Abstract
Despite abundant research into the neurobiology of mental disorders, to date neurobiological insights have had very little impact on psychiatric diagnosis or treatment. In this review, we contend that the search for neuroimaging biomarkers—neuromarkers—of mental disorders is a highly promising avenue toward improved psychiatric healthcare. However, many of the traditional tools used for psychiatric neuroimaging are inadequate for the identification of neuromarkers. Specifically, we highlight the need for larger samples and for multivariate analysis. Approaches such as machine learning are likely to be beneficial for interrogating high-dimensional neuroimaging data. We suggest that broad, population-based study designs will be important for developing neuromarkers of mental disorders, and will facilitate a move away from a phenomenological definition of mental disorder categories and toward psychiatric nosology based on biological evidence. We provide an outline of how the development of neuromarkers should occur, emphasizing the need for tests of external and construct validity, and for collaborative research efforts. Finally, we highlight some concerns regarding the development, and use of, neuromarkers in psychiatric healthcare.
Highlights
According to figures from the World Health Organization, the projected risk for developing some form of mental disorder across the lifetime is between 18 and 55% [1]
Summary we have contended that the integration of neuromarkers into the diagnosis and treatment of mental disorders would be of great benefit to both patients and clinicians
We will address each of these areas, outlining why they pose a threat to the clinical applicability of neuroimaging research
Summary
According to figures from the World Health Organization, the projected risk for developing some form of mental disorder across the lifetime is between 18 and 55% [1]. Psychiatric neuroimaging research typically involves a group of patients, and a group of healthy control participants (normally matched to the patient group in terms of various demographic characteristics) These are compared in terms of their brain structure or function. To account for the high risk of false positive findings (see Glossary), mass univariate analyses are ordinarily reported using corrected statistical significance thresholds This approach has produced important insights into the neuropathology underlying many psychiatric conditions including addiction [e.g., [8]]; schizophrenia [e.g., [9]]; social anxiety disorder [e.g., [10]], Attention deficit hyperactivity disorder [ADHD; e.g., [11]], and anorexia nervosa [e.g., [12]].
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