Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

The effect of top-down attention on empathy fatigue

Prolonged exposure to others' suffering can lead to empathy fatigue, especially when individuals struggle to effectively regulate their empathic capacity. Shifting active attention away from emotional components toward cognitive components of others' suffering is an effective strategy for mitigating empathy fatigue. This research investigated how top-down attentional manipulation modulates empathy fatigue in both auditory (Study 1) and visual (Study 2) modalities. Participants completed two tasks in both studies: (i) the attention to cognitive empathy task (A-C task) and (ii) the attention to emotional empathy task (A-E task). Each task included three blocks (Time Block 1, Time Block 2, and Time Block 3) designed to induce empathy fatigue. Study 1 revealed that the A-C task reduced empathy fatigue and N1 amplitudes than the A-E task in Time Block 3, indicating that attention to cognitive empathy might decrease auditory empathy fatigue. Study 2 indicates that the A-C task caused a longer N2 latency than the A-E task, signifying a decelerated emotional empathic response when attention was on cognitive empathy in the visual modality. Overall, prioritizing cognitive empathy seems to conserve mental resources and reduce empathy fatigue. This research documented the relationship between top-down attention and empathy fatigue and the possible neural mechanism.

Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier

Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n = 29) and those that do not (n = 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.

Individual variation in the neurophysiological representation of negative emotions in virtual reality is shaped by sociability

Negative emotions play a dominant role in daily human life, and mentalizing and empathy are also basic sociability in social life. However, little is known regards the neurophysiological pattern of negative experiences in immersive environments and how people with different sociabilities respond to the negative emotional stimuli at behavioral and neural levels. The present study investigated the neurophysiological representation of negative affective experiences and whether such variations are associated with one's sociability. To address this question, we examined four types of negative emotions that frequently occurred in real life: angry, anxious, fearful, and helpless. We combined naturalistic neuroimaging under virtual reality, multimodal neurophysiological recording, and behavioral measures. Inter-subject representational similarity analysis was conducted to capture the individual differences in the neurophysiological representations of negative emotional experiences. The behavioral and neurophysiological indices revealed that although the emotion ratings were uniquely different, a similar electroencephalography response pattern across these negative emotions was found over the parieto-occipital electrodes. Furthermore, the neurophysiological representations indeed reflected interpersonal variations regarding mentalizing and empathic abilities. Our findings yielded a common pattern of neurophysiological responses toward different negative affective experiences in VR. Moreover, the current results indicate the potential of taking a sociability perspective for understanding the interpersonal variations in the neurophysiological representation of emotion.