Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands.
Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands.
Prog Neuropsychopharmacol Biol Psychiatry. 2019 Apr 20;91:49-59. doi: 10.1016/j.pnpbp.2018.08.005. Epub 2018 Aug 11.
As of yet, no diagnostic biomarkers are available for obsessive-compulsive disorder (OCD), and its diagnosis relies entirely upon the recognition of behavioural features assessed through clinical interview. Neuroimaging studies have shown that various brain structures are abnormal in OCD patients compared to healthy controls. However, the majority of these results are based on average differences between groups, which limits diagnostic usage in clinical practice. In recent years, a growing number of studies have applied multivariate pattern analysis (MVPA) techniques on neuroimaging data to extract patterns of altered brain structure, function and connectivity typical for OCD. MVPA techniques can be used to develop predictive models that extract regularities in data to classify individual subjects based on their diagnosis. In the present paper, we reviewed the literature of MVPA studies using data from different imaging modalities to distinguish OCD patients from controls. A systematic search retrieved twelve articles that fulfilled the inclusion and exclusion criteria. Reviewed studies have been able to classify OCD diagnosis with accuracies ranging from 66% up to 100%. Features important for classification were different across imaging modalities and widespread throughout the brain. Although studies have shown promising results, sample sizes used are typically small which can lead to high variance of the estimated model accuracy, cohort-specific solutions and lack of generalizability of findings. Some of the challenges are discussed that need to be overcome in order to move forward toward clinical applications.
目前，还没有用于强迫症（OCD）的诊断生物标志物，其诊断完全依赖于通过临床访谈评估的行为特征的识别。神经影像学研究表明，与健康对照组相比，强迫症患者的各种大脑结构异常。然而，这些结果中的大多数都是基于组间的平均差异，这限制了其在临床实践中的诊断应用。近年来，越来越多的研究将多元模式分析（MVPA）技术应用于神经影像学数据，以提取强迫症患者特有的大脑结构、功能和连接改变模式。MVPA 技术可用于开发预测模型，以提取数据中的规律，根据患者的诊断对个体进行分类。在本文中，我们回顾了使用不同成像模式的数据进行 MVPA 研究的文献，以区分强迫症患者和对照组。系统搜索检索到符合纳入和排除标准的十二篇文章。综述研究能够以 66%至 100%的准确率对 OCD 诊断进行分类。用于分类的特征在不同的成像模式之间有所不同，并且广泛分布在整个大脑中。尽管这些研究显示出了有希望的结果，但使用的样本量通常较小，这可能导致估计模型准确性的方差较大、特定队列的解决方案以及研究结果缺乏可推广性。为了朝着临床应用的方向前进，需要讨论一些需要克服的挑战。
