The neurasthenia–depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD). Both electronic medical information records and EEG records from patients with neurasthenia and MDD were gathered. The demographic and clinical characteristics, EEG power spectral density, and functional connectivity were compared between the neurasthenia and MDD groups. Machine Learning methods such as random forest, logistic regression, support vector machines, and k nearest neighbors were also used for classification between groups to extend the identification that there is a significant different pattern between neurasthenia and MDD. We analyzed 305 patients with neurasthenia and 45 patients with MDD. Compared with the MDD group, patients with neurasthenia reported more somatic symptoms and less emotional symptoms (p < 0.05). Moreover, lower theta connectivity was observed in patients with neurasthenia compared to those with MDD (p < 0.01). Among the classification models, random forest performed best with an accuracy of 0.93, area under the receiver operating characteristic curve of 0.97, and area under the precision-recall curve of 0.96. The essential feature contributing to the model was the theta connectivity. This is a retrospective study, and medical records may not include all the details of a patient’s syndrome. The sample size of the MDD group was smaller than that of the neurasthenia group. Neurasthenia and MDD are different not only in symptoms but also in brain activities.

Guillain-Barré syndrome (GBS) is a group of immune-mediated peripheral neuropathies that causes acute flaccid paralysis. The global incidence of GBS was 0.6-4/100 000, and the incidence in China was 0.698/100 000. Although the diagnosis and treatment of GBS has made rapid progress, approximately 20

OBJECTIVE:To investigate the neurophysiological mechanisms underlying cerebellar repetitive transcranial magnetic stimulation (rTMS) in essential tremor (ET) using source-level electroencephalography (EEG) phase-locking value analysis. METHODS:EEG recordings and clinical assessments of ET patients (n = 20) before and after 4-week bilateral cerebellar rTMS were studied and evaluated for correlations. Brain source activities were reconstructed using standardized low-resolution brain electromagnetic tomography (LORETA). Functional connectivity and local network metrics in the alpha band (8-13 Hz) were compared with those of healthy controls (n = 20). RESULTS:ET patients had lower nodal efficiency in visual areas and reduced inter-regional occipital connectivity than controls. Local network metrics in the left superior occipital gyrus, left cuneus, and right fusiform gyrus negatively correlated with tremor severity and significantly improved with rTMS. Connectivity changes between the left inferior occipital gyrus (IOG) and superior parietal gyrus (SPG) and the right SPG and midcingulate cortex (MCC), strongly correlated with improvements in activities of daily living. Performance-related connectivity changes involved the left IOG-SPG, right SPG-MCC, and left middle occipital gyrus to the supplementary motor area. CONCLUSIONS:Cerebellar rTMS may alleviate ET by reorganizing functional connectivity within the visuomotor network. SIGNIFICANCE:Our findings provide preliminary mechanistic insights and potential biomarkers for ET treatment, pending validation in future sham-controlled studies.

A 52-year-old male with early-stage Alzheimer's disease and long-standing anxiety received 30 repetitive transcranial magnetic stimulation sessions over 8 months and 20-month Mediterranean diet intervention. Neuropsychological assessments [Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Clinical Dementia Rating, Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), Pittsburgh Sleep Quality Index) and resting-state electroencephalogram (rsEEG) were conducted at baseline, during treatment, and at 6-month follow-up. After treatment, MoCA and MMSE scores improved by 6 and 5 points; HAMA and HAMD scores declined by 7 and 3 points. rsEEG showed progressive increases in individual alpha peak frequency (8.69 to 10.22 Hz), enhancement of alpha power, and reduction in theta power. Cerebrospinal fluid amyloid-β 42 levels also normalized. The patient reported marked mental well-being.

Large-scale, high-density EEG datasets offer significant potential to uncover the neural mechanisms of disease and advance clinical applications, yet they remain scarce. Here, we present a dataset of 64-channel resting-state EEG recordings comprising 8,416 resting-state EEG recordings from 8,132 participants, encompassing a wide range of 15 neurological and neuropsychiatric disorder categories. Along with EEG recordings, the dataset includes demographic characteristics, diagnostic classifications, and neuropsychological assessment scores. All data underwent rigorous quality control and were fully de-identified in accordance with ethical and regulatory guidelines. This resource provides an unprecedented opportunity for subgroup and cross-disease comparisons, and the development of robust machine learning models for clinical EEG interpretation. Access to the dataset is managed under institutional agreements ensuring privacy and ethical compliance, supporting both fundamental research and translational applications in neurology and psychiatry.

BackgroundPost-stroke depression (PSD) is a prevalent complication that adversely affects recovery following stroke. Repetitive transcranial magnetic stimulation (rTMS) has garnered attention as a potential therapeutic intervention for PSD. This pilot double-blind randomized trial aimed to assess the feasibility and preliminary effects of high- and low-frequency rTMS in PSD, while exploring potential neural mechanisms using electroencephalography.MethodsChronic stroke survivors diagnosed with PSD were randomly allocated to receive either high-frequency rTMS targeting the left dorsolateral prefrontal cortex or low-frequency rTMS targeting the right dorsolateral prefrontal cortex for 20 sessions. Hamilton Depression Rating Scale were assessed, and resting-state electroencephalography were recorded at baseline, mid-treatment, and post-treatment.ResultsBoth high- and low-frequency rTMS were well tolerated and reduced depressive symptoms at mid- and post-treatment. Electroencephalography analysis did not reveal divergent neural signatures associated with the two protocols. However, altered connectivity linking posterior divisions of the middle frontal gyrus and specific regions in the theta- and beta-band frequencies were associated with the improvement in Hamilton Depression Rating Scale scores.ConclusionThis pilot study provides preliminary evidence that rTMS is feasible for managing PSD across both high- and low-frequency protocols. EEG analyses suggest potential neurobiological mechanisms, which may inform future research on treatment optimization.Clinical trial registrationchictr.org.cn, ChiCTR1900021168.

Predicting responsvienss to repetitive transcranial magnetic stimulation (rTMS) can facilitate personalized treatments with improved efficacy; however, predictive features related to this response are still lacking. We explored whether resting-state electroencephalography (rsEEG) functional connectivity measured at baseline or during treatment could predict the response to 10-day rTMS targeted to the right dorsolateral prefrontal cortex (DLPFC) in 36 patients with chronic insomnia disorder (CID). Pre- and post-treatment rsEEG scans and the Pittsburgh Sleep Quality Index (PSQI) were evaluated, with an additional rsEEG scan conducted after four rTMS sessions. Machine-learning approaches were employed to assess the ability of each connectivity measure to distinguish between responders (PSQI improvement > 25%) and non-responders (PSQI improvement ≤ 25%). Furthermore, we analyzed the connectivity trends of the two subgroups throughout the treatment. Our results revealed that the machine learning model based on baseline theta connectivity achieved the highest accuracy (AUC = 0.843) in predicting treatment response. Decreased baseline connectivity at the stimulated site was associated with higher responsiveness to TMS, emphasizing the significance of functional connectivity characteristics in rTMS treatment. These findings enhance the clinical application of EEG functional connectivity markers in predicting treatment outcomes.

The pathogenesis of essential tremor (ET) remains unclear, and the efficacy of related drug treatment is inadequate for proper tremor control. Hence, in the current study, consecutive low-frequency repetitive transcranial magnetic stimulation (rTMS) modulation on cerebellum was accomplished in a population of ET patients, along with pre - and post -treatment resting-state electroencephalogram (EEG) networks being constructed. The results primarily clarified the decreasing of resting-state network interactions occurring in ET, especially the weaker frontal-parietal connectivity, compared to healthy individuals. While after the rTMS stimulation, promotions in both network connectivity and properties, as well as clinical scales, were identified. Furthermore, significant correlations between network characteristics and clinical scale scores enabled the development of predictive models for assessing rTMS intervention efficacy. Using a multivariable linear model, clinical scales after one-month rTMS treatment were accurately predicted, underscoring the potential of brain networks in evaluating rTMS effectiveness for ET. The findings consistently demonstrated that repetitive low-frequency rTMS neuromodulation on cerebellum can significantly improve the manifestations of ET, and individual networks will be reliable tools for evaluating the rTMS efficacy, thereby guiding personalized treatment strategies for ET patients.

Background: Repetitive transcranial magnetic stimulation (rTMS) is an advanced and noninvasive technology that uses pulse stimulation to treat cognitive impairment. However, its specific effects have always been mixed with those of cognitive training, and the optimal parameter for Alzheimer’s disease (AD) intervention is still ambiguous. Objective: This study aimed to summarize the therapeutic effects of pure rTMS on AD, excluding the influence of cognitive training, and to develop a preliminary rTMS treatment plan. Methods: Between 1 January 2010 and 28 February 2023, we screened randomized controlled clinical trials from five databases (PubMed, Web of Science, Embase, Cochrane, and ClinicalTrials. gov). We conducted a meta-analysis and systematic review of treatment outcomes and rTMS treatment parameters. Result: A total of 4,606 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles, comprising 655 participants (308 males and 337 females), were included in the final analysis. The findings revealed that rTMS significantly enhances both global cognitive ability (p = 0.0002, SMD = 0.43, 95% CI = 0.20–0.66) and memory (p = 0.009, SMD = 0.37, 95% CI = 0.09–0.65). Based on follow-up periods of at least 6 weeks, the following stimulation protocols have demonstrated efficacy for AD: stimulation sites (single or multiple targets), frequency (20 Hz), stimulation time (1–2 s), interval (20–30 s), single pulses (≤2500), total pulses (>20000), duration (≥3 weeks), and sessions (≥20). Conclusions: This study suggests that rTMS may be an effective treatment option for patients with AD, and its potential therapeutic capabilities should be further developed in the future.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) has been increasingly used as a treatment modality for chronic insomnia disorder (CID). However, our understanding of the mechanisms underlying the efficacy of rTMS is limited.ObjectiveThis study aimed to investigate rTMS-induced alterations in resting-state functional connectivity and to find potential connectivity biomarkers for predicting and tracking clinical outcomes after rTMS.MethodsThirty-seven patients with CID received a 10-session low frequency rTMS treatment applied to the right dorsolateral prefrontal cortex. Before and after treatment, the patients underwent resting-state electroencephalography recordings and a sleep quality assessment using the Pittsburgh Sleep Quality Index (PSQI).ResultsAfter treatment, rTMS significantly increased the connectivity of 34 connectomes in the lower alpha frequency band (8–10 Hz). Additionally, alterations in functional connectivity between the left insula and the left inferior eye junction, as well as between the left insula and medial prefrontal cortex, were associated with a decrease in PSQI score. Further, the correlation between the functional connectivity and PSQI persisted 1 month after the completion of rTMS as evidenced by subsequent electroencephalography (EEG) recordings and the PSQI assessment.ConclusionBased on these results, we established a link between alterations in functional connectivity and clinical outcomes of rTMS, which suggested that EEG-derived functional connectivity changes were associated with clinical improvement of rTMS in treating CID. These findings provide preliminary evidence that rTMS may improve insomnia symptoms by modifying functional connectivity, which can be used to inform prospective clinical trials and potentially for treatment optimization.

Background:Somatic symptom disorder (SSD) commonly presents in general hospital settings, posing challenges for healthcare professionals lacking specialised psychiatric training. The Neuro-11 Neurosis Scale (Neuro-11) offers promise in screening and evaluating psychosomatic symptoms, comprising 11 concise items across three dimensions: somatic symptoms, negative emotions and adverse events. Prior research has validated the scale's reliability, validity and theoretical framework in somatoform disorders, indicating its potential as a valuable tool for SSD screening in general hospitals.Aims:This study aimed to establish the reliability, validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD. Through this comparative analysis, we aimed to validate the effectiveness and precision of the Neuro-11, enhancing its utility in clinical settings.Methods:Between November 2020 and December 2021, data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People's Hospital in China for various physical discomforts. The patients completed multiple questionnaires, including the Neuro-11, Short Form 36 Health Survey, Patient Health Questionnaire 15 items, Hamilton Anxiety Scale and Hamilton Depression Scale. Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results:The Neuro-11 demonstrated strong content reliability and structural consistency, correlating significantly with internationally recognised and widely used questionnaires. Despite its brevity, the Neuro-11 exhibited significant correlations with other questionnaires. A test-retest analysis yielded a correlation coefficient of 1.00, Spearman-Brown coefficient of 0.64 and Cronbach's α coefficient of 0.72, indicating robust content reliability and internal consistency. Confirmatory factor analysis confirmed the validity of the three-dimensional structure (p<0.001, comparative fit index=0.94, Tucker-Lewis index=0.92, root mean square error of approximation=0.06, standardised root mean square residual=0.04). The threshold of the Neuro-11 is set at 10 points based on the maximum Youden's index from the receiver operating characteristic curve analysis. In terms of diagnostic efficacy, the Neuro-11 has an area under the curve of 0.67.Conclusions:(1) The Neuro-11 demonstrates robust associations with standard questionnaires, supporting its validity. It is applicable in general hospital settings, assessing somatic symptoms, negative emotions and adverse events. (2) The Neuro-11 exhibits strong content reliability and validity, accurately capturing the intended constructs. The three-dimensional structure demonstrates robust construct validity. (3) The threshold of the Neuro-11 is set at 10 points.

Background: Mild cognitive impairment (MCI) is a condition between normal aging and dementia; nearly 10-15% of MCI patients develop dementia annually. There are no effective interventions for MCI progression. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that has attempted to improve the overall cognitive function of MCI patients. However, it does not affect episodic memory improvement. Methods: In this study, we engaged 15 clinically diagnosed MCI patients and normal controls to explore the effect of dual-targeted rTMS on progressing cognitive function, particularly episodic memory in MCI patients. Resting-state EEG recordings and neuropsychological assessments were conducted before and after the intervention. EEG features were extracted using an adaptive algorithm to calculate functional connectivity alterations in relevant brain regions and the mechanisms of altered brain functional networks in response to dual-target rTMS. Results: The study revealed that the functional brain connectivity between the right posterior cingulate gyrus (PCC) and the right dorsal caudate nucleus (DC) was significantly reduced in MCI patients compared to normal controls (p < 0.001). Dual-target rTMS increased the strength of the reduced functional connectivity (p < 0.001), which was related to cognitive enhancement (p < 0.05). Conclusion: This study provides a new stimulation protocol for rTMS intervention. Improving the functional connectivity of the right PCC to the right DC is a possible mechanism by which rTMS improves overall cognitive and memory function in MCI patients.

ObjectiveHypertensive disorders of pregnancy (HDP) are associated with an increased risk of adverse neonatal outcomes. Although twin pregnancies had a higher risk of developing HDP, it is not known whether HDP in twins will increase the risk of adverse neonatal outcomes. We aimed to assess whether this association differed in singleton and twin pregnancies in women who conceived with assisted reproductive technology (ART).MethodsWe finally included 193,590 live births born via ART from the National Vital Statistics System (NVSS) for the years 2015–2019. We used Log-binomial regression to evaluate the associations between HDP and the risk of adverse neonatal outcomes in ART mothers.ResultsAmong 193,590 ART-treated mothers, there were 140,870 and 52,720 mothers who had singleton pregnancies and twin pregnancies, respectively. Those ART mothers with twin pregnancies had a higher rate of HDP than singleton pregnancies (20.5% vs. 11.0%). In singleton pregnancies, the risks of preterm birth [adjusted risk ratio (aRR)): 2.80, 95% CI 2.67–2.93], low birth weight (aRR: 2.80, 95% CI 2.67–2.93), small for gestational age (aRR: 1.41, 95% CI 1.34–1.49), 5 min Apgar <7 (aRR: 1.66, 95% CI 1.50–1.83) and cesarean section (aRR: 1.23, 95% CI 1.21–1.25) were significantly higher in HDP mothers than in non-HDP mothers respectively. However, in contrast to singleton pregnancies, these associations were weak or reversed in twin pregnancies, after adjusting for confounding factors.ConclusionIn ART-treated women, although twin pregnancies had a higher HDP rate, the risk of adverse neonatal outcomes associated with HDP was lower than that of singletons.

Abstract Background Subjective cognitive decline (SCD) is the subjective perception of a decline in cognition and is recognized as the earliest symptomatic manifestation of Alzheimer’s disease (AD). Detection and treatment strategies for SCD are urgently needed to slow or halt AD progression. Methods Fifty-four participants were included. Electroencephalography (EEG) and repetitive transcranial magnetic stimulation (rTMS) were applied. Results SCD showed a decreased power spectrum, enhanced functional connectivity (FC), and increased global and local network topological features mostly restricted to the β band. 10 Hz rTMS significantly improved cognition in SCD by acting on the FC feature. The interaction between the four EEG features contributed to the detection of disease progression and prediction of rTMS modulations with an adjusted R2 of 0.856 and an accuracy of 77.8%. Conclusions EEG-based features may be considered as the earliest biomarkers of AD. rTMS significantly mitigate cognitive function in SCD may through modulating FC. Trial registration Chinese Clinical Trial Registry (ChiCTR1800019199)

Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection. Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC). Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated. Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification. Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.

Objective Cognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study. Methods We applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition. Results We observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG]. Conclusion These preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.

Background: Increasing evidence demonstrates that repetitive transcranial magnetic stimulation (rTMS) treatment of the dorsolateral prefrontal cortex is beneficial for improving cognitive function in patients with Alzheimer’s disease (AD); however, the underlying mechanism of its therapeutic effect remains unclear.Objectives/Hypothesis: The aim of this study was to investigate the impact of rTMS to the dorsolateral prefrontal cortex on functional connectivity along with treatment response in AD patients with different severity of cognitive impairment.Methods: We conducted a 2-week treatment course of 10-Hz rTMS over the left dorsolateral prefrontal cortex in 23 patients with AD who were split into the mild or moderate cognitive impairment subgroup. Resting state electroencephalography and general cognition was assessed before and after rTMS. Power envelope connectivity was used to calculate functional connectivity at the source level. The functional connectivity of AD patients and 11 cognitively normal individuals was compared.Results: Power envelope connectivity was higher in the delta and theta bands but lower in the beta band in the moderate cognitive impairment group, compared to the cognitively normal controls, at baseline (p < 0.05). The mild cognitive impairment group had no significant abnormities. Montreal Cognitive Assessment scores improved after rTMS in the moderate and mild cognitive impairment groups. Power envelope connectivity in the beta band post-rTMS was increased in the moderate group (p < 0.05) but not in the mild group. No significant changes in the delta and theta band were found after rTMS in both the moderate and mild group.Conclusion: High-frequency rTMS to the dorsolateral prefrontal cortex modulates electroencephalographic functional connectivity while improving cognitive function in patients with AD. Increased beta connectivity may have an important mechanistic role in rTMS therapeutic effects.

Background: Accumulating evidence suggests that low frequency repetitive transcranial magnetic stimulation (rTMS), which generally decreases cortical excitability and remodels plastic connectivity, improves sleep quality in patients with insomnia disorder. However, the effects of rTMS vary substantially across individuals and treatment is sometimes unsatisfactory, calling for biomarkers for predicting clinical outcomes. Objective: This study aimed to investigate whether functional connectivity of the target network in electroencephalography is associated with the clinical response to low frequency rTMS in patients with insomnia disorder. Methods: Twenty-five patients with insomnia disorder were subjected to 10 sessions of treatment with 1 Hz rTMS over the right dorsolateral prefrontal cortex. Resting-state electroencephalography was collected before rTMS. Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Mini-Mental State Exam were performed before and after rTMS treatment, with a follow-up after one month. Electroencephalographic connectivity was measured by the power envelope connectivity at the source level. Partial least squares regression identified models of connectivity that maximally accounted for the rTMS response. Results: Scores of Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale were decreased after rTMS and one-month later. Baseline weaker connectivity of a network in the beta and alpha bands between a brain region approximating the stimulated right dorsolateral prefrontal cortex and areas located in the frontal, insular, and limbic cortices was associated with a greater change in Pittsburgh Sleep Quality Index and Hamilton Depression Rating Scale following rTMS. Conclusions: Low frequency rTMS could improve sleep quality and depressive moods in patients with insomnia disorder. Moreover, electroencephalographic functional connectivity would potentially be a robust biomarker for predicting the therapeutic effects. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

目的 将躯体症状、情感症状及负性事件三者结合,编制一份适合在综合医院实施的躯体形式障碍筛查量表并进一步验证其信度、效度.方法 通过查阅文献、参考中国精神疾病分类中躯体形式障碍的诊断标准(第3版)(Chinese Classification of Mental Disorders,third edition,CCMD-3)、国际疾病分类(International Classification of Diseases,ICD)-10躯体形式障碍诊断标准,结合临床经验构建躯体形式障碍三维度量表模型,包括躯体症状、负性情感、负性事件3项条目,并进行预调查条目分析和验证预问卷模型,形成躯体形式障碍筛查量表(简称Neuro-11).对2014年6月-2016年6月就诊于深圳市人民医院神经内科的461例患者进行基本资料、躯体形式障碍筛查量表、广泛性焦虑量表(the seven-item generalized anxiety disorder scale, GAD-7)、9项患者健康问卷(patient health questionnaire-9,PHQ-9)的调查及测评,精神科医师根据ICD-10躯体形式障碍诊断标准进行电话访谈诊断,诊断结果为躯体形式障碍诊断的金标准.分析各人口学特点的得分情况,通过ROC曲线分析该量表的临界值,分析量表的内部信度及效标效度,分析量表3个维度之间的关系.结果 Neuro-11包含3个维度及11个条目,3个维度分别为躯体症状、负性情感及负性事件.当量表得分≥1 0分时,可认为患者有躯体形式障碍倾向,所对应的敏感度为0.88,特异度为0.72.Neuro-11的内部一致性Cronbach α系数为0.73,分半信度系数为0.68;并且随着Neuro-11的得分升高,GAD-7、PHQ-9的得分也升高.结论 综合医院躯体形式障碍筛查量表的三维理论合理,对躯体形式障碍的诊断价值良好,具有良好的信度、效度,并且可作为躯体形式障碍严重程度的衡量工具.

目的 基于静息态脑电图探索卒中后抑郁(post-stroke depression,PSD)脑网络特征异常改变,提取客观生物标志物.方法 回顾性分析深圳市人民医院脑电数据库中缺血性卒中慢性期患者病例资料,收集静息态脑电图与汉密尔顿抑郁量表(Hamilton depression scale,HAMD)、MMSE及NIHSS评分资料.以HAMD评分≥20分为分界值,并通过病灶位置及体积匹配将患者分为PSD组和卒中后非抑郁(post stroke nondepression,PSND)组.脑电图数据预处理后,分别基于相干性虚部及能量包络在皮层源层面建立不同频段功能连接矩阵,采用基于网络的统计方法分析两组间差异.结果 与PSND组比较,PSD组患者①基于相干性虚部的脑网络连接在δ频段减弱,以顶叶脑区连接减弱更明显;θ频段减弱,以左侧额顶颞枕、边缘叶及右侧额叶连接减弱更明显;γ频段增强,以左侧额叶、边缘叶及右侧顶叶脑区连接增强更明显;②基于能量包络的脑网络连接在α频段增强,以双侧顶枕叶脑区连接增强更明显.结论 PSD患者脑网络发生异常改变,静息态脑电图是揭示这种改变的有效工具.