Affective neuroscience: applications for sexual medicine research and clinical practice.

INTRODUCTION: Affective neuroscience is the study of the brain substrates of emotional, embodied experiences. Affective neuroscience theory (ANT) is based on experimental evidence that all mammals are hardwired with 7 primary subcortical emotional operating systems, or "core emotions," that have overlapping but distinct circuits buried in the deep, ancient parts of the brain. Imbalances in the 7 core emotions can affect multiple aspects of the individual's psychosocial well-being (eg, depression, anxiety, substance abuse). Here, we propose that core emotions can also influence sexual function and, specifically, that imbalances in core emotions are the bridge connecting psychiatric symptoms (eg, anhedonia) to sexual dysfunction (eg, anorgasmia). OBJECTIVES: In this targeted review and commentary, we outline potential connections between ANT and sexual medicine research and clinical practice. We summarize ANT by defining the 3-level BrainMind and core emotions; examining how they relate to personality, behavior, and mental health; and determining the implications for sexual health research and clinical practice. METHODS: A targeted literature review was conducted. Case studies were adapted from client files and clinician interviews and then anonymized. RESULTS: We propose a novel organizational schema for implementing affective balance therapies for sexual dysfunction, which integrate psychoeducational, somatic, and cognitive therapeutic approaches under the ANT framework. We provide 3 patient case studies (anorgasmia, hypersexuality, spinal cord injury) outlining the implementation of this approach and patient outcomes. CONCLUSION: ANT has practical translational applications in sexual health research and clinical practice. By integrating our understanding of the role of core emotions in human sexuality, clinicians can better tailor treatments to address sexual dysfunction.

Protocol optimization and reducing dropout in online research.

Online research has advantages over in-person research; it's cost-efficient, scalable, and may increase diversity. Researchers collecting data online can assess protocol performance with classification models like a decision tree. However, challenges include attrition, lack of testing environment controls, technical limitations, and lack of face-to-face rapport and real time feedback. It is necessary to consider human factors of the teleresearch process from recruitment to data collection. Here we document the impact of protocol optimizations on social media engagement and retention between a pilot sample of Veterans (n = 26) and a post-optimization sample of both Veterans and civilians (n = 220) recruited from Facebook advertisements. Two-sided tests for equality of proportions were statistically significant: advertisement views leading to clicks increased by 23.8% [X2(1) = 130.3, p < 0.001] and completion of behavioral tasks increased by 31.2% [X2(1) = 20.74, p < 0.001]. However, a proportion of participants dropped out of the study before completion for both samples. To explore why, a C5.0 decision tree was used to find features that classify participant dropout. The features chosen by the algorithm were nicotine use (100%) and cannabis use (25.6%). However, for those completing the study, data quality of cognitive performance was similar for users and nonusers. Rather than determining eligibility, participants who endorse using nicotine, or both nicotine and cannabis, may have individual differences that require support in online protocols to reduce drop out, such as extra breaks. An introduction page that humanizes participants' lifestyle habits as a naturalistic benefit of remote research may also be helpful. Strategies are discussed to increase engagement and improve data quality. The findings have implications for the feasibility of conducting remote research, an increasingly popular approach that has distinct challenges compared to in-person studies.

Association between mental health symptoms and behavioral performance in younger vs. older online workers.

Background: The COVID-19 pandemic has been associated with increased rates of mental health problems, particularly in younger people. Objective: We quantified mental health of online workers before and during the COVID-19 pandemic, and cognition during the early stages of the pandemic in 2020. A pre-registered data analysis plan was completed, testing the following three hypotheses: reward-related behaviors will remain intact as age increases; cognitive performance will decline with age; mood symptoms will worsen during the pandemic compared to before. We also conducted exploratory analyses including Bayesian computational modeling of latent cognitive parameters. Methods: Self-report depression (Patient Health Questionnaire 8) and anxiety (General Anxiety Disorder 7) prevalence were compared from two samples of Amazon Mechanical Turk (MTurk) workers ages 18-76: pre-COVID 2018 (N = 799) and peri-COVID 2020 (N = 233). The peri-COVID sample also completed a browser-based neurocognitive test battery. Results: We found support for two out of three pre-registered hypotheses. Notably our hypothesis that mental health symptoms would increase in the peri-COVID sample compared to pre-COVID sample was not supported: both groups reported high mental health burden, especially younger online workers. Higher mental health symptoms were associated with negative impacts on cognitive performance (speed/accuracy tradeoffs) in the peri-COVID sample. We found support for two hypotheses: reaction time slows down with age in two of three attention tasks tested, whereas reward function and accuracy appear to be preserved with age. Conclusion: This study identified high mental health burden, particularly in younger online workers, and associated negative impacts on cognitive function.