Wang Yingchan, Wang Jijun, Su Wenjun, Hu Hao, Xia Mengqing, Zhang Tianhong, Xu Lihua, Zhang Xia, Taylor Hugh, Osipowicz Karol, Young Isabella M, Lin Yueh-Hsin, Nicholas Peter, Tanglay Onur, Sughrue Michael E, Tang Yingying, Doyen Stephane
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Psychiatry Res. 2023 May;323:115122. doi: 10.1016/j.psychres.2023.115122. Epub 2023 Feb 26.
This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology.
T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia.
The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology.
We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
本文旨在对精神分裂症症状背后的解剖学回路进行建模，并探索受精神病理学影响的脑网络之间异常连接的模式。
对总共126名招募来参与本研究的精神分裂症患者进行了T1磁共振成像（MRI）、扩散加权成像（DWI）和静息态功能MRI（rsfMRI）检查。使用全知软件（https://www.o8t.com）对图像进行处理。我们进一步应用空心树超级（HoTS）方法来深入了解哪些脑区存在可能与精神分裂症症状相关的异常连接。
阳性与阴性症状量表分为6个因子。每种症状都与特定的解剖学异常和回路相对应。各因子之间的比较显示，因子1和因子2中的脑区存在共同出现的情况。多个大规模网络参与了精神分裂症的症状表现，其中默认模式网络（DMN）和中央执行网络（CEN）区域内的功能连接与精神病理学测量最为相关。
我们总结了皮质区域相关解剖结构，这是为理解其在精神分裂症中的作用所做的更大努力的一部分。这种独特的机器学习类型方法通过连接诊断亚型并分析连接组特征，将症状映射到特定的脑区和回路。
