (PDSS). Group comparisons were additionally conducted using analysis of covariance (ANCOVA), controlling for relevant sociodemographic and clinical variables. Pearson correlation and hierarchical multiple regression analyses were performed. Results: Patients with PD had significantly higher CSS (80.70 ± 22.71 vs. 60.62 ± 17.22) and ASI-3 total scores (35.66 ± 17.87 vs. 12.25 ± 10.18) than healthy controls. In the PD group, CYB was positively correlated with AS (r = 0.38, p < 0.01), whereas no significant association was found between CYB and PD severity (r = 0.09, p > 0.05). AS showed a moderate positive correlation with PD severity (r = 0.46, p < 0.01). In hierarchical regression analyses, CYB did not predict PD severity. Adding AS significantly increased the explained variance; however, in the final model, only general anxiety severity (BAI) remained a significant predictor of PD severity. Conclusions: Patients with PD exhibit elevated levels of CYB and AS, which are positively associated with each other. Nevertheless, PD severity appears to be primarily driven by general anxiety symptoms rather than CYB. These findings suggest that CYB may represent a parallel maladaptive coping behavior rather than a direct determinant of symptom severity, with potential implications for assessment and intervention. Longitudinal studies are warranted to clarify causal relationships. Full article

Background: Moyamoya disease (MMD) is a rare, progressive cerebrovascular arteriopathy characterized by stenosis and occlusion of the distal internal carotid arteries with the development of compensatory collateral networks. In children, MMD is a major cause of ischemic stroke; however, neurological morbidity frequently extends beyond cerebrovascular events to include epilepsy, headache, cognitive impairment, and psychiatric manifestations. Data regarding the long-term evolution of these complications in Caucasian pediatric patients remains limited. Case Report: We present the longitudinal case of a Caucasian female diagnosed with advanced MMD after an ischemic stroke at the age of 7 years, followed by indirect surgical revascularization (encephalo-duro-arterio-synangiosis) and chronic antiplatelet therapy. Four years later, she developed recurrent focal aware sensory–motor seizures associated with chronic post-ischemic cortical injury. Despite stable vascular imaging and absence of recurrent infarction, the patient experienced persistent neurological sequelae, including residual spastic hemiparesis, episodic tension-type headaches, and evolving neuropsychological complications. Cognitive assessment initially suggested mild neurocognitive impairment, with subsequent improvement during adolescence. In late follow-up, prominent anxiety symptoms emerged, and psychiatric evaluation confirmed panic disorder requiring psychological and pharmacological support. The patient remained neurologically stable into adulthood under continued multidisciplinary care. This case illustrates the broad spectrum of neurological and psychiatric complications that may accompany pediatric MMD, even in the absence of new ischemic events. The accompanying literature review emphasizes that epilepsy, headache, cognitive dysfunction, and psychiatric disorders represent clinically significant components of the long-term disease burden in children with MMD. Conclusions: Pediatric moyamoya disease should be regarded not only as a cause of childhood stroke, but also as a chronic condition with long-term epileptic, cognitive, and psychiatric sequelae that may evolve independently of recurrent ischemic injury. By providing longitudinal follow-up from childhood into adulthood in a Caucasian patient, this report underscores the importance of integrating neuropsychological and psychiatric surveillance into standard care pathways, alongside vascular and surgical management, to better address the full spectrum of morbidity and improve quality of life. Full article

Precise modeling and simulation of pedestrian flow are crucial for public space safety design and emergency management. This study proposes an interdisciplinary method integrating computer vision and cellular automata (CA). First, unidirectional pedestrian flow video data with different densities were collected from an overpass scene via controlled experiments. High-precision pedestrian trajectory extraction and tracking were achieved using the YOLO 11 model and DeepSORT algorithm, with image distortion corrected by perspective transformation. For the first time, the probability distribution of pedestrian turning angles derived from trajectory analysis was converted into data-driven transition probabilities for the Moore neighborhood in the CA model. An improved evacuation model was then constructed, comprehensively considering real-data-based transition probabilities, speed–density distribution, panic coefficient, individual life value, and hazard source dynamics. Multi-scenario simulations show that moderate panic may shorten evacuation time, while excessive panic causes behavioral disorders; group movement is constrained by the slowest individual, and increased hazard source speed reduces the proportion of safe pedestrians. This study provides new insights and methodological support for refined pedestrian evacuation simulation and safety management. Full article

Background: The clinical presentation of temporal lobe epilepsy (TLE) and panic disorder can sometimes overlap, particularly when the seizure symptoms include paroxysmal episodes of intense fear and autonomic symptoms. As a result, patients with TLE can be misdiagnosed with a primary psychiatric illness, which leads to inappropriate treatment, worsening of the underlying condition and decreased function and quality of life. Clinical case: We present the case of a 46-year-old woman, known for a 20-year history of generalized epilepsy and major depressive disorder with panic attacks that were refractory and persistent despite trials of SSRIs, benzodiazepines and cognitive behavioral therapy (CBT). While hospitalized for video-EEG monitoring in the context of worsening epilepsy, she was found to have TLE seizures presenting as what the patient had described as panic attacks, and that sometimes progressed to secondarily generalized seizures. Following a transition from a medication regimen targeting generalized epilepsy to one more appropriate for focal seizures, the patient experienced clinical improvement with a decrease in the magnitude and frequency of panic symptoms. Conclusions: This case, in combination with other case reports in the literature, demonstrates the need for clinical suspicion of TLE in patients presenting with atypical panic-like episodes or a refractory panic disorder, especially in cases known for epilepsy or having risk factors for seizure disorder. It also highlights the importance of comprehensive diagnostic evaluation in neuropsychiatric presentations, including EEG and brain imaging, to ensure accurate diagnosis and appropriate management. Full article

Background/Objectives: The treatment of dual disorders (DDs) must be comprehensive and multidisciplinary. Evidence supports the effectiveness of psychotherapy in treating DDs. The second part of this consensus synthesizes the available evidence on psychological treatment for specific DDs. Methods: Two consensus methods were sequentially implemented: the nominal group technique and the Delphi method. Results: This consensus review encompassed a compilation of recommendations for the psychological treatment of neurodevelopmental, anxiety, post-traumatic stress, somatic symptom, eating, and personality disorders. Finally, recommendations for the future research agenda on the psychological treatment of DD were included. Conclusions: (1) Psychological treatment, particularly integrated treatment, is effective. (2) In the case of dual autism, interventions for substance use disorders should be adapted to this population’s characteristics. (3) More research is needed on dual social anxiety, panic, generalized anxiety, somatic symptom, and eating disorders, for which Cognitive Behavioral Therapy (CBT) is the most commonly used treatment. (4) For dual attention deficit hyperactivity disorder, multicomponent treatment is recommended (psychoeducation, CBT, and peer or family support). (5) For dual anxiety disorders, CBT is the first-line treatment. (6) For dual post-traumatic stress disorder, CBT (cognitive processing therapy and prolonged exposure therapy), acceptance and commitment therapy, stress inoculation training, and Eye Movement Desensitization and Reprocessing (EMDR) are effective. (7) For dual personality disorders, evidence is scarce. (8) For borderline personality disorder, dialectical behavior therapy, dynamic deconstructive psychotherapy, and dual-focus schema therapy show promise. (9) For antisocial personality disorder, CBT, contingency management, and counseling on impulsive lifestyles may be useful. (10) Much more evidence is needed from studies that overcome the methodological limitations of existing ones. Full article

Background: PTSD diagnosis is challenging. Symptoms overlap with depression and panic attacks. This causes misdiagnosis and delayed treatment. Current methods lack objective biomarkers. This study presents a hybrid AI framework. It combines CNNs and SVMs. The system detects PTSD from ECG signals. Methods: ECG data from 79 participants were analyzed. Four groups were included. PTSD patients numbered 20. Depression patients numbered 20. Panic attack patients numbered 19. Healthy controls numbered 20. Wavelet transform created scalograms. Three CNN models were tested. AlexNet, GoogLeNet, and ResNet50 were used. Deep features were extracted. SVMs classified the features. Five-fold validation was performed. Statistical tests confirmed significance. Results: Hybrid models performed robustly. ResNet50 + SVM and AlexNet + SVM achieved statistically equivalent results with accuracies of 97.05% and 97.26%, respectively. AUC reached 1.00 for multi-class tasks. PTSD detection was highly accurate. The system distinguished PTSD from other disorders. Hybrid models beat standalone CNNs. SVM integration improved results significantly. Conclusions: This is the first ECG-based AI for PTSD diagnosis. The hybrid approach achieves clinical-level accuracy. PTSD is distinguished from depression and panic attacks. Objective biomarkers support psychiatric assessment. Early intervention becomes possible. Full article

This paper reexamines the contested categories of sex addiction and Compulsive Sexual Behavior Disorder (CSBD) through a feminist-critical synthesis of 63 peer-reviewed articles published between 2000 and 2024. Rather than treating these diagnoses as neutral clinical entities, the review situates them within broader systems of normative regulation, emphasizing how psychiatric discourse, cultural anxieties, and digital infrastructures converge to define sexual deviance. The analysis is organized around the following three themes: (1) clinical ambivalence, where blurred thresholds of disorder mirror the opaque judgments of algorithmic moderation; (2) moral panic, which persists less as episodic reaction than as a durable strategy of governance embedded in media and platform logics; and (3) the pathologization of margins, whereby diagnostic and digital regimes disproportionately target queer, racialized, and gender-nonconforming sexualities. The paper introduces the concept of digital moral regulation to describe how platform architectures extend older traditions of moral governance, embedding cultural judgments into technical systems of visibility and suppression. By reframing CSBD as part of this regulatory formation, the review underscores that debates over compulsive sexuality are not solely matters of diagnostic precision, but of power: who defines harm, whose desires are legitimized, and how infrastructures translate cultural unease into regimes of control. Full article

Background: Anxiety disorders, including panic disorder, agoraphobia, specific phobias, and generalized anxiety disorder, are among the most frequent psychiatric conditions in primary care. They often present with somatic symptoms such as dyspnea, palpitations, chest or gastrointestinal discomfort, sweating, or flushing. Adaptol^® is a non-benzodiazepine anxiolytic with nootropic properties that modulates the limbic-reticular system, hypothalamic emotional centers, and multiple neurotransmitter systems. This study aimed to assess the association between Adaptol^® use and changes in anxiety symptoms, including somatic manifestations, in routine practice. Methods: A noninterventional observational study was conducted in 100 adults diagnosed with anxiety disorders in primary care. All received Adaptol^® 500 mg as prescribed. Patients had to have mild-to-moderate anxiety (5–14 points according GAD-7) to be enrolled. Exclusion criteria ruled out individuals with concomitant psychiatric or severe somatic conditions and those with use of other medications or any interventions that could affect the symptoms. Anxiety severity and somatic symptom burden were assessed at baseline and after treatment. Results: Adaptol^® treatment was associated with reduction in anxiety and somatic complaints. Improvements were reported in palpitations, chest discomfort, gastrointestinal disturbances, and autonomic symptoms. Greater benefit was observed in male patients, though without significance testing, and in those with severe baseline anxiety, as demonstrated by correlation between GAD-7 scores at baseline and changes after the treatment (r = 0.5). No unexpected adverse events occurred. Conclusions: In this real-world study, Adaptol^® showed anxiolytic efficacy and good tolerability, improving both psychological and somatic manifestations of anxiety disorders. These findings support its use in primary care, especially in severe cases of anxiety. Controlled trials are needed to support these results. Full article

The aim of this paper is to contribute to the further development of a coherent theory of music performance anxiety (MPA) and its treatment. I have previously proposed three forms of MPA-focal, MPA with social anxiety, and MPA with panic and/or depression. An attachment disorder was proposed as a possible underlying psychopathology for this third type of MPA. Accordingly, open-ended in-depth assessment interviews of three professional musicians presenting with severe MPA that included panic attacks and depressed mood were analyzed from an attachment theory perspective. Two of these musicians participated in short-term psychodynamic psychotherapy. It was hypothesized that the musical performance setting re-triggers unprocessed feelings related to early attachment trauma, and that performance anxiety can be a manifestation of the emergence into consciousness of these powerful early feelings. As hypothesized, severely anxious musicians suffered both early and current relational trauma that was expressed through symptomatology in their MPA manifestations. The assessment interview of the first musician demonstrated how MPA can arise in the midst of other challenging current life circumstances that re-trigger feelings about early attachment failures and the importance of taking a full life history from severely performance-anxious musicians. Excerpts from the two musicians’ short-term psychodynamic psychotherapy demonstrated resolution of their severe MPA. Failure to identify and treat underlying attachment disorders in severely anxious musicians may render other forms of treatment ineffective or short-lived. Full article

Objectives: Virtual reality (VR) has emerged as a promising tool for assessing anxiety-related disorders through immersive exposure and physiological monitoring. This study aimed to evaluate whether multimodal data, including heart rate variability (HRV), skin conductance response (SCR), and self-reported anxiety, collected during VR exposure could classify patients with panic disorder and agoraphobia using machine learning models. Methods: Seventy-six participants (38 patients with panic disorder and agoraphobia, 38 healthy controls) completed 295 total VR exposure sessions. Each session involved two road and two supermarket scenarios designed to induce anxiety. Inside the sessions, self-reported anxiety was measured along with physiological signals recorded by photoplethysmography and SCR sensors. HRV measures of heart rate, standard deviation of normal-to-normal intervals, and low-frequency to high-frequency ratio were extracted along with SCR peak frequency and average amplitude. These features were analyzed using Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (k-NN), Logistic Ridge Regression (LRR), C-Support Vector Machine (SVC), Random Forest (RF), and Stochastic Gradient Boosting (SGB) classifiers. Results: The best model achieved an accuracy of 0.83. Most models showed specificity and precision ≥0.80, while sensitivity varied across models, with several reaching ≥0.82. Performance was stable across major hyperparameters, VR-stimulus settings, and medication status. The patients reported higher subjective anxiety but exhibited blunted physiological responses, particularly in SCR amplitude. Self-reported anxiety demonstrated higher feature importance scores compared to other physiological properties. Conclusion: VR exposure with self-reported anxiety and physiological measures may serve as a feasible diagnostic aid for panic disorder and agoraphobia. Further refinement is needed to improve sensitivity and clinical applicability. Full article

Background: Arterial hypertension (HT) is one of the most prevalent diseases, causing increased morbidity and mortality. Treatment of HT might be complicated by multiple drug intolerance syndrome (MDIS), defined as intolerance to three or more drug classes. This systematic review and meta-analysis aimed to assess the prevalence of MDIS in patients with hypertension and investigate its impact on disease control. Materials and Methods: MEDLINE, Scopus, Web of Science, and Embase were thoroughly searched in June 2024. The data concerning MDIS prevalence, MDIS risk factors, number of adverse drug reactions (ADRs), and types of reactions was extracted. Quality assessment was done with the use of the Newcastle-Ottawa Scale. Meta-analysis was conducted to determine the pooled prevalence of MDIS and ADRs. The study was conducted according to the PRISMA guidelines. Results: This review included four studies (2508 patients). The pooled prevalence of MDIS was 10% (95% CI: 4%–19%). Headache was the most frequent ADR reported in the studies (71.47%, 95% CI: 56.5%–84.44%), while pain in other body parts was present in 53.08% of patients. Female sex was found to increase the risk of MDIS in each included study. Other risk factors identified were older age, gastrointestinal disorders, anxiety disorders, panic attacks, and depression. Blood pressure was higher in the MDIS group, and in this group, more patients had uncontrolled hypertension. Conclusions: The prevalence of MDIS in the hypertensive population can be considered high. Furthermore, blood pressure control is worse in patients with MDIS. However, this phenomenon is still studied inadequately, and further research is necessary. Full article

Background/Objectives: Recent evidence suggests that gut microbiota plays an important role in anxiety and stress-related disorders through interactions along the gut–brain axis. Our aim was to determine the microbiological diversity of intestinal microorganisms in individuals with acute and remission phases of PD when compared to healthy individuals. Another aim was also to analyze the differences in the metabolic pathways occurring in the intestinal microbiota of individuals from the three analyzed groups. Methods: A diagnosis was established using the Mini-International Neuropsychiatric Interview (M.I.N.I). The gut’s microbiota composition was analyzed through bacterial 16S rRNA gene sequencing (V1–V2 regions). The clinical evaluations included a BMI measurement, Short Form-36 Health Survey (SF-36), Hamilton Anxiety Scale (HAM-A), Montgomery–Åsberg Depression Rating Scale (MADRS), Columbia-Suicide Severity Rating Scale (C-SSRS), and State-Trait Anxiety Inventory (STAI). Results: We recruited 62 participants (31 PD and 31 controls). After conducting quality control filtering, data from 54 participants were analyzed (25 PD, 11 acute, 14 remission, and 29 controls). Observed richness was lower in the acute PD (63) group than in the control (74) and remission (66) (p = 0.038) groups, whereas the Shannon and Simpson indices and beta diversity (PERMANOVA) were not significantly different. The Ruminococcus gnavus group was enriched in acute PD; no other deconfounded differences in microbial composition were detected. Predicted functional differences were detected by edgeR only and included the pathways that are related to steroid biosynthesis and innate immune signaling. Conclusions: Distinct gut microbial signatures were associated with PD, implicating both the metabolic and inflammatory pathways in disease pathophysiology. Full article

Background: Food addiction (FA) is an emerging construct that mirrors the behavioral and neurobiological characteristics of substance use disorders. Despite growing interest, its association with specific psychiatric disorders among bariatric patients remains understudied. Objective: Our aim was to examine the prevalence and strength of associations between FA and seven major psychiatric disorders in individuals who underwent bariatric surgery. Methods: In a sample of 100 post-bariatric patients referred for psychiatric evaluation, FA was assessed using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), and psychiatric disorders were diagnosed using the Mini International Neuropsychiatric Interview (MINI). Logistic regression models were used to estimate adjusted odds ratios (aORs) for the association between FA and each psychiatric disorder, controlling for sex, age, body mass index (BMI), employment status, the number of children, clinical comorbidities, physical activity, family psychiatric history, and region of residence. Results: FA was present in 51% of the sample. Descriptive analyses revealed a significantly higher prevalence of major depressive disorder, panic disorder, generalized anxiety disorder, social anxiety disorder, agoraphobia, obsessive–compulsive disorder, and bulimia nervosa among individuals with FA. Multivariate models showed robust associations between FA and bulimia nervosa (aOR = 19.42, p < 0.05), generalized anxiety disorder (aOR = 2.88, p < 0.05), obsessive–compulsive disorder (aOR = 6.64, p < 0.05), agoraphobia (aOR = 3.14, p < 0.05), social anxiety disorder (aOR = 4.28, p < 0.05) and major depressive disorder (aOR = 2.79, p < 0.05). Conclusions: FA is strongly associated with a range of psychiatric comorbidities in post-bariatric patients, reinforcing the need for comprehensive mental health screening in this population. These findings underscore the potential role of FA as a clinical marker for stratified risk assessment, supporting more personalized approaches to mental health monitoring and intervention following bariatric surgery. Full article