Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model - Kuang, Qi-Jie; Zhou, Su-Miao; Liu, Yi; Wu, Hua-Wang; Bi, Tai-Yong; She, Sheng-Lin; Zheng, Ying-Jun (Frontiers in Psychiatry, 2022)
Objective: There were few studies had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In the present study, we developed a model for the prediction of FER ability in Chinese Han patients with first-episode schizophrenia (FSZ). Methods: 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan. Results: Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients. Conclusion: Our study provide an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.