Role of per- and polyfluoroalkyl substances in the cardiorenal system: Unraveling crosstalk from the network of pollutants and phenotypes.

Although per- and polyfluoroalkyl substances (PFAS) have been frequently linked to cardiovascular and renal disease separately, evidence remains scarce regarding their systematic effect. Therefore, we recruited 546 newly diagnosed acute coronary syndrome (ACS) patients and detected seven myocardial enzymes and six kidney function biomarkers. Twelve PFAS were also assessed with ultra-high-performance liquid chromatography-tandem mass spectrometry. Generalized linear model and restricted cubic spline model were applied to single pollutant analysis. Quantile g-computation was used for mixture analysis. Network model was utilized to identify central and bridge nodes of pollutants and phenotypes. In the present study, perfluorohexane sulfonic acid was positively associated with uric acid (UA) (ß= 0.04, 95% confidence interval (CI): 0.01, 0.07), and perfluorobutanoic acid was negatively associated with estimated glomerular filtration rate (ß= -0.04, 95% CI: -0.07, -0.01) but positively associated with UA (ß= 0.03, 95% CI: 0.01, 0.06). In mixture analysis, each quantile increase in the PFAS mixture was significantly associated with UA (ß= 0.08, 95% CI: 0.04, 0.11). Network analysis revealed that perfluorooctanoate, UA, and myoglobin were denoted as bridge nodes, and the first principal component of lactate dehydrogenase and creatine kinase- myocardial band was identified as the node with the highest strength and expected influence. This study investigates the systematic impact of PFAS exposure through cardiorenal interaction network, which highlights that PFAS may serve as an upstream approach in UA-modulated cardiorenal network to affect cardiorenal system comprehensively.

Amizade e deficiência: análise das redes de conhecimento / Friendship and disability: An analysis of knowledge networks

Resumo Introdução: A amizade influencia positivamente o desenvolvimento humano. Todavia, pessoas com deficiencia enfrentam dificuldades para construir e manter esse vínculo. Este estudo objetiva explorar e analisar as redes de conhecimento da literatura sobre amizade e deficiencia de 2017 a 2022. Método: Utilizando critérios Prisma, a busca foi realizada nas bases de dados Scopus e Web of Science. Os softwares VOSviewer e Pajek foram usados para construir e analisar as redes. Resultados: Foram analisados 68 artigos. Os Estados Unidos lideram em publicaçoes, entretanto o Reino Unido é mais influente na rede. As principais investigaçoes na área sao: transtorno do espectro autista, deficiencia intelectual, inclusao social, habilidades sociais e violencia contra a pessoa com deficiencia. Documentos influentes abordam análise temática em psicologia, amizade de pessoas com transtorno do espectro autista e deficiencia intelectual. Conclusões: Os mapas de conhecimento e análise de redes ajudam a explorar a literatura, o que permite compreender tendencias, estrutura intelectual base e correlações científicas.

Abstract Introduction: Friendship positively influences human development. However, people with disabilities face difficulties in building and maintaining this bond. This study aims to explore and analyze the knowledge networks in the literature on friendship and disabilities from 2017 to 2022. Method: Using PRISMA criteria, the search was conducted in the Scopus and Web of Science databases. VOSviewer and Pajek software were used to construct and analyze the networks. Results: Sixty-eight articles were analyzed. The United States leads in publications, but the United Kingdom is more influential in the network. The main research areas are autism spectrum disorder, intellectual disability, social inclusion, social skills, and violence against people with disabilities. Influential documents address thematic analysis in psychology, and friendship among individuals with autism spectrum disorder and intellectual disability. Conclusions: Knowledge maps and network analysis help explore the literature, allowing an understanding of trends, the foundational intellectual structure, and scientific correlations.

Network analysis of subjective happiness, hope and resilience in the Paraguayan general population during the post-COVID-19 pandemic period / Análisis en red de felicidad subjetiva, esperanza y resiliencia en la población general paraguaya en la pospandemia del COVID-19

Abstract Introduction/objective: In this cross-sectional study, the connections between indicators of subjective happiness, hope, and resilience were investigated in 591 adult Paraguayans (average age 37.7 years; SD = 11.35) during the COVID-19 post-pandemic period, using network analysis for the first time. Method: The indicators of subjective happiness, hope, and resilience were assessed using the Subjective Happiness Scale, the Adult Hope Scale, and the 10-item Connor-Davidson Resilience Scale, respectively. Result: The results indicated that "Enjoy life in spite of it all," "Pursuing goals," and "Coping with stress" were the most central indicators of the resilience, hope, and subjective happiness network. While stronger conditional relationships were observed between indicators of the same network variable, potential bridge indicators were also noted that could link resilience, hope, and subjective happiness, such as "I am a strong person," "Enjoy life in spite of it all," "Pursuing goals," and "I have been successful in life." Conclusions: The results suggest that timely and multilevel interventions targeted at central and bridge indicators can help promote positive emotions that impact mental health.

Resumen Introducción/objetivo: En este estudio transversal se investigaron las conexiones entre los indicadores de felicidad subjetiva, esperanza y resiliencia en 591 paraguayos adultos (edad promedio 37.7 años; DE = 11.35) en el período pospandemia del COVID-19 utilizando análisis de redes por primera vez. Método: Los indicadores de felicidad subjetiva, esperanza y resiliencia se evaluaron utilizando la Escala de Felicidad Subjetiva, la Escala de Esperanza del Adulto y la Escala de Resiliencia de Connor-Davidson de 10 ítems, respectivamente. Resultados: Los resultados indicaron que "Disfrutar la vida a pesar de todo", "Perseguir metas" y "Afrontar el estrés" fueron los indicadores más centrales de la red de resiliencia, esperanza y felicidad subjetiva. Si bien se observaron relaciones condicionales más fuertes entre indicadores de la misma variable de red, también se observaron posibles indicadores puente que podrían vincular la resiliencia, la esperanza y la felicidad subjetiva, como "Soy una persona fuerte", "Disfruta la vida a pesar de todo", "Persiguiendo metas" y "He tenido éxito en la vida". Conclusiones: Los resultados sugieren que las intervenciones oportunas y multinivel dirigidas a indicadores centrales y puente pueden ayudar a promover emociones positivas que impacten la salud mental.

Revolutionizing prognostic predictions in colorectal cancer: Macrophage­driven transcriptional insights from single­cell RNA sequencing and gene co­expression network analysis.

Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.

Identification of signaling pathways induced by porcine parvovirus in porcine kidney-15 cells based on weighted correlation network analysis.

Porcine parvovirus (PPV) is an important trigger of reproductive issues in pigs. Infection of the porcine kidney-15 (PK-15) cells with PPV induces cell death and inflammation. To explore the impact of PPV infection on gene expression in PK-15 cells and to identify the associated signaling pathways, we performed weighted correlation network analysis (WGCNA) on both PPV-infected and uninfected cells. We identified the blue and brown modules, with the blue module demonstrating decreased gene expression and the brown module showing increased gene expression at 48 hr post-PPV induction. Gene Ontology (GO) analysis revealed that genes in the blue module were predominantly associated with cellular components, while those in the brown module were enriched in biological processes, including the immunological response to PPV infection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the modules indicated that the "Cytokine-cytokine receptor interaction" was linked to PPV. Furthermore, we screened 124 target genes involved in more than 100 pathways that interact with C-X-C motif chemokine ligand 8 (CXCL8), which plays an essential role in regulating numerous biological processes. The signaling pathways we identified facilitate a comprehensive examination of the mechanisms of interactions initiated by PPV infection.

Network analysis of correlations between suicide exposure, depression, and anxiety symptoms in adolescents.

BACKGROUND: Adolescents with a history of suicide exposure, defined as experiencing the suicide death of a family member, friend, or other acquaintances, are more likely to experience mental health issues such as depression or anxiety. AIMS: This study aimed to explore prevalence rates and the network of adolescents' suicide exposure, depression, and anxiety symptoms, and to clarify the correlations between suicide exposure and symptoms of depression and anxiety. METHOD: A total of 8,957 adolescents were included in this cross-sectional study. Data regarding general information, symptoms of depression and anxiety, and suicide exposure were collected from mid-September to early October 2021. Network analysis was employed to assess relationships between suicide exposure and individual symptoms of both depression and anxiety. Central symptoms were identified by strength; the flow network was visualized to identify symptoms directly related to suicide exposure. RESULTS: The prevalence rates of suicide exposure, depression, and anxiety were 5.28%, 12.87%, and 10.48%. Results indicated that suicide exposure was associated with both depression and anxiety, and had the strongest positive association with suicidal ideation. Central symptoms of the network were sad mood, nervousness, fatigue, irritability, and uncontrollable worry. Bridge symptoms were suicidal ideation and irritability. Appetite changes, suicidal ideation, uncontrollable worry, sleep difficulties, and irritability were symptoms directly related to suicide exposure. CONCLUSIONS: There were significant inter-symptom associations between suicide exposure, depression, and anxiety in adolescents. It is recommended that future studies explore whether targeted interventions and long-term monitoring concerning these inter-symptom associations can protect adolescents with suicide exposure.

Assessing restorative effects of soundscapes in VR through EEG and HRV.

This study aimed to extend the application of soundscape analysis by utilizing electroencephalogram (EEG) analysis as a physiological evaluation tool to determine the actual restorative impact on individual environmental perceptions and psycho-physiological responses stemming from various soundscape experiences. Initially, we constructed three distinct virtual reality (VR) environments: waterfront, urban, and green areas, each accompanied by three content variations. A total of 60 subjects participated in the study. Data were gathered through a survey assessing individual characteristics, psychological restorative responses, and soundscape perceptions. Additionally, quantitative physiological responses such as heart rate variability (HRV) and EEG, were measured. Subsequently, subjects were categorized into restoration and non-restoration groups based on HRV responses using cluster analysis. The analysis revealed positive HRV changes indicative of reduced stress levels. In EEG analysis, differences were observed in network connectivity rather than power spectral density. As a result of connectivity analysis, global efficiency increased overall in the restoration group, and differences in nodal efficiency occurred in a total of eight brain regions, enabling soundscape experience to efficiently process cognitive functions in the cerebrum, thereby having a positive effect on neural communication. This study is significant as it represents the first examination of soundscape restorative responses in terms of brain wave connectivity, underscoring the feasibility of employing physiological evaluations, including brain wave analysis, in the study of soundscapes.

A network analysis of voice hearing, emotional distress and subjective recovery before and after cognitive behavioural interventions.

BACKGROUND: There is a need to identify and to better understand key processes involved in voice hearing, which can inform the targeting and development of psychological interventions for distressing voices. The current study aimed to examine interrelations between the negative impact of voices, voice characteristics, emotional distress and recovery before and after cognitive behavioural interventions for voices (Coping Strategy Enhancement, guided self-help Cognitive Behavioural Therapy, Relating Therapy and Person-Based Cognitive Therapy). METHODS: The sample consisted of 172 participants from the Sussex Voices Clinic who completed pre- and post-treatment assessments. The negative impact of voices, voice characteristics, emotional distress and recovery were used to estimate two networks, before and after cognitive behavioural interventions, using the graphical lasso method with the extended Bayesian information criterion. Centrality indices were also computed, and the two networks were compared on connectivity, structure and individual edge weights. RESULTS: Depression, anxiety and the negative impact of voices were identified as key central symptoms and acted as bridge symptoms in pre- and post-treatment networks. There were no significant differences in network structure (M = 0.155, p = .57), global strength (S = 0.188, p = .07) and centralities (C = -0.318, p = -.06) between the two networks. CONCLUSION: Our findings suggest that anxiety and depression are promising treatment targets, that can lead to reductions in voice-related distress, whereas the characteristics of voices and subjective recovery play little role in the network structure. Limitations include the lack of a control group and the lack of diversity within the sample.

Soil-Mulching Treatment Enhances the Content of Stilbene in Grape Berries: A Transcriptomic and Metabolomic Analysis.

Soil mulching is a useful agronomic practice that promotes early fruit maturation and affects fruit quality. However, the regulatory mechanism of fruit metabolites under soil-mulching treatments remains unknown. In this study, variations in the gene sets and metabolites of grape berries after mulching (rice straw + felt + plastic film) using transcriptome and metagenomic sequencing were investigated. The results of the cluster analysis and orthogonal projection to latent structures discriminant analysis of the metabolites showed a difference between the mulching and control groups, as did the principal component analysis results for the transcriptome. In total, 36 differentially expressed metabolites were identified, of which 10 (resveratrol, ampelopsin F, piceid, 3,4'-dihydroxy-5-methoxystilbene, ε-viniferin, trans resveratrol, epsilon-viniferin, 3'-hydroxypterostilbene, 1-methyl-resveratrol, and pterostil-bene) were stilbenes. Their content increased after mulching, indicating that stilbene synthase activity increased after mulching. The weighted gene co-expression network analysis revealed that the turquoise and blue modules were positively and negatively related to stilbene compounds. The network analysis identified two seed genes (VIT_09s0054g00610, VIT_13s0156g00260) and two transcription factors (VIT_13s0156g00260, VIT_02s0025g04590). Overall, soil mulching promoted the accumulation of stilbene compounds in grapes, and the results provided key genetic information for further studies.

Anxiety, depression, and post-traumatic stress disorder in nurses exposed to horizontal violence: a network analysis.

BACKGROUND: Horizontal violence can cause serious mental health problems for nurses, particularly anxiety, depression, and post-traumatic stress disorder. However, the intrinsic linkage mechanism between mental symptoms of anxiety, depression, and post-traumatic stress disorder in nurses exposed to horizontal violence is unclear. This study aims to elucidate the characteristics of anxiety, depression, and post-traumatic stress disorder networks among nurses with horizontal violence exposure. METHODS: Data for this cross-sectional study were obtained from the baseline portion of a short longitudinal survey conducted at four tertiary hospitals in Shandong Province, China. A total of 510 nurses with horizontal violence exposure completed the General Information Scale, the Negative Acts Questionnaire, the Seven-item Generalized Anxiety Disorder Scale, the Nine-item Patient Health Questionnaire, and the Four-item SPAN. The network model was constructed using network analysis. The expected influence and the bridge expected influence of nodes were calculated. The stability and accuracy of the network were estimated. RESULTS: The results show that A4 (Trouble relaxing) and P1 (Startle) had the highest expected influence in the network. D9 (Suicidality ideation) and A5 (Restlessness) were the key bridge symptoms. CONCLUSIONS: "Trouble relaxing", "Startle", "Suicidality ideation", and "Restlessness" are all mental symptoms that need to be urgently improved the most in nurses exposed to horizontal violence. Nursing administrators and policymakers should implement mental health intervention programs for these symptoms as early as possible to maximize nurses' mental health.

Bioinformatics approaches for studying molecular sex differences in complex diseases.

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

The network analysis of anxiety, depression and academic burnout symptom relationships throughout early, middle, and late adolescence.

BACKGROUND: Previous research has explored the associations between anxiety, depression, and academic burnout primarily from a variable-level perspective. However, there is limited understanding of which symptoms might play a significant role in anxiety, depression, and academic burnout among adolescents at different stages. METHODS: This study included 7,286 adolescents aged 10 to 18. Questionnaires assessed participants' anxiety, depression, and academic burnout. Network analysis was conducted on the overall sample and segmented by early, middle, and late adolescence to explore relationships between symptoms and variations in symptom expression across these stages, aiming to propose effective interventions targeting anxiety, depression, and academic burnout symptoms in early, middle, and late adolescence. RESULTS: The study found that "feeling that studying is meaningless" emerged as a core symptom in the overall sample. Additionally, "acting or speaking slowly" emerged as a core symptom in early adolescence, while "the thought of dying or hurting" and "feeling bad about yourself, letting your family down" were prominent in middle adolescence, and "easily annoyed or irritable" and "feeling tired" may be prioritized in late adolescence. The varying central symptoms across different adolescent stages suggest the need for targeted interventions. CONCLUSION: These findings underscore the importance of interventions tailored to specific symptoms to meet the unique needs of adolescents at different developmental stages.

Working in a relational way is everything: Perceptions of power and value in a drug policy-making network.

BACKGROUND: The development of drug policies has been a major focus for policy-makers across North America in light of the ongoing public health emergency caused by the overdose crisis. In this context, the current study examined stakeholders' experiences and perceptions of power and value in a drug policy-making process in a North American city using qualitative, questionnaire, and social network data. METHODS: We interviewed 18 people who participated in the development of a drug policy proposal between October 2021 and March 2022. They represented different groups and organizations, including government (n = 3), people who use drugs-led advocacy organizations (n = 5), other drug policy advocacy organizations (n = 5), research (n = 3) and police (n = 2). Most of them identified as men (n = 8) and white (n = 16), and their ages ranged between 30 and 80 years old (median = 50). Social network analysis questionnaires and semi-structured qualitative interviews were administered via Zoom. Social network data were analysed using igraph in R, and qualitative data were analysed using thematic analysis. The analyses explored perceptions of value and power within a drug policy-making network. RESULTS: The policy-making network showed that connections could be found across participants from different groups, with government officials being the most central. Qualitative data showed that inclusion in the network and centrality did not necessarily translate into feeling powerful or valued. Many participants were dissatisfied with the process despite having structurally advantageous positions or self-reporting moderately high quantitative value scores. Participants who viewed themselves as more valued acknowledged many process shortcomings, but they also saw it as more balanced or fair than those who felt undervalued. CONCLUSIONS: While participation can make stakeholders and communities feel valued and empowered, our findings highlight that inclusion, position and diversity of connections in a drug policy-making network do not, in and of itself, guarantee these outcomes. Instead, policy-makers must provide transparent terms of reference guidelines and include highly skilled facilitators in policy discussions. This is particularly important in policy processes that involve historical power imbalances in the context of a pressing public health emergency.

Microbial inoculants using spent mushroom substrates as carriers improve soil multifunctionality and plant growth by changing soil microbial community structure.

Peat is typically used as a carrier for microbial inoculants; however, due to its non-renewable nature alternatives need to be identified as reliable and renewable carriers for mineral-solubilizing inoculants. In pot experiments, solid microbial inoculants were comprised of peat (P), biochar (BC), and spent mushroom substrates (SMS) using Medicago sativa L. as experimental materials, and the purpose of this study is to assess the effect of solid microbial inoculants on soil multifunctionality and plant growth. The results revealed that the SMS microbial inoculant had the greatest positive impact on plant biomass and significantly stimulated soil multifunctionality which is typically managed or assessed based on various soil functions or processes that are crucial for sustaining productivity, in contrast to the peat microbial inoculant, particularly at a supply level of 100 g/pot. There was no significant correlation between soil multifunctionality and bacterial/fungal microbial diversity. However, according to the co-occurrence network of bacteria and fungi, soil multifunctionality was intimately correlated with the biodiversity of the main ecological clusters (modules) of bacteria and fungi, rather than to the entire soil microbial community structure. The keystone species of module hubs and connectors play critical roles in maintaining the stability of ecological clusters of microbial co-occurrence networks and linkages between ecological clusters. Soil pH is a major predictor of changes in plant biomass, and leads to changes therein by affecting the major ecological clusters of bacterial and fungal co-occurrence networks. These results suggested that SMS may serve as a good alternative to peat as a carrier of mineral-solubilizing microorganisms to maintain soil multifunctionality and promote plant growth.

Variations in media framing of movements in China, France, and the U.S.: An intersectional approach.

Current literature often links contentious protests with media hostility, showing that news outlets typically portray protests involving disruption or violence in a negative light. Contesting this literature, this work introduces an intersectional approach-focusing on geopolitics, protest goals and actions-to theorize divergences in the media framing of protests that entail violence. To illustrate these divergences, we use mixed methods-network analysis and content analysis-to examine an original dataset on U.S. media coverage of three large movements in different countries. These movements share similarities in their anti-status quo goals and contentious actions but differ in geopolitical locations: one taking place in the U.S., the second in a U.S. ally country, and the third in a non-ally country. As the first to apply network analysis in movement-media studies, this comparative study contributes to a systematic examination of media framing variations both within and across social movements. This work also complicates our understanding of violence and media representation by introducing a theoretically-informed approach that considers multiple factors simultaneously.

Using network analysis to provide evidence for brain health as a unified construct relevant to aging with HIV.

PURPOSE: Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV. METHODS: Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network. RESULTS: The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops," indicating strong connectivity. The most central symptoms were "How much do you enjoy life?" and "How often do you have negative feelings?". CONCLUSION: The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. METHODS: Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. RESULTS: Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. CONCLUSIONS: The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Network Analysis to Visualize Qualitative Results: Example From a Qualitative Content Analysis of The National Child Abuse Hotline.

Data visualization, such as figures created through network analysis, may be one way to present more complete information from qualitative analysis. Segments of qualitatively coded data can be treated as objects in network analysis, thus creating visual representations of the code frequency (i.e., nodes) and the co-occurrence (i.e., edges). By sharing an example of network analysis applied to qualitative data, and then comparing our process with other applications, our goal is to help other researchers reflect on how this approach may support their interpretation and visualization of qualitative data. A total of 265 de-identified transcripts between help-seekers and National Child Abuse Hotline crisis counselors were included in the network analysis. Post-conversation surveys, including help-seekers' perceptions of the conversations, were also included in the analysis. Qualitative content analysis was conducted, which was quantified as the presence or absence of each code within a transcript. Then, we divided the dataset based on help-seekers' perceptions. Individuals who responded that they "Yes/Maybe" felt more hopeful after the conversation were in the "hopeful" dataset, while those who answered "No" were in the "unhopeful" dataset. This information was imported to UCINET to create co-occurrence matrices. Gephi was used to visualize the network. Overall, code co-occurrence networks in hopeful conversations were denser. Furthermore, the average degree was higher in these hopeful conversations, suggesting more codes were consistently present. Codes in hopeful conversations included information, counselor support, and problem-solving. Conversely, non-hopeful conversations focused on information. Overall, network analysis revealed patterns that were not evident through traditional qualitative analysis.

Salience network segregation mediates the effect of tau pathology on mild behavioral impairment.

INTRODUCTION: A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the joint contributions of Alzheimer's disease (AD) pathology and brain function to MBI remain unclear. METHODS: We test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks, using data from 128 participants with diagnosis of amnestic mild cognitive impairment or mild dementia-AD type. RESULTS: We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI. DISCUSSION: Our findings suggest that tau pathology contributes to MBI primarily by disrupting salience network function and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations. HIGHLIGHTS: Network segregation mediates Alzheimer's disease (AD) pathology impact on mild behavioral impairment (MBI). The salience network is pivotal in linking tau pathology and MBI. This study used path analysis with AD biomarkers and network integrity. The study evaluated the roles of salience, default mode, and frontoparietal networks. This is the first study to integrate MBI with AD biomarkers and network functionality.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students.

BACKGROUND: A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. METHODS: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). RESULTS: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. CONCLUSION: These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. TRIAL REGISTRATION: Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).