Data_Sheet_1_Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features.PDF

BackgroundSome studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR.MethodsFifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine.ResultsAll groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures.ConclusionOur findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.

背景：诸多研究表明，在精神分裂症患者群体中，高达40%的死亡原因可归因于自杀，与普通人群相比，精神分裂症患者的自杀风险（SR）高出8.5倍。基于生物指标，准确可靠地预测精神分裂症患者的自杀风险，具有至关重要的意义。然而，尚不清楚精神分裂症中的自杀风险是否与自发脑活动改变相关，或是否可以将静息态功能性磁共振成像（rsfMRI）指标与机器学习（ML）算法相结合，以识别具有SR的患者。方法：本研究纳入了59名参与者，包括具有和未具有SR的精神分裂症患者，以及年龄和性别相匹配的健康人，他们接受了13分钟的静息态功能性磁共振成像。计算了静态和动态的低频振幅波动（ALFF）、低频振幅波动分数（fALFF）、区域一致性以及功能连接（FC）指标，并将这些指标作为五个机器学习算法的输入：梯度提升（GB）、LASSO、逻辑回归（LR）、随机森林和支持向量机。结果：所有组在腹侧默认模式网络（DMN）和前额叶皮层（SN）的内网络功能连接方面均表现出不同的特征。在功能连接的LASSO算法中，最佳性能达到70%的准确率和0.76的AUROC（p < 0.05）。使用fALFF和ALFF指标，GB和LR也表现出了显著的分类能力。结论：我们的研究结果提示，精神分裂症患者的自杀风险可以在默认模式网络（DMN）和前额叶皮层（SN）的功能连接改变层面上观察到。机器学习算法能够显著区分具有SR的患者。我们的研究结果对于基于非侵入性rsfMRI开发精神分裂症自杀风险的神经标志物可能具有实用价值。