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OBECTIVE:: High homocysteine (Hct) has been causatively linked to Alzheimer disease (AD) and vascular dementia (VaD) in old age, but research methodologies and outcome measures are heterogeneous. It remains unclear whether the findings can be generalized across studies. METHODS: : Random-effects meta-analyses were conducted on studies examining the relationship between Hct level and risk of developing dementia/cognitive decline between comparison groups. Meta-regression identified patient- and trial-related factors, which may contribute to heterogeneity. RESULTS: : Seventeen relevant studies (6,122 participants; 13 cross-sectional and fourprospective studies) were included. Compared with controls, Hct was significantly elevated in AD (pooled standardized mean difference [SMD]: 0.59; 95% confidence interval [CI]: 0.38-0.80; significant heterogeneity: = 0.105) and VaD (pooled SMD: 1.30; 95% CI: 0.75-1.84; significant heterogeneity: = 0.378). Meta-regression identified mean age as significant moderator for AD versus controls and mean age and mean folate levels as significant moderators for VaD versus controls. Hct was significantly higher in VaD relative to AD (pooled SMD: 0.48; 95% CI: 0.23-0.73; moderately significant heterogeneity: = 0.076); proportion of men and mean folate levels were significant moderators. High-Hct level was not associated with risk of developing dementia in prospective studies (pooled odds ratio: 1.34; 95% CI: 0.94-1.91, nonsignificant heterogeneity: = 0.048). CONCLUSION: : Individuals with AD and VaD have higher Hct levels than controls; however, a causal relationship between high-Hct level and risk of developing dementia is not supported. More prospective studies and randomized controlled trials are required to test the therapeutic benefits of lowering Hct levels. [ABSTRACT FROM AUTHOR]

To assess how culture influences the behavior of people, multilevel models are an immediate choice for modeling the relationship at the levels of the individual and culture. The authors propose structural equation modeling (SEM) to test the universality of psychological processes at the individual and culture levels. Specifically, the structural equivalence of the measurement (where the instrument is measuring the same construct across countries) is first tested with meta-analytic SEM. If the measurement is structurally equivalent, cross-level equivalence (where the instrument is measuring similar constructs at different levels) will then be tested with multilevel SEM. A large data set on social axioms with 7,590 university students from 40 cultural groups was used to illustrate the procedures. The results showed that the structural equivalence of the social axioms was well supported at the individual level across 40 cultural groups, whereas the cross-level equivalence was partially supported. The superiority of the SEM approach and the theoretical meaning of its solution are discussed. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy and safety of a dexamethasone intracanalicular insert (DEX) for treatment of allergic conjunctivitis (AC)., Patients and Methods: In this multicenter, randomized, double-masked, placebo-controlled phase 3 study, adults (≥18 years) with AC were randomized 1:1 to DEX or placebo insert (PBO) placed bilaterally. Subjects underwent repetitive conjunctival allergen challenges (CAC) across 30 days and were assessed for changes in AC signs and symptoms. The primary endpoint was ocular itching score at 3, 5, and 7 minutes post-CAC at Day 8 (7 days post-insertion). This trial is registered on ClinicalTrials.gov (NCT04050865)., Results: Ninety-six subjects were randomized (n=48 DEX, n=48 PBO). Compared to PBO, there were statistically significant treatment differences favoring DEX for the primary endpoint of mean ocular itching score at Day 8 (-0.86, -0.98, -0.96 at 3, 5, and 7 minutes post-CAC respectively; P <0.0001 for all). Treatment differences favored DEX for all 24 time points across 6 visits and were statistically significant ( P <0.05) except for the first post-insertion (Day 7, 3 minutes). For the 18 time points at which conjunctival redness was assessed, DEX had lower scores than PBO ( P <0.05 for all). The most common ocular adverse events (AEs) in DEX subjects were eye discharge and irritation. No serious AEs, elevated intraocular pressure, dacryocanaliculitis, or use of rescue medications were reported., Conclusion: Results of this study support the potential use of dexamethasone insert as a physician-administered, preservative-free treatment for AC, with significant improvements in ocular itching and conjunctival redness compared with placebo. The dexamethasone insert was generally safe with a favorable safety profile., Competing Interests: Kenneth Kenyon, Eugene McLaurin, Steven Silverstein, John Meyer, Erik Anderson, and Ravi Patel were study investigators and have no conflicts of interest to disclose for this work. Paul Gomes is an employee of Ora, Inc. Erin Reilly, Srilatha Vantipalli, and Matthew Cheung are employees of Ocular Therapeutix, Inc. Michael H. Goldstein is a former employee of Ocular Therapeutix, Inc. The authors report no other conflicts of interest in this work., (© 2024 Kenyon et al.)

Despite the number of empirical contributions on the topic, scientists have offered contrasting perspectives on the role of adaptive versus maladaptive emotion regulation (ER) strategies in suicidality. Moreover, suicidal attempts and suicidal ideation are likely to be differentially related to single ER strategies. To provide more systematic knowledge that can be used to draw sound conclusions and formulate clinical indications, we carried out a systematic review and meta-analysis that we reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards (Moher et al., 2009). From an initial pool of 16,530 articles retrieved from scientific databases (APA PsycInfo, APA PsycArticles, Medline, Scopus, Web of Science, and PubMed) and a search for gray literature, 226 articles were selected to perform 15 meta-analyses. In addition, metaregressions were carried out to test a series of moderators, including the type of suicidality investigated. Among adaptive strategies, results evidenced the role of reappraisal, mindfulness, and several aspects of problem solving. In contrast to our hypothesis, reflective attitude was positively associated with suicidality, calling into question the traditional distinction between adaptive and maladaptive strategies. Regarding maladaptive ER strategies, suppression, avoidance, rumination, brooding, negative problem orientation, and both impulsive and avoidant problem solving proved to be significantly associated with suicidality. Finally, several moderation effects involving age, gender composition, and type of suicidality were observed, supporting the importance of adopting a complex perspective when approaching the topic. Despite the interesting preliminary results, additional research is needed to provide a greater understanding of the interplay between the different ER strategies and suicidality and to develop effective protocols of intervention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Principal component regression (PCR) is a popular technique in data analysis and machine learning. However, the technique has two limitations. First, the principal components (PCs) with the largest variances may not be relevant to the outcome variables. Second, the lack of standard error estimates for the unstandardized regression coefficients makes it hard to interpret the results. To address these two limitations, we propose a model-based approach that includes two mean and covariance structure models defined for multivariate PCR. By estimating the defined models, we can obtain inferential information that will allow us to test the explanatory power of individual PCs and compute the standard error estimates for the unstandardized regression coefficients. A real example is used to illustrate our approach, and simulation studies under normality and nonnormality conditions are presented to validate the standard error estimates for the unstandardized regression coefficients. Finally, future research topics are discussed., (© 2023 British Psychological Society.)

Redundancy analysis (RA) is a multivariate method that maximizes the mean variance of a set of criterion variables explained by a small number of redundancy variates (i.e., linear combinations of a set of predictor variables). However, two challenges exist in RA. First, inferential information for the RA estimates might not be readily available. Second, the existing methods addressing the dimensionality problem in RA are limited for various reasons. To aid the applications of RA, we propose a direct covariance structure modeling approach to RA. The proposed approach (1) provides inferential information for the RA estimates, and (2) allows the researcher to use a simple yet practical criterion to address the dimensionality problem in RA. We illustrate our approach with an artificial example, validate some standard error estimates by simulations, and demonstrate our new criterion in a real example. Finally, we conclude with future research topics.

Previous studies have reported impaired humoral responses after SARS-CoV-2 mRNA vaccination in patients with immune-mediated inflammatory diseases (IMIDs), particularly those treated with anti-TNF biologics. We previously reported that IMID patients diagnosed with inflammatory bowel disease, psoriasis, psoriatic arthritis, ankylosing spondylitis, or rheumatoid arthritis exhibited greater waning of Ab and T cell responses than healthy control subjects after SARS-CoV-2 vaccine dose 2. Fewer data are available on the effects of third and fourth doses. This observational cohort study collected plasma and PBMCs from healthy control subjects and untreated or treated patients with IMIDs prevaccination and after one to four doses of SARS-CoV-2 mRNA vaccine (BNT162b2 or mRNA-1273). SARS-CoV-2-specific Ab levels, neutralization, and T cell cytokine release were measured against wild-type and Omicron BA.1 and BA.5 variants of concern. Third vaccine doses substantially restored and prolonged Ab and T cell responses in patients with IMIDs and broadened responses against variants of concern. Fourth-dose effects were subtle but also prolonged Ab responses. However, patients with IMIDs treated with anti-TNF, especially patients with inflammatory bowel disease, exhibited lower Ab responses even after the fourth dose. Although T cell IFN-γ responses were maximal after one dose, IL-2 and IL-4 production increased with successive doses, and early production of these cytokines was predictive of neutralization responses at 3-4 mo postvaccination. Our study demonstrates that third and fourth doses of the SARS-CoV-2 mRNA vaccines sustain and broaden immune responses to SARS-CoV-2, supporting the recommendation for three- and four-dose vaccination regimens in patients with IMIDs., (Copyright © 2023 by The American Association of Immunologists, Inc.)

Objective: To examine sociodemographic factors associated with having unmet needs in medications, mental health, and food security among older adults during the COVID-19 pandemic., Data Sources and Study Setting: Primary data and secondary data from the electronic health records (EHR) in an age-friendly academic health system in 2020 were used., Study Design: Observational study examining factors associated with having unmet needs in medications, food, and mental health., Data Collecting/extraction Methods: Data from a computer-assisted telephone interview and EHR on community-dwelling older patients were analyzed., Principle Findings: Among 3400 eligible patients, 1921 (53.3%) (average age 76, SD 11) responded, with 857 (45%) of respondents having at least one unmet need. Unmet needs for medications were present in 595 (31.0%), for food in 196 (10.2%), and for mental health services in 292 (15.2%). Racial minorities had significantly higher probabilities of having unmet needs for medicine and food, and of being referred for services related to medications, food, and mental health. Patients living in more resource-limited neighborhoods had a higher probability of being referred for mental health services., Conclusions: Age-friendly health systems (AFHS) and their recognition should include assessing and addressing social risk factors among older adults. Proactive efforts to address unmet needs should be integral to AFHS., (© 2022 Health Research and Educational Trust.)

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary and meta-analytic data has received little attention, particularly when IPD are drawn from complex sampling surveys. Complex sampling surveys often feature clustering, stratification, and multistage sampling to obtain nationally or internationally representative data from a target population. Furthermore, IPD from these studies is likely to provide more than one effect size. To address these complexities, we propose a two-stage meta-analytic approach that generates model-based effect sizes in Stage 1 and synthesizes them in Stage 2. We present a sequence of steps, illustrate their implementation, and discuss the methodological decisions and options within. Given its flexibility to deal with the complex nature of the primary and meta-analytic data and its ability to combine multiple IPD sets or IPD with aggregated data, the proposed two-stage approach opens up new analytic possibilities for synthesizing knowledge from complex sampling surveys. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The extent and nature of cognitive impairment in brief psychotic disorder remains unclear, being rarely studied unlike schizophrenia. The present study hence sought to directly compare the visual cognitive dysfunction and its associated brain networks in brief psychotic disorder and schizophrenia. Data from picture completion (a complex visual task) and whole-brain functional connectome from resting-state fMRI were acquired from a sample of clinically stable patients with an established psychotic disorder (twenty with brief psychotic disorder, twenty with schizophrenia) and twenty-nine healthy controls. Group differences and the inter-relationships in task performances and brain networks were tested. Picture completion task deficits were found in brief psychotic disorder compared with healthy controls, though the deficits were less than schizophrenia. Task performance also correlated with severity of psychotic symptoms in patients. The task performance was inversely correlated with the functional connectivity between peripheral visual and attentional networks (dorsal attention and salience ventral attention), with increased functional connectivity in brief psychotic disorder compared with healthy controls and in schizophrenia compared with brief psychotic disorder. Present findings showed pronounced visual cognitive impairments in brief psychotic disorder that were worse in schizophrenia, underpinned by abnormal interactions between higher-order attentional and lower-order visual processing networks., Competing Interests: Declaration of Competing Interest All authors report no financial conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD)., Purpose: To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD., Study Type: Systematic review and meta-analysis., Data Sources: MEDLINE, Embase, and PsycINFO were searched through October 2021., Field Strength: 1.0 T, 1.5 T, or 3.0 T., Assessment: Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2)., Statistical Tests: Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results., Results: Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value., Data Conclusion: While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution., Evidence Level: 3 TECHNICAL EFFICACY: Stage 3., (© 2021 International Society for Magnetic Resonance in Medicine.)

BACKGROUNDLimited information is available on the impact of immunosuppressants on COVID-19 vaccination in patients with immune-mediated inflammatory diseases (IMID).METHODSThis observational cohort study examined the immunogenicity of SARS-CoV-2 mRNA vaccines in adult patients with inflammatory bowel disease, rheumatoid arthritis, ankylosing spondylitis, or psoriatic disease, with or without maintenance immunosuppressive therapies. Ab and T cell responses to SARS-CoV-2, including neutralization against SARS-CoV-2 variants, were determined before and after 1 and 2 vaccine doses.RESULTSWe prospectively followed 150 subjects, 26 healthy controls, 9 patients with IMID on no treatment, 44 on anti-TNF, 16 on anti-TNF with methotrexate/azathioprine (MTX/AZA), 10 on anti-IL-23, 28 on anti-IL-12/23, 9 on anti-IL-17, and 8 on MTX/AZA. Ab and T cell responses to SARS-CoV-2 were detected in all participants, increasing from dose 1 to dose 2 and declining 3 months later, with greater attrition in patients with IMID compared with healthy controls. Ab levels and neutralization efficacy against variants of concern were substantially lower in anti-TNF-treated patients than in healthy controls and were undetectable against Omicron by 3 months after dose 2.CONCLUSIONSOur findings support the need for a third dose of the mRNA vaccine and for continued monitoring of immunity in these patient groups.FUNDINGFunded by a donation from Juan and Stefania Speck and by Canadian Institutes of Health (CIHR)/COVID-Immunity Task Force (CITF) grants VR-1 172711 and VS1-175545 (to THW and ACG), CIHR FDN-143250 (to THW), GA2-177716 (to VC, ACG, and THW), and GA1-177703 (to ACG) and the CIHR rapid response network to SARS-CoV-2 variants, CoVaRR-Net (to ACG).

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the performance of four approaches to handling missing data in these models: a joint ordinal-continuous full information maximum likelihood (FIML) approach and three multiple imputation approaches (fully conditional specification, fully conditional specification with latent variable formulation, and expectation-maximization with bootstrapping) combined with the weighted least squares with mean and variance adjustment (WLSMV) estimator. In a Monte-Carlo simulation, the FIML approach produced unbiased estimations of factor loadings and standard errors in almost all conditions. Fully conditional specification combined with WLSMV was second best, producing accurate estimates when the sample size was large. However, FIML encountered slight non-convergence issues when certain ordinal categories have extremely low frequencies, which is typical of skewed data. If the sample is large, fully conditional specification combined with weighted least squares is recommended when the FIML approach is not feasible (e.g., non-convergence, impractical computation durations, and variables that predict missingness are not of interest to the analysis)., (© 2021. The Psychonomic Society, Inc.)

In this paper, we show how the methods of systematic reviewing and meta-analysis can be used in conjunction with structural equation modeling to summarize the results of studies in a way that will facilitate the theory development and testing needed to advance prevention science. We begin with a high-level overview of the considerations that researchers need to address when using meta-analytic structural equation modeling (MASEM) and then discuss a research project that brings together theoretically important cognitive constructs related to depression to (a) show how these constructs are related, (b) test the direct and indirect effects of dysfunctional attitudes on depression, and (c) test the effects of study-level moderating variables. Our results suggest that the indirect effect of dysfunctional attitudes (via negative automatic thinking) on depression is two and a half times larger than the direct effect of dysfunctional attitudes on depression. Of the three study-level moderators tested, only sample recruitment method (clinical vs general vs mixed) yielded different patterns of results. The primary difference observed was that the dysfunctional attitudes → automatic thoughts path was less strong for clinical samples than it was for general and mixed samples. These results illustrate how MASEM can be used to compare theoretically derived models and predictions resulting in a richer understanding of both the empirical results and the theories underlying them., (© 2021. Society for Prevention Research.)

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom., (© 2021. The Author(s).)

Objective: According to the theory of planned behavior, individuals are more likely to act on their behavioral intentions, and report intentions aligned with their attitudes and subjective norm, when their perceived behavioral control (PBC) is high. We tested these predictions meta-analytically by estimating the moderating effect of PBC on the attitude-intention, subjective norm-intention, and the intention-behavior relations in studies applying the theory in the health behavior domain., Method: We conducted a preregistered secondary analysis of studies ( k = 39, total N = 13,121) from two programs of research. Each study measured participants' attitude, subjective norms, PBC, and intentions in relation to health behaviors, and most ( k = 36) measured health behavior at follow-up. Data were analyzed using meta-analytic structural equation modeling. Behavior type, scale score coverage, sample age, and publication states were included as moderators of model effects., Results: PBC moderated the intention-behavior relation but not the attitude-intention and subjective norm-intention relations. All moderation effects exhibited significant heterogeneity. Analysis of moderators indicated that the PBC moderation effects on intention varied according to scale score coverage but not by the other moderator variables tested., Conclusions: Results support moderation of the intention-behavior relation by PBC in health behaviors. However, substantial unresolved heterogeneity in the effect across studies remained. Further, these effects may not generalize to other populations and moderator analyses were confined to broad categories. More research that tests these moderation effects in health behavior contexts and reports sufficient data necessary for conducting a meta-analysis is needed to enable moderator analyses with greater fidelity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Aims: A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the predictor's effort on the outcome variable after controlling for the mediator., Methods: A single study rarely provides enough evidence to answer research questions in a particular domain. Replications are generally recommended as the gold standard to conduct scientific research. When a sufficient number of studies have been conducted addressing similar research questions, a meta-analysis can be used to synthesize those studies' findings., Results: The main objective of this paper is to introduce two frameworks to integrating studies using mediation analysis. The first framework involves calculating standardized indirect effects and direct effects and conducting a multivariate meta-analysis on those effect sizes. The second one uses meta-analytic structural equation modeling to synthesize correlation matrices and fit mediation models on the average correlation matrix. We illustrate these procedures on a real dataset using the R statistical platform., Conclusion: This paper closes with some further directions for future studies., (© The Author(s) 2021. Medical Council on Alcohol and Oxford University Press. All rights reserved.)

This article describes source data from a systematic review and meta-analysis of electroencephalography (EEG) and magnetoencephalography (MEG) studies investigating functional connectivity in idiopathic generalized epilepsy. Data selection, analysis and reporting was performed according to PRISMA guidelines. Eligible studies for review were identified from human case-control, and cohort studies. Twenty-two studies were included in the review. Extracted descriptive data included sample characteristics, acquisition of EEG or MEG recordings and network construction. Reported differences between IGE and control groups in functional connectivity or network metrics were extracted as the main outcome measure. Qualitative group differences in functional connectivity were synthesized through narrative review. Meta-analysis was performed for group-level, quantitative estimates of common network metrics clustering coefficient, path length, mean degree and nodal strength. Six studies were included in the meta-analysis. Risk of bias was assessed across all studies. Raw and synthesized data for included studies are reported, alongside effect size and heterogeneity statistics from meta-analyses. Network neurosciences is a rapidly expanding area of research, with significant potential for clinical applications in epilepsy. This data article provides novel, statistical estimates of brain network differences from patients with IGE relative to healthy controls, across the existing literature. Increasing data accessibility supports study replication and improves study comparability for future reviews, enabling a better understanding of network characteristics in IGE., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier Inc.)

Posttraumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account and applied this framework in the first-ever meta-analytic study of PTSD symptom networks. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561 and estimated a single pooled network model underlying the data sets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (a) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (b) While several clear symptom-links, interpretable clusters, and significant differences between strength of edges and centrality of nodes can be identified in the network, no single or small set of nodes that clearly played a more central role than other nodes could be pinpointed, except for the symptom "amnesia" that was clearly the least central symptom. (c) Despite large between-study heterogeneity, we found that network models estimated from single samples can lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for., Competing Interests: Declaration of Competing Interest W. D’Souza has received support from: travel, investigator-initiated, and speaker honoraria from UCB Pharma; investigator and speaker honoraria from Eisai Australia educational grants from Novartis Pharmaceuticals, Pfizer Pharmaceuticals and Sanofi-Synthelabo; educational, travel and fellowship grants from GSK Neurology Australia, and honoraria from SciGen Pharmaceuticals and Liva Nova. His salary is part-funded by the University of Melbourne. The remaining authors have no declarations of interest., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Meta-analytic structural equation modeling (MASEM) refers to fitting structural equation models (SEMs) (such as path models or factor models) to meta-analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. This app implements the one-stage MASEM approach, and allows users to apply MASEM in a user-friendly way. The aim of this article is to provide a tutorial on one-stage MASEM and a practical guide to webMASEM. We will pay specific attention to how the data should be structured and prepared for webMASEM, because mistakes in this step may lead to faulty results without receiving an error message. The use of webMASEM is illustrated with an analysis of a meta-analytic path model in which the path coefficients are moderated by a study-level variable, a meta-analytic factor model in which the factor loadings are moderated by a study-level variable, and a meta-analytic panel model in which the effects are moderated by a study-level variable. All used datafiles and R scripts are available online., (© 2021 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized model on meta-analytic data. We propose a novel MASEM method, one-stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-stage MASEM allows researchers to incorporate continuous or categorical moderators into the MASEM, in which any parameter in the structural equation model (e.g., path coefficients and factor loadings) can be modeled by the moderator variable, while the method does not require complete data for the primary studies included in the meta-analysis. We illustrate the new method on two real data sets, evaluate its empirical performance via a computer simulation study, and provide user-friendly R-functions and annotated syntax to assist researchers in applying one-stage MASEM. We close the article by presenting several future research directions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Conventional meta-analytic procedures assume that effect sizes are independent. When effect sizes are not independent, conclusions based on these conventional procedures can be misleading or even wrong. Traditional approaches, such as averaging the effect sizes and selecting one effect size per study, are usually used to avoid the dependence of the effect sizes. These ad-hoc approaches, however, may lead to missed opportunities to utilize all available data to address the relevant research questions. Both multivariate meta-analysis and three-level meta-analysis have been proposed to handle non-independent effect sizes. This paper gives a brief introduction to these new techniques for applied researchers. The first objective is to highlight the benefits of using these methods to address non-independent effect sizes. The second objective is to illustrate how to apply these techniques with real data in R and Mplus. Researchers may modify the sample R and Mplus code to fit their data.

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.

Meta-analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM., (© 2018 John Wiley & Sons, Ltd.)

In the social and behavioral sciences, it is recommended that effect sizes and their sampling variances be reported. Formulas for common effect sizes such as standardized and raw mean differences, correlation coefficients, and odds ratios are well known and have been well studied. However, the statistical properties of multivariate effect sizes have received less attention in the literature. This study shows how structural equation modeling (SEM) can be used to compute multivariate effect sizes and their sampling covariance matrices. We focus on the standardized mean difference (multiple-treatment and multiple-endpoint studies) with or without the assumption of the homogeneity of variances (or covariance matrices) in this study. Empirical examples were used to illustrate the procedures in R. Two computer simulation studies were used to evaluate the empirical performance of the SEM approach. The findings suggest that in multiple-treatment and multiple-endpoint studies, when the assumption of the homogeneity of variances (or covariance matrices) is questionable, it is preferable not to impose this assumption when estimating the effect sizes. Implications and further directions are discussed.

Meta-analytic structural equation modeling (MASEM) is a statistical technique to pool correlation matrices and test structural equation models on the pooled correlation matrix. In Stage 1 of MASEM, correlation matrices from independent studies are combined to obtain a pooled correlation matrix, using fixed- or random-effects analysis. In Stage 2, a structural model is fitted to the pooled correlation matrix. Researchers applying MASEM may have hypotheses about how certain model parameters will differ across subgroups of studies. These moderator hypotheses are often addressed using suboptimal methods. The aim of the current article is to provide guidance and examples on how to test hypotheses about group differences in specific model parameters in MASEM. We illustrate the procedure using both fixed- and random-effects subgroup analysis with two real datasets. In addition, we present a small simulation study to evaluate the effect of the number of studies per subgroup on convergence problems. All data and the R-scripts for the examples are provided online.

This study used the latent profile transition analysis (LPTA) to analyze whether emotional labor profiles change across time and how these profiles relate to occupational well-being (i.e., job satisfaction, quality of work life, psychological distress, and work-family conflict). A total of 155 full-time Chinese employees completed the questionnaire survey at two time points. Three latent profiles were identified at Time 1 and the same profiles were replicated at Time 2. We determined that the majority of the participants retained the original profiles. Lastly, occupational well-being differed significantly across the identified profiles. The limitations and implications of this study were also provided.

Meta-analytic structural equation modeling (MASEM) is becoming increasingly popular for testing theoretical models from a pool of correlation matrices in management and organizational studies. One limitation of the conventional MASEM approaches is that the proposed structural equation models are only tested on the average correlation matrix. It remains unclear how far the proposed models can be generalized to other populations when the correlation matrices are heterogeneous. Recently, Yu, Downes, Carter, and O'Boyle (2016) proposed a full-information MASEM approach to address this limitation by fitting structural equation models from the correlation matrices generated from a parametric bootstrap. However, their approach suffers from several conceptual issues and technical errors. In this study, we reran some of the simulations in Yu et al. by correcting all of the errors in their original studies. The findings showed that bootstrap credible intervals (CVs) work reasonably well, whereas test statistics and goodness-of-fit indices do not. We advise researchers on what they can and cannot achieve by applying the full-information MASEM approach. We recommend fitting MASEM with the two-stage structural equation modeling approach, which works well for the simulation studies. If researchers want to inspect the heterogeneity of the parameters, they may use the bootstrap CVs from the full-information MASEM approach. All of these analyses were implemented in the open-source R statistical platform; researchers can easily apply and verify the findings. This article concludes with several future directions to address the issue of heterogeneity in MASEM. (PsycINFO Database Record, ((c) 2018 APA, all rights reserved).)

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables. In this study, we modify the currently optimal MASEM-method to deal with missing correlation coefficients, and compare its performance with existing methods. This study is the first to evaluate the performance of fixed-effects MASEM methods under different levels of missing correlation coefficients. We found that the often used univariate methods performed very poorly, while the multivariate methods performed well overall.

To develop a consensus on the definition and measurement of Internet gaming disorder (IGD), several recent studies have used the DSM-5's proposed criteria for IGD as the basis in scale construction. This study contributes to this emerging consensus by developing and validating a new Chinese Internet Gaming Disorder Scale (C-IGDS) based on the DSM-5 criteria. A representative sample of Hong Kong community adults (n=502, 50% men, mean age=37.1, age range=18-60) was recruited for a telephone survey with random digit dialing. Various statistical techniques were used to assess the psychometric properties of the C-IGDS. The C-IGDS had good reliability (Cronbach's α=0.91) and structural validity (CFA model fit: RMSEA=0.027, CFI=0.991, TLI=0.988) in our sample. Moderate to moderately strong correlations with depressive symptoms (r=0.617, p<0.001), social anxiety symptoms (r=0.366, p<0.001), and gaming hours (r=0.412, p<0.001) supported the criterion validity of the C-IGDS. In addition, the C-IGDS exhibited strict measurement invariance for sex and at least strong measurement invariance for age. In addition to providing the first Chinese scale for measuring IGD based on the DSM-5's proposed criteria, this study provides empirical support for the validity of these diagnostic criteria as the basis for a universal measure of IGD. Most important, this study is the first to reveal the criteria's measurement invariance, thereby indicating their suitability for use with diverse demographic groups., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Statistical methods play an important role in behavioural, medical, and social sciences. Two recent statistical advances are structural equation modelling (SEM) and meta-analysis. SEM is used to test hypothesised models based on substantive theories, which can be path, confirmatory factor analytic, or full structural equation models. Meta-analysis is used to synthesise research findings in a particular topic. This article demonstrates another recent statistical advance - meta-analytic structural equation modelling (MASEM) - that combines meta-analysis and SEM to synthesise research findings for the purpose of testing hypothesised models. Using the theory of planned behaviour as an example, we show how MASEM can be used to address important research questions that cannot be answered by univariate meta-analyses on Pearson correlations. Specifically, MASEM allows researchers to: (1) test whether the proposed models are consistent with the data; (2) estimate partial effects after controlling for other variables; (3) estimate functions of parameter estimates such as indirect effects; and (4) include latent variables in the models. We illustrate the procedures with an example on the theory of planned behaviour. Practical issues in MASEM and suggested solutions are discussed.

The Looming Maladaptive Style Questionnaire (LMSQ) is a self-report measure designed to assess the looming cognitive style, a tendency to interpret threats as rapidly approaching and increasing in magnitude. To date, no systematic evaluation on the psychometric properties of the LMSQ across diverse cultural contexts has been done. In the present research, the measurement invariance of the LMSQ test scores was examined in 10 countries (N=4000). Confirmatory factor analysis suggested that a two-factor model (i.e., physical looming and social looming) fitted the data well across countries. Partial measurement invariance was established for the LMSQ scores across the countries whereas full measurement invariance was achieved across gender. Meta-analytic structural equation modeling was applied to examine the unique contributions of the two looming factors to anxiety and depression symptoms. Results indicated that the test scores underlying two looming factors were crucial and valid predictors of symptoms. The LMSQ shows promise as a measure with cross-cultural generalizability and opens new avenues for its use in diverse cultural settings., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Vividness is an aspect of consciousness related to mental imagery and prospective episodic memory. Despite being harshly criticized in the past for failing to demonstrate robust correlations with behavioral measures, currently this construct is attracting a resurgent interest in cognitive neuroscience. Therefore, an updated examination of the validity of this construct is timely. A corpus of peer-reviewed literature was analyzed through meta-analysis, which compared the two main formats used to measure vividness [trial-by-trial vividness ratings (VR) and the Vividness of Visual Imagery Questionnaire (VVIQ)]. These two formats were compared in relation to all available behavioral/cognitive (BC) and neuroscience (NS) measures in Phase 1 (3542 statistical observations representing 393 journal articles); and then in relation to all available BC, EEG and fMRI literature in Phase 2 (3624 observations representing 402 articles). Both Phases observed significantly larger effect size estimates (ESEs) for VR than VVIQ, and larger ESEs for NS than BC measures. ESEs for EEG and fMRI were not significantly different in Phase 2, but were greater than BC ESEs. These data suggest VR are a more reliable self-report measure than VVIQ, and may reflect a more direct route of reportability than the latter. Furthermore, both VR and VVIQ are more strongly associated with the neural, than the cognitive and behavioural correlates of imagery. If one establishes neuroscience measures as the criterion variable, then self-reports of vividness show higher construct validity than behavioural/cognitive measures of imagery. We discuss how the present findings contribute to current issues on measurement of reportability; and how this study advances our understanding of vividness as a phenomenological characteristic of imagery, and other forms of conscious experience which do not necessarily involve imagery.

Background: Small studies done during the past decade have demonstrated same-day discharge after appendectomy as an option for non-perforated appendicitis. Here we have examined a large cohort to confirm that same-day discharge in acute non-perforated appendicitis is a safe option., Study Design: This was a retrospective study of patients from 14 Southern California Region Kaiser Permanente medical centers. All patients older than 18 years of age with acute, non-perforated appendicitis who underwent a laparoscopic appendectomy between 2010 and 2014 were included. We compared patients discharged on the day of surgery with patients hospitalized for 1 night. We examined readmission rates, complication rates, postoperative emergency department visits, postoperative diagnostic or therapeutic radiology visits, reoperations, and cost of treatment., Results: The cohort was composed of 12,703 patients; 6,710 patients were in the same-day discharge group and 5,993 patients were in the hospitalized group. Patients in the same-day discharge group had a lower rate of readmission within 30 days when compared with the hospitalized group (2.2% vs 3.1%; p < 0.005). In both groups, postoperative rates of visits to emergency or radiology department for diagnostic or therapeutic imaging studies were statistically similar. Postoperative general surgery department visits were slightly higher in the hospitalized group (85% vs 81%; p < 0.001)., Conclusions: Adult patients with acute, non-perforated appendicitis can be discharged safely on the day of surgery without higher rates of postoperative complication or readmission rates compared with those hospitalized after surgery. In addition, same-day discharge in this patient group is cost-effective., (Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.)

Colorectal cancer is the third most common cancer worldwide. Aberrant overexpression of antiapoptotic BCL-2 (B-cell lymphoma 2) family proteins is closely linked to tumorigenesis and poor prognosis in colorectal cancer. Obatoclax is an inhibitor targeting all antiapoptotic BCL-2 proteins. A previous study has described the antiproliferative action of obatoclax in one human colorectal cancer cell line without elucidating the underlying mechanisms. We herein reported that, in a panel of human colorectal cancer cell lines, obatoclax inhibits cell proliferation, suppresses clonogenicity, and induces G₁-phase cell cycle arrest, along with cyclin D1 downregulation. Notably, ectopic cyclin D1 overexpression abrogated clonogenicity suppression but also G₁-phase arrest elicited by obatoclax. Mechanistically, pre-treatment with the proteasome inhibitor MG-132 restored cyclin D1 levels in all obatoclax-treated cell lines. Cycloheximide chase analyses further revealed an evident reduction in the half-life of cyclin D1 protein by obatoclax, confirming that obatoclax downregulates cyclin D1 through induction of cyclin D1 proteasomal degradation. Lastly, threonine 286 phosphorylation of cyclin D1, which is essential for initiating cyclin D1 proteasomal degradation, was induced by obatoclax in one cell line but not others. Collectively, we reveal a novel anticancer mechanism of obatoclax by validating that obatoclax targets cyclin D1 for proteasomal degradation to downregulate cyclin D1 for inducing antiproliferation., Competing Interests: The authors declare no conflict of interest.

Health locus of control (HLOC) refers to beliefs regarding how one's health is influenced by oneself, others, or fate. This meta-analysis investigated whether three HLOC dimensions (internality/I-HLOC, powerful others/P-HLOC, chance/C-HLOC) were related to both specific health behaviours and global health appraisal, and whether these relationships were moderated by gender and age compositions, individualism, and power distance. Three-level mixed-effects meta-analysis was performed on studies examining the associations of HLOC with specific health behaviour (k = 76, N = 76,580, 57% women, M age  = 43.75) and global health appraisal (k = 95, N = 12,068, 57% women, M age  = 45.44), respectively. For specific health behaviour, the averaged correlations with the HLOC dimensions were generally weak (r's = -.07 to .10). However, the links between P-HLOC and exercise were moderated by all four demographic moderators, and gender composition and individualism moderated the association between the HLOC dimensions and diet. For global health appraisal, all of the averaged correlations were statistically significant (r's = -.16 to .21), except that between P-HLOC and mental quality of life. The results further showed individualism and power distance to moderate the links between the HLOC dimensions and both mental and physical quality of life, and gender composition to moderate those between these dimensions and two indicators of emotional problems (depression and anxiety).

Meta-analysis is widely accepted as the preferred method to synthesize research findings in various disciplines. This paper provides an introduction to when and how to conduct a meta-analysis. Several practical questions, such as advantages of meta-analysis over conventional narrative review and the number of studies required for a meta-analysis, are addressed. Common meta-analytic models are then introduced. An artificial dataset is used to illustrate how a meta-analysis is conducted in several software packages. The paper concludes with some common pitfalls of meta-analysis and their solutions. The primary goal of this paper is to provide a summary background to readers who would like to conduct their first meta-analytic study.

Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM. Random-effects models are well known in conventional meta-analysis but are less studied in MASEM. The primary objective of this paper was to address issues related to random-effects models in MASEM. Specifically, we compared two different random-effects models in MASEM-correlation-based MASEM and parameter-based MASEM-and explored their strengths and limitations. Two examples were used to illustrate the similarities and differences between these models. We offered some practical guidelines for choosing between these two models. Future directions for research on random-effects models in MASEM were also discussed. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists-and probably the most crucial one-is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

This multinational study simultaneously tested three prominent hypotheses--universal disposition, cultural relativity, and livability--that explained differences in subjective well-being across nations. We performed multilevel structural equation modeling to examine the hypothesized relationships at both individual and cultural levels in 33 nations. Participants were 6,753 university students (2,215 men; 4,403 women; 135 did not specify), and the average age of the entire sample was 20.97 years (SD = 2.39). Both individual- and cultural-level analyses supported the universal disposition and cultural relativity hypotheses by revealing significant associations of subjective well-being with Extraversion, Neuroticism, and independent self-construal. In addition, interdependent self-construal was positively related to life satisfaction at the individual level only, whereas aggregated negative affect was positively linked with aggregate levels of Extraversion and interdependent self-construal at the cultural level only. Consistent with the livability hypothesis, gross national income (GNI) was related to aggregate levels of negative affect and life satisfaction. There was also a quadratic relationship between GNI and aggregated positive affect. Our findings reveal that universal disposition, cultural self-construal, and national income can elucidate differences in subjective well-being, but the multilevel analyses advance the literature by yielding new findings that cannot be identified in studies using individual-level analyses alone., (© 2014 Wiley Periodicals, Inc.)

The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.

Meta-analysis is an indispensable tool used to synthesize research findings in the social, educational, medical, management, and behavioral sciences. Most meta-analytic models assume independence among effect sizes. However, effect sizes can be dependent for various reasons. For example, studies might report multiple effect sizes on the same construct, and effect sizes reported by participants from the same cultural group are likely to be more similar than those reported by other cultural groups. This article reviews the problems and common methods to handle dependent effect sizes. The objective of this article is to demonstrate how 3-level meta-analyses can be used to model dependent effect sizes. The advantages of the structural equation modeling approach over the multilevel approach with regard to conducting a 3-level meta-analysis are discussed. This article also seeks to extend the key concepts of Q statistics, I2, and R2 from 2-level meta-analyses to 3-level meta-analyses. The proposed procedures are implemented using the open source metaSEM package for the R statistical environment. Two real data sets are used to illustrate these procedures. New research directions related to 3-level meta-analyses are discussed., (PsycINFO Database Record (c) 2014 APA, all rights reserved.)

Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation Modeling 16:28-53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed.

Chlorogenic acid (3-O-caffeoylquinic acid, CA) is the active component in several botanical beverage, vegetables, fruits, and herbal drugs. The effect of water boiling on the bioactivity of CA was studied. CA could be isomerized to 4-O-caffeoylquinic acid (4-O-CA) and 5-O-caffeoylquinic acid (5-O-CA) in decoctive extraction, and each of the isomers occupied about one-third of the total caffeoylquinic acids. A novel method, using water elution of microsphere resin, was used to purify CA and its 2 isomers. The yield of CA, 4-O-CA, and 5-O-CA was 82%, 5.6%, and 50%, with the purity of 98%, 97%, and 99%, respectively. The DPPH radical scavenging assay showed that 4-O-CA, 5-O-CA, and CA exhibited similar activity. However, there was no significant difference between 4-O-CA and 5-O-CA when used against CCl₄-induced toxicity in hepG2 cells. Our studies show that isomerization is the main transformation of CA in boiling, and the decoction could not decrease the anti-oxidant activity of CA., (© 2014 Institute of Food Technologists®)