Bringing the laboratory into the home: A protocol for remote biobehavioral data collection in pregnant women with emotion dysregulation and their infants

Pregnant women struggling with emotion dysregulation may be more likely to engage in a wide range of health risk behaviors. This protocol describes a study on intergenerational transmission of emotion dysregulation from the third trimester of pregnancy to 18 months postpartum. Biobehavioral markers of emotion dysregulation are typically measured in laboratory settings which was prohibited by many universities during the COVID-19 pandemic. We describe how markers of emotion dysregulation (e.g. maternal, fetal, and infant heart rate variability) are collected remotely. We detail how data collection can be augmented to reach diverse populations who may not otherwise participate in laboratory-based research.

Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

Trait Disinhibition and NoGo Event-Related Potentials in Violent Mentally Disordered Offenders and Healthy Controls

Trait disinhibition may function as a dispositional liability toward maladaptive behaviors relevant in the treatment of mentally disordered offenders (MDOs). Reduced amplitude and prolonged latency of the NoGo N2 and P3 event-related potentials have emerged as promising candidates for transdiagnostic, biobehavioral markers of trait disinhibition, yet no study has specifically investigated these two components in violent, inpatient MDOs. Here, we examined self-reported trait disinhibition, experimentally assessed response inhibition, and NoGo N2 and P3 amplitude and latency in male, violent MDOs (N = 27) and healthy controls (N = 20). MDOs had a higher degree of trait disinhibition, reduced NoGo P3 amplitude, and delayed NoGo P3 latency compared to controls. The reduced NoGo P3 amplitude and delayed NoGo P3 latency in MDOs may stem from deficits during monitoring or evaluation of behavior. NoGo P3 latency was associated with increased trait disinhibition in the whole sample, suggesting that trait disinhibition may be associated with reduced neural efficiency during later stages of outcome monitoring or evaluation. Findings for NoGo N2 amplitude and latency were small and non-robust. With several limitations in mind, this is the first study to demonstrate attenuated NoGo P3 amplitude and delayed NoGo P3 latency in violent, inpatient MDOs compared to healthy controls.

Prisoners of Addictive Cues: Biobehavioral Markers of Overweight and Obese Adults with Food Addiction

Obesity is associated with food and eating addiction (FA), but the biobehavioral markers of this condition are poorly understood. To characterize FA, we recruited 18 healthy controls and overweight/obese adults with (n = 31) and without (n = 17) FA (H-C, FAOB, NFAOB, respectively) to assess alpha brain asymmetry at rest using electroencephalogram; event-related potentials following exposure to high-calorie food (HCF), low-calorie food (LCF), and nonfood (NF) images in a Stroop paradigm; reaction time reflective of the Stroop bias; and symptoms of depression and disordered eating behavior. The FAOB group had the greatest emotional and uncontrollable eating, depressive, and binge-eating symptoms. The FAOB group displayed lower resting left alpha brain asymmetry than that of the NFAOB group. Differently from the other groups, the FAOB group presented attenuated Stroop bias following exposure to HCF relative to NF images, as well as a lower late positive potential component (LPPb; 450–495 ms) in both frontal and occipital regions. In the total cohort, a correlation was found between the Stroop bias and the LPPb amplitude. These results point to biobehavioral hypervigilance in response to addictive food triggers in overweight/obese adults with FA. This resembles other addictive disorders but is absent in overweight/obesity without FA.

0045 Biobehavioral Markers for Sleep/Wake Disturbance and Fatigue in Young Childhood Brain Tumor Survivors

Introduction Survivors of childhood and adolescent brain tumors and subsequent treatment may experience many neurological processes involving the forebrain, brainstem, and hypothalamus as well as the symptom cluster of stress, sleep, and fatigue. As a result, the impact of brain tumor treatment (chemotherapy/biotherapy, radiotherapy, and surgery) may have lasting biobehavioral effects. Description of symptoms during early survivorship is not always evident in the literature. Methods Convenience sampling and the following inclusion criteria were utilized: brain tumor survivors ages 8–17 years; ≥6 months, <6 years from completion of treatment; disease free or stable disease. Participants completed polysomnography (PSG) followed by a multiple sleep latency test (MSLT), and subjective measures of sleep, fatigue, stress, and pubertal status. Collection of salivary biomarkers for stress (cortisol) and sleep (melatonin) was completed the evening of and morning after the PSG. Results Analysis of the first 12 participants (5 males; 3 Hispanic/Latino; average age 14 years; 9–72 months post treatment) revealed mean (minutes) total sleep time (TST) 442, sleep latency (SL) 42 and waking (WASO) 88; sleep efficiency (SE) mean 83%, There were large magnitude correlations between several variables of interest, notably PM Cortisol with fatigue, TST (r= .472; -.453); AM Cortisol with SL (r=.479); AM Melatonin with SE, SL, WASO (r= -.459; .692; .458). Average AM melatonin level (26.6 pg/dl) was higher than PM (6.66 pg/dl). Seven participants were diagnosed with clinical sleep disorders, including one with narcolepsy and two with hypersomnia. Conclusion During early survivorship after pediatric brain tumor treatment, survivors may be at high risk for sleep/wake disturbance (SWD). Morning melatonin and biomarker correlations with sleep and fatigue in this sample warrant further exploration and may be related to first night effect versus circadian rhythm differences or clinical sleep disorder. Recommendations for future practice include developmentally matched protocols and routine screening of biobehavioral markers to assess risk for stress, SWD, and fatigue. Support 1. Center for Oncology Education and Research Harris College of Nursing & Health Sciences Texas Christian University 2. Neuro-Oncology Program Hematology/Oncology Center Cook Children’s Health Care System 3. Nursing Research and Evidence-Based Practice James A. “Buddy” Davidson Endowed Fund