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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?

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Abstract

Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neuroimaging, the classification performance of ASD remains far away from clinical application. This raises the question of how we can overcome these barriers to develop early biomarkers for ASD. One approach might be to rethink how we operationalize the theoretical basis of this disease in machine learning models. Here we introduced unsupervised graph representations that explicitly map the neural mechanisms of a core aspect of ASD, deficits in dyadic social interaction, as assessed by dual brain recordings, termed hyperscanning, and evaluated their predictive performance. The proposed method differs from existing approaches in that it is more suitable to capture social interaction deficits on a neural level and is applicable to young children and infants. First results from functional near-infrared spectroscopy data indicate potential predictive capacities of a task-agnostic, interpretable graph representation. This first effort to leverage interaction-related deficits on neural level to classify ASD may stimulate new approaches and methods to enhance existing models to achieve developmental ASD biomarkers in the future.

M. Schulte-Rüther and V. Reindl—Shared last authorship.

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Author information

Authors and Affiliations

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

    Christian Gerloff, Kerstin Konrad, Jana Kruppa & Vanessa Reindl

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

    Christian Gerloff, Kerstin Konrad & Vanessa Reindl

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

    Christian Gerloff

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

    Jana Kruppa & Martin Schulte-Rüther

  5. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany

    Martin Schulte-Rüther

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

    Vanessa Reindl

Authors
  1. Christian Gerloff

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  2. Kerstin Konrad

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  3. Jana Kruppa

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  4. Martin Schulte-Rüther

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  5. Vanessa Reindl

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Corresponding author

Correspondence toChristian Gerloff.

Editor information

Editors and Affiliations

  1. Lausanne University Hospital, Lausanne, Switzerland

    Ahmed Abdulkadir

  2. Indian Institute of Technology Ropar, Rupnagar, India

    Deepti R. Bathula

  3. Yale University, New Haven, CT, USA

    Nicha C. Dvornek

  4. The University of Texas Health Science Center, San Antonio, TX, USA

    Mohamad Habes

  5. Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  6. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Vinod Kumar

  7. University of Tübingen, Tübingen, Germany

    Thomas Wolfers

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Gerloff, C., Konrad, K., Kruppa, J., Schulte-Rüther, M., Reindl, V. (2022). Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?. In: Abdulkadir, A.,et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_15

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