Chunxia Yang

Major Depressive Disorder (MDD) is characterized by cognitive dysfunction that persists as one of the most profoundly disabling mental health conditions wo

It was found that a significant number of patients with major depressive disorder (MDD) did not respond to the treatment, leading to high ongoing costs and disease burden. The main objective of this study was to find neurobiological indicators that can predict the effectiveness of antidepressant treatment using diffusion tensor imaging (DTI). A group of 103 patients who were experiencing their first episode of MDD were included in the study. After 2 weeks of SSRI treatment, the group of patients was split into two categories: ineffectiveand effective. The FMRIB Software Library (FSL) was used for diffusion data preprocessing to obtain tensor-based parameters such as FA, MD, AD, and RD. Tract-Based Spatial Statistical (TBSS) voxel-wise statistical analysis of the tensor-based parameters was carried out using the TBSS procedure in FSL. We conducted an investigation to determine if there were notable variations in neuroimaging attributes among the three groups. Compared to HC, the effective group showed significantly higher AD and MD values in the left CgH. Correlating neuroimaging characteristics and clinical manifestations revealed a significant positive correlation between CgH-l FA and clinical 2-week HAMD-17 total scores and a significant positive correlation between CgH-r FA and clinical 2-week HAMD-17 total scores. Functional damage to the cingulum bundle in the hippocampal region may predispose patients to MDD and predict antidepressant treatment outcomes. More extensive multicenter investigations are necessary to validate these MRI findings that indicate treatment effectiveness and assess their potential significance in practical therapeutic decision-making.

Background: Bipolar disorder (BD) is a complex mental condition of which the mechanism of onset remains unclear. Mendelian randomization (MR) allows evaluation of the causal effects of biomarkers by minimizing the risks of reverse causation and confounding factors. In this study, MR was used to assess the causal relationships between neurodevelopment‐related proteins and BD, thereby providing potential evidence for the neurodevelopmental hypothesis of this mental disorder.Methods: Leveraging data from large‐scale genome‐wide association studies (GWASs), the associations between six neurodevelopment‐related proteins and BD were analyzed using five MR approaches; namely, inverse‐variance weighted, weighted median, MR–Egger, simple mode, and weighted mode methods. The neurodevelopment‐related proteins were selected in the study with 5368 European descents. GWAS of BD come from the Psychiatric Genomics Consortium (N_Case = 41,917, N_Control = 371,549).Results: The analyses identified robust causal relationships between BD and the proteins inter‐alpha‐trypsin inhibitor heavy chain (ITIH)5 (OR = 1.08, 95% CI = 1.00–1.17, p = 0.04) and neurofascin (NFASC) (OR = 0.96, 95% CI = 0.92–1.00, p = 0.042). Initial findings for ITIH1 and ITIH3 were deemed unreliable due to pleiotropy (ITIH1: MR–Egger intercept p = 0.025) or heterogeneity (ITIH3: Cochran's Q p = 0.001). Furthermore, the MR analyses failed to yield evidence supporting a causal effect of liability to BD on neurodevelopment‐related proteins.Conclusion: The MR analysis indicated potential causal relationships between two neurodevelopment‐related proteins (NFASC and ITIH5) and BD. Further studies are required to validate these results and elucidate the specific functions of these proteins in the development of this mental disorder.

Background: Magnetic Seizure Therapy (MST) is an emerging psychiatric physical therapy in recent years, and the MST technique induces seizures by stimulating the local cortex. Numerous studies have shown that compared to electroconvulsive therapy (ECT), magnetic shock therapy (MST) has relatively little effect on cognitive function, and thus has certain advantages in clinical application. However, as far as magnetic shock therapy itself is concerned, there are few studies specifically focusing on its efficacy in depth, and the mechanism of MST in treating schizophrenia is still unclear. Methods: This study centered on the key question of “the efficacy of magnetic stunning therapy for patients with schizophrenia and the combined fMRI-EEG to explore the regulatory mechanisms of brain networks”. The study protocol describes our interventional clinical trial aimed at developing magnetic convulsive therapy as an effective and safe treatment for SCZ. The study population consisted of SCZ patients and healthy controls (Hc). Clinical symptoms, cognitive function, EEG and fMRI data were collected from the patient group at baseline, post-treatment and follow-up phases. We systematically assessed the clinical features and cognitive functions of the patients, combined with EEG and fMRI indexes to explore the brain network connectivity abnormalities in SCZ patients, and jointly explored the neuroimaging mechanisms in SCZ patients. Conclusion: Positive results from this trial may have a direct and significant impact on patients with SCZ. Once the MST demonstrates significant antipsychotic efficacy with high perceived safety, it will have significant implications for clinical practice. Brain network abnormality may be one of the core pathological mechanisms of SCZ. Therefore, by combining neurophysiological imaging techniques to scientifically verify the abnormality of SCZ brain network, the elucidation of this issue can help to further reveal the pathological mechanism of SCZ and provide a scientific basis for the effective and precise treatment of the disease. Trial Registration: ChiCTR2000038361 Registered on September 21, 2020.

Background: Depression is a chronic psychiatric condition that places significant burdens on individuals, families, and societies. The rapid evolution of non-invasive brain stimulation techniques has facilitated the extensive clinical use of Transcranial Magnetic Stimulation (TMS) for depression treatment. In light of the substantial recent increase in related research, this study aims to employ bibliometric methods to systematically review the global research status and trends of TMS in depression, providing a reference and guiding future studies in this field.Methods: We retrieved literature on TMS and depression published between 1999 and 2023 from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases within the Web of Science Core Collection (WoSCC). Bibliometric analysis was performed using VOSviewer and CiteSpace software to analyze data on countries, institutions, authors, journals, keywords, citations, and to generate visual maps.Results: A total of 5,046 publications were extracted covering the period from 1999 to 2023 in the field of TMS and depression. The publication output exhibited an overall exponential growth trend. These articles were published across 804 different journals, BRAIN STIMULATION is the platform that receives the most articles in this area. The literature involved contributions from over 16,000 authors affiliated with 4,573 institutions across 77 countries. The United States contributed the largest number of publications, with the University of Toronto and Daskalakis ZJ leading as the most prolific institution and author, respectively. Keywords such as “Default Mode Network,” “Functional Connectivity,” and “Theta Burst” have recently garnered significant attention. Research in this field primarily focuses on TMS stimulation patterns, their therapeutic efficacy and safety, brain region and network mechanisms under combined brain imaging technologies, and the modulation effects of TMS on brain-derived neurotrophic factor (BDNF) and neurotransmitter levels.Conclusion: In recent years, TMS therapy has demonstrated extensive potential applications and significant implications for the treatment of depression. Research in the field of TMS for depression has achieved notable progress. Particularly, the development of novel TMS stimulation patterns and the integration of TMS therapy with multimodal techniques and machine learning algorithms for precision treatment and investigation of brain network mechanisms have emerged as current research hotspots.

Background: Post-stroke depression (PSD) is most prevalent during the rehabilitative period following a stroke. Recent studies verified the effects of repetitive transcranial magnetic stimulation therapy (rTMS) and mindfulness-based stress reduction (MBSR) in patients with depression. However, the effectiveness and prospect of application in PSD patients remain unclear. This study sought to evaluate the effectiveness of a combined intervention based on rTMS and MBSR for the physical and mental state of PSD patients. Methods: A randomized, double-blind, sham-controlled study design was employed. Participants were recruited from the Rehabilitation Medicine Centre and randomly assigned to receive either MBSR combined with active or sham rTMS or sham rTMS combined with general psychological care. We used a 17-item Hamilton Depression Rating Scale (HAMD-17), a mini-mental state examination (MMSE), the Modified Barthel Index (MBI), and the Pittsburgh Sleep Quality Index (PSQI) to evaluate depressed symptoms, cognitive function, activities of daily living (ADL), and sleep quality at baseline, post-intervention, and the 8-week follow-up. A two-factor analysis of variance was used to compare differences between groups, and Pearson’s linear correlation was used to analyze the possible relationship between variables and potential predictors of depression improvement. Results: Seventy-two participants were randomized to rTMS–MBSR (n = 24), sham rTMS–MBSR (n = 24), or sham rTMS–general psychological care (n = 24). A total of 71 patients completed the questionnaire, a 99% response rate. There were significant time and group interaction effects in HAMD-17, MMSE, MBI, and PSQI scores (p < 0.001). The repeated-measure ANOVA showed a significant improvement of all variables in rTMS–MBSR compared to sham rTMS–MBSR and sham rTMS combined with general psychological care (p < 0.05). Additional results demonstrated that cognitive function, sleep quality, and activities of daily living are associated with depressive symptoms, and cognitive function is a potential variable for improved depression. Conclusion: Depressive symptoms can be identified early by assessing cognitive function, and rTMS–MBSR might be considered a potentially helpful treatment for PSD.

Objective: Repetitive transcranial magnetic stimulation (rTMS) is an effective and evidence-based treatment for major depressive disorder (MDD). This retrospective study aimed to explore the efficacy of add-on iTBS treatment in MDD in real-world clinical practice. Methods: One hundred and fifty-nine inpatients with MDD in a general hospital were included in this study. These patients were treated with at least 8 sessions of iTBS, in addition to antidepressants and supportive psychotherapy. Symptoms of depression and anxiety were assessed with the Hamilton Depression Rating Scale (HDRS) and the Hamilton Rating Scale for Anxiety (HAMA) at baseline and after 2– 4 weeks of treatment. The improvement degree of depressive and anxious symptoms was compared between the first-episode MDD (n=107) and recurrent MDD (n=52) groups. Results: Depressive and anxious symptoms were reduced significantly after the add-on iTBS treatment; the response and remission rates in the first-episode MDD group were 55.14% and 28.97%, which were 63.46% and 28.85% for the recurrent MDD group, respectively (P> 0.05). The response rate and remission rate of anxiety in the first-episode MDD group was 64.13% and 57.45% for HAMA, and 66.67% and 62.50% for the recurrent MDD group (P> 0.05). Conclusion: Our findings indicated that antidepressant and anti-anxiety efficacy of add-on iTBS treatment remains equivocal in real-world clinical practice, regardless of a first-episode depression diagnosis or recurrent depression.

Background: Meteorological factors and air pollutants have been reported to be associated with hand, foot, and mouth disease (HFMD) epidemics before the introduction of vaccine. However, there is limited evidence for studies with long-term dimensions. Methods: We collected the daily HFMD counts, weather and air pollution data from 2014 to 2020 in Chengdu. Distributed lag non-linear models (DLNM) were used to assess the associations of meteorological factors and air pollutants on HFMD cases. Results: From 2014–2020, high relative humidity and precipitation and extremely high and low levels of PM₁₀, O₃, SO₂ and CO increased the risk of HFMD. In pre-vaccination period, extreme high and low temperatures, PM₁₀ and NO₂, low precipitation and high concentrations of PM_2.5 and O₃ significantly increase the risk of HFMD; In post-vaccination period, high relative humidity and low level of CO can significantly increase the incidence of HFMD; During the period of COVID-19, only low temperature will significantly increase the risk of HFMD; Low concentration of air pollutants has the greatest impact on the 6–14 age group, while the high concentration of air pollutants has the greatest impact on the 0–1 age group. Conclusions: Our study suggest that high relative humidity and precipitation and extremely high and low levels of PM₁₀, O₃, SO₂ and CO increased the risk of HFMD from 2014 to 2020. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.