Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation.

INTRODUCTION: It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features. METHODS: Three cohorts (homosexual men, heterosexual men, and a mixed sex cohort), one pretrained network on sex classification, and one newly trained network for sexual orientation classification were used to classify sex. Further, Grad-CAM methodology and source localization were used to identify the spatiotemporal patterns that were used for differentiation by the networks. RESULTS: Using a pretrained network for classification of males and females, no differences existed between classification of homosexual and heterosexual males. The newly trained network was able, however, to correctly classify the cohorts with a total accuracy of 83%. The retrograde activation using Grad-CAM technology yielded distinctive functional EEG patterns in the Brodmann area 40 and 1 when combined with Fourier analysis and a source localization. DISCUSSION: This study shows that electrophysiological trait markers of male sexual orientation can be identified using deep learning. These patterns are different from the differentiating signatures of males and females in a resting-state EEG.

Neuroelectric Correlates of Human Sexuality: A Review and Meta-Analysis.

Many reviews on sexual arousal in humans focus on different brain imaging methods and behavioral observations. Although neurotransmission in the brain is mainly performed through electrochemical signals, there are no systematic reviews of the electrophysiological correlates of sexual arousal. We performed a systematic search on this subject and reviewed 255 studies including various electrophysiological methods. Our results show how neuroelectric signals have been used to investigate genital somatotopy as well as basic genital physiology during sexual arousal and how cortical electric signals have been recorded during orgasm. Moreover, experiments on the interactions of cognition and sexual arousal in healthy subjects and in individuals with abnormal sexual preferences were analyzed as well as case studies on sexual disturbances associated with diseases of the nervous system. In addition, 25 studies focusing on brain potentials during the interaction of cognition and sexual arousal were eligible for meta-analysis. The results showed significant effect sizes for specific brain potentials during sexual stimulation (P3: Cohen's d = 1.82, N = 300, LPP: Cohen's d = 2.30, N = 510) with high heterogeneity between the combined studies. Taken together, our review shows how neuroelectric methods can consistently differentiate sexual arousal from other emotional states.

Automaticity of Early Sexual Attention: An Event-Related Potential Study.

A promising line of research on forensic assessment of paraphilic sexual interest focuses on behavioral measures of visual attention using sexual stimuli as distractors. The present study combined event-related potentials (ERPs) with behavioral measures to investigate whether detection of a hidden sexual preference can be improved with ERPs. Normal variants of sexual orientation were used for a proof-of-concept investigation. Accordingly, 40 heterosexual and 40 gay men participated in the study. Within each group, half of the participants were instructed to hide their sexual orientation. The results showed that a match between sexual orientation and stimulus delays responses and influences ERP before motor responses. Late ERP components showed higher potential in differentiating hidden sexual preferences than motor responses, thereby showing how ERPs can be used in combination with reaction time measures to potentially facilitate the detection of hidden sexual preferences.

The Effects of Acute Transcranial Direct Current Stimulation on Attentional Bias in Pedophilic Disorder: A Preregistered Pilot Study.

OBJECTIVES: Individuals with pedophilic disorder (PD) experience personal and interpersonal difficulties and are at risk of sexually offending against children. As such, innovative and empirically validated treatments are needed. Recent studies have indicated that men who have sexually offended against children (SOC) with PD display an automatic attention bias for child-related stimuli as well as reduced activity in the dorsolateral prefrontal cortex (dlPFC), a brain area involved in cognitive control, including control over sexual arousal. In this preregistered pilot study, we are the first to investigate whether acutely increasing prefrontal activity could reduce the putative pedophilic attention bias. MATERIALS AND METHODS: We delivered a single 20-min session of active anodal versus sham transcranial direct current stimulation (tDCS) over the left dlPFC to 16 SOC with PD and 16 matched healthy controls, while they performed a task requiring controlled attention to computer-generated images of clothed and nude children and adults. We collected responses unobtrusively by recording eye movements. RESULTS: Our results did not support the presence of the expected automatic attention bias across outcome measures. Nonetheless, we found a response facilitation with child targets in patients and, unexpectedly, in controls, likely due to unwanted salience effects. Active versus sham tDCS reduced this bias across groups, as indicated by a significant group*condition interaction (p = 0.04). However, no attentional bias and no tDCS effects on attentional responses to child and adult images emerged following tDCS. CONCLUSIONS: These results suggest enhanced cognitive control in response to salient stimuli during active tDCS. Thus, to assist future studies on neuromodulation in PD, we provide suggestions for design improvement.

Emotion-Induced Frontal Alpha Asymmetry as a Candidate Predictor of Relapse After Discontinuation of Antidepressant Medication.

BACKGROUND: One in 3 patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of major depressive disorder. However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by functional magnetic resonance imaging has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. METHODS: Patients (n = 56) who had remitted from a depressive episode while taking antidepressants underwent electroencephalography (EEG) recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a 6-month follow-up period. Thirty five healthy control participants were also tested. Current source density of the EEG power in the alpha band (8-13 Hz) was extracted and alpha asymmetry was computed by comparing the power across 2 hemispheres at frontal electrodes (F5 and F6). RESULTS: Sad mood induction was robust across all groups. Reactivity of alpha asymmetry to sad mood did not distinguish healthy control participants from patients with remitted major depressive disorder on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in alpha asymmetry compared with patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy and a positive predictive value of 0.75). CONCLUSIONS: A simple EEG-based measure of emotional reactivity may have potential to contribute to clinical prediction models of antidepressant discontinuation. Given the very small sample size, this finding must be interpreted with caution and requires replication in a larger study.