Brain and behavioral alterations in subjects with social anxiety dominated by empathic embarrassment.

Social-anxiety disorder involves a fear of embarrassing oneself in the presence of others. Taijin-kyofusho (TKS), a subtype common in East Asia, additionally includes a fear of embarrassing others. TKS individuals are hypersensitive to others' feelings and worry that their physical or behavioral defects humiliate others. To explore the underlying neurocognitive mechanisms, we compared TKS ratings with questionnaire-based empathic disposition, cognitive flexibility (set-shifting), and empathy-associated brain activity in 23 Japanese adults. During 3-tesla functional MRI, subjects watched video clips of badly singing people who expressed either authentic embarrassment (EMBAR) or hubristic pride (PRIDE). We expected the EMBAR singers to embarrass the viewers via emotion-sharing involving affective empathy (affEMP), and the PRIDE singers to embarrass via perspective-taking involving cognitive empathy (cogEMP). During affEMP (EMBAR > PRIDE), TKS scores correlated positively with dispositional affEMP (personal-distress dimension) and with amygdala activity. During cogEMP (EMBAR < PRIDE), TKS scores correlated negatively with cognitive flexibility and with activity of the posterior superior temporal sulcus/temporoparietal junction (pSTS/TPJ). Intersubject correlation analysis implied stronger involvement of the anterior insula, inferior frontal gyrus, and premotor cortex during affEMP than cogEMP and stronger involvement of the medial prefrontal cortex, posterior cingulate cortex, and pSTS/TPJ during cogEMP than affEMP. During cogEMP, the whole-brain functional connectivity was weaker the higher the TKS scores. The observed imbalance between affEMP and cogEMP, and the disruption of functional brain connectivity, likely deteriorate cognitive processing during embarrassing situations in persons who suffer from other-oriented social anxiety dominated by empathic embarrassment.

Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. OBJECTIVE: We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. METHODS: To validate the method, Bayesian optimization was employed using participants' binary judgements about the intensity of phosphenes elicited through tACS. RESULTS: We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations. CONCLUSION: Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals.

Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods.

An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.