Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Neuroimage Clin. 2021;32:102860. doi: 10.1016/j.nicl.2021.102860. Epub 2021 Oct 18.
Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.
先前的脑结构磁共振成像研究报告称，精神分裂症患者存在脑结构异常，这些异常可用于区分精神分裂症患者和正常对照者。然而，大多数现有研究仅在单个地点识别出精神分裂症患者，且未考虑与高遗传性精神分裂症密切相关的遗传特征。在这项研究中，我们对脑结构磁共振图像和遗传数据进行了标准化特征提取，以将精神分裂症患者与正常对照者区分开来。共有 1010 名参与者，其中 508 名精神分裂症患者和 502 名正常对照者来自中国 8 个独立的研究地点。使用不同的机器学习方法和输入特征进行分类实验。我们测试了支持向量机、逻辑回归和集成学习策略，使用了 3 个感兴趣的特征集：（1）成像特征：灰质体积；（2）遗传特征：多基因风险评分；（3）成像特征和遗传特征的融合。通过留一站点交叉验证评估性能。最后，确定了一些重要的大脑和遗传特征。我们发现，同时输入成像和遗传特征的模型比单独输入的模型表现更好。在交叉验证中表现最好的分类模型的平均准确率为 71.6%。测量与精神分裂症最相关的遗传变异累积风险的遗传特征对分类的贡献最大。我们的工作首次考虑了大规模多站点精神分裂症分类中的结构性脑改变和全基因组遗传因素。我们的发现可能为精神分裂症的潜在病理生理学和风险机制提供了一些见解。
