Stoyanov Drozdstoy, Kandilarova Sevdalina, Aryutova Katrin, Paunova Rositsa, Todeva-Radneva Anna, Latypova Adeliya, Kherif Ferath
Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
Centre for Research in Neuroscience-Department of Clinical Neurosciences, CHUV-UNIL, 1010 Lausanne, Switzerland.
Diagnostics (Basel). 2020 Dec 24;11(1):19. doi: 10.3390/diagnostics11010019.
Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.
传统的精神病学诊断过度依赖自我报告测量方法（内省）或临床评定量表（访谈）。这就产生了与神经科学等生物医学学科之间所谓的解释鸿沟，而神经科学本应提供疾病的生物学解释。在这种情况下，在传统统计框架下，精神病学中的神经生物学评估和临床评估仍然存在差异且不可通约。新兴的转化神经影像学领域试图通过同时应用临床评估工具和功能磁共振成像来弥合解释鸿沟，而当用标准统计方法进行分析时，这也被证明存在问题。为了克服这个问题，我们团队设计了一种新颖的机器学习技术——多元线性方法（MLM），它可以从基于体素的形态测量、静息态功能和任务相关神经成像以及相关临床测量中获取趋同数据。在本文中，我们报告了与抑郁症患者样本相比，精神分裂症患者疾病生物学特征的趋同交叉验证结果。我们的模型提供了证据，表明MLM分析中的神经成像数据和临床数据相结合可以在增量效度方面为鉴别诊断提供信息。
