The Social Determinants of Emotional and Behavioral Problems in Adolescents Experiencing Early Puberty.

PURPOSE: Earlier pubertal timing is an important predictor of emotional and behavioral problems during adolescence. The current study undertook a comprehensive investigation of whether the social environment can buffer or amplify the associations between pubertal timing and emotional and behavioral problems. METHODS: Research questions were examined in the Adolescent Brain Cognitive Development (ABCD) Study, a large population representative sample in the United States. We examined interactions between pubertal timing and the shared effects of a range of proximal and distal social environmental influences (i.e., parents, peers, schools, neighborhoods, socioeconomic status) in 10- to 13-year-olds. RESULTS: Results revealed significant interaction between timing and proximal social influences (i.e., the "microsystem") in predicting emotional and behavioral problems. In general, adolescents with earlier pubertal timing and unfavorable (high levels of negative and low levels of positive) influences in the microsystem exhibited greater problems. Both males and females exhibited such associations for rule-breaking problems, while females alone exhibited associations for depressive problems. Results also illustrate the relative strength of each social context at moderating risk for emotional and behavioral problems in earlier versus later pubertal maturers. DISCUSSION: These findings highlight the importance of proximal social influences in buffering vulnerability for emotional and behavioral problems related to earlier puberty. Findings also illustrate the broad implications of latent environmental factors, reflecting common variance of multiple social influences that typically covary with one another.

Large-scale analysis of interindividual variability in single and paired-pulse TMS data.

OBJECTIVE: This study brought together over 60 transcranial magnetic stimulation (TMS) researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability. METHODS: Authors of previously published studies were contacted and asked to share deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and paired-pulse TMS data. RESULTS: 687 healthy participant's data were pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual's single-pulse TMS amplitude. Baseline motor evoked potential amplitude, motor cortex hemisphere, and motor threshold (MT) significantly predicted short-interval intracortical inhibition response. Baseline motor evoked potential amplitude, test stimulus intensity, interstimulus interval, and MT significantly predicted intracortical facilitation response. Age, hemisphere, and TMS machine significantly predicted MT. CONCLUSIONS: This large-scale analysis has identified a number of factors influencing participants' responses to single and paired-pulse TMS. We provide specific recommendations to minimise interindividual variability in single and paired-pulse TMS data. SIGNIFICANCE: This study has used large-scale analyses to give clarity to factors driving variance in TMS data. We hope that this ongoing collaborative approach will increase standardisation of methods and thus the utility of single and paired-pulse TMS.

Large-scale analysis of interindividual variability in theta-burst stimulation data: Results from the 'Big TMS Data Collaboration'.

BACKGROUND: Many studies have attempted to identify the sources of interindividual variability in response to theta-burst stimulation (TBS). However, these studies have been limited by small sample sizes, leading to conflicting results. OBJECTIVE/HYPOTHESIS: This study brought together over 60 TMS researchers to form the 'Big TMS Data Collaboration', and create the largest known sample of individual participant TBS data to date. The goal was to enable a more comprehensive evaluation of factors driving TBS response variability. METHODS: 118 corresponding authors of TMS studies were emailed and asked to provide deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to iTBS and cTBS response variability. RESULTS: 430 healthy participants' TBS data was pooled across 22 studies (mean age = 41.9; range = 17-82; females = 217). Baseline MEP amplitude, age, target muscle, and time of day significantly predicted iTBS-induced plasticity. Baseline MEP amplitude and timepoint after TBS significantly predicted cTBS-induced plasticity. CONCLUSIONS: This is the largest known study of interindividual variability in TBS. Our findings indicate that a significant portion of variability can be attributed to the methods used to measure the modulatory effects of TBS. We provide specific methodological recommendations in order to control and mitigate these sources of variability.