Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume - Zhu, Wenjing; Wang, Zan; Yu, Miao; Zhang, Xiangrong; Zhang, Zhijun (Frontiers in Neuroscience, 2023)
Objective: 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 have explored the differences of functional connectivity (FC) between the different subtypes based on region of interest (ROI) from GMV (Gray matter volume). 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. Methods: Total 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, 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. Results: The relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between right inferior parietal lobule (IPL.R) and bilateral thalamus, and lingual gyrus, between right inferior temporal gyrus (ITG.R) and Salience Network (SN) in NDS. The FC between THA.R and Visual network (VN), between ITG.R and right superior occipital gyrus in DS group were 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 DS group. The thalamic FC imbalance was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with SANS scale in DS group, but positively correlated with SAPS scale in NDS group. Conclusion: Using SVM classification method and based on ROI from GMV, we highlighted the difference of 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.