Zhu Wenjing, Wang Zan, Yu Miao, Zhang Xiangrong, Zhang Zhijun
Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China.
Affiliated Mental Health Center, Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Front Neurosci. 2023 Mar 27;17:1132607. doi: 10.3389/fnins.2023.1132607. eCollection 2023.
Schizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.
A total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.
The relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.
Using an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.
精神分裂症可根据原发性和持续性阴性症状的有无分为缺陷型精神分裂症（DS）和非缺陷型精神分裂症（NDS）。到目前为止，很少有研究基于来自灰质体积（GMV）的感兴趣区域（ROI）来探究不同亚型之间的功能连接（FC）差异，尤其是因为脑网络的特征仍不清楚。本研究旨在基于通过机器学习算法和差异GMV获得的ROI来研究DS和NDS之间功能连接的改变。然后，分析这些改变与临床症状之间的关系。此外，进一步探究两组中的丘脑功能连接失衡情况。
共有16名DS患者、31名NDS患者和38名健康对照（HC）接受静息态功能磁共振成像扫描，患者组将进一步通过临床量表进行评估，包括简明精神病评定量表（BPRS）、阴性症状评定量表（SANS）和阳性症状评定量表（SAPS）。基于GMV图像数据，使用支持向量机（SVM）对DS和NDS进行分类。在全脑FC分析和丘脑FC失衡分析中，将分类中权重较高的脑区用作种子点。最后，偏相关分析探究了两种亚型中FC改变与临床量表之间的关系。
基于SVM获得了相对较高的分类准确率。与HC相比，NDS中右侧顶下小叶（IPL.R）与双侧丘脑、舌回之间，以及右侧颞下回（ITG.R）与突显网络（SN）之间的FC增加。DS组中右侧丘脑（THA.R）与视觉网络（VN）之间，以及ITG.R与右侧枕上回之间的FC高于HC。此外，与NDS相比，DS组中ITG.R与左侧额上回和额中回之间的FC降低。在两种亚型中均发现了以额颞叶 - THA.R低连接性和感觉运动网络（SMN） - THA.R高连接性为特征的丘脑FC失衡。DS组中THA.R与SMN的FC值与SANS评分呈负相关，而在NDS组中与SAPS评分呈正相关。
使用SVM分类方法并基于GMV的ROI，我们从局部到脑网络突出了DS和NDS之间功能连接的差异，这为探索精神分裂症两种亚型的神经生理病理学提供了新信息。
