Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score.
Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score.
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通过将结构磁共振成像数据与多基因风险评分相结合进行多部位精神分裂症分类。
DOI：
10.1016/j.nicl.2021.102860
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Liu B
中科院分区：
文献类型：
--
作者：
Hu K;Wang M;Liu Y;Yan H;Song M;Chen J;Chen Y;Wang H;Guo H;Wan P;Lv L;Yang Y;Li P;Lu L;Yan J;Wang H;Zhang H;Zhang D;Wu H;Ning Y;Jiang T;Liu B
使用来自八个地点的大量样本数据研究精神分裂症的分类。脑图像和遗传因素作为分类的特征。进行了机器学习方法和留一个站点的交叉验证。先前的脑结构磁共振成像研究报道，精神分裂症患者有脑结构异常，这已被用于区分精神分裂症患者与正常对照。然而，大多数现有的研究都是在单一位点鉴定精神分裂症患者，而没有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基因数据进行了标准化的特征提取，以区分精神分裂症患者和正常人。共有1010名参与者，508名精神分裂症患者和502名正常对照，从中国8个独立的站点招募。使用不同的机器学习方法和输入特征进行分类实验。我们使用3个感兴趣的特征集测试了支持向量机、逻辑回归和集成学习策略：(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。通过留一个点的交叉验证来评估性能。最后，确定了一些重要的大脑和遗传特征。我们发现，将成像和遗传特征作为输入的模型比单独使用任何一种输入的模型表现得更好。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的累积风险的遗传特征对分类贡献最大。我们的工作迈出了在大规模多位点精神分裂症分类中考虑大脑结构改变和全基因组遗传因素的第一步。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见解。
Schizophrenia classification was studied using a large sample data from eight sites. Both brain images and genetic factors were used as features for classification. Machine learning methods and leave-one-site-out cross-validation were performed. 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.
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DOI：
10.12703/p6-57
发表时间：
2014
期刊：
F1000prime reports
影响因子：
--
作者：
Haller CS;Padmanabhan JL;Lizano P;Torous J;Keshavan M
通讯作者：
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DOI：
10.1093/cercor/bhw157
发表时间：
2016-08
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Fan L;Li H;Zhuo J;Zhang Y;Wang J;Chen L;Yang Z;Chu C;Xie S;Laird AR;Fox PT;Eickhoff SB;Yu C;Jiang T
通讯作者：
Jiang T
影响因子：
10.6
作者：
Camchong, Jazmin;Dyckman, Kara A.;McDowell, Jennifer E.
通讯作者：
McDowell, Jennifer E.
影响因子：
4.5
作者：
Dickey, CC;Salisbury, DF;Shenton, ME
通讯作者：
Shenton, ME
影响因子：
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作者：
Lee JJ;Kim HJ;Čeko M;Park BY;Lee SA;Park H;Roy M;Kim SG;Wager TD;Woo CW
通讯作者：
Woo CW