Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia.

A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman's rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.

White matter tract alterations in schizophrenia identified by DTI-based probabilistic tractography: a multisite harmonisation study.

INTRODUCTION: It has been suggested that schizophrenia involves dysconnectivity between functional brain regions and also the white matter structural disorganisation. Thus, diffusion tensor imaging (DTI) has widely been used for studying schizophrenia. However, most previous studies have used the region of interest (ROI) based approach. We, therefore, performed the probabilistic tractography method in this study to reveal the alterations of white matter tracts in the schizophrenia brain. METHODS: A total of four different datasets consisted of 189 patients with schizophrenia and 213 healthy controls were investigated. We performed retrospective harmonisation of raw diffusion MRI data by dMRIharmonisation and used the FMRIB Software Library (FSL) for probabilistic tractography. The connectivities between different ROIs were then compared between patients and controls. Furthermore, we evaluated the relationship between the connection probabilities and the symptoms and cognitive measures in patients with schizophrenia. RESULTS: After applying Bonferroni correction for multiple comparisons, 11 different tracts showed significant differences between patients with schizophrenia and healthy controls. Many of these tracts were associated with the basal ganglia or cortico-striatal structures, which aligns with the current literature highlighting striatal dysfunction. Moreover, we found that these tracts demonstrated statistically significant relationships with few cognitive measures related to language, executive function, or processing speed. CONCLUSION: We performed probabilistic tractography using a large, harmonised dataset of diffusion MRI data, which enhanced the statistical power of our study. It is important to note that most of the tracts identified in this study, particularly callosal and cortico-striatal streamlines, have been previously implicated in schizophrenia within the current literature. Further research with harmonised data focusing specifically on these brain regions could be recommended.

Structural impairment in superficial and deep white matter in schizophrenia.

OBJECTIVE: Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS: We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS: Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION: We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.

Introduction: The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods: In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion: The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels.

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals' smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Loss-Framed Adaptive Microcontingency Management for Preventing Prolonged Sedentariness: Development and Feasibility Study.

BACKGROUND: A growing body of evidence shows that financial incentives can effectively reinforce individuals' positive behavior change and improve compliance with health intervention programs. A critical factor in the design of incentive-based interventions is to set a proper incentive magnitude. However, it is highly challenging to determine such magnitudes as the effects of incentive magnitude depend on personal attitudes and contexts. OBJECTIVE: This study aimed to illustrate loss-framed adaptive microcontingency management (L-AMCM) and the lessons learned from a feasibility study. L-AMCM discourages an individual's adverse health behaviors by deducting particular expenses from a regularly assigned budget, where expenses are adaptively estimated based on the individual's previous responses to varying expenses and contexts. METHODS: We developed a mobile health intervention app for preventing prolonged sedentary lifestyles. This app delivered a behavioral mission (ie, suggesting taking an active break for a while) with an incentive bid when 50 minutes of uninterrupted sedentary behavior happened. Participants were assigned to either the fixed (ie, deducting the monotonous expense for each mission failure) or adaptive (ie, deducting varying expenses estimated by the L-AMCM for each mission failure) incentive group. The intervention lasted 3 weeks. RESULTS: We recruited 41 participants (n=15, 37% women; fixed incentive group: n=20, 49% of participants; adaptive incentive group: n=21, 51% of participants) whose mean age was 24.0 (SD 3.8; range 19-34) years. Mission success rates did not show statistically significant differences by group (P=.54; fixed incentive group mean 0.66, SD 0.24; adaptive incentive group mean 0.61, SD 0.22). The follow-up analysis of the adaptive incentive group revealed that the influence of incentive magnitudes on mission success was not statistically significant (P=.18; odds ratio 0.98, 95% CI 0.95-1.01). On the basis of the qualitative interviews, such results were possibly because the participants had sufficient intrinsic motivation and less sensitivity to incentive magnitudes. CONCLUSIONS: Although our L-AMCM did not significantly affect users' mission success rate, this study configures a pioneering work toward adaptively estimating incentives by considering user behaviors and contexts through leveraging mobile sensing and machine learning. We hope that this study inspires researchers to develop incentive-based interventions.

Impact of the early phase of the COVID-19 pandemic on the use of mental health services in South Korea: a nationwide, health insurance data-based study.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has profoundly affected the utilization of mental health services. Existing evidence investigating this issue at the nationwide level is lacking, and it is uncertain whether the effects of the COVID-19 pandemic on the use of psychiatric services differs based on psychiatric diagnosis. METHODS: Data from the claims database between October 2015 and August 2020 was obtained from the Health Insurance Review and Assessment agency in South Korea. Based on the main diagnostic codes, psychiatric patients were identified and categorized into diagnostic groups (anxiety disorders, bipolar and related disorders, depressive disorders, and schizophrenia spectrum disorders). We calculated the number of psychiatric inpatients and outpatients and the medication adherence of patients for each month. We compared the actual and predicted values of outcomes during the COVID-19 pandemic and performed interrupted time-series analyses to test the statistical significance of the impact of the pandemic. RESULTS: During the COVID-19 pandemic, the number of inpatients and admissions to psychiatric hospitals decreased for bipolar and related disorders and depressive disorders. In addition, the number of patients admitted to psychiatric hospitals for schizophrenia spectrum disorders decreased. The number of psychiatric outpatients showed no significant change in all diagnostic groups. Increased medication adherence was observed for depressive, schizophrenia spectrum, and bipolar and related disorders. CONCLUSIONS: In the early phase of the COVID-19 pandemic, there was a trend of a decreasing number of psychiatric inpatients and increasing medication adherence; however, the number of psychiatric outpatients remained unaltered.

Delay in psychiatric hospitalization from the diagnosis of first-episode schizophrenia and its association with clinical outcomes and direct medical costs: a nationwide, health insurance data-based study.

BACKGROUND: Early intervention is essential for improving the prognosis in patients with first-episode schizophrenia (FES). The Mental Health Act limits involuntary hospitalization in South Korea to cases where an individual exhibits both a mental disorder and a potential for harming themselves or others, which could result in a delay in the required treatment in FES. We investigated the effect of delay in the first psychiatric hospitalization on clinical outcomes in FES. METHODS: The South Korean Health Insurance Review Agency database (2012-2019) was used. We identified 15,994 patients with FES who had a record of at least one psychiatric hospitalization within 1 year from their diagnosis. A multivariate linear regression model and a generalized linear model with a gamma distribution and log link were used to examine associations between the duration from the diagnosis to the first psychiatric admission and clinical outcomes as well as direct medical costs after 2 and 5 years. RESULTS: Within both the 2-year and the 5-year period, longer durations from the diagnosis to the first psychiatric admission were associated with an increase in the number of psychiatric hospitalizations (2-y: B = 0.003, p = 0.003, 5-y: B = 0.007, p = 0.001) and an increase in direct medical costs (total: 2-y: B = 0.005, p < 0.001, 5-y: B = 0.004, p = 0.005; inpatient care: 2-y: B = 0.005, p < 0.001, 5-y: B = 0.004, p = 0.017). CONCLUSIONS: Earlier psychiatric admission from the diagnosis is associated with a decrease in the number of psychiatric admissions as well as in direct medical costs in patients with FES.

One-Year Clinical Outcomes Following Electroconvulsive Therapy for Patients with Schizophrenia: A Nationwide Health Insurance Data-Based Study.

Background: Although the use of electroconvulsive therapy (ECT) in the treatment of schizophrenia has decreased since the advent of antipsychotic drugs, ECT is still implemented in several clinical indications. However, a few population-based studies have examined its real-world effectiveness in schizophrenia. Methods: We used data from 2010 to 2019 from the Health Insurance Review and Assessment Service database in the Republic of Korea. We selected 380 schizophrenia patients having more than six ECT sessions and 1140 patient controls matched for age, sex, calendar year at entry, and the number of psychiatric hospitalizations before the time point of start of psychiatric hospitalization for ECT. Antipsychotic treatment discontinuation, psychiatric hospitalization, and direct medical costs were used as measures of clinical outcomes. Multiple regression analysis was used for any group-by-time interaction effect, and 1-year pre- and post-ECT periods were compared within and between the groups. Results: We found a significantly lower number of antipsychotic treatment discontinuations in the ECT group during the 1-year post-ECT period (t=2.195, p=0.028). A larger decrease was found in the number of psychiatric hospitalizations in the ECT group, with a group-by-time interaction effect (p=0.043). The direct medical costs in the 1-year pre- (t=-8.782, p<0.001) and post-ECT periods (t=-9.107, p<0.001) were higher in the ECT group than in the control group, with no significant change across both periods. Conclusion: We found that the ECT group had a larger decrease in the number of psychiatric hospitalizations in the 1-year post-ECT period than the control group.

A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset.

Accelerometer data collected from wearable devices have recently been used to monitor physical activities (PAs) in daily life. While the intensity of PAs can be distinguished with a cut-off approach, it is important to discriminate different behaviors with similar accelerometry patterns to estimate energy expenditure. We aim to overcome the data imbalance problem that negatively affects machine learning-based PA classification by extracting well-defined features and applying undersampling and oversampling methods. We extracted various temporal, spectral, and nonlinear features from wrist-, hip-, and ankle-worn accelerometer data. Then, the influences of undersampilng and oversampling were compared using various ML and DL approaches. Among various ML and DL models, ensemble methods including random forest (RF) and adaptive boosting (AdaBoost) exhibited great performance in differentiating sedentary behavior (driving) and three walking types (walking on level ground, ascending stairs, and descending stairs) even in a cross-subject paradigm. The undersampling approach, which has a low computational cost, exhibited classification results unbiased to the majority class. In addition, we found that RF could automatically select relevant features for PA classification depending on the sensor location by examining the importance of each node in multiple decision trees (DTs). This study proposes that ensemble learning using well-defined feature sets combined with the undersampling approach is robust for imbalanced datasets in PA classification. This approach will be useful for PA classification in the free-living situation, where data imbalance problems between classes are common.

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management.

Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels.

The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.

Comparative effectiveness of antipsychotic monotherapy and polypharmacy in schizophrenia patients with clozapine treatment: A nationwide, health insurance data-based study.

Clozapine is the most effective antipsychotic for treatment-resistant schizophrenia (TRS). However, it remains uncertain whether antipsychotic augmentation to clozapine has the superior effectiveness over clozapine alone and the effect size of clozapine compared to other antipsychotic drugs in TRS. Therefore, we examined the comparative effectiveness of antipsychotic monotherapy and polypharmacy on the risk of psychiatric admission and treatment discontinuation in TRS. Data were collected from the Health Insurance Review Agency database between January 2010 and December 2019 in South Korea. Among prevalent patients with schizophrenia, we defined 22,327 patients with TRS as those who had been prescribed with clozapine at least once during the entire observation period. Stratified Cox proportional hazards regressions were performed using data on all antipsychotic prescriptions of patients with TRS to investigate the risk of psychiatric hospitalization and treatment discontinuation associated with antipsychotic treatment. In individual comparisons, clozapine monotherapy was the most effective for the risk of psychiatric hospitalization compared to no use (hazard ratio [HR] = 0.23, 95% confidence interval [CI] = 0.22-0.25). In group comparisons, clozapine with long-acting injectable (LAI) second-generation antipsychotics (SGA) was superior to clozapine monotherapy for the risk of psychiatric hospitalization (HR = 0.60, 95%CI = 0.41-0.88). Clozapine monotherapy was associated with the lowest risk of treatment discontinuation in the individual and group comparisons. This retrospective observational population-based study reports that clozapine with LAI SGA is more effective in lowering the risk of psychiatric hospitalization in antipsychotic group comparison with the reference of clozapine monotherapy.

Risk of treatment discontinuation and psychiatric hospitalization associated with early dose reduction of antipsychotic treatment in first-episode schizophrenia: A nationwide, health insurance data-based study.

AIM: We investigated the impact of early dose reduction of antipsychotic treatment on the risk of treatment discontinuation and psychiatric hospitalization in patients with first-episode schizophrenia (FES). METHODS: The Health Insurance Review Agency database in South Korea was used to include 16 153 patients with FES. At 6 months from their diagnosis, the patients were categorized by the magnitude of dose reduction (no reduction, 0%-50%, and >50%). With a reference of no reduction, the risk of treatment discontinuation and psychiatric hospitalization associated with dose reduction in the 1-year follow-up period after the first 6 months was examined with a Cox proportional hazard ratio model stratified by the mean daily olanzapine-equivalent dose in the first 3 months (<10, 10 to 20, >20 mg/day). RESULTS: A >50% dose reduction was associated with an increased risk of treatment discontinuation in all subgroups (<10 mg/day: hazard ratio [HR] =1.44, 95% confidence interval [CI] =1.24-1.67 [P <0.01]; 10-20 mg/day: HR =1.60, 95% CI =1.37-1.86 [P <0.01]; and >20 mg/day: HR =1.62, 95% CI =1.37-1.91 [P <0.01]). In the subgroup taking <10 mg/day, an association of 0%-50% dose reduction with an increased risk of treatment discontinuation was observed (HR =1.20, 95% CI =1.09-1.31; P <0.01). A > 50% dose reduction was associated with increased risk of psychiatric hospitalization only in the subgroup taking <10 mg/day (HR =1.48, 95% CI =1.21-1.80; P <0.01). CONCLUSIONS: Our results suggest that an above certain dose of antipsychotic drugs is required to prevent psychiatric hospitalization, and extensive dose reduction of antipsychotic drugs could result in a higher risk of treatment discontinuation.

Diagnostic conversion from unipolar to bipolar affective disorder-A population-based study.

OBJECTIVES: It is essential to clinically distinguish bipolar affective disorder from unipolar affective disorders. However, patients previously diagnosed with unipolar affective disorder are sometimes later diagnosed with bipolar affective disorder, known as diagnostic conversion. Here we investigated diagnostic conversion using data from a nationwide population-based register. METHODS: We obtained claims data from 2007 to 2020 in Korea's Health Insurance Review Agency database and identified a cohort of patients who were diagnosed with unipolar depression in 2009 without prior psychiatric diseases within the previous 2 years. We studied the rate of diagnostic conversion and risk factors, especially antidepressants. RESULTS: About 6.5% of patients underwent diagnostic conversion during the observation period. Younger age at disease onset and usage of antidepressants increased the relative risk for diagnostic conversion. Patients using serotonin-norepinephrine reuptake inhibitors (SNRI) showed more than twice the risk compared to no usage of antidepressant. LIMITATION: First, this study was based on the population-based register data. Thus, we defined the patient cohort diagnosed with unipolar depression with strict inclusion criteria. Second, the exposure time differed between different antidepressants. Third, we estimated the relative risk for diagnostic conversion compared to no use of antidepressants. Moreover, we could not rule out the potential influence of antidepressant polypharmacy. CONCLUSION: We confirmed diagnostic conversion in some patients and identified younger age or usage of antidepressants, especially SNRI, as risk factors. Because unipolar and bipolar affective disorders show different disease courses or prognoses and have different treatment strategies, clinicians should be mindful of diagnostic conversion.

Effects of comorbid alcohol use disorder on the clinical outcomes of first-episode schizophrenia: a nationwide population-based study.

BACKGROUND: Alcohol use disorder (AUD) is a common psychiatric comorbidity in schizophrenia, associated with poor clinical outcomes and medication noncompliance. Most previous studies on the effect of alcohol use in patients with schizophrenia had limitations of small sample size or a cross-sectional design. Therefore, we used a nationwide population database to investigate the impact of AUD on clinical outcomes of schizophrenia. METHODS: Data from the Health Insurance Review Agency database in South Korea from January 1, 2007 to December 31, 2016 were used. Among 64,442 patients with first-episode schizophrenia, 1598 patients with comorbid AUD were selected based on the diagnostic code F10. We performed between- and within-group analyses to compare the rates of psychiatric admissions and emergency room (ER) visits, and medication possession ratio (MPR) between the patients with comorbid AUD and control patients matched for the onset age, sex, and observation period. RESULTS: The rates of psychiatric admissions and ER visits in both groups decreased after the time point of diagnosis of AUD; however, the decrease was significantly greater in the patients with comorbid AUD compared to the control patients. While the comorbid AUD group showed an increase in MPR after the diagnosis of AUD, MPR decreased in the control group. The rates of psychiatric admissions, ER visits, and MPR were worse in the comorbid AUD group both before and after the diagnosis of AUD. CONCLUSIONS: The results emphasize an importance of psychiatric comorbidities, especially AUD, in first-episode schizophrenia and the necessity of further research for confirmative findings of the association of AUD with clinical outcomes of schizophrenia.

Hypoparathyroidism, sensorineural deafness, and renal dysgenesis syndrome with a GATA3 mutation.

Hypoparathyroidism, sensorineural deafness, and renal dysgenesis syndrome is an autosomal dominant disease caused by mutations in the GATA3 gene on chromosome 10p15. We identified a patient diagnosed with hypoparathyroidism who also had a family history of hypoparathyroidism and sensorineural deafness, present in the father. The patient was subsequently diagnosed and found to be a heterozygote for an insertion mutation c.255_256ins4 (GTGC) in exon 2 of GATA3. His father was also confirmed to have the same mutation in GATA3.