AUTHOR=Zhao Zongya , Li Jun , Niu Yanxiang , Wang Chang , Zhao Junqiang , Yuan Qingli , Ren Qiongqiong , Xu Yongtao , Yu Yi 

TITLE=Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.651439

DOI=10.3389/fnins.2021.651439

ISSN=1662-453X

ABSTRACT=At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HC) during a learning task, and then combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HC. Our results indicated that an excellent classification performance (accuracy=95.16%, specificity=94.44%, sensitivity=96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15 which included 12 PDC and 3 PLI connectivity. The selected effective connectivity features were mainly located between frontal/temporal/central and visual/parietal lobe and the selected functional connectivity features were mainly located between frontal/temporal and visual cortex of right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared to HC, whereas all the selected functional connectivity decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.