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Using the syntactic priming paradigm, this study investigated abstract syntactic knowledge of Chinese transitive structures (i.e., subject-verb-object [SVO], BA, and BEI) in deaf children with cochlear implants (CIs). Specifically, we focused on the differences in the development of various syntactic structures (within CI children and compared with their typically hearing children) and the possible individual differences during this process. Results showed that both CI and hearing children exhibited structural priming for all syntactic structures (i.e., SVO, SbaOV structure [agent-patient ordering], and ObeiSV structure [patient-agent ordering]) after comprehending and repeating the prime sentence regardless of verb repetition. However, verb repetition induced an intense abstract priming effect in CI children but not hearing children, with the lexical boost effect more significant for SVO and BA structures. In addition, CI children’s working memory capability modulated the production of the BA structure but not SVO and BEI structures.
Psychostimulants and nonstimulants have partially overlapping pharmacological targets on attention-deficit/hyperactivity disorder (ADHD), but whether their neuroimaging underpinnings differ is elusive. We aimed to identify overlapping and medication-specific brain functional mechanisms of psychostimulants and nonstimulants on ADHD.
Methods
After a systematic literature search and database construction, the imputed maps of separate and pooled neuropharmacological mechanisms were meta-analyzed by Seed-based d Mapping toolbox, followed by large-scale network analysis to uncover potential coactivation patterns and meta-regression analysis to examine the modulatory effects of age and sex.
Results
Twenty-eight whole-brain task-based functional MRI studies (396 cases in the medication group and 459 cases in the control group) were included. Possible normalization effects of stimulant and nonstimulant administration converged on increased activation patterns of the left supplementary motor area (Z = 1.21, p < 0.0001, central executive network). Stimulants, relative to nonstimulants, increased brain activations in the left amygdala (Z = 1.30, p = 0.0006), middle cingulate gyrus (Z = 1.22, p = 0.0008), and superior frontal gyrus (Z = 1.27, p = 0.0006), which are within the ventral attention network. Neurodevelopmental trajectories emerged in activation patterns of the right supplementary motor area and left amygdala, with the left amygdala also presenting a sex-related difference.
Conclusions
Convergence in the left supplementary motor area may delineate novel therapeutic targets for effective interventions, and distinct neural substrates could account for different therapeutic responses to stimulants and nonstimulants.
Depression is associated with serious disease burden. Despite the multitude of antidepressant options available, the adherence rate is often low. Accounting for patient preferences can potentially boost adherence to antidepressant medication and elevate patient satisfaction. However, limited evidence exists regarding patient preferences for antidepressant selection. This study aims to elicit patient preferences regarding the benefits, risks, and cost attributes of antidepressants in China.
Methods
A best-worst scaling profile case experiment was conducted using a face-to-face survey administered to patients diagnosed with depression. Patients were recruited from general and psychiatric hospitals. We utilized a multiphase approach that integrated literature review, expert consultation, and best-worst scaling to develop attributes within choice sets. The attributes with each varying across two or three levels encompassed remission rate, sleep disorders, risk of headache or dizziness, risk of gastrointestinal adverse events, risk of liver or kidney injury, and monthly out-of-pocket costs. Each respondent answered seven choice tasks, including a dominant task. Data were analyzed using conditional logit, mixed logit, and generalized multinomial logit models. Subgroup analyses were conducted to explore preference heterogeneity.
Results
A total of 331 participants completed the survey and met the inclusion criteria. Almost all attribute levels were statistically significant. Overall, the most desirable characteristics of antidepressant medications were higher remission rates (80% and 55% rates; p<0.05), lower risk of liver or kidney injury (1% rate; p<0.05), and fewer monthly out-of-pocket costs (CNY100 [USD13.93, EUR12.75]; p<0.05). Risks of gastrointestinal adverse events (60% and 35% rates) and insomnia were the least preferred features. Regarding attributes, efficacy, the risk of gastrointestinal adverse events, and sleep disorders were relatively important factors influencing patient choice. Preferences differed slightly by age, degree of education, personal annual income, and treatments currently received.
Conclusions
Our study suggests that efficacy, gastrointestinal adverse effects, sleep disorders, and treatment costs are critical drivers behind medication choices among patients with depression. Preference heterogeneity also exists regarding individual and therapeutic characteristics, which need more samples and further analyses to identify. These discoveries hold the potential to enrich the shared decision-making process between physicians and patients within healthcare settings.
Ultrasonic scalpels are widely used in urological surgery. Although portable ultrasonic scalpels are convenient to use and install, the existing evidence on their safety and effectiveness is scarce. This study aimed to compare the safety and effectiveness of portable ultrasonic scalpels in urological surgery with traditional ultrasonic scalpels to aid clinical decision-making.
Methods
A multicenter, prospective, non-randomized controlled trial was conducted from February to August 2023 in three tertiary hospitals in China. The intervention group included 90 prospectively enrolled patients undergoing urological surgery during the same period of hospitalization: 45 with portable ultrasonic scalpels and 45 with traditional scalpels. Demographic and clinical data of patients in the study were collected. Data on quality of life were obtained using the EuroQol EQ-5D-5L scale preoperatively, at discharge, and one month and three months after surgery. Descriptive analysis and a generalized linear model were used in the data analysis.
Results
A total of 82 patients were included in the study: 39 in the intervention group and 43 in the control group. The average hospital stay and intraoperative and postoperative blood loss in the intervention group were lower than in the control group (p>0.05). From baseline to discharge, the decrease in quality-adjusted life-years (QALYs) in the intervention group was smaller (–0.134 versus –0.287; p<0.05) than in the control group. During the follow-up period, there were no significant differences in the changes in QALYs between the two groups. The decline in QALYs was significantly influenced by variables such as intraoperative blood loss and surgical site.
Conclusions
There were no significant differences in baseline characteristics or changes in QALYs between the intervention and control groups. Portable ultrasonic scalpels in urological surgery may be as equally effective as traditional scalpels with respect to clinical outcomes, with additional benefits in reducing QALY decline at discharge. Further research with large samples and long-term follow-up should be conducted.
Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation–maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.
We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_{1}$$\end{document} penalty term to the log-likelihood. The computation is carried out by the expectation–maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.
Diagnostic classification models (DCMs) are widely used for providing fine-grained classification of a multidimensional collection of discrete attributes. The application of DCMs requires the specification of the latent structure in what is known as the \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document} matrix. Expert-specified \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document} matrices might be biased and result in incorrect diagnostic classifications, so a critical issue is developing methods to estimate \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document} in order to infer the relationship between latent attributes and items. Existing exploratory methods for estimating \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document} must pre-specify the number of attributes, K. We present a Bayesian framework to jointly infer the number of attributes K and the elements of \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document}. We propose the crimp sampling algorithm to transit between different dimensions of K and estimate the underlying \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document} and model parameters while enforcing model identifiability constraints. We also adapt the Indian buffet process and reversible-jump Markov chain Monte Carlo methods to estimate \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\varvec{Q}}$$\end{document}. We report evidence that the crimp sampler performs the best among the three methods. We apply the developed methodology to two data sets and discuss the implications of the findings for future research.
Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.
Hidden Markov models (HMMs) have been applied in various domains, which makes the identifiability issue of HMMs popular among researchers. Classical identifiability conditions shown in previous studies are too strong for practical analysis. In this paper, we propose generic identifiability conditions for discrete time HMMs with finite state space. Also, recent studies about cognitive diagnosis models (CDMs) applied first-order HMMs to track changes in attributes related to learning. However, the application of CDMs requires a known \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{Q}$$\end{document} matrix to infer the underlying structure between latent attributes and items, and the identifiability constraints of the model parameters should also be specified. We propose generic identifiability constraints for our restricted HMM and then estimate the model parameters, including the \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{Q}$$\end{document} matrix, through a Bayesian framework. We present Monte Carlo simulation results to support our conclusion and apply the developed model to a real dataset.
The eddy-viscosity model, as initially used to model the mean Reynolds stress, has been widely used in the linear analysis of turbulence recently by direct extension. In this study, the mechanism of the eddy viscosity in improving the prediction of fluctuation structures with linear analysis is clarified by investigating the statistical properties of forcing, eddy-viscosity term and their correlations. From the direct numerical simulation (DNS) results of turbulent channel flows with $Re_{\tau }=186$–$2003$, the spatial correlation of forcing is partially cancelled due to its interaction with eddy-viscosity terms. The stochastic forcing after excluding the eddy-viscosity term is nearly uncorrelated spatially, which better matches the condition where the resolvent modes are consistent with the spectral proper orthogonal decomposition (SPOD) modes from DNS. With the above findings, an optimisation framework is developed by minimising the spatial correlations of the stochastic forcing. The optimised eddy-viscosity profiles nearly overlap with the mean-quantity-based model in the near-wall region, but have different maximum values. Compared with the mean-quantity-based model, the optimised results enhance the consistency between the resolvent and DNS results significantly. Based on the optimised results, a new modelling framework is further abstracted, leaving only one to-be-modelled parameter of the maximum value of the eddy-viscosity profile. This parameter follows distinctive rules with spanwise flow scales, based on which a simplified predictive model is constructed. The resolvent modes predicted by the new model exhibit high consistency with SPOD modes, which are essentially comparable to the optimised results for wide ranges of streamwise and spanwise scales.
Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC) = 0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.
This study investigates the impact of coronavirus disease 2019 (COVID-19) pandemic on HTAsiaLink members at the organizational level and provides recommendations for mitigating similar challenges in the future.
Methods
A survey was disseminated among HTAsiaLink members to assess the COVID-19 impact in three areas: (i) inputs, (ii) process, and (iii) outputs of the Health Technology Assessment organizations’ (HTAOs) research operations and HTA process in general.
Results
Survey results showed that most HTAOs hired more staff and secured similar or higher funding levels during COVID-19. Nevertheless, some organizations reported high staff turnover. COVID-19-relevant research was prioritized, and most of the organizations had to adapt their research design to meet the needs of policymakers. Time constraints in conducting research and inability to collect primary data were reported as impacts on the research process. Overall, the number of research projects and accessibility of respondents’ publications increased during COVID-19.
Conclusions
Research demand for HTAOs increased during COVID-19 and impacted their research process; however, they demonstrated resilience and adaptability to provide timely evidence for policymakers. With the growing reliance on HTA, HTAOs require adequate financial support, continuous capacity building, collaboration, and partnership, innovative HTA methods, and a pragmatic yet robust, evidence-to-policy process in preparation for future pandemics.
The maser instability associated with the loss-cone distribution has been widely invoked to explain the radio bursts observed in the astrophysical plasma environment, such as aurora and corona. In the laboratory plasma of a tokamak, events reminiscent of these radio bursts have also been frequently observed as an electron cyclotron emission (ECE) burst in the microwave range ($\mathrm{\sim }2{f_{\textrm{ce}}}$ near the last closed flux surface) during transient magnetohydrodynamic events. These bursts have a short duration of ~10 μs and display a radiation spectrum corresponding to a radiation temperature ${T_{e,\textrm{rad}}}$ of over $30\ \textrm{keV}$ while the edge thermal electron temperature ${T_e}$ is only in the range of $1\ \textrm{keV}$. Suprathermal electrons can be generated through magnetic reconnection, and a loss-cone distribution can be generated through open stochastic field lines in the magnetic mirror of the near-edge region of a tokamak plasma. Radiation modelling shows that a sharp distribution gradient $\partial f/\partial {v_ \bot } > 0$ at the loss-cone boundary can cause a negative absorption of ECE radiation through the maser instability. The negative absorption then amplifies the radiation so that the microwave intensity is significantly stronger than the thermal value. The significant ${T_{e,\textrm{rad}}}$ from the simulations suggests the potential role of the loss-cone maser instability in generating the ECE burst in a tokamak.
Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.
This study aims to evaluate the impact of low-carbohydrate diet, balanced dietary guidance and pharmacotherapy on weight loss among individuals with overweight or obesity over a period of 3 months. The study involves 339 individuals with overweight or obesity and received weight loss treatment at the Department of Clinical Nutrition at the Second Affiliated Hospital of Zhejiang University, School of Medicine, between 1 January 2020 and 31 December 2023. The primary outcome is the percentage weight loss. Among the studied patients, the majority chose low-carbohydrate diet as their primary treatment (168 (49·56 %)), followed by balanced dietary guidance (139 (41·00 %)) and pharmacotherapy (32 (9·44 %)). The total percentage weight loss for patients who were followed up for 1 month, 2 months and 3 months was 4·98 (3·04, 6·29) %, 7·93 (5·42, 7·93) % and 10·71 (7·74, 13·83) %, respectively. Multivariable logistic regression analysis identified low-carbohydrate diet as an independent factor associated with percentage weight loss of ≥ 3 % and ≥ 5 % at 1 month (OR = 0·461, P < 0·05; OR = 0·349, P < 0·001). The results showed that a low-carbohydrate diet was an effective weight loss strategy in the short term. However, its long-term effects were comparable to those observed with balanced dietary guidance and pharmacotherapy.
Human alveolar echinococcosis is a hard-to-treat and largely untreated parasitic disease with high associated health care costs. The current antiparasitic treatment for alveolar echinococcosis relies exclusively on albendazole, which does not act parasiticidally and can induce severe adverse effects. Alternative, and most importantly, improved treatment options are urgently required. A drug repurposing strategy identified the approved antimalarial pyronaridine as a promising candidate against Echinococcus multilocularis infections. Following a 30-day oral regimen (80 mg kg−1 day−1), pyronaridine achieved an excellent therapeutic outcome in a clinically relevant hepatic alveolar echinococcosis murine model, showing a significant reduction in both metacestode size (72.0%) and counts (85.2%) compared to unmedicated infected mice, which revealed significantly more potent anti-echinococcal potency than albendazole treatment at an equal dose (metacestode size: 42.3%; counts: 4.1%). The strong parasiticidal activity of pyronaridine was further confirmed by the destructive damage to metacestode tissues observed morphologically. In addition, a screening campaign combined with computational similarity searching against an approved drug library led to the identification of pirenzepine, a gastric acid-inhibiting drug, exhibiting potent parasiticidal activity against protoscoleces and in vitro cultured small cysts, which warranted further in vivo investigation as a promising anti-echinococcal lead compound. Pyronaridine has a known drug profile and a long track record of safety, and its repurposing could translate rapidly to clinical use for human patients with alveolar echinococcosis as an alternative or salvage treatment.
Suicide and suicidal behaviour strongly contribute to overall male youth mortality. An understanding of worldwide data contextualises suicide and suicidal behaviour in young men within any given country.
Method:
Members and colleagues of the World Federation of Societies of Biological Psychiatry’s Task Force on Men’s Mental Health review the relevant data from several regions of the world. The review identifies notable findings across regions of relevance to researchers, policymakers, and clinicians.
Results:
Male suicide and suicidal behaviour in adolescence and emerging adulthood within North America, Latin America and the Caribbean, Europe, the Mediterranean and the Middle East, Continental Africa, South Asia, East Asia, China, and Oceania share similarities as well as significant points of divergence.
Conclusions:
International data provide an opportunity to obtain a superior understanding of suicide and suicidal behaviour amongst young men.
The World Health Organization declared the current monkeypox outbreak a public health emergency of international concern (PHEIC) on July 23, 2022, as it has posed a great threat to human health. This bibliometric analysis aimed to explore the current research hotspots focused on monkeypox.
Methods
A systematic search of the Web of Science Core Collection database was conducted for published articles on monkeypox from database inception to February 23, 2023. VOSviewer software was used for analysis and visualization of research results.
Results
A total of 1646 publications on monkeypox virus were included for bibliometric analysis. Results showed that (1) the number of publications about monkeypox virus increased significantly in 2022, (2) smallpox and monkeypox virus were popular research keywords, (3) the United States has made the most significant contribution to the study of monkeypox virus, (4) Journal of Virology was the most active journal in publishing articles about monkeypox, and (5) research themes mainly included the body’s reaction after monkeypox infection, epidemiology, diagnosis, and pathological mechanisms.
Conclusions
Future research should focus on early sensitive diagnostic measures of monkeypox and the development of vaccines based on the characteristics of the virus. Study findings also provided key areas for public health experts to focus on and collaborate with policymakers.
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).
Methods
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.
Results
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.
Conclusions
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.
Persistent cognitive deficits and functional impairments are associated with bipolar disorder (BD), even during the euthymic phase. The dysfunction of default mode network (DMN) is critical for self-referential and emotional mental processes and is implicated in BD. The current study aims to explore the balance of excitatory and inhibitory neurotransmitters, i.e. glutamate and γ-aminobutyric acid (GABA), in hubs of the DMN during the euthymic patients with BD (euBD).
Method
Thirty-four euBD and 55 healthy controls (HC) were recruited to the study. Using proton magnetic resonance spectroscopy (1H-MRS), glutamate (with PRESS sequence) and GABA levels (with MEGAPRESS sequence) were measured in the medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC) and the posterior cingulate gyrus (PCC). Measured concentrations of excitatory glutamate/glutamine (Glx) and inhibitory GABA were used to calculate the excitatory/inhibitory (E/I) ratio. Executive and attentional functions were respectively assessed using the Wisconsin card-sorting test and continuous performance test.
Results
euBD performed worse on attentional function than controls (p = 0.001). Compared to controls, euBD had higher E/I ratios in the PCC (p = 0.023), mainly driven by a higher Glx level in the PCC of euBD (p = 0.002). Only in the BD group, a marginally significant negative association between the mPFC E/I ratio (Glx/GABA) and executive function was observed (p = 0.068).
Conclusions
Disturbed E/I balance, particularly elevated Glx/GABA ratio in PCC is observed in euBD. The E/I balance in hubs of DMN may serve as potential biomarkers for euBD, which may also contribute to their poorer executive function.