Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score - Wang, Meng; Hu, Ke; Fan, Lingzhong; Yan, Hao; Li, Peng; Jiang, Tianzi; Liu, Bing (Frontiers in Genetics, 2022)
Background: Prior studies have separately demonstrated that magnetic resonance imaging (MRI) and schizophrenia polygenic risk score (PRS) are predictive of antipsychotic medication treatment outcomes in schizophrenia. However, it remains unclear whether MRI combined with PRS can provide superior prognostic performance. Besides, the relative importance of these measures in predictions is not investigated. Methods: We collected 57 patients with schizophrenia, all of which had baseline MRI and genotype data. All these patients received approximately 6 weeks of antipsychotic medication treatment. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up. We divided these patients into responders (N = 20) or non-responders (N = 37) based on whether their percentages of PANSS total reduction were above or below 50%. 9 categories of MRI measures and PRSs with 145 different P-value thresholding ranges were calculated. We trained machine learning classifiers with these baseline predictors to identify whether a patient was a responder or non-responder. Results: The extreme gradient boosting (XGBoost) technique was applied to build binary classifiers. Using a leave-one-out cross-validation scheme, we achieved an accuracy of 86% with all MRI and PRS features. Other common classifier metrics were also estimated, including sensitivity (85%), specificity (86.5%), F1-score (81%), and area under the receiver operating characteristic curve (0.86). We found excluding a single feature category of gray matter volume (GMV), amplitude of low-frequency fluctuation (ALFF), and surface curvature could lead to a maximum accuracy drop of 10.6%. These 3 categories contributed more than half of the top 10 important features. Besides, removing PRS features gave rise to a modest accuracy drop (8.8%), which was not the least decrease (1.8%) among all feature categories. Conclusions: Our classifier using both MRI and PRS features was stable and not biased to predicting either responder or non-responder. Combining with MRI measures, PRS could provide certain extra predictive power of antipsychotic medication treatment outcomes in patients with schizophrenia. The relative importance of PRS was between maximum and minimum, lower than some neuroimaging measures such as GMV, ALFF, and surface curvature. Our findings inform the contributions of PRS in such machine learning studies.