A

Machine learning–based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia

Schizophrenia Research, vol. 297, pp. 120–128

Authors 5

  1. Han‐Gue Jo corresponding

    Kunsan National University

    Affiliation as printed

    Department of AI Convergence, College of Computer and Software, Kunsan National University, Gunsan, Republic of Korea. Electronic address: hgjo@kunsan.ac.kr

  2. Forschungszentrum Jülich · RWTH Aachen University

    Affiliation as printed

    Institute for Translational Neuroscience and Clinical Psychology, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute for Translational Neuroscience, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany

  3. Forschungszentrum Jülich · RWTH Aachen University

    Affiliation as printed

    Institute for Translational Neuroscience and Clinical Psychology, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute for Translational Neuroscience, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany

  4. Forschungszentrum Jülich · RWTH Aachen University

    Affiliation as printed

    Institute for Translational Neuroscience and Clinical Psychology, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute for Translational Neuroscience, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany; Center for Computational Life Sciences, RWTH Aachen

  5. Forschungszentrum Jülich · RWTH Aachen University

    Affiliation as printed

    Institute for Translational Neuroscience and Clinical Psychology, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute for Translational Neuroscience, INM-10, Institute of Neuroscience and Medicine, Jülich Research Centre, Jülich, Germany

Cited by 0 stored of 0

No patents citing this paper on Lens.org (checked 2026-10-06).

References 36