Suicidal thoughts, behaviors, and event‐related potentials: A systematic review and meta‐analysis

Gallyer, Austin J.; Dougherty, Sean; Burani, Kreshnik; Albanese, Brian J.; Joiner, Thomas E.; Hajcak, Greg

doi:10.1111/psyp.13939

Cited by 14 publications

(8 citation statements)

References 108 publications

(128 reference statements)

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“…Finally, this study did not find other significant whole-brain connectivity between-group differences. Results are consistent with recent meta-analyses of event-related potential studies, which found small or no effects in larger studies comparing those with suicide attempt to suicidal ideation alone (Gallyer et al, 2021). Alternatively, it may be the case that history of suicidal ideation, as suggested by Ordaz et al (2018), is associated with brain network alterations rather than attempts as conceptualized in the current study, Regardless, null findings across methodologies using stringent control groups may reflect the need for more nuanced investigation and interpretation of the interplay between biopsychosocial factors to differentiate those with lifetime history of suicide attempt(s) from SDVT alone (Klonsky & May, 2015).…”

Section: Discussionsupporting

confidence: 91%

Resting-state network analysis of suicide attempt history in the UK Biobank

Thompson

Ghahramanlou‐Holloway

Murphy

et al. 2023

Psychol. Med.

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Background Prior research has identified altered brain structure and function in individuals at risk for self-directed violence thoughts and behaviors. However, these studies have largely utilized healthy controls and findings have been inconsistent. Thus, this study examined differences in resting-state functional network connectivity among individuals with lifetime suicide attempt(s) v. lifetime self-directed violence thoughts alone. Methods Using data from the UK Biobank, this study utilized a series of linear regressions to compare individuals with lifetime suicide attempt(s) (n = 566) v. lifetime self-directed violence thoughts alone (n = 3447) on within- and between- network resting-state functional connectivity subnetworks. Results There were no significant between-group differences for between-network, within-network, or whole-brain functional connectivity after adjusting for age, sex, ethnicity, and body mass index and performing statistical corrections for multiple comparisons. Resting-state network measures may not differentiate between individuals with lifetime suicide attempt(s) and lifetime self-directed violence thoughts alone. Conclusions Null findings diverge from results reported in smaller neuroimaging studies of suicide risk, but are consistent with null findings in other large-scale studies and meta-analyses. Strengths of the study include its large sample size and stringent control group. Future research on a wider array of imaging, genetic, and psychosocial risk factors can clarify relative contributions of individual and combined variables to suicide risk and inform scientific understanding of ideation-to-action framework.

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Section: Discussionsupporting

confidence: 91%

Resting-state network analysis of suicide attempt history in the UK Biobank

Thompson

Ghahramanlou‐Holloway

Murphy

et al. 2023

Psychol. Med.

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“…Only considering our study and the study by Tsypes and colleagues, it is difficult to decide between these two scenarios. We recently conducted a meta-analysis on the relationship between ERPs and suicidal ideation and found a very small (Hedges’s g = −.06) blunting of the reward positivity in individuals experiencing suicidal ideation, compared with controls (Gallyer et al, 2021). For reasons we discuss in that meta-analytic review, including the small effect size and the high correlation between depression and suicidal ideation (Rogers et al, 2018), it cannot be ruled out that this blunting of the reward positivity is a false positive.…”

Section: Discussionmentioning

confidence: 99%

“…We chose two effect-size ranges, which resulted in two separate equivalence tests: (a) lower bound d = −0.24 and upper bound d = 0.24 and (b) lower bound d = −0.60 and upper bound d = 0.60. Our first effect-size estimate was based on recent meta-analytic evidence that the upper bound on the relationship between ERPs and suicidal ideation is Hedges’s g = .24, and the second effect-size range was based on the effect reported in the study we sought to conceptually replicate (Gallyer et al, 2021; Tsypes et al, 2019). For our equivalence tests, we used the PCA Δreward positivity as our dependent variable and ideation group (no suicidal ideation, recent suicidal ideation) as the independent variable.…”

Section: Methodsmentioning

confidence: 99%

Examining Blunted Initial Response to Reward and Recent Suicidal Ideation in Children and Adolescents Using Event-Related Potentials: Failure to Conceptually Replicate Across Two Independent Samples

Gallyer

Burani

Mulligan

et al. 2022

Clinical Psychological Science

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A previous study found that children with recent suicidal ideation had blunted neural reward processing (as measured by the reward positivity), compared with matched controls, and that this difference was driven by reduced neural responses to monetary loss rather than to reward. Here, we aimed to conceptually replicate and extend these findings in two samples ( n = 264, 27 with suicidal ideation; n = 314, 49 with suicidal ideation at baseline) of children and adolescents (11–15 years old and 8–15 years old, respectively). Results from both samples showed no evidence that children and adolescents with suicidal ideation have abnormal reward or loss processing nor that reward processing predicts suicidal ideation 2 years later. The results highlight the need for greater statistical power as well as continued research examining the neural underpinnings of suicidal thoughts and behaviors.

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“…Pooling meta-analysis data into a larger analysis is also known as a “meta-meta-analysis” (hence the package name metameta , because this was the original motive for developing the package). The second use case is the implementation of the metameta package when reporting novel meta-analyses (Boen et al, 2022; Gallyer et al, 2021). For example, Gallyer and colleagues (2021) performed a meta-analysis on the link between event-related-potentials derived from electroencephalograms and suicidal thoughts and behaviors and found no significant relationships.…”

mentioning

confidence: 99%

“…The second use case is the implementation of the metameta package when reporting novel meta-analyses (Boen et al, 2022; Gallyer et al, 2021). For example, Gallyer and colleagues (2021) performed a meta-analysis on the link between event-related-potentials derived from electroencephalograms and suicidal thoughts and behaviors and found no significant relationships. An early implementation of the metameta package was used to complement this analysis, which revealed that most included studies were considerably underpowered to detect meaningful effect sizes for the field.…”

mentioning

confidence: 99%

A Guide for Calculating Study-Level Statistical Power for Meta-Analyses

Quintana

2023

Advances in Methods and Practices in Psychological Science

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Meta-analysis is a popular approach in the psychological sciences for synthesizing data across studies. However, the credibility of meta-analysis outcomes depends on the evidential value of studies included in the body of evidence used for data synthesis. One important consideration for determining a study’s evidential value is the statistical power of the study’s design/statistical test combination for detecting hypothetical effect sizes of interest. Studies with a design/test combination that cannot reliably detect a wide range of effect sizes are more susceptible to questionable research practices and exaggerated effect sizes. Therefore, determining the statistical power for design/test combinations for studies included in meta-analyses can help researchers make decisions regarding confidence in the body of evidence. Because the one true population effect size is unknown when hypothesis testing, an alternative approach is to determine statistical power for a range of hypothetical effect sizes. This tutorial introduces the metameta R package and web app, which facilitates the straightforward calculation and visualization of study-level statistical power in meta-analyses for a range of hypothetical effect sizes. Readers will be shown how to reanalyze data using information typically presented in meta-analysis forest plots or tables and how to integrate the metameta package when reporting novel meta-analyses. A step-by-step companion screencast video tutorial is also provided to assist readers using the R package.

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Suicidal thoughts, behaviors, and event‐related potentials: A systematic review and meta‐analysis

Cited by 14 publications

References 108 publications

Resting-state network analysis of suicide attempt history in the UK Biobank

Resting-state network analysis of suicide attempt history in the UK Biobank

Examining Blunted Initial Response to Reward and Recent Suicidal Ideation in Children and Adolescents Using Event-Related Potentials: Failure to Conceptually Replicate Across Two Independent Samples

A Guide for Calculating Study-Level Statistical Power for Meta-Analyses

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