Department of Psychology, Vanderbilt University, Nashville, Tennessee.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Biol Psychiatry. 2020 Jul 1;88(1):51-62. doi: 10.1016/j.biopsych.2019.12.015. Epub 2019 Dec 23.
Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.
精神障碍表现出高共病率和临床表现的非特异性,同时在诊断类别内表现出显著的异质性。值得注意的是,许多这些精神障碍首先在青少年时期表现出来。我们通过识别神经回路中的异常来综述在青年精神症状异质性方面的进展和下一步工作。为了解决精神病学中的这一基本挑战,已经提出了许多方法。我们对这些方法进行了概述,大致分为维度方法和类别方法,以及单视图方法和多视图方法。维度方法包括因子分析和典型相关分析,旨在在从健康到疾病的连续谱上捕捉精神病理学和大脑测量之间的维度关联。相比之下,类别方法,如聚类和社区检测,旨在识别症状或大脑特征类别内的个体亚型。我们重点介绍了一些将这些方法应用于青少年样本的研究,并讨论了在使用这些方法时需要考虑的问题。最后,我们通过突出未来研究的途径来结束本文。