Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR).

INTRODUCTION: Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research. METHODS AND ANALYSIS: We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE. ETHICS AND DISSEMINATION: Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials. TRIAL REGISTRATION NUMBER: NCT06108102.

Joint Latent Space Model for Social Networks with Multivariate Attributes.

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation-maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package "jlsm" is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf .

Effects of an inulin fiber diet on the gut microbiome, colon, and inflammatory biomarkers in aged mice.

Due to the increasing human life expectancy and limited supply of healthcare resources, strategies to promote healthy aging and reduce associated functional deficits are of public health importance. The gut microbiota, which remodels with age, has been identified as a significant contributor to the aging process that is modifiable by diet. Since prebiotic dietary components such as inulin have been shown to impart positive benefits with regards to aging, this study used C57Bl6 mice to investigate whether 8 weeks on a 2.5 % inulin enhanced AIN-93M 1 % cellulose diet could offset age-associated changes in gut microbiome composition and markers of colon health and systemic inflammation in comparison to a AIN 93M 1 % cellulose diet with 0 % inulin. Our results demonstrated that, in both age groups, dietary inulin significantly increased production of butyrate in the cecum and induced changes in the community structure of the gut microbiome but did not significantly affect systemic inflammation or other markers of gastrointestinal health. Aged mice had different and less diverse microbiomes when compared to adult mice and were less sensitive to inulin-induced microbiome community shifts, evidenced by longitudinal differences in differentially abundant taxa and beta diversity. In aged mice, inulin restored potentially beneficial taxa including Bifidobacterium and key butyrate producing genera (e.g. Faecalibaculum). Despite inducing notable taxonomic changes, however, the 2.5 % inulin diet reduced alpha diversity in both age groups and failed to reduce overall community compositional differences between age groups. In conclusion, a 2.5 % inulin enhanced diet altered gut microbiome α and ß diversity, composition, and butyrate production in both adult and aged mice, with more potent effects on ß diversity and greater number of taxa significantly altered in adult mice. However, significant benefits in age-associated changes in systemic inflammation or intestinal outcomes were not detected.

Heywood Cases in Unidimensional Factor Models and Item Response Models for Binary Data.

Heywood cases are known from linear factor analysis literature as variables with communalities larger than 1.00, and in present day factor models, the problem also shows in negative residual variances. For binary data, factor models for ordinal data can be applied with either delta parameterization or theta parametrization. The former is more common than the latter and can yield Heywood cases when limited information estimation is used. The same problem shows up as non convergence cases in theta parameterized factor models and as extremely large discriminations in item response theory (IRT) models. In this study, we explain why the same problem appears in different forms depending on the method of analysis. We first discuss this issue using equations and then illustrate our conclusions using a small simulation study, where all three methods, delta and theta parameterized ordinal factor models (with estimation based on polychoric correlations and thresholds) and an IRT model (with full information estimation), are used to analyze the same datasets. The results generalize across WLS, WLSMV, and ULS estimators for the factor models for ordinal data. Finally, we analyze real data with the same three approaches. The results of the simulation study and the analysis of real data confirm the theoretical conclusions.

Effects of Broad-Spectrum Antibiotic Treatment or Germ-Free Status on Endurance Performance and Exercise Adaptations in Mice.

PURPOSE: Endurance exercise alters the gut microbiome independently of diet. The extent to which gut microbes are responsible for physiologic adaptations to exercise training is unknown. The purpose of these experiments was to determine the role of gut microbes in performance and muscle adaptation to 6 wk of voluntary wheel running (VWR) in mice. METHODS: We depleted microbes with broad-spectrum antibiotic (ABX) treatment and used germ-free (GF) mice to determine effects on adaptations to VWR. Male and female C57Bl/6 mice ( n = 56) were assigned to daily VWR or sedentary conditions. After the intervention, treadmill endurance and glucose tolerance were assessed, and gastrocnemius and soleus tissues were harvested and analyzed for citrate synthase (CS) enzyme activity and expression of exercise training-sensitive genes. RESULTS: ABX treatment and GF status resulted in VWR volumes ~22% and 26% lower than controls, respectively. Analysis of variance revealed that, although VWR increased treadmill endurance, ABX had no effect. GF status significantly reduced treadmill performance in trained GF mice after training. VWR increased gastrocnemius CS enzyme activity in all groups, and ABX and GF status did not reduce the VWR effect. VWR also increased muscle expression of PGC1a, but this was not affected by ABX treatment. CONCLUSIONS: ABX treatment and GF status reduced VWR behavior but did not affect VWR-induced adaptations in endurance capacity, CS activity, or expression of muscle metabolic genes. However, GF status reduced endurance capacity. These data indicated that reducing microbes in adulthood does not inhibit endurance training adaptations in C57Bl/6 mice, but that GF mice possess a reduced responsiveness to endurance exercise training, perhaps because of a developmental defect associated with lack of microbes from birth.

Dietary Fiber as a Counterbalance to Age-Related Microglial Cell Dysfunction.

With increasing age, microglia shift toward a pro-inflammatory phenotype that may predispose individuals to neurodegenerative disease. Because fiber fermentation in the colon produces bioactive short-chain fatty acids (SCFAs; e.g., acetate, butyrate, and propionate) that signal through the gut-brain axis, increasing dietary fiber may prevent or reverse age-related dysregulation of microglia. Adult (3-4 months old) and aged (23-24 months old) male and female mice were given ad libitum access to a modified AIN-93M diet with 1% cellulose or the same diet with 2.5 or 5.0% inulin for 8 weeks. Several adult and aged male mice fed 0 or 5% inulin were randomly selected for whole brain single-cell RNA sequencing (scRNA-seq) and differential gene expression analysis to classify brain microglia according to gene expression profile; and identify additional genetic markers of aging as possible targets for dietary interventions. Microglia were isolated from remaining mice and expression of selected aging-, inflammatory-, and sensome-related genes was assessed by Fluidigm as was the ex vivo secretion of tumor necrosis factor-alpha (TNF-α). SCFAs were measured in samples collected from the cecum. Microglia from adult and aged mice segregated into distinct phenotypes according to their gene expression profile. In aged mice, a considerably greater proportion of the population of microglia was identified being "activated" and a considerably smaller proportion was identified being "quiescent." These findings using whole brain scRNA-seq were largely corroborated using highly purified microglia and Fluidigm analysis to assess a selected panel of genes. Aged mice compared to adults had lower levels of SCFA's in cecum. Dietary inulin increased SCFAs in cecum and mostly restored microglial cell gene expression and TNF-α secretion to that seen in adults. Sex differences were observed with females having lower levels of SCFAs in cecum and increased neuroinflammation. Overall, these data support the use of fiber supplementation as a strategy to counterbalance the age-related microglial dysregulation.

Understanding the role of subpopulations and reliability in between-group studies.

The replication crisis has led to a renewed discussion about the impacts of measurement quality on the precision of psychology research. High measurement quality is associated with low measurement error, yet the role of reliability in the quality of experimental research is not always well understood. In this study, we attempt to understand the role of reliability through its relationship with power while focusing on between-group designs for experimental studies. We outline a latent variable framework to investigate this nuanced relationship through equations. An under-evaluated aspect of the relationship is the variance and homogeneity of the subpopulation from which the study sample is drawn. Higher homogeneity implies a lower reliability, but yields higher power. We proceed to demonstrate the impact of this relationship between reliability and power by imitating different scenarios of large-scale replications with between-group designs. We find negative correlations between reliability and power when there are sizable differences in the latent variable variance and negligible differences in the other parameters across studies. Finally, we analyze the data from the replications of the ego depletion effect (Hagger et al., 2016) and the replications of the grammatical aspect effect (Eerland et al., 2016), each time with between-group designs, and the results align with previous findings. The applications show that a negative relationship between reliability and power is a realistic possibility with consequences for applied work. We suggest that more attention be given to the homogeneity of the subpopulation when study-specific reliability coefficients are reported in between-group studies.

Recent Integrations of Latent Variable Network Modeling With Psychometric Models.

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.

Effects of Exercise on Stress-induced Attenuation of Vaccination Responses in Mice.

Studies suggest that exercise can improve vaccination responses in humans. Chronic stress can lead to immunosuppression, and there may be a role for exercise in augmenting immune responses. PURPOSE: To investigate the effects of acute eccentric exercise (ECC) and voluntary wheel exercise training (VWR) on antibody and cell-mediated immune responses to vaccination in chronically stressed mice. We hypothesized that both ECC and VWR would attenuate chronic stress-induced reductions in vaccination responses. METHODS: Mice were randomized into four groups: control (CON), stress (S)-ECC, S-VWR, and S-sedentary (SED). Stressed groups received chronic restraint stress for 6 h·d, 5 d·wk for 3 wk. After the first week of stress, S-ECC were exercised at 17 m·min speed at -20% grade for 45 min on a treadmill and then intramuscularly injected with 100 µg of ovalbumin (OVA) and 200 µg of alum adjuvant. All other groups were also vaccinated at this time. Stress-VWR mice voluntarily ran on a wheel for the entire experiment. Plasma was collected before, and at 1, 2, and 4 wk postvaccination. Enzyme-linked immunosorbent assay was performed to analyze anti-OVA IgG and IgM antibodies. After 3 wk of chronic stress, all mice were injected with OVA into the ear to determine the delayed-type hypersensitivity. RESULTS: We found that chronic restraint stress significantly reduced body weight and caused adrenal hypertrophy. We also found both S-ECC and S-VWR groups had significantly elevated anti-OVA IgG (P < 0.05), whereas no significant differences between the two exercise groups. Neither S-ECC nor S-VWR altered anti-OVA IgM or delayed-type hypersensitivity responses compared with S-SED group. CONCLUSIONS: Acute eccentric exercise and voluntary exercise training alleviated the chronic stress-induced anti-OVA IgG reductions in vaccination responses.