Neuroscience
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Functional plasticity of the adult brain is well established. Recently, the structural counterpart to such plasticity has been suggested by neuroimaging studies showing experience-dependent differences in gray matter (GM) volumes. Within the primary and secondary olfactory cortices, reduced GM volumes have been demonstrated in patients with olfactory loss. ⋯ We found significantly increased post-operative GM volumes within the primary (left piriform cortex, right amygdala) and secondary (right orbitofrontal cortex, caudate nucleus, hippocampal-parahippocampal complex and bilateral temporal poles) olfactory networks, and decreased GM volumes within the secondary network only (left caudate nucleus and temporal pole, bilateral hippocampal-parahippocampal complex). As a control measure, we assessed GM change within V1, S1 and A1, where there were no suprathreshold voxels. To our knowledge, this is the first study to demonstrate GM structural plasticity within the primary and secondary olfactory cortices, following restoration of olfaction.
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Cortical involvement in postural control is well recognized, however the role of non-visual afferents remains unclear. Parietal cortical areas are strongly implicated in vestibulo-spatial functions, but topographical localization during balance tasks remains limited. Here, we use electroencephalography (EEG) during continuous balance tasks of increasing difficulty at single electrode positions. ⋯ Our results demonstrate the functional importance of bilateral central and parietal cortices in continuous balance control. The hemispheric asymmetry observed implies that the non-dominant hemisphere is involved with online monitoring of postural control. Although the posterior parietal asymmetry found may relate to vestibular, somatosensory or multisensory feedback processing, we argue that the finding relates to active balance control rather than simple sensory-intake or reflex circuit activation.
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Pragmatics may be defined as the ability to communicate by expressing and recognizing intentions. The objective of this meta-analysis was to identify neural substrates for comprehension of pragmatic content in general, as well as the differences between pragmatic forms, and to describe if there is differential recruitment of brain areas according to natural language. This meta-analysis included 48 functional magnetic resonance imaging studies that reported pragmatic versus literal language contrasts. ⋯ In conclusion, pragmatic language comprehension involves classical language areas in bilateral perisylvian regions, along with the medial prefrontal cortex, an area involved in social cognition. Together, these areas could represent the "pragmatic language network". Nonetheless, when proposing a universal neural substrate for all forms of pragmatic language, the diversity among studies in terms of pragmatic form, and configuration, must be taken into consideration.
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Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in deception remain unclear. In this study, we sought to address this issue by employing a machine-learning approach to predict individuals' deceptive propensity based on the topological properties of whole-brain resting-state functional connectivity (RSFC). ⋯ The machine-learning model sufficiently decoded individual differences in deception using three brain networks based on RSFC, including the executive controlling network (dorsolateral prefrontal cortex, middle frontal cortex, and orbitofrontal cortex), the social and mentalizing network (the temporal lobe, temporo-parietal junction, and inferior parietal lobule), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding the mental states of others and the reward or values of deception or honesty, and integrating this information to make a final decision about being deceptive or honest. These findings suggest the potential of using RSFC as a task-independent neural trait for predicting deceptive propensity, and shed light on using machine-learning approaches in deception detection.