Related Experiment Video
Updated: Sep 12, 2026
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
Personalized prediction of treatment response during the acute phase of schizophrenia based on the connectome-based
Meiqi Yan1, Hongxing Zhang2,3, Yiqun He4
1Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
Background:
Schizophrenia shows great variability in symptoms and treatment response. Integrating neuroimaging techniques with data-driven models may help to predict patient responses and develop personalized therapies.
Methods:
Using functional connectivity (FC) changes before and after treatment as the neuroimaging feature and reduction rate (RR) of different symptom dimensions as the clinical feature, this study established a prediction model for the acute-phase treatment response of patients with schizophrenia using the connectome-based predictive modeling (CPM) method. The model was then validated in independent samples. Transcriptome-neuroimaging correlation analysis was conducted to identify genes associated with FC changes.
Results:
Prediction models were established, which involved the total score of PANSS and scores of positive and affective symptoms. The models were Y = 0.029Xpos + 0.500 (r = 0.407, P = 0.040) for the RR of the total score of PANSS, Y = 0.010Xpos - 0.010Xneg + 0.542 (r = 0.421, P = 0.038) for the RR of positive symptoms, and Y = -0.037Xneg + 0.475 (r = 0.486, P = 0.040) for the RR of affective symptoms. The positive and negative network models for predicting the RR of positive symptoms (P = 0.011, R² = 0.184) and the negative network model for the RR of affective symptoms (P = 0.041, R² = 0.125) were validated. Gene enrichment analysis linked the models to synaptic structures and cell channel activation.
Conclusions:
Our study established and validated a three-network predictive model for predicting treatment response regarding positive/affective symptoms, which may help individualized treatment monitoring and efficacy prediction.
