Jollans Lee, Whelan Robert
School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
Front Psychiatry. 2018 Jun 6;9:242. doi: 10.3389/fpsyt.2018.00242. eCollection 2018.
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.
尽管对精神障碍的神经生物学进行了大量研究，但迄今为止，神经生物学见解对精神病诊断或治疗的影响微乎其微。在本综述中，我们认为寻找精神障碍的神经影像学生物标志物——神经标志物——是改善精神卫生保健的一条极有前景的途径。然而，许多用于精神科神经影像学的传统工具不足以识别神经标志物。具体而言，我们强调需要更大的样本量和多变量分析。机器学习等方法可能有助于分析高维神经影像学数据。我们认为，广泛的基于人群的研究设计对于开发精神障碍的神经标志物很重要，并且将有助于摆脱基于现象学的精神障碍类别定义，转向基于生物学证据的精神病学分类学。我们概述了神经标志物的开发应如何进行，强调了外部效度和结构效度测试以及合作研究努力的必要性。最后，我们强调了在精神卫生保健中开发和使用神经标志物的一些问题。
