The multifaceted role of emotion regulation in suicidality: Systematic reviews and meta-analytic evidence.

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).

International Classification of Functioning, Disability, and Health-based rehabilitation program promotes activity and participation of post-stroke patients.

Background: The International Classification of Functioning, Disability, and Health (ICF) model has been applied in post-stroke rehabilitation, yet limited studies explored its clinical application on enhancing patients' Activity and Participation (ICF-A&P) level. Purpose: This study gathered evidence of the effects of an ICF-based post-stroke rehabilitation program (ICF-PSRP) in enhancing community reintegration in terms of ICF-A&P of post-stroke patients. Methods: Fifty-two post-stroke patients completed an 8 to 12 weeks multidisciplinary ICF-PSRP after setting personal treatment goals in an outpatient community rehabilitation center. Intake and pre-discharge assessments were administered for primary outcomes of Body function (ICF-BF; e.g., muscle strength) and ICF-A&P (e.g., mobility), and secondary outcomes of perceived improvements in ability (e.g., goal attainment and quality of life). Results: There were significantly higher levels in the ICF-BF and ICF-A&P domains, except cognitive function under the ICF-BF. Improvements in the primary outcomes predicted corresponding secondary outcomes. Firstly, expressive and receptive functions (ICP-BF) were mediated by the everyday language (ICF-A&P) which predicted patients' satisfaction with the language-related quality of life. Secondly, upper extremity function (ICP-BF) was mediated by the lower extremity mobility (ICF-A&P) predicting work and productivity-related quality of life. Content analyses showed that combined ICF-BF and ICF-A&P contents throughout the ICF-PSRP contributed to the positive treatment effects. Conclusion: The ICF-PSRP was effective in promoting body function, and activity and participation levels of post-stroke patients. Positive treatment effects are characterized by goal-setting process, cross-domain content design, and community-setting delivery.Clinical trial registration: https://clinicaltrials.gov/study/NCT05941078?id=NCT05941078&rank=1, identifier NCT05941078.

Goal-setting and personalization under the International Classification of Functioning, Disability, and Health framework: Community reintegration program for post-stroke patients.

Background: Body functions and structures, activities, and participation are the core components in the International Classification of Functioning, Disability, and Health (ICF) to identify post-stroke patients' health conditions. The specification of health conditions enhances the outcomes of post-stroke rehabilitation. Purpose: This study aimed to explore the extent and the processes in an ICF-based post-stroke rehabilitation program (ICF-PSRP) that could enhance patients' community reintegration level. Methods: Post-stroke patients who completed the ICF-PSRP participated in intake and pre-discharge individual face-to-face semi-structured interviews. In addition, case therapists were invited to a face-to-face semi-structured group interview. Clinician experts were invited to complete an interview with the same interview contents as case therapists but in an online format. All interview recordings were analyzed with the Framework analysis. Patients' treatment goals were mapped with the ICF Core Set for Stroke. Results: Out of 37 invited post-stroke patients, thirty-three of them completed the interview. Three case therapists and five clinicians completed the interviews. The goals set by the patients and their caregivers showed a broadening of their scope over the course of the program. The changes in scope ranged from the activities to the participation and environmental components. Increases in patient-therapist interactions played an essential role in the goal-setting process, which were integral to personalizing the treatment content. These characteristics were perceived by all parties who contributed to the program outcomes. Conclusion: The application of ICF's principles and core components offers a useful framework for enhancing post-stroke patients' community reintegration level. Future studies should explore the way in which patient-therapist interaction, exposure to environmental factors, and personalized interventions maximize the benefits of applying this framework to the community integration of post-stroke patients.

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients.

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.

A primer on synthesizing individual participant data obtained from complex sampling surveys: A two-stage IPD meta-analysis approach.

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).

Statistical Inference in Redundancy Analysis: A Direct Covariance Structure Modeling Approach.

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.

Abnormalities in visual cognition and associated impaired interactions between visual and attentional networks in schizophrenia and brief psychotic disorder.

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.

Second-line immunotherapy and functional outcomes in autoimmune encephalitis: A systematic review and individual patient data meta-analysis.

Autoimmune encephalitis (AE) is a neurological disorder caused by autoimmune attack on cerebral proteins. Experts currently recommend staged immunotherapeutic management, with first-line immunotherapy followed by second-line immunotherapy if response to first-line therapy is inadequate. Meta-analysis of the evidence base may provide higher quality evidence to support this recommendation. We undertook a systematic review of observational cohort studies reporting AE patients treated with either second-line immunotherapy or first-line immunotherapy alone, and outcomes reported using the modified Rankin Scale (mRS; search date: April 22, 2020). We performed several one-stage multilevel individual patient data (IPD) meta-analyses to examine the association between second-line immunotherapy and final mRS scores (PROSPERO ID CRD42020181805). IPD were obtained for 356 patients from 25 studies. Most studies were rated as moderate to high risk of bias. Seventy-one patients (71/356, 19%) were treated with second-line immunotherapy. We did not find a statistically significant association between treatment with second-line immunotherapy and final mRS score for the cohort overall (odds ratio [OR] = 1.74, 95% confidence interval [CI] = .98-3.08, p = .057), or subgroups with anti-N-methyl-D-aspartate receptor encephalitis (OR = 1.03, 95% CI = .45-2.38, p = .944) or severe AE (maximum mRS score > 2; OR = 1.673, 95% CI = .93-3.00, p = .085). Treatment with second-line immunotherapy was associated with higher final mRS scores in subgroups with anti-leucine-rich glioma-inactivated 1 AE (OR = 6.70, 95% CI = 1.28-35.1, p = .024) and long-term (at least 12 months) follow-up (OR = 3.94, 95% CI = 1.67-9.27, p = .002). We did not observe an association between treatment with second-line immunotherapy and lower final mRS scores in patients with AE. This result should be interpreted with caution, given the risk of bias, limited adjustment for disease severity, and insensitivity of the mRS in estimating psychiatric and cognitive disability.

Perceived behavioral control moderating effects in the theory of planned behavior: A meta-analysis.

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).

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis.

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.

Translation and validation of the Reaction to Impairment and Disability Inventory for Chinese population in Hong Kong.

PURPOSE: This study translated the reaction to impairment and disability inventory (RIDI) to Chinese and validated it for use in Hong Kong. METHODS: We conducted an instrument validation of the Chinese RIDI, with a sample of 244 persons with CID. The research questionnaire collected demographic information, illness-related variables, the Chinese version of RIDI (C-RIDI), and measures of resilience and well-being. We examined the factor structure, internal consistency, convergent validity, and criterion-related validity of the C-RIDI. RESULTS: The C-RIDI has good content validity and no major changes to the translated items were needed for the use in Hong Kong. For factor structure, we replicated the results of Livneh et al. The C-RIDI has two second-order factors of adaptive and nonadaptive scales, which interact with the two denial subscales. Internal consistency of the subscales is satisfactory except for the three-item denial subscales. Correlations of the C-RIDI subscales with illness-related variables, resilience, and mental well-being are consistent with our hypotheses and provide support for the convergent and criterion-related validity of the scale. CONCLUSIONS: The C-RIDI has satisfactory psychometric properties. The study results support its internal consistency, convergent validity, criterion-related validity, and factorial validity.IMPLICATIONS FOR REHABILITATIONEmotional adjustment to chronic illness and disability is a key determinant of illness self-management, mental well-being, and quality of life.The study translated the reaction to impairment and disability inventory into Chinese and conducted a psychometric evaluation of the translated instrument.The Chinese RIDI had a similar second-order factor structure as in the validation studies of the English version, and result of this confirmatory factor analysis support the theory underlying the design of the RIDI.The Chinese RIDI had satisfactory convergent and criterion-related validity and internal consistency, and is ready for application in rehabilitation practice and research in the Chinese context.

Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data.

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).

A Primer on Meta-Analytic Structural Equation Modeling: the Case of Depression.

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.

Synthesizing Indirect Effects in Mediation Models With Meta-Analytic Methods.

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.

Meta-analytic Gaussian Network Aggregation.

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.

Source data from a systematic review and meta-analysis of EEG and MEG studies investigating functional connectivity in idiopathic generalized epilepsy.

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.

Impact of glucocorticoids on the incidence of lupus-related major organ damage: a systematic literature review and meta-regression analysis of longitudinal observational studies.

OBJECTIVE: In systemic lupus erythematosus (SLE), disease activity and glucocorticoid (GC) exposure are known to contribute to irreversible organ damage. We aimed to examine the association between GC exposure and organ damage occurrence. METHODS: We conducted a literature search (PubMed (Medline), Embase and Cochrane January 1966-October 2021). We identified original longitudinal observational studies reporting GC exposure as the proportion of users and/or GC use with dose information as well as the occurrence of new major organ damage as defined in the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index. Meta-regression analyses were performed. Reviews, case-reports and studies with <5 years of follow-up, <50 patients, different outcomes and special populations were excluded. RESULTS: We selected 49 articles including 16 224 patients, 14 755 (90.9%) female with a mean age and disease duration of 35.1 years and of 37.1 months. The mean follow-up time was 104.9 months. For individual damage items, the average daily GC dose was associated with the occurrence of overall cardiovascular events and with osteoporosis with fractures. A higher average cumulative dose adjusted (or not)/number of follow-up years and a higher proportion of patients on GC were associated with the occurrence of osteonecrosis. CONCLUSIONS: We confirm associations of GC use with three specific damage items. In treating patients with SLE, our aim should be to maximise the efficacy of GC and to minimise their harms.

Network models of posttraumatic stress disorder: A meta-analysis.

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).

Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies.

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.