Search

Panic disorder (PD) may accompany elevated social avoidance and distress (SAD). Higher SAD in patients with PD predicts a poorer prognosis and response to treatment. As heart rate variability (HRV) reflects the self‐regulatory capacity underlying SAD, applying HRV to distinguish PD heterogeneity would be clinically beneficial in formulating personalized treatment strategies. We hypothesized that HRV would be lower in patients with PD and severe SAD (PD‐SAD group) than in those without SAD (PD group). A total of 288 patients met the eligibility criteria, with complete collection of variables of interest and HRV. The inverse probability of treatment weighting (IPTW) method was used to balance the groups for the baseline characteristics of patients. Following IPTW adjustment, the average treatment effects were computed using inverse propensity weighting with regression adjustment (IPW‐RA). All characteristics were similar in both groups after IPTW adjustment. The PD‐SAD group showed a decreased tendency in time domain parameters, including mean heart rate, SDNN, RMSSD, and pNN50 with a nonlinear domain of SD1. However, no significant intergroup differences were observed in the frequency domain. Higher SAD in PD was associated with reduced HRV, mainly in the time domain, which may be attributed to the shared neural networks between dysfunctional self‐regulation statuses, as indexed by reduced HRV. As respiratory sinus arrhythmia is mainly reflected in the frequency domain, the time domain may be more reliable for identifying heterogeneity within patients with PD, who are frequently associated with respiratory pattern abnormalities. [ABSTRACT FROM AUTHOR]

Background: Theta-gamma coupling (TGC) describes the modulation of gamma oscillations by the theta phasic activity, which is crucial for processes such as the ordering of information during working memory (WM) performance. The mental arithmetic (MA), which involves performing calculations with numbers, is a crucial tool for evaluating and understanding the sensory processing and management abilities of WM. Evaluating TGC may provide greater insight into the neural mechanisms mediating WM deficits in panic disorder (PD). Methods: Medical and electroencephalography (EEG) records of psychiatric outpatient clinic between 1 March 2020 and 30 September 2023 were retrospectively reviewed. A total of 34 PD patients and 34 age- and sex-matched healthy controls (HCs) underwent EEG to assess the overall functional interaction of the brain using multi-channel EEG analysis, focusing on specific brain regions including the frontal, temporal, parietal, and occipital lobes. EEG recordings were conducted during two sessions: a 5-min eyes-closed resting-state (RS) and a subsequent 5-min eyes-closed MA. The TGC and the spectral power of the theta and gamma frequency bands, which are well known to be associated with WM, were analysed. Results: Compared to those in HCs, TGC and theta power were significantly attenuated in PD patients. When analysing both HCs and PD patients together, RS TGC and relative theta power were negatively correlated with state anxiety and perceived stress scores, respectively. In contrast, TGC and relative theta power during the MA condition were positively correlated with the MA performance. Specifically, in PD patients, RS theta power across all electrodes was significantly negatively correlated with the Hamilton Anxiety Scale (HAMA) score. Linear regression analysis revealed that theta power in the T5 channel remained negatively correlated with pathological anxiety as measured by the HAMA score, even after controlling for other confounding factors. Conclusions: This study highlights significant alterations in TGC and theta power in PD patients. PD patients exhibit reduced TGC and theta power compared to HCs, indicating deficits in the neural mechanisms underlying anxiety and/or WM in PD. These insights contribute to a better understanding of the neural basis of WM deficits in PD and suggest potential avenues for targeted therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear. Aims: This study aimed to assess the effects of PD and paroxetine on brain functional hubs in drug-free patients and to identify neuroimaging biomarkers for diagnosing and predicting TR in patients with PD. Methods: Imaging data from 54 medication-free PD patients and 54 matched healthy controls (HCs) underwent DC and functional connectivity (FC) analyses before and after a 4-week paroxetine treatment. Diagnosis and prediction of TR models for PD were constructed using support vector machine (SVM) and support vector regression (SVR), with DC as features. Results: Patients with PD showed aberrant DC and FC in the anterior cingulum, temporal, and occipital areas compared with HCs at baseline. After treatment, DC of the patients increased in the calcarine cortex, lingual gyrus, and cerebellum IV/V, along with improved clinical symptoms. Utilizing voxel-wise DC values at baseline, the SVM effectively distinguished patients with PD from HCs with an accuracy of 83.33%. In SVR, the predicted TR significantly correlated with the observed TR (correlation coefficient (r) = 0.893, Mean Squared Error = 0.009). Conclusion: Patients with PD exhibited abnormal DC and FC, notably in the limbic network, temporal, and occipital regions. Paroxetine ameliorated patients' symptoms while altering their brain FC. SVM and SVR models, utilizing baseline DC, effectively distinguished the patients from HCs and accurately predicted TR. [ABSTRACT FROM AUTHOR]

Despite the high prevalence, and high functional impairment associated with panic disorder, programs targeting the prevention of panic psychopathology are still very limited. This article aims to guide panic disorder prevention programs, by analyzing different psychological risk factors, and their interactions in the pathogenesis of the psychopathology of panic disorder. The results show, on the one hand, that neuroticism, perceived control, emotional clarity, and anxiety sensitivity are causal risk factors for the development of panic disorder. On the other hand, several causal pathways involving complex chains of these factors lead to the development of panic disorder. These results may help guide panic disorder prevention interventions by guiding, on the one hand, decisions concerning the populations to be targeted for prevention efforts as well as, on the other hand, by identifying the risk and protective factors to be targeted to reduce the probability of occurrence of panic disorder. [ABSTRACT FROM AUTHOR]

Panic disorder is costly, and while evidence-based interventions for panic disorder are effective, obtaining a diagnosis often precludes access to such treatments. This is problematic because the categorical diagnosis of panic disorder (i.e. “you have it, or you don’t”) supported by modern diagnostic manuals contradicts empirically supported dimensional models of panic disorder. Taxometric analyses, which test the dimensional or categorical latent structure of constructs, have consistently revealed dimensional latent structures when applied to other anxiety disorders and panic-related processes, but taxometric analyses have never been applied to panic disorder. To address this gap in the literature, three nonredundant taxometric procedures were applied to seven theoretically-relevant indicators of panic disorder derived from Panic Disorder Severity Scale data collected from 663 participants recruited via Amazon Mechanical Turk. Simulated comparison plots and objective fit indices were also evaluated. The collective results provided consistent empirical support for a dimensional model of panic disorder, with an overall mean CCFI score of .39. The implications of the present findings for the measurement, assessment, diagnosis, and treatment of panic disorder are discussed. [ABSTRACT FROM AUTHOR]

Objective: Variants within genes encoding microRNAs (miRNAs) may alter the expression of both miRNAs and their target genes, thus contributing to the etiology of psychiatric disorders. The involvement of miRNAs in neuronal differentiation and synaptic plasticity supported this hypothesis. We aimed to investigate the links between miR-155 rs767649/miR-22 rs8076112 and the risk of panic disorder (PD) in a sample of Turkish population. Methods: In this experimental study, 134 PD patients and 140 healthy controls were recruited. Genotyping was carried out using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) method. To evaluate PD phenotypes, Panic Disorder Severity Scale (PDSS) was also administered to patients to clarify possible associations between the scale and risk variants analyzed. Results: The genotype analysis of miR-155 rs767649 did not show an association with PD risk and it was not related to the disease severity. For miR-22 rs8076112 variant, a statistically significant association was determined; CC genotypes were lower in patients compared to controls. Logistic regression analysis proved the highly protective effect (80.4%) of CC genotype against PD ( p = 0.041; OR = 0.196, 95% CI = 0.041-0.934). Though its significance in disease liability, miR-22 rs8076112 was not associated with the disease severity. Conclusion: Our findings firstly report the combined analysis of miR-155 rs767649 and miR-22 rs8076112 in PD in terms of both disease susceptibility and severity. These findings await replication in independent cohorts with enrichment of other miRNA gene variants. Thus, certain miRNAs and their target genes involved in the etiology and phenotypes of PD could be enlightened. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a chronic sleep-related breathing disorder that is highly prevalent in individuals with posttraumatic stress disorder (PTSD). The reason for this high prevalence remains unclear. We hypothesized that breathing instability, one of the key contributors to OSA, may be altered in PTSD and predispose OSA. Healthy participants (214 females, 98 males) aged 17 to 42 (M = 19.92; SD = 2.85) completed online questionnaires measuring PTSD symptomatology, sleep disturbances, and self-reported breathlessness. A subset of these participants (16 females, 14 males) aged 18 to 42 (M = 23.50; SD = 7.18) completed an in-lab breathing instability assessment, whereby they performed a series of 20-second and maximal duration breath-holds. PTSD severity positively predicted subjective perceptions of breathlessness (P < 0.001) but not objective measures of breathing instability, namely ventilation following 20-s breath-holds (P = 0.93) and maximal breath-hold duration (P = 0.41). These results suggest that breathing instability may not be the driving factor behind the high prevalence of OSA in PTSD. Instead, other factors such as a low arousal threshold, elevated ventilatory responses to arousal, or coexisting insomnia may explain the high rates of OSA in PTSD. One explanation for the discrepancy between subjective breathlessness and breathing stability measures relating to PTSD severity may be that hypervigilance and increased anxiety impacted self-perceptions of breathlessness while not altering breathing instability per se. NEW & NOTEWORTHY: Obstructive sleep apnea (OSA) is more common in individuals with posttraumatic stress disorder (PTSD) than the general population for unknown reasons. This study assessed one of the key contributors to OSA, respiratory control instability, in young students with a range of posttraumatic stress symptoms. Although individuals with high PTSD symptoms reported increased subjective breathlessness, objectively measured breathing instability was not altered, suggesting respiratory instability is unlikely to increase the risk of OSA in PTSD. [ABSTRACT FROM AUTHOR]

Objective: Despite advancements in understanding panic disorder (PD), its etiology and pathogenesis remain elusive, particularly concerning the influence of psychosocial factors. This study aims to explore the mediating roles of neuroticism and anxiety sensitivity (AS) in the relationship between childhood trauma and PD severity. Methods: We conducted a cross-sectional analysis involving 84 individuals diagnosed with PD and 112 age- and gender-matched healthy controls (HC). Childhood trauma was assessed using the validated Childhood Trauma Questionnaire (CTQ), while anxiety sensitivity levels were measured using the Anxiety Sensitivity Index-3 (ASI-3). Neuroticism was evaluated using the Chinese Big Five Personality Inventory (CBF-PI-B). The severity of PD was determined using the Panic Disorder Severity Scale (PDSS). Statistical analysis encompassed both correlation and mediation techniques to thoroughly examine the relationships among childhood trauma, neuroticism, AS, and PD severity. Results: Individuals with PD exhibited significantly higher CTQ, ASI-3, and neuroticism scores compared to HC (all P<0.05). Within the PD group, strong positive correlations were found between CTQ scores, ASI-3 scores, neuroticism levels, and PDSS scores, with correlation coefficients ranging from 0.336 to 0.504 (all P<0.001). Additionally, our results indicated that neuroticism and AS acted as mediating factors in the relationship between childhood trauma and the severity of PD, with the 95% confidence interval for the indirect effects ranging from 0.028 to 0.116. Conclusion: This study highlights that PD patients exhibit significantly higher levels of childhood trauma, neuroticism, and AS compared to HC. Furthermore, childhood trauma indirectly influences PD severity through a chain mediation involving neuroticism and AS. These findings suggest the importance of psychological factors in moderating the impact of early adverse experiences on the development and progression of PD. [ABSTRACT FROM AUTHOR]

Panic disorder (PD) is considered one of the destructive ailments, with various individuals experiencing a critical functional disorder. As the range of remission for PD is achieved only between 20% and 50% with the help of regular pharmacotherapy, modern solutions are expected to deal with this issue. By taking the advantage of Internet of Things (IoT), a novel IoT-inspired behaviour monitoring framework is proposed for the analysis of panic disorder in a particular context. A quantum probability-inspired quantification measure is calculated to determine the scale of health irregularity. In addition, Temporal Data Mining (TDM) is performed for the formulation of temporal data granules to measure Individual Health Index (IHI) by utilising the Multi-scaled Gated Recurrent Unit (M-GRU) technique of deep learning. Moreover, a two-phased alert generation approach is proposed for notifying the current health condition of an individual to the concerned caretaker or medical specialist for assistive or medical services. In the comparative analysis, the proposed framework has outperformed the state-of-the-art approaches by achieving a considerable classification accuracy of 96.89% for event determination and 94.14% accuracy for health severity determination. Similarly, a considerable improvement with respect to Specificity, Sensitivity, and F-measure has been observed for the proposed framework. [ABSTRACT FROM AUTHOR]

Background: A review of previous studies shows that, among the variables that affect the underlying mechanisms of panic and depression disorders, are body awareness, difficulty in emotional regulation, and patient's self-management. Considering the lack of research background on the comparison of the above three components in patients with panic and depression disorders, the present study seeks to answer the question whether these three components are different in patients with panic, depression and nonaffected persons? Aims: The purpose of this research was to compare body awareness, difficulty in emotion regulation and self-management among people with panic disorders, depression and nonaffected persons. Methods: The current research design was descriptive and causal-comparative. The statistical population of the research included all people suffering from panic disorders, depression and non-suffering people in the city of Babol in the autumn of 2022. The sampling method was available and the sample size was 30 people for each group. The data collection tools in this study included three body awareness questionnaires of Shields (1989), the difficulty in emotion regulation questionnaire of Gratz & Roemer (2004) and the self-management questionnaire of Houthun and Neck (2000). The collected data were analyzed by multivariate analysis of variance and Benferoni post hoc test using SPSS22 software. Results: The obtained results showed that there is no significant difference in body awareness between people suffering from panic disorders, depression and non-suffering people. There was no significant difference in other variables, including difficulty in emotion regulation and self-management, between the two panic and depressed groups, but both groups had a significant difference compared to the group of non-diseased people. It was also found that three groups of people suffering from panic, depression and non-suffering people have a significant difference in the three strategies of self-management, including behavioral strategies, natural reward and constructive thought pattern and difficulty in emotional regulation (P< 0.05). Conclusion: According to the obtained results, it can be concluded that people with panic disorders and depression have problems in emotion regulation and self-management compared to non-afflicted people. Therefore, it is suggested that psychiatrists and psychotherapists pay attention to the three variables of the present study in the therapeutic interventions of patients suffering from panic and depression in order to prevent the progression and exacerbation of disease symptoms. [ABSTRACT FROM AUTHOR]

Importance: This report details a case of effectively managing severe panic attacks in a panic disorder (PD) patient with agoraphobia by acupuncture treatment. Case presentation: A 38-year-old Japanese woman suffering from PD with agoraphobia presented at our acupuncture clinic with complaints of repeated panic attacks and anxiety. Initially starting to experience symptoms in her late teens, she avoided psychiatric consultation due to reluctance toward psychotropic medications. An unbiased psychiatrist used the Panic Disorder Severity Scale (PDSS) to assess her PD severity. The PDSS score decreased from 21 on the first to 12 points on the sixth visit. The patient experienced reduced frequency and severity of panic attacks, with restored confidence to go into public despite agoraphobia. Conclusions and Relevance: This is the first report to demonstrate the effectiveness of acupuncture on PD with agoraphobia using PDSS, suggesting its potential as a nonpharmacological treatment for patients with PD and agoraphobia. [ABSTRACT FROM AUTHOR]

Abstract Background Theta-gamma coupling (TGC) describes the modulation of gamma oscillations by the theta phasic activity, which is crucial for processes such as the ordering of information during working memory (WM) performance. The mental arithmetic (MA), which involves performing calculations with numbers, is a crucial tool for evaluating and understanding the sensory processing and management abilities of WM. Evaluating TGC may provide greater insight into the neural mechanisms mediating WM deficits in panic disorder (PD). Methods Medical and electroencephalography (EEG) records of psychiatric outpatient clinic between 1 March 2020 and 30 September 2023 were retrospectively reviewed. A total of 34 PD patients and 34 age- and sex-matched healthy controls (HCs) underwent EEG to assess the overall functional interaction of the brain using multi-channel EEG analysis, focusing on specific brain regions including the frontal, temporal, parietal, and occipital lobes. EEG recordings were conducted during two sessions: a 5-min eyes-closed resting-state (RS) and a subsequent 5-min eyes-closed MA. The TGC and the spectral power of the theta and gamma frequency bands, which are well known to be associated with WM, were analysed. Results Compared to those in HCs, TGC and theta power were significantly attenuated in PD patients. When analysing both HCs and PD patients together, RS TGC and relative theta power were negatively correlated with state anxiety and perceived stress scores, respectively. In contrast, TGC and relative theta power during the MA condition were positively correlated with the MA performance. Specifically, in PD patients, RS theta power across all electrodes was significantly negatively correlated with the Hamilton Anxiety Scale (HAMA) score. Linear regression analysis revealed that theta power in the T5 channel remained negatively correlated with pathological anxiety as measured by the HAMA score, even after controlling for other confounding factors. Conclusions This study highlights significant alterations in TGC and theta power in PD patients. PD patients exhibit reduced TGC and theta power compared to HCs, indicating deficits in the neural mechanisms underlying anxiety and/or WM in PD. These insights contribute to a better understanding of the neural basis of WM deficits in PD and suggest potential avenues for targeted therapeutic interventions.

Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life. Early detection and intervention are crucial for managing panic disorder and improving the well-being of those affected. Technology plays a pivotal role in facilitating early detection through data-driven approaches that employ algorithms to identify patterns of behavior or symptoms associated with panic disorder. Accurate classification of panic disorder is crucial for effective diagnosis and treatment. However, machine learning models trained on imbalanced datasets, such as those containing panic disorder patients, are prone to overfitting, leading to poor generalization performance. This study investigates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing overfitting in panic disorder dataset classification using the Random Forest algorithm. The results demonstrate that SMOTE significantly improves the classification performance of Random Forest. By mitigating overfitting and improving generalization to unseen data, SMOTE increases accuracy by 15 percentage points. Before using SMOTE, the accuracy was 82%, and after using SMOTE it is 97%. The findings underscore the promise of SMOTE as a tool for boosting the performance of machine learning algorithms in classifying panic disorder from imbalanced data.

Background: Non-cardiac chest pain, a term that serves as a broad canvas, has a multitude of potential causes in which panic disorder emerges as a prevailing cause. The recognition and accurate diagnosis of panic disorder among patients with non-cardiac chest pain is important. Hence a study was conducted to understand the prevalence of panic disorder among patients with non-cardiac chest pain in a tertiary care center in Kerala. Methods: A hospital-based, cross-sectional study was done among 118 patients who were diagnosed with non-cardiac chest pain by a cardiologist. The study was conducted in the cardiology OPD in a tertiary care center in Kerala. Mini-International Neuropsychiatric Interview Panic disorder – Version 6.0.0 (M.I.N.I.) was used to diagnose panic disorder among these patients. Results: It was found that panic disorder was present in 39.8% (95% Confidence Interval - 30.9-49.3%). Conclusion: The prevalence of panic disorder in patients diagnosed with non-cardiac chest pain was 39.8%. Consultation liaison treatment using pharmacotherapy and/or psychotherapy with the help of a psychiatrist may benefit such patients.