Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.

This study aimed to retrospectively examine the computed tomography (CT) features of lung adenocarcinoma across different demographic groups. Preoperative chest CT findings from 1266 surgically resected lung adenocarcinoma cases were retrospectively analyzed. Lung adenocarcinomas were categorized based on CT characteristics into pure ground glass (pGGO), nodule-containing ground glass opacity (mGGO), and pure solid without containing ground glass opacity (pSD). These categories were correlated with sex, age, EGFR status, and five histopathological subtypes. The diameters of pGGO, mGGO, and pSD significantly increased across all patient groups (P < 0.05). Males exhibited a significantly higher proportion of pSD than females (P = 0.002). The mean diameters of pGGO and pSD were significantly larger in males than in females (P = 0.0017 and P = 0.043, respectively). The frequency of pGGO was higher in the younger age group (≤ 60 years) compared to the older group (> 60 years) for both males (P = 0.002) and females (P = 0.027). The frequency of pSD was higher in the older age group for both sexes. A linear correlation between age and diameter was observed in the entire cohort as well as in the male and female groups (P < 0.0001 for all groups). EGFR mutations were less frequent in pSD compared to pGGO (P = 0.0002) and mGGO (P < 0.0001). The frequency of lesions containing micropapillary components increased from pGGO to mGGO and pSD (P < 0.0001 for all). The frequency of lesions containing solid components also increased from pGGO to mGGO and pSD (P = 0.045, P < 0.0001, and P < 0.0001, respectively). The CT features of lung adenocarcinoma exhibit differences across genders and age groups. Male gender and older age are risk factors for lung adenocarcinoma growth.

Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials (LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon\'s brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon\'s brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.

Post-stroke depression (PSD) is one of the most common mental sequelae after a stroke and can damage the brain. Although PSD has garnered increasing attention in recent years, the precise mechanism remains unclear. Studies have indicated that the expression of DAPK1 is elevated in various neurodegenerative conditions, including depression, ischemic stroke, and Alzheimer\'s disease. However, the specific molecular mechanism of DAPK1-mediated cognitive dysfunction and neuronal apoptosis in PSD rats is unclear. In this study, we established a rat model of PSD, and then assessed depression-like behaviors and cognitive dysfunction in rats using behavioral tests. In addition, we detected neuronal apoptosis and analyzed the expression of DAPK1 protein and proteins related to the ERK/CREB/BDNF signaling pathway. The findings revealed that MCAO combined with CUMS can induce more severe depression-like behaviors and cognitive dysfunction in rats, while overexpression of DAPK1 may hinder the downstream ERK/CREB/BDNF signaling pathways, resulting in neuronal loss and exacerbation of brain tissue damage. In this study, we will focus on DAPK1 and explore its role in PSD.

Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain\'s blood vessels, or when these vessels rupture. And, the cognitive impairment\'s evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

UNASSIGNED: To investigate the effects of nonpharmacological interventions (NPIs) on poststroke depression (PSD) in stroke patients.
UNASSIGNED: Computer searches were conducted on the PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang databases from their establishment to December 2023. The selection was made using the inclusion and exclusion criteria, and 40 articles were included to compare the effects of the 17 NPIs on patients with PSD.
UNASSIGNED: Forty studies involving seventeen interventions were included. The network findings indicated that compared with conventional therapy (COT), superior PSD improvement was observed for cognitive behavioral therapy (CBT) + acupoint acupuncture (CBTA) (mean difference [MD], -4.25; 95% CI, -5.85 to -2.65), team positive psychotherapy (MD, -4.05; 95% CI, -5.53 to -2.58), music therapy (MT) + positive psychological intervention (MD, -2.25; 95% CI, -3.65 to -0.85), CBT (MD, -1.52; 95% CI, -2.05 to -0.99), mindfulness-based stress reduction (MD, -1.14; 95% CI, -2.14 to -0.14), MT (MD, -0.95; 95% CI, -1.39 to -0.52), acupoint acupuncture + MT (AAMT) (MD, -0.69; 95% CI, -1.25 to -0.14). Furthermore, CBT (MD, -3.87; 95% CI, -4.57 to -3.17), AAMT (MD, -1.02; 95% CI, -1.41 to -0.62), acupressure + MT (MD, -0.91; 95% CI, -1.27 to -0.54), and narrative care + acupressure (MD, -0.74; 95% CI, -1.19 to -0.29) demonstrated superior Pittsburgh Sleep Quality Index (PSQI) improvement compared with COT.
UNASSIGNED: Evidence from systematic reviews and meta-analyses suggests that CBTA improves depression in patients with PSD. Moreover, CBT improves sleep in these patients. Additional randomized controlled trials are required to further investigate the efficacy and mechanisms of these interventions.

BACKGROUND: Depression is the common mental disease after stroke. Our objective was to investigate the correlation of Life\'s Essential 8 (LE8), the recently updated evaluation of cardiovascular health, with the occurrence of post-stroke depression (PSD) and all-cause mortality among United States (US) adults.
METHODS: Participants with stroke were chosen from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The relationship between LE8 and the risk of PSD was assessed through weighted multiple logistic models. A restricted cubic spline was employed for the examination of correlations. To demonstrate the stability of the results, sensitivity analysis and subgroup analysis were carried out. Furthermore, Cox regression models were used for the correlation between LE8 and all-cause mortality.
RESULTS: In this study, a total of 1071 participants were included for analysis. It was observed that LE8 score and PSD risk shared an inverse relationship in per 10 points increase [OR = 0.62 (0.52-0.74, P < 0.001)] in logistic regression models. The analysis of restricted cubic spline demonstrated approximately a noticeable inverse linear association between LE8 score and PSD risk. Sensitivity analysis verified the stability of the findings. Moreover, no statistically significant interactions were identified in subgroup analysis. A reverse association between LE8 score and all-cause mortality was also observed with a 10-point increase [HR = 0.85 (0.78-0.94, P < 0.001)] in cox regression models.
CONCLUSIONS: A negative correlation was discovered between LE8 score and PSD and all-cause mortality risk among US adults. We need to conduct large-scale prospective studies to further validate our results.

BACKGROUND: Post-stroke depression (PSD) is a common neuropsychiatric complication that affects approximately one-third of stroke patients. The treatment and prognosis of this disease are poor. Socioeconomic status (SES) is closely related to health outcomes; however, only a few previous studies have focused on the association between SES and PSD. Given the substantial population of stroke patients in China, it is crucial to examine the potential risk factors associated with PSD. Conducting studies on this population and investigating the influence of economic conditions can provide valuable guiding theoretical insights into PSD prevention and management.
METHODS: We used data from the 2018 China Health and Retirement Longitudinal Study and selected appropriate samples for analysis. Depression was estimated using the Center of Epidemiologic Studies Depression Scale-10, a validated tool for assessing depression in the general population. Multiple logistic regression analysis was employed to assess the association between SES and PSD and to evaluate any urban-rural differences.
RESULTS: Of the 749 respondents, 370 (49.4%) had depression. Stroke patients with a middle school education demonstrated a greater risk of developing depression than those with a primary school education or below after adjusting for all control variables (odds ratio (OR) = 1.60, 95% confidence interval (CI): 1.03-2.51, P = 0.036). However, stroke patients with a high school education or above had a lower risk of developing depression than those with a primary school education or below (OR = 0.50, 95% CI: 0.28-0.88, P = 0.016). In rural areas, stroke patients with a high school or above education level had lower rates of depression than those with a primary school education or below (OR = 0.44, 95% CI: 0.21-0.91, P = 0.027). This difference was not significant in urban areas.
CONCLUSIONS: SES significantly influences the occurrence of PSD, which is reflected by education attainment and annual household expenditures. Education attainment was an independent influence on PSD, with a more pronounced effect in rural versus urban areas. We hope to reduce the prevalence of PSD and enhance the comprehensive management of this disease by modifying the influencing factors. Sex, self-reported health status, activities of daily living, night-time sleep duration, and life satisfaction also influenced the occurrence of PSD.

The postsynaptic density (PSD) is a collection of specialized proteins assembled beneath the postsynaptic membrane of dendritic spines. The PSD proteome comprises ~1000 proteins, including neurotransmitter receptors, scaffolding proteins and signalling enzymes. Many of these proteins have essential roles in synaptic function and plasticity. During brain development, changes are observed in synapse density and in the stability and shape of spines, reflecting the underlying molecular maturation of synapses. Synaptic protein composition changes in terms of protein abundance and the assembly of protein complexes, supercomplexes and the physical organization of the PSD. Here, we summarize the developmental alterations of postsynaptic protein composition during synapse maturation. We describe major PSD proteins involved in postsynaptic signalling that regulates synaptic plasticity and discuss the effect of altered expression of these proteins during development. We consider the abnormality of synaptic profiles and synaptic protein composition in the brain in neurodevelopmental disorders such as autism spectrum disorders. We also explain differences in synapse development between rodents and primates in terms of synaptic profiles and protein composition. Finally, we introduce recent findings related to synaptic diversity and nanoarchitecture and discuss their impact on future research. Synaptic protein composition can be considered a major determinant and marker of synapse maturation in normality and disease.