Paunova Rositsa, Kandilarova Sevdalina, Todeva-Radneva Anna, Latypova Adeliya, Kherif Ferath, Stoyanov Drozdstoy
Department of Psychiatry and Medical Psychology, Medical University Plovdiv, 4002 Plovdiv, Bulgaria.
Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria.
Diagnostics (Basel). 2022 Feb 12;12(2):469. doi: 10.3390/diagnostics12020469.
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.
我们对两组精神分裂症和抑郁症患者的结构、静息态及任务相关功能磁共振成像（fMRI）数据采用了多变量方法，以确定与这些疾病鉴别诊断显著相关的几个区域。这些区域包括左侧颞横回（PP）、左侧额下回岛盖部（OpIFG）、眶内侧回（MOrG）、后岛叶（PIns）和海马旁回（PHG）。本研究提供了证据表明多模态神经影像学方法可能会提高精神疾病诊断的有效性。结构、静息态或任务相关的功能磁共振成像模式无法提供独立的生物标志物。进一步的研究需要考虑并实施一种将临床测量与不同神经影像学模式相结合的递增效度模型，以区分抑郁症和精神分裂症。神经影像学水平上的疾病生物学特征更有可能支撑精神病学中更广泛的疾病分类实体。
