The heterogeneity of chronic post-COVID neuropsychiatric symptoms (PCNPS), especially after infection by the Omicron strain, has not been adequately explored.

To explore the clustering pattern of chronic PCNPS in a cohort of patients having their first COVID infection during the ‘Omicron wave’ and discover phenotypes of patients based on their symptoms’ patterns using a pre-registered protocol.

We assessed 1205 eligible subjects in Hong Kong using app-based questionnaires and cognitive tasks.

Partial network analysis of chronic PCNPS in this cohort produced two major symptom clusters (cognitive complaint–fatigue and anxiety–depression) and a minor headache–dizziness cluster, like our pre-Omicron cohort. Participants with high numbers of symptoms could be further grouped into two distinct phenotypes: a cognitive complaint–fatigue predominant phenotype and another with symptoms across multiple clusters. Multiple logistic regression showed that both phenotypes were predicted by the level of pre-infection deprivation (adjusted P-values of 0.025 and 0.0054, respectively). The severity of acute COVID (adjusted P = 0.023) and the number of pre-existing medical conditions predicted only the cognitive complaint–fatigue predominant phenotype (adjusted P = 0.003), and past suicidal ideas predicted only the symptoms across multiple clusters phenotype (adjusted P < 0.001). Pre-infection vaccination status did not predict either phenotype.

Our findings suggest that we should pursue a phenotype-driven approach with holistic biopsychosocial perspectives in disentangling the heterogeneity under the umbrella of chronic PCNPS. Management of patients complaining of chronic PCNPS should be stratified according to their phenotypes. Clinicians should recognise that depression and anxiety cannot explain all chronic post-COVID cognitive symptoms.

Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.

Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t-test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.

Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.

Evidence suggests the crucial role of dysfunctional default mode (DMN), salience and frontoparietal (FPN) networks, collectively termed the triple network model, in the pathophysiology of treatment-resistant depression (TRD).

Using the graph theory- and seed-based functional connectivity analyses, we attempted to elucidate the role of low-dose ketamine in the triple networks, namely the DMN, salience and FPN.

Resting-state functional connectivity magnetic resonance imaging (rs–fcMRI) data derived from two previous clinical trials of a single, low-dose ketamine infusion were analysed. In clinical trial 1 (Trial 1), patients with TRD were randomised to either a ketamine or normal saline group, while in clinical trial 2 (Trial 2) those patients with TRD and pronounced suicidal symptoms received a single infusion of either 0.05 mg/kg ketamine or 0.045 mg/kg midazolam. All participants underwent rs–fcMRI pre and post infusion at Day 3. Both graph theory- and seed-based functional connectivity analyses were performed independently.

Trial 1 demonstrated significant group-by-time effects on the degree centrality and cluster coefficient in the right posterior cingulate cortex (PCC) cortex ventral 23a and b (DMN) and the cluster coefficient in the right supramarginal gyrus perisylvian language (salience). Trial 2 found a significant group-by-time effect on the characteristic path length in the left PCC 7Am (DMN). In addition, both ketamine and normal saline infusions exerted a time effect on the cluster coefficient in the right dorsolateral prefrontal cortex a9-46v (FPN) in Trial 1.

These findings may support the utility of the triple-network model in elucidating ketamine’s antidepressant effect. Alterations in DMN, salience and FPN function may underlie this effect.

Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well studied. In this population-based cohort study with quasi-experimental design, we examined both the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.

Using the territory-wide electronic medical records in Hong Kong, we identified patients with new diagnoses of depression from 2014 to 2022. An interrupted time-series (ITS) analysis examined changes in incidence of depression before and during the pandemic. We then divided patients into nine cohorts based on year of incidence and studied their initial and ongoing service use until December 2022. Generalized linear modeling compared the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among preexisting patients.

There was an immediate increase in depression incidence (RR=1.21; 95% CI: 1.10, 1.33; p<0.001) in the population since the pandemic with a nonsignificant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11 percent fewer resources than the prepandemic patients in the first diagnosis year. Preexisting depression patients also had an immediate decrease of 16 percent in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound.

During the COVID-19 pandemic, service provision for depression was suboptimal in the face of greater demand generated by the increasing depression incidence. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.

We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.

The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.

There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.

A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.

The right inferior frontal gyrus (RIFG) is a potential beneficial brain stimulation target for autism. This randomized, double-blind, two-arm, parallel-group, sham-controlled clinical trial assessed the efficacy of intermittent theta burst stimulation (iTBS) over the RIFG in reducing autistic symptoms (NCT04987749).

Conducted at a single medical center, the trial enrolled 60 intellectually able autistic individuals (aged 8–30 years; 30 active iTBS). The intervention comprised 16 sessions (two stimulations per week for eight weeks) of neuro-navigated iTBS or sham over the RIFG. Fifty-seven participants (28 active) completed the intervention and assessments at Week 8 (the primary endpoint) and follow-up at Week 12.

Autistic symptoms (primary outcome) based on the Social Responsiveness Scale decreased in both groups (significant time effect), but there was no significant difference between groups (null time-by-treatment interaction). Likewise, there was no significant between-group difference in changes in repetitive behaviors and exploratory outcomes of adaptive function and emotion dysregulation. Changes in social cognition (secondary outcome) differed between groups in feeling scores on the Frith-Happe Animations (Week 8, p = 0.026; Week 12, p = 0.025). Post-hoc analysis showed that the active group improved better on this social cognition than the sham group. Dropout rates did not vary between groups; the most common adverse event in both groups was local pain. Notably, our findings would not survive stringent multiple comparison corrections.

Our findings suggest that iTBS over the RIFG is not different from sham in reducing autistic symptoms and emotion dysregulation. Nonetheless, RIFG iTBS may improve social cognition of mentalizing others' feelings in autistic individuals.

Older age bipolar disorder (OABD) is commonly defined as bipolar disorder in individuals aged 60 or more. General principles of pharmacotherapy in guidelines for treating OABD are greatly like those for younger adults. We aimed to investigate prescription changes among OABD patients discharged from two public mental hospitals in Taiwan from 2006 to 2019.

OABD patients discharged from the two study hospitals, from 1 January 2006 to 31 December 2019 (n = 1072), entered the analysis. Prescribed drugs at discharge, including mood stabilizers (i.e., lithium, valproate, carbamazepine, and lamotrigine), antipsychotics (i.e., second- and first-generation antipsychotics; SGAs & FGAs), and antidepressants, were investigated. Complex polypharmacy was defined as the use of 3 or more agents among the prescribed drugs. Temporal trends of each prescribing pattern were analyzed using the Cochran-Armitage Trend test.

The most commonly prescribed drugs were SGAs (72.0%), followed by valproate (48.4%) and antidepressants (21.7%). The prescription rates of SGAs, antidepressants, antidepressants without mood stabilizers, and complex polypharmacy significantly increased over time, whereas the prescription rates of mood stabilizers, lithium, FGAs, and antidepressants plus mood stabilizers significantly decreased.

Prescribing patterns changed remarkably for OABD patients over a 14- year period. The decreased use of lithium and increased use of antidepressants did not reflect bipolar treatment guidelines. Future research should examine whether such prescribing patterns are associated with adverse clinical outcomes.

National health insurance (NHI) Taiwan has provided additional markups on dental service fees for people with specific disabilities, and the expenditure has increased significantly from TWD473 million (USD15 million) in 2016 to TWD722 million (USD24 million) in 2022. The purpose of this study was to determine oral health risk and to develop a risk assessment model for capitation outpatient dental payments in children with Autism.

Based on the literature and expert opinion, we developed a level of oral health risk model from the claim records of 2019. The model uses oral outpatient claim data to analyze: (i) the degree of caries disease; (ii) the level of dental fear or cooperation; and (iii) the level of tooth structure. Each factor was given a score from zero to four and a total score was calculated. Low-, medium-, and high-risk groups were formed based on the total points. The oral health risk capitation models are estimated by ordinary least squares using an individual’s annual outpatient dental expenditure in 2019 as the dependent variable. For subgroups based on age group and level of disability, expenditures predicted by the models are compared with actual outpatient dental expenditures. Predictive R-squared and predictive ratios were used to evaluate the model’s predictability.

The demographic variables, level of oral health risk, preventive dental care, and the type of dental health care predicted 30 percent of subsequent outpatient dental expenditure in children with autism. For subgroups (age group and disability level) of high-risk patients, the model substantially overpredicted the expenditure, whereas underprediction occurred in the low-risk group.

The risk-adjusted model based on principal oral health was more accurate in predicting an individual’s future expenditure than the relevant study in Taiwan. The finding provides insight into the important risk factor in the outpatient dental expenditure of children with autism and the fund planning of dental services for people with specific disabilities.

To evaluate the mental health of paediatric cochlear implant users and analyse the relationship between six dimensions (movements, cognitive ability, emotion and will, sociality, living habits and language) and hearing and speech rehabilitation.

Eighty-two cochlear implant users were assessed using the Mental Health Survey Questionnaire. Age at implantation, time of implant use and listening modes were investigated. Categories of Auditory Performance and the Speech Intelligibility Rating Scale were used to score hearing and speech abilities.

More recipients scored lower in cognitive ability and language. Age at implantation was statistically significant (p < 0.05) for movements, cognitive ability, emotion and will, and language. The time of implant usage and listening mode indicated statistical significance (p < 0.05) in cognitive ability, sociality and language.

Timely attention should be paid to the mental health of paediatric cochlear implant users, and corresponding psychological interventions should be implemented to make personalised rehabilitation plans.

Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.

Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.

CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features.

This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.