BACKGROUND AND AIMS: Compulsive Sexual Behavior Disorder (CSBD) is characterized by increased reactivity to erotic reward cues. Cue-encoded reward parameters, such as type (e.g. erotic or monetary) or probability of anticipated reward, shape reward-related motivational processes, increase the attractiveness of cues and therefore might enhance maladaptive behavioral patterns in CSBD. Studies on the neural patterns of cue processing in individuals with CSBD have been limited mainly to ventral striatal responses. Therefore, here we aimed to examine the cue reactivity of multiple key structures in the brains reward system, taking into account not only the type of predicted reward but also its probability. METHODS: Twenty Nine men seeking professional help due to CSBD and 24 healthy volunteers took part in an fMRI study with a modified Incentive Delay Task with erotic and monetary rewards preceded by cues indicating a 25%, 50%, or 75% chance of reward. Analyses of functional patterns of activity related to cue type and probability were conducted on the whole-brain and ROI levels. RESULTS: Increased anticipatory response to cues predictive of erotic rewards was observed among CSBD participants when compared to controls, in the ventral striatum and anterior orbitofrontal cortex (aOFC). The activity in aOFC was modulated by reward probability. DISCUSSION AND CONCLUSIONS: Type of anticipated reward (erotic vs monetary) affects reward-related behavioral motivation in CSBD more strongly than reward probability. We present evidence of abnormal aOFC function in CSBD by demonstrating the recruitment of additional subsections of this region by erotic reward cues.

Conversations between people are characterized by complex nonlinear combinations of nonverbal and neurocognitive responses complementing the words that are spoken. New tools are needed to integrate these multimodal components into coherent models of conversation. We present a study and analysis pipeline for integrating multimodal measures of conversation. Data were collected using video recordings and functional near-infrared spectroscopy (fNIRS), a portable neuroimaging technology, during dyadic conversations among strangers (N=70 dyads). Rather than running discrete analyses of neural and nonverbal data, we introduce a pipeline to combine time series data from each modality into multimodal deep neural networks (DNNs) – including channel-based fNIRS signals and OpenFace data that quantifies facial expressions over time – using S2S-RNN-Autoencoders. We explored two measures to examine the resulting t-SNE space: distance and synchrony. We found that across the dimensions integrating neural and nonverbal input features, conversing dyads tend to stay closer together than permuted pairs. Dyads exhibit significantly higher synchrony in their covariation in this space compared to permuted pairs. The results suggest a mixed methodological integration may contribute to a deeper understanding of the dynamics of communication.