Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.

Journal: Biological psychiatry. Cognitive neuroscience and neuroimaging
Published Date: Jun 21, 2019

Abstract

BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development.

Authors

  • Emily A Boeke
    Department of Psychology, New York University, New York, New York.
  • Avram J Holmes
    Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut.
  • Elizabeth A Phelps
    Department of Psychology, Harvard University, Cambridge Massachusetts. Electronic address: phelps@fas.harvard.edu.

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