Predicting treatment outcomes in patients with panic disorder: Cross-sectional and two-year longitudinal structural connectome analysis using machine learning methods.

PURPOSE: This study examined the relationship between structural brain networks and long-term treatment outcomes in patients with panic disorder (PD) using machine learning methods. METHOD: The study involved 80 participants (53 PD patients and 27 healthy controls) and included clinical assessments and MRI scans at baseline and after two years (160 MRIs). Patients were categorized based on their response to two-year pharmacotherapy. Brain networks were analyzed using white matter tractography and network-based statistics. RESULTS: Results showed structural network changes in PD patients, particularly in the extended fear network, including frontal regions, thalamus, and cingulate gyrus. Longitudinal analysis revealed that increased connections to the amygdala, hippocampus, and insula were associated with better treatment response. Conversely, overconnectivity in the amygdala and insula at baseline was associated with poor response, and similar patterns were found in the insula and parieto-occipital cortex related to non-remission. This study found that SVM and CPM could effectively predict treatment outcomes based on network pattern changes in PD. CONCLUSIONS: These findings suggest that monitoring structural connectome changes in limbic and paralimbic regions is critical for understanding PD and tailoring treatment. The study highlights the potential of using personalized biomarkers to develop individualized treatment strategies for PD.

Childhood Trauma and Panic Disorder: The Impact of History of Child Abuse on Illness Severity and Treatment Response.

OBJECTIVE: Patients who have experienced child abuse often have complex clinical presentations; whether a history of child abuse (HCA) affects psychotherapy outcomes is unclear. The authors examined relationships between HCA, clinical baseline variables, and change in these variables after three different psychotherapies for panic disorder (PD). METHODS: Two hundred adults with PD (with or without agoraphobia) were randomly assigned to one of three treatments across two sites: panic-focused psychodynamic psychotherapy (PFPP), cognitive-behavioral therapy (CBT), or applied relaxation training (ART). Differences in demographic and clinical variables between those with and without HCA were compared. The primary analysis addressed odds of meeting clinical response criteria on the Panic Disorder Severity Scale (PDSS) between treatments, as moderated by HCA. This effect was examined via continuous outcomes on the PDSS and psychosocial functioning (Sheehan Disability Scale). RESULTS: Compared with patients without HCA (N=154), patients with HCA (N=46) experienced significantly more severe symptoms of PD (d=0.60), agoraphobia (d=0.47), and comorbid depression (d=0.46); significantly worse psychosocial impairment (d=0.63) and anxiety sensitivity (d=0.75); greater personality disorder burden (d=0.45)-particularly with cluster C disorders (d=0.47)-and more severe interpersonal problems (d=0.54). HCA significantly moderated the likelihood of clinical response, predicting nonresponse to ART (B=-2.05, 95% CI=-4.17 to -0.30, OR=0.13, z=-2.14, p=0.032) but not CBT or PFPP. HCA did not interact with treatment condition to predict slopes of PDSS change. CONCLUSIONS: The results of this study highlight the importance of HCA in formulating treatment recommendations. Increased awareness of HCA's effects on severity of PD and treatment responsiveness among patients with PD may improve outcomes.

The cognitive theory of panic disorder: A systematic narrative review.

BACKGROUND: The cognitive theory of panic disorder proposes that individuals with panic disorder have a relatively enduring tendency to catastrophically misinterpret bodily sensations resulting in panic attacks. AIMS: We investigated whether the evidence is consistent with the theory and its predictions, if updates are required and sought to identify future research considerations. METHODS: We searched Scopus, Web of Science, PsycInfo, EMBASE, MEDLINE and CINAHL (1986 to July 2024). Inclusion criteria were studies collecting quantitative data derived from panic disorder patients, testing one of the predictions and using appropriate outcome measures. Exclusion criteria were non-English language publications, all participants under the age of 18 and studies that were not published in a peer-reviewed journal. Quality was assessed using 'QualSyst' and synthesis was based on each prediction tested. PROPSERO registration #CRD42022332211. RESULTS: 53 studies were identified amongst 49 publications. There was substantial evidence for all predictions. Three studies did not support the prediction tested and none were inconsistent. LIMITATIONS: Most studies were 'medium' in quality and were predominately from female samples. CONCLUSIONS: Findings are consistent with the theory and its predictions. Higher quality research is needed and implications for future research are discussed.

Reflection impulsivity in patients with panic disorder.

BACKGROUND: Individuals with panic disorder (PD) often exhibit a proclivity to conclude that an imminent catastrophe looms, drawing such conclusions from limited physical information. Reflection impulsivity, characterized by decision-making without adequate information, likely affects this bias. This study examines the relationship between reflection impulsivity and self-report impulsive features in individuals with PD. METHODS: Fifty patients with PD and 50 healthy controls (HC) participated to this study. Reflection impulsivity was assessed using the Information Sampling Task (IST), while trait impulsivity was measured using the Barratt Impulsiveness Scale (BIS). Participants also completed the Beck Anxiety Inventory, Panic and Agoraphobia Scale (P&A), and Anxiety Sensitivity Index-3 (ASI-3). RESULTS: Individuals with PD exhibited higher reflection impulsivity compared to HC. Increased reflection impulsivity correlated positively with PD severity as assessed by the P&A and cognitive concerns measured by ASI-3. No significant correlations were found with physical or social concerns. PD patients had higher BIS scores than HC. Higher trait impulsivity was linked to recurrent emergency department visits among PD patients. DISCUSSION: The findings of this study reveal the notion of increased impulsivity among individuals with PD and its relationship with cognitive concerns, as well as recurrent visits to the emergency department stemming from panic attacks.

Exercise and Anxiety.

Anxiety disorders are among the most commonly diagnosed mental health disorders in the United States - affecting over 40 million adults per year. Although anxiety disorders are commonly treated via psychotherapy and/or pharmacotherapy, there is also accumulating evidence to suggest that physical activity and exercise may play an important role in prevention and treatment. This chapter provides an extensive overview of literature examining the effects of physical activity, acute bouts of exercise, and chronic exercise training on several anxiety outcomes, primarily in adults. The collective evidence to date suggests that: (1) an acute bout of aerobic or resistance exercise generally results in reductions in state anxiety among adults with and without clinical anxiety disorders, (2) greater levels of physical activity are associated with fewer anxiety symptoms and a reduced likelihood of developing an anxiety disorder, (3) exercise training (aerobic and resistance) appears to reduce general anxiety symptoms in adults without a clinical anxiety disorder and adults with a chronic illness (e.g., cardiovascular disease) and may reduce disorder-specific symptoms of anxiety in adults with a clinical anxiety disorder. Although the collective body of evidence is promising, there is a need for additional well-designed and adequately powered randomized controlled trials, especially among adults with clinical anxiety disorders.

A Review of Current Digital Mental Health Care Applications for Anxiety Symptoms and Future Prospects.

OBJECTIVE: Since the impact of the coronavirus disease-2019 pandemic, the need for efficiency in medical services has become more urgent than ever. The digital treatment market is rapidly growing worldwide and digital therapeutics (DTx), a major part of the digital medical services, is also emerging as a new paradigm for treatment, with its industry growing rapidly as well. Increasing research is done on the effectiveness of mobile DTx in improving mental health conditions such as insomnia, panic, and depression. METHODS: This review paper investigates 1) the functions and characteristics of mobile digital mental health care applications for the treatment of anxiety symptoms, 2) extracts common attributes of the applications, and 3) compares them with existing traditional treatment mechanisms. RESULTS: Among the 20,000 mental health management applications that have been developed so far, 8 applications that are relatively widely used were selected and reviewed. Check-in, self-help tips, quick relief, journal, courses for practice are common features of the digital mental health care applications for anxiety and are also widely used feature in the cognitive behavioral therapy. CONCLUSION: Based on this review, we have proposed the essential elements and directions for the development of a Korean digital mental health care applications for anxiety disorders.

The functional and structural alterations in brain regions related to the fear network model in panic disorder: A resting-state fMRI and T1-weighted imaging study.

Abnormal functional connectivity (FC) within the fear network model (FNM) has been identified in panic disorder (PD) patients, but the specific local structural and functional properties, as well as effective connectivity (EC), remain poorly understood in PD. The purpose of this study was to investigate the structural and functional patterns of the FNM in PD. Magnetic resonance imaging data were collected from 33 PD patients and 35 healthy controls (HCs). Gray matter volume (GMV), degree centrality (DC), regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) were used to identify the structural and functional characteristics of brain regions within the FNM in PD. Subsequently, FC and EC of abnormal regions, based on local structural and functional features, and their correlation with clinical features were further examined. PD patients exhibited preserved GMV, ReHo, and ALFF in the brain regions of the FNM compared with HCs. However, increased DC in the bilateral amygdala was observed in PD patients. The amygdala and its subnuclei exhibited altered EC with rolandic operculum, insula, medial superior frontal gyrus, supramarginal gyrus, opercular part of inferior frontal gyrus, and superior temporal gyrus. Additionally, Hamilton Anxiety Scale score was positively correlated with EC from left lateral nuclei (dorsal portion) of amygdala to right rolandic operculum and left superior temporal gyrus. Our findings revealed a reorganized functional network in PD involving brain regions regulating exteroceptive-interoceptive signals, mood, and somatic symptoms. These results enhance our understanding of the neurobiological underpinnings of PD, suggesting potential biomarkers for diagnosis and targets for therapeutic intervention.

Predicting Dropout From Cognitive Behavioral Therapy for Panic Disorder Using Machine Learning Algorithms.

Background: Attrition is an important problem in clinical practice and research. However, the predictors of dropping out from cognitive behavioral therapy (CBT) for panic disorder (PD) are not fully understood. In this study, we aimed to build a dropout prediction model for CBT for PD using machine learning (ML) algorithms. Methods: We treated 208 patients with PD applying group CBT. From baseline data, the prediction analysis was carried out using two ML algorithms, random forest and light gradient boosting machine. The baseline data included five personality dimensions in NEO Five Factor Index, depression subscale of Symptom Checklist-90 Revised, age, sex, and Panic Disorder Severity Scale. Results: Random forest identified dropout during CBT for PD showing that the accuracy of prediction was 88%. Light gradient boosting machine showed that the accuracy was 85%. Conclusions: The ML algorithms could detect dropout after CBT for PD with relatively high accuracy. For the purpose of clinical decision-making, we could use this ML method. This study was conducted as a naturalistic study in a routine clinical setting. Therefore, our results in ML approach could be generalized to regular clinical settings.

Abnormal Insula Network Characteristics in Panic Disorder.

BACKGROUND: Panic disorder (PD) is a common disabling condition characterized by recurrent panic attacks. Emotional and behavioral impairments are associated with functional connectivity (FC) and network abnormalities. We used the whole brain FC, modular networks, and graph-theory analysis to investigate extensive network profiles in PD. METHOD: The functional MRI data from 82 PD and 97 controls were included. Intrinsic FC between each pair of 160 regions, 6 intra-networks, and 15 inter-networks were analyzed. The topological properties were explored. RESULTS: PD patients showed altered FCs within the right insula, between frontal cortex-posterior cingulate cortex (PCC), frontal cortex-cerebellum, and PCC-occipital cortex (corrected P values < 0.001). Lower connections within the Sensorimotor Network (SMN) and SMN-Occipital Network (OCN) were detected (P values < 0.05). Various decreased global and local network features were found in PD (P values < 0.05). In addition, significant correlations were found between PD symptoms and nodal efficiency (Ne) in the insula (r = -0.273, P = 0.016), and the FC of the intra-insula (r = -0.226, P = 0.041). CONCLUSIONS: PD patients present with abnormal functional brain networks, especially the decreased FC and Ne within insula, suggesting that dysfunction of information integration plays an important role in PD.

Anxiety, a significant risk factor for coronary artery disease: what is the best index.

BACKGROUND: Coronary artery disease (CAD) is known as the leading cause of disability and death globally. Anxiety disorders are also recognized as common types of mental disorders that substantially impact global health. Iran ranks among the countries with a high incidence of CAD and anxiety disorders. Therefore, the present study aims to determine the potential association and epidemiological aspects of anxiety and CAD within the population of Mashhad, the second most popoulos city in Iran. METHODS: The present study is based on extracted data from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) study which is a 10-year prospective cohort study intended to assess the effects of various CAD risk factors among Mashhad city residents. Anxiety scores were assessed at the baseline using Beck Anxiety Inventory and individuals were classified based on the BAI 4-factor structure model which included autonomic, cognitive, panic, and neuromotor components. Accordingly, the association between baseline anxiety scores and the BAI four-factor model with the risk of CAD events was analyzed using SPSS software version 21. RESULTS: Based on the results, 60.4% of the sample were female, and 5.6% were classified as having severe forms of anxiety. Moreover, severe anxiety was more prevalent in females. Results showed a 1.7% risk of CAD (p-value < 0.001) over 10 years with one unit increase in anxiety score. Based on the 4-factor model structure, we found that only panic disorder could significantly increase the risk of CAD by 1.1% over the 10-year follow-up (p-value < 0.001). CONCLUSION: Anxiety symptoms, particularly panic disorder, are independently and significantly associated with an increased overall risk of developing CAD over a 10-year period. Therefore, further studies are warranted to investigate the mechanisms through which anxiety may cause CAD, as well as possible interventions to mitigate these processes.

Elevated accuracy in recognition of subliminal happy facial expressions in patients with panic disorder after psychotherapy.

Background: Individuals with anxiety disorders (ADs) often display hypervigilance to threat information, although this response may be less pronounced following psychotherapy. This study aims to investigate the unconscious recognition performance of facial expressions in patients with panic disorder (PD) post-treatment, shedding light on alterations in their emotional processing biases. Methods: Patients with PD (n=34) after (exposure-based) cognitive behavior therapy and healthy controls (n=43) performed a subliminal affective recognition task. Emotional facial expressions (fearful, happy, or mirrored) were displayed for 33 ms and backwardly masked by a neutral face. Participants completed a forced choice task to discriminate the briefly presented facial stimulus and an uncovered condition where only the neutral mask was shown. We conducted a secondary analysis to compare groups based on their four possible response types under the four stimulus conditions and examined the correlation of the false alarm rate for fear responses to non-fearful (happy, mirrored, and uncovered) stimuli with clinical anxiety symptoms. Results: The patient group showed a unique selection pattern in response to happy expressions, with significantly more correct "happy" responses compared to controls. Additionally, lower severity of anxiety symptoms after psychotherapy was associated with a decreased false fear response rate with non-threat presentations. Conclusion: These data suggest that patients with PD exhibited a "happy-face recognition advantage" after psychotherapy. Less symptoms after treatment were related to a reduced fear bias. Thus, a differential facial emotion detection task could be a suitable tool to monitor response patterns and biases in individuals with ADs in the context of psychotherapy.

A threat from within: Learning to fear by observing aversive bodily symptoms in others.

Although observational fear learning has been implicated in the development of phobic-related fears, studies investigating observational learning of fear of bodily symptoms remain scarce. Therefore, the aim of the present study was to investigate whether fear in response to bodily symptoms can be acquired simply by observing a fearful reaction to provocation of aversive bodily symptoms in others. Forty healthy participants underwent an observational fear conditioning paradigm consisting of two phases. In the first phase, participants observed a demonstrator reacting to an aversive bodily symptom provocation (unconditioned stimulus or US, i.e., labored breathing) paired with one conditioned stimulus (CS+) but not with the other one (CS-, both CSs were geometric symbols presented on a screen the demonstrator was watching). In the second phase, participants were directly presented with the same conditioned stimuli, but in the absence of the US. Our results revealed enhanced conditioned fear responses in the beginning of the second phase to the CS + as compared to CS-, as indexed by greater skin conductance and subjective fear responses, as well as greater potentiation of startle eyeblink responses to the CS + as compared to the ITI. Taken together, these findings implicate that fear of bodily symptoms can be learned through observation of others, that is, without first-hand experience of bodily threat.

Biobehavioral approach to distinguishing panic symptoms from medical illness.

Panic disorder is a common psychiatric diagnosis characterized by acute, distressing somatic symptoms that mimic medically-relevant symptoms. As a result, individuals with panic disorder overutilize personal and healthcare resources in an attempt to diagnose and treat physical symptoms that are often medically benign. A biobehavioral perspective on these symptoms is needed that integrates psychological and medical knowledge to avoid costly treatments and prolonged suffering. This narrative review examines six common somatic symptoms of panic attacks (non-cardiac chest pain, palpitations, dyspnea, dizziness, abdominal distress, and paresthesia), identified in the literature as the most severe, prevalent, or critical for differential diagnosis in somatic illness, including long COVID. We review somatic illnesses that are commonly comorbid or produce panic-like symptoms, their relevant risk factors, characteristics that assist in distinguishing them from panic, and treatment approaches that are typical for these conditions. Additionally, this review discusses key factors, including cultural considerations, to assist healthcare professionals in differentiating benign from medically relevant symptoms in panic sufferers.

Epigenetic signatures of social anxiety, panic disorders and stress experiences: Insights from genome-wide DNA methylation risk scores.

Social anxiety disorder (SAD) and panic disorder (PD) are prevalent anxiety disorders characterized by a complex interplay of genetic and environmental factors. Both disorders share overlapping features and often coexist, despite displaying distinct characteristics. Childhood life adversity, overall stressful life events, and genetic factors contribute to the development of these disorders. DNA methylation, an epigenetic modification, has been implicated in the pathogenesis of these diseases. In this study, we investigated whether whole-genome DNA methylation risk scores (MRSs) for SAD risk, severity of social anxiety, childhood life adversity, PD risk, and overall stressful life events were associated with SAD or PD case‒control status. Preliminary epigenome-wide association studies (EWASs) for SAD risk, severity of social anxiety, and childhood life adversity were conducted in 66 SAD individuals and 77 healthy controls (HCs). Similarly, EWASs for PD risk and overall stressful life events were performed in 182 PD individuals and 81 HCs. MRSs were calculated from these EWASs. MRSs derived from the EWASs of SAD risk and severity of social anxiety were greater in PD patients than in HCs. Additionally, MRSs derived from the EWASs of overall stressful life events, particularly in PD individuals, were lower in SAD individuals than in HCs. In contrast, MRSs for childhood life adversity or PD risk were not significantly associated with PD or SAD case‒control status. These findings highlight the epigenetic features shared in both disorders and the distinctive epigenetic features related to social avoidance in SAD patients, helping to elucidate the epigenetic basis of these disorders.

Neuronavigated Right Orbitofrontal 20 Hz Theta Burst Transcranial Magnetic Stimulation Augmentation for Obsessive-Compulsive Disorder with Comorbid Depression and Anxiety Disorders: An Open-Label Study.

BACKGROUND: Despite the availability of pharmacotherapy and psychotherapy for treating obsessive-compulsive disorder (OCD), alternative approaches need to be explored due to the high likelihood of treatment resistance. Neuronavigated 20 Hz theta burst stimulation (TBS-20 Hz), targeting the bilateral dorsolateral prefrontal cortex (DLPFC) augmented with the right orbitofrontal cortex (ROFC), was tested for treating OCD comorbid with depression and anxiety disorders. METHODS: A retrospective chart review was performed on fourteen patients treated for moderate-to-severe OCD in a private outpatient clinic. Twelve patients had comorbid major depressive disorder (MDD), and thirteen patients had either generalized anxiety disorder (GAD) or panic disorder (PD). Patients completed the Y-BOCS-SR, BDI-II, and BAI rating scales weekly, which were used to measure the changes in OCD, depression, and anxiety symptoms, respectively. RESULTS: Neuronavigated TBS-20 Hz was sequentially applied to the right DLPFC (RDLPFC), left DLPFC (LDLPFC), and ROFC. A total of 64% (9/14) of patients achieved remission from OCD (Y-BOCS-SR ≤ 14) in an average of 6.1 weeks of treatment (SD = 4.0). A total of 58% (7/12) of patients remitted from MDD (BDI < 13) in an average of 4.1 weeks (SD = 2.8), and 62% (8/13) of patients remitted from GAD/PD (BAI < 8) in an average of 4.3 weeks (SD = 2.5). CONCLUSIONS: The neuronavigated TBS-20 Hz sequential stimulation of RDLPFC and LDLPFC, followed by ROFC, significantly reduced OCD, MDD, and GAD/PD symptoms. Randomized sham controls are warranted to validate these results.

A Case of Anti-Leucine-Rich Glioma-Inactivated Protein 1 (Anti-LGI1) Limbic Encephalitis With New-Onset Panic Attacks.

Anti-leucine-rich glioma-inactivated protein 1 (anti-LGI1) limbic encephalitis is a rare autoimmune neurologic disorder with antibodies against LGI1. It was first recognized as a disease in 2010 and represents the second most common cause of autoimmune encephalitis. Clinically, it is characterized by subacute changes in cognition, memory, and behavior, associated with hyponatremia and faciobrachial dystonic seizures (FBDS). This report discusses a unique onset of anti-LGI1 limbic encephalitis where an elderly female presented with symptoms of new-onset panic attacks and rhythmic facial movements for one week. She was then admitted to neurology for further serum, cerebrospinal fluid(CSF), and lab testing. She was eventually found to be positive for antibodies against LGI1 voltage-gated potassium channels, which confirmed the diagnosis of limbic encephalitis. The quick recognition of symptoms and escalation of management allowed the patient to experience drastic improvements after the initiation of steroids, immunotherapy, and lacosamide. Since anti-LGI1 limbic encephalitis is underdiagnosed, it can lead to irreversible long-term cognitive sequelae (i.e., memory deficits). Thus, awareness of the typically associated findings of FBDS, cognitive disturbances, psychiatric changes, and hyponatremia can aid in early diagnosis and prompt treatment with immunotherapy, allowing for more favorable outcomes.

Absolute and relative outcomes of psychotherapies for eight mental disorders: a systematic review and meta-analysis.

Psychotherapies are first-line treatments for most mental disorders, but their absolute outcomes (i.e., response and remission rates) are not well studied, despite the relevance of such information for health care users, providers and policy makers. We aimed to examine absolute and relative outcomes of psychotherapies across eight mental disorders: major depressive disorder (MDD), social anxiety disorder, panic disorder, generalized anxiety disorder (GAD), specific phobia, post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), and borderline personality disorder (BPD). We used a series of living systematic reviews included in the Metapsy initiative (www.metapsy.org), with a common strategy for literature search, inclusion of studies and extraction of data, and a common format for the analyses. Literature search was conducted in major bibliographical databases (PubMed, PsycINFO, Embase, and the Cochrane Register of Controlled Trials) up to January 1, 2023. We included randomized controlled trials comparing psychotherapies for any of the eight mental disorders, established by a diagnostic interview, with a control group (waitlist, care-as-usual, or pill placebo). We conducted random-effects model pairwise meta-analyses. The main outcome was the absolute rate of response (at least 50% symptom reduction between baseline and post-test) in the treatment and control conditions. Secondary outcomes included the relative risk (RR) of response, and the number needed to treat (NNT). Random-effects meta-analyses of the included 441 trials (33,881 patients) indicated modest response rates for psychotherapies: 0.42 (95% CI: 0.39-0.45) for MDD; 0.38 (95% CI: 0.33-0.43) for PTSD; 0.38 (95% CI: 0.30-0.47) for OCD; 0.38 (95% CI: 0.33-0.43) for panic disorder; 0.36 (95% CI: 0.30-0.42) for GAD; 0.32 (95% CI: 0.29-0.37) for social anxiety disorder; 0.32 (95% CI: 0.23-0.42) for specific phobia; and 0.24 (95% CI: 0.15-0.36) for BPD. Most sensitivity analyses broadly supported these findings. The RRs were significant for all disorders, except BPD. Our conclusion is that most psychotherapies for the eight mental disorders are effective compared with control conditions, but absolute response rates are modest. More effective treatments and interventions for those not responding to a first-line treatment are needed.

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning.

OBJECTIVE: Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. METHODS: Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. RESULTS: All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. CONCLUSIONS: These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

The Effectiveness of a Digital App for Reduction of Clinical Symptoms in Individuals With Panic Disorder: Randomized Controlled Trial.

BACKGROUND: Panic disorder is a common and important disease in clinical practice that decreases individual productivity and increases health care use. Treatments comprise medication and cognitive behavioral therapy. However, adverse medication effects and poor treatment compliance mean new therapeutic models are needed. OBJECTIVE: We hypothesized that digital therapy for panic disorder may improve panic disorder symptoms and that treatment response would be associated with brain activity changes assessed with functional near-infrared spectroscopy (fNIRS). METHODS: Individuals (n=50) with a history of panic attacks were recruited. Symptoms were assessed before and after the use of an app for panic disorder, which in this study was a smartphone-based app for treating the clinical symptoms of panic disorder, panic symptoms, depressive symptoms, and anxiety. The hemodynamics in the frontal cortex during the resting state were measured via fNIRS. The app had 4 parts: diary, education, quest, and serious games. The study trial was approved by the institutional review board of Chung-Ang University Hospital (1041078-202112-HR-349-01) and written informed consent was obtained from all participants. RESULTS: The number of participants with improved panic symptoms in the app use group (20/25, 80%) was greater than that in the control group (6/21, 29%; χ21=12.3; P=.005). During treatment, the improvement in the Panic Disorder Severity Scale (PDSS) score in the app use group was greater than that in the control group (F1,44=7.03; P=.01). In the app use group, the total PDSS score declined by 42.5% (mean score 14.3, SD 6.5 at baseline and mean score 7.2, SD 3.6 after the intervention), whereas the PDSS score declined by 14.6% in the control group (mean score 12.4, SD 5.2 at baseline and mean score 9.8, SD 7.9 after the intervention). There were no significant differences in accumulated oxygenated hemoglobin (accHbO2) at baseline between the app use and control groups. During treatment, the reduction in accHbO2 in the right ventrolateral prefrontal cortex (VLPFC; F1,44=8.22; P=.006) and the right orbitofrontal cortex (OFC; F1,44=8.88; P=.005) was greater in the app use than the control group. CONCLUSIONS: Apps for panic disorder should effectively reduce symptoms and VLPFC and OFC brain activity in patients with panic disorder. The improvement of panic disorder symptoms was positively correlated with decreased VLPFC and OFC brain activity in the resting state. TRIAL REGISTRATION: Clinical Research Information Service KCT0007280; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=21448.