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      Gyrus rectus asymmetry predicts trait alexithymia, cognitive empathy, and social function in neurotypical adults

      , , , , ,
      Cerebral Cortex
      Oxford University Press (OUP)

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          Abstract

          Reduced empathy and elevated alexithymia are observed in autism spectrum disorder (ASD), which has been linked to altered asymmetry in brain morphology. Here, we investigated whether trait autism, empathy, and alexithymia in the general population is associated with brain morphological asymmetry. We determined left–right asymmetry indexes for cortical thickness and cortical surface area (CSA) and applied these features to a support-vector regression model that predicted trait autism, empathy, and alexithymia. Results showed that less leftward asymmetry of CSA in the gyrus rectus (a subregion of the orbitofrontal cortex) predicted more difficulties in social functioning, as well as reduced cognitive empathy and elevated trait alexithymia. Meta-analytic decoding of the left gyrus rectus annotated functional items related to social cognition. Furthermore, the link between gyrus rectus asymmetry and social difficulties was accounted by trait alexithymia and cognitive empathy. These results suggest that gyrus rectus asymmetry could be a shared neural correlate among trait alexithymia, cognitive empathy, and social functioning in neurotypical adults. Left–right asymmetry of gyrus rectus influenced social functioning by affecting the cognitive processes of emotions in the self and others. Interventions that increase leftward asymmetry of the gyrus rectus might improve social functioning for individuals with ASD.

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Empathy for pain involves the affective but not sensory components of pain.

            Our ability to have an experience of another's pain is characteristic of empathy. Using functional imaging, we assessed brain activity while volunteers experienced a painful stimulus and compared it to that elicited when they observed a signal indicating that their loved one--present in the same room--was receiving a similar pain stimulus. Bilateral anterior insula (AI), rostral anterior cingulate cortex (ACC), brainstem, and cerebellum were activated when subjects received pain and also by a signal that a loved one experienced pain. AI and ACC activation correlated with individual empathy scores. Activity in the posterior insula/secondary somatosensory cortex, the sensorimotor cortex (SI/MI), and the caudal ACC was specific to receiving pain. Thus, a neural response in AI and rostral ACC, activated in common for "self" and "other" conditions, suggests that the neural substrate for empathic experience does not involve the entire "pain matrix." We conclude that only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy.
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              Measuring the thickness of the human cerebral cortex from magnetic resonance images.

              Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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                Author and article information

                Journal
                Cerebral Cortex
                Oxford University Press (OUP)
                1047-3211
                1460-2199
                March 01 2023
                February 20 2023
                May 15 2022
                March 01 2023
                February 20 2023
                May 15 2022
                : 33
                : 5
                : 1941-1954
                Article
                10.1093/cercor/bhac184
                35567793
                384c3d3e-5adb-4b0b-87c6-85e87696d3a8
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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