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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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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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