CITATION.cff

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title: >-
  Can We Diagnose Mental Disorders in Children? A
  Large-Scale Assessment of Machine Learning on Structural
  Neuroimaging of 6916 Children in the ABCD Study
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Richard
    family-names: Gaus
    orcid: 'https://orcid.org/0000-0001-5999-0174'
    affiliation: >-
      The Lab for Artificial Intelligence in Medical Imaging
      (AI-Med), Department of Child and Adolescent
      Psychiatry, Ludwig-Maximilians-Universität, Munich,
      Germany
  - family-names: Pölsterl
    given-names: Sebastian
    orcid: 'https://orcid.org/0000-0002-1607-7550'
    affiliation: >-
      The Lab for Artificial Intelligence in Medical Imaging
      (AI-Med), Department of Child and Adolescent
      Psychiatry, Ludwig-Maximilians-Universität, Munich,
      Germany
  - family-names: Greimel
    given-names: Ellen
    affiliation: >-
      Department of Child and Adolescent Psychiatry,
      Psychosomatics and Psychotherapy, University Hospital,
      Ludwig-Maximilians-Universität, Munich, Germany
  - given-names: Gerd
    family-names: Schulte-Körne
    affiliation: >-
      Department of Child and Adolescent Psychiatry,
      Psychosomatics and Psychotherapy, University Hospital,
      Ludwig-Maximilians-Universität, Munich, Germany
  - given-names: Christian
    family-names: Wachinger
    affiliation: >-
      Technical University of Munich, School of Medicine,
      Department of Radiology, Munich, Germany
    orcid: 'https://orcid.org/0000-0002-3652-1874'
repository-code: 'https://github.com/ai-med/abcd_study'
abstract: >-
  Background: Prediction of mental disorders based on
  neuroimaging is an emerging area of research with
  promising first results in adults. However, research on
  the unique demographic of children is underrepresented and
  it is doubtful whether findings obtained on adults can be
  transferred to children. 

  Methods: Using data from 6,916 children aged 9-10 in the
  multicenter Adolescent Brain Cognitive Development (ABCD)
  study, we extracted 136 regional volume and thickness
  measures from structural magnetic resonance images to
  rigorously evaluate the capabilities of machine learning
  to predict ten different psychiatric disorders: major
  depressive disorder, bipolar disorder (BD), psychotic
  symptoms, attention deficit hyperactivity disorder (ADHD),
  oppositional defiant disorder, conduct disorder,
  post-traumatic stress disorder, obsessive-compulsive
  disorder, generalized anxiety disorder, and social anxiety
  disorder. For each disorder, we performed cross-validation
  and assessed whether models discovered a true pattern in
  the data via permutation testing.

  Results: Two of ten disorders can be detected with
  statistical significance when using advanced models that
  (i) allow for non-linear relationships between
  neuroanatomy and disorder, (ii) model interdependencies
  between disorders, and (iii) avoid confounding due to
  sociodemographic factors: ADHD (AUROC = 0.567, p = 0.002)
  and BD (AUROC = 0.551, p = 0.002). In contrast,
  traditional models perform consistently worse and predict
  only ADHD with statistical significance (AUROC = 0.529, p
  = 0.002).

  Conclusion: While the modest absolute classification
  performance does not warrant application in the clinic,
  our results provide empirical evidence that embracing and
  explicitly accounting for the complexities of mental
  disorders via advanced machine learning models can
  discover patterns that would remain hidden with
  traditional models.
keywords:
  - neuroimaging
  - machine learning
  - ABCD study
  - mental disorders
  - confounding
license: MIT