Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score | |
Wang, Meng1,2,3; Hu, Ke1,2,3; Fan, Lingzhong1,2,3,4; Yan, Hao5,6; Li, Peng5,6; Jiang, Tianzi1,2,3,4,7,8; Liu, Bing9,10 | |
发表期刊 | FRONTIERS IN GENETICS |
2022-02-02 | |
卷号 | 13页码:11 |
摘要 | 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%. Nine 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 metrics were also estimated, including sensitivity (85%), specificity (86%), 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 10.5%. These three categories contributed more than half of the top 10 important features. Besides, removing PRS features caused 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 schizophrenia. PRS exhibited medium importance in predictions, lower than GMV, ALFF, and surface curvature, but higher than measures of cortical thickness, cortical volume, and surface sulcal depth. Our findings inform the contributions of PRS in predictions of treatment outcomes in schizophrenia. |
关键词 | schizophrenia treatment prediction XGBoost polygenic risk score magnetic resonance imaging |
DOI | 10.3389/fgene.2022.848205 |
关键词[WOS] | STRUCTURAL BRAIN ABNORMALITIES ; FUNCTIONAL CONNECTIVITY ; RECOMMENDATIONS ; REGISTRATION ; ASSOCIATION ; MARKERS ; MRI |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Basic Research and Development Program (973)[2011CB707800] ; Natural Science Foundation of China[81771451] ; Natural Science Foundation of China[82071505] |
项目资助者 | National Key Basic Research and Development Program (973) ; Natural Science Foundation of China |
WOS研究方向 | Genetics & Heredity |
WOS类目 | Genetics & Heredity |
WOS记录号 | WOS:000759140600001 |
出版者 | FRONTIERS MEDIA SA |
七大方向——子方向分类 | 脑网络分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47973 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Liu, Bing |
作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China 5.Peking Univ, Hosp 6, Inst Mental Hlth, Beijing, Peoples R China 6.Peking Univ, Key Lab Mental Hlth, Minist Hlth, Beijing, Peoples R China 7.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Key Lab NeuroInformat, Minist Educ, Chengdu, Peoples R China 8.Chinese Acad Sci, Innovat Acad Artificial Intelligence, Beijing, Peoples R China 9.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China 10.Chinese Inst Brain Res, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Meng,Hu, Ke,Fan, Lingzhong,et al. Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score[J]. FRONTIERS IN GENETICS,2022,13:11. |
APA | Wang, Meng.,Hu, Ke.,Fan, Lingzhong.,Yan, Hao.,Li, Peng.,...&Liu, Bing.(2022).Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score.FRONTIERS IN GENETICS,13,11. |
MLA | Wang, Meng,et al."Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score".FRONTIERS IN GENETICS 13(2022):11. |
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