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Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?

Lecture notes in computer science, pp. 147–157

Authors 4

  1. Jülich Aachen Research Alliance · RWTH Aachen University

    Affiliation as printed

    Chair II of Mathematics, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany

    Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany

    JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen and Research Centre Juelich, Aachen, Germany

  2. Jülich Aachen Research Alliance · RWTH Aachen University

    Affiliation as printed

    Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany

    JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen and Research Centre Juelich, Aachen, Germany

  3. Universitätsmedizin Göttingen · Leibniz ScienceCampus Primate Cognition · University of Göttingen

    Affiliation as printed

    Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany

    Leibniz ScienceCampus Primate Cognition, Göttingen, Germany

  4. Nanyang Technological University · Jülich Aachen Research Alliance · RWTH Aachen University

    Affiliation as printed

    Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany

    JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen and Research Centre Juelich, Aachen, Germany

    Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore

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References 27