Advancing our understanding of cognitive development and motor vehicle crash risk: A multiverse representation analysis

Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia

Connectome-based lesion-symptom mapping relates behavioural impairments to disruption of structural brain connectivity. Connectome-based lesion-symptom mapping can be based on different approaches (diffusion MRI versus lesion mask), network scales (whole brain versus regions of interest) and measure types (tract-based, parcel-based, or network-based metrics). We evaluated the similarity of different connectome-based lesion-symptom mapping processing choices and identified factors that influence the results using multiverse analysis-the strategy of conducting and displaying the results of all reasonable processing choices. Metrics derived from lesion masks and diffusion-weighted images were tested for association with Boston Naming Test and Token Test performance in a sample of 50 participants with aphasia following left hemispheric stroke. 'Direct' measures were derived from diffusion-weighted images. 'Indirect' measures were derived by overlaying lesion masks on a white matter atlas. Parcel-based connectomes were constructed for the whole brain and regions of interest (14 language-relevant parcels). Numerous tract-based and network-based metrics were calculated. There was a high discrepancy across processing approaches (diffusion-weighted images versus lesion masks), network scales (whole brain versus regions of interest) and metric types. Results indicate weak correlations and different connectome-based lesion-symptom mapping results across the processing choices. Substantial methodological work is needed to validate the various decision points that arise when conducting connectome-based lesion-symptom mapping analyses. Multiverse analysis is a useful strategy for evaluating the similarity across different processing choices in connectome-based lesion-symptom mapping.

Post-selection Inference in Multiverse Analysis (PIMA): An Inferential Framework Based on the Sign Flipping Score Test

When analyzing data, researchers make some choices that are either arbitrary, based on subjective beliefs about the data-generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper, we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that considers for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e., preprocessing) and any generalized linear model; it allows testing the null hypothesis that a given predictor is not associated with the outcome, by combining information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate allowing researchers to claim that the null hypothesis can be rejected for any specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. We formally prove that the Type I error rate is controlled, and compute the statistical power of the test through a simulation study. Finally, we apply the PIMA procedure to the analysis of a real dataset on the self-reported hesitancy for the COronaVIrus Disease 2019 (COVID-19) vaccine before and after the 2020 lockdown in Italy. We conclude with practical recommendations to be considered when implementing the proposed procedure.

The Role of Emotional Dysregulation, Impulsivity Traits and Aggressive Behaviors in Adolescents Who Sustain Multiple Motor-Vehicle Crashes

Adolescents tend to experience multiple motor-vehicle crashes (MVCs). Recent literature has thoroughly explored the psychological consequences following an MVC, but prior psychological functioning, the ability to regulate one's emotions and tendencies to impulsivity and aggressive behaviors have been far less explored. This study aims to explore the emotional-behavioral functioning, measured with the Youth Self-Report (YSR); emotional dysregulation, measured with the Difficulties in Emotional Regulation Scale (DERS); impulsivity traits, measured with the Barratt Impulsiveness Scale (BIS-11); and the capacity to be mindful, measured with the Mindful Attention Awareness Scale (MAAS), in adolescents that have experienced one or more MVCs in a year. N = 295 adolescents who visited an emergency department for MVCs were divided in two groups based on the number of MVCs they had sustained over the course of a year. The adolescents in Group 1 sustained more than three MVCs, and adolescents in Group 2 sustained only one. Participants were assessed through self-report questionnaires. Adolescents showed difficulties in emotional regulation, impulsivity traits, aggressive behaviors and a low capacity to be mindful. These results may be useful in the creation of intervention and prevention programs focused on emotional awareness targets for adolescents.