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<p>Humans often judge others egocentrically, assuming that they feel or think similarly to themselves. Emotional egocentricity bias (EEB) occurs in situations when others feel differently to oneself. Using a novel paradigm, we investigated the neurocognitive mechanisms underlying the developmental capacity to overcome such EEB in children compared with adults. We showed that children display a stronger EEB than adults and that this correlates with reduced activation in right supramarginal gyrus (rSMG) as well as reduced coupling between rSMG and left dorsolateral prefrontal cortex (lDLPFC) in children compared with adults. Crucially, functional recruitment of rSMG was associated with age-related differences in cortical thickness of this region. Although in adults the mere presence of emotional conflict occurs between self and other recruited rSMG, rSMG-lDLPFC coupling was only observed when implementing empathic judgements. Finally, resting state analyses comparing connectivity patterns of rSMG with that of right temporoparietal junction suggested a unique role of rSMG for self-other distinction in the emotional domain for adults as well as for children. Thus, children’s difficulties in overcoming EEB may be due to late maturation of regions distinguishing between conflicting socio-affective information and relaying this information to regions necessary for implementing accurate judgments.</p>
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Mindfulness meditation has been shown to promote emotional stability. Moreover, during the processing of aversive and self-referential stimuli, mindful awareness is associated with reduced medial prefrontal cortex (MPFC) activity, a central default mode network (DMN) component. However, it remains unclear whether mindfulness practice influences functional connectivity between DMN regions and, if so, whether such impact persists beyond a state of meditation. Consequently, this study examined the effect of extensive mindfulness training on functional connectivity within the DMN during a restful state. Resting-state data were collected from 13 experienced meditators (with over 1000 h of training) and 11 beginner meditators (with no prior experience, trained for 1 week before the study) using functional magnetic resonance imaging (fMRI). Pairwise correlations and partial correlations were computed between DMN seed regions’ time courses and were compared between groups utilizing a Bayesian sampling scheme. Relative to beginners, experienced meditators had weaker functional connectivity between DMN regions involved in self-referential processing and emotional appraisal. In addition, experienced meditators had increased connectivity between certain DMN regions (e.g. dorso-medial PFC and right inferior parietal lobule), compared to beginner meditators. These findings suggest that meditation training leads to functional connectivity changes between core DMN regions possibly reflecting strengthened present-moment awareness.

An extensive body of research defines the default-mode network (DMN) to be one of the critical networks of the human brain, playing a pivotal functional role in processes of internal mentation. Alterations in the connectivity of this network as a function of aging have been found, with reductions associated with functional ramifications for the elderly population. This study examined associations between integrity of the DMN and trait levels of mindfulness disposition, defined by our ability to exert attentional and emotional control in the present moment, and, thereby, bring awareness to immediate experiences. Twenty-five older adults participated in the study and underwent a brief functional magnetic resonance imaging session and filled out questionnaires related to their overall health and mindfulness disposition. Mindfulness disposition was associated with greater connectivity of the DMN, specifically, in the dorsal posterior cingulate cortex and the precuneus. Mindfulness disposition, thus, explains variance in the connectivity of one of the more intrinsic networks of the human brain, known to be critical for promoting self-relevant mental explorations and building cognitive and affective control.

We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes by forming a Fourier series expansion. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we propose to filter out only the significant eigenfunctions by imposing l1-penalty. The new sparse framework can further avoid additional surface-based smoothing often used in the field. The proposed approach is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shapes in the normal population. In addition, we show how the emotional response is related to the anatomy of the subcortical structures.
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The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.
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<p>Previous functional imaging studies have shown key roles of the dorsal anterior insula (dAI) and anterior midcingulate cortex (aMCC) in empathy for the suffering of others. The current study mapped structural covariance networks of these regions and assessed the relationship between networks and individual differences in empathic responding in 94 females. Individual differences in empathy were assessed through average state measures in response to a video task showing others' suffering, and through questionnaire-based trait measures of empathic concern. Overall, covariance patterns indicated that dAI and aMCC are principal hubs within prefrontal, temporolimbic, and midline structural covariance networks. Importantly, participants with high empathy state ratings showed increased covariance of dAI, but not aMCC, to prefrontal and limbic brain regions. This relationship was specific for empathy and could not be explained by individual differences in negative affect ratings. Regarding questionnaire-based empathic trait measures, we observed a similar, albeit weaker modulation of dAI covariance, confirming the robustness of our findings. Our analysis, thus, provides novel evidence for a specific contribution of frontolimbic structural covariance networks to individual differences in social emotions beyond negative affect.</p>
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