Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity.
Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity.
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DOI：
10.1371/journal.pone.0014277
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发表时间：
2010-12-09
期刊：
影响因子：
3.7
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Deshpande G;Li Z;Santhanam P;Coles CD;Lynch ME;Hamann S;Hu X
脑状态分类已经使用诸如从功能性磁共振成像（fMRI）数据导出的体素强度之类的特征作为诸如支持向量机（SVM）之类的有效分类器的输入来完成，并且基于脑功能的空间定位模型。随着大脑功能的联结模型的出现，来自大脑网络的特征可以为大脑状态分类提供增加的辨别力。在这项研究中，我们引入了一个新的框架，其中在两个功能连接（FC）的基础上的瞬时时间相关性和有效的连接（EC）的基础上的因果关系在大脑网络中被用作特征的SVM分类器。为了得到这些功能，我们采用了一种新的方法，最近推出的我们称为相关清除格兰杰因果关系（CPGC），以获得FC和EC从fMRI数据同时没有污染的瞬时相关格兰杰因果关系。此外，通过将递归聚类消除（RCE）算法与SVM分类器相结合，加速了统计学习，提高了性能准确性。我们证明了基于CPGC的RCE-SVM方法的有效性，使用以疾病状态预测为例的大脑状态分类的特定实例。因此，我们表明，这种方法能够预测90.3%的准确性是否任何给定的人类受试者产前暴露于可卡因或没有，即使没有显着的行为差异，发现暴露和健康受试者之间。这项工作中采用的框架在性质上是相当普遍的，产前接触可卡因只是这种方法的力量的一个说明性例子。在使用神经成像数据的任何大脑状态分类方法中，包括方向连接信息可以证明是性能增强剂。当大脑状态分类用于疾病状态预测时，我们的方法可以帮助临床医生在非神经影像学生物标志物可能无法确定地进行鉴别诊断的情况下进行更准确的疾病诊断。
Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification. In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects. The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.
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DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
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DOI：
10.1073/pnas.0505210102
发表时间：
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影响因子：
11.1
作者：
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DOI：
10.1196/annals.1376.041
发表时间：
2006-01-01
期刊：
RESILIENCE IN CHILDREN
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1109/tbme.2009.2037808
发表时间：
2010-06
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Deshpande G;Sathian K;Hu X
通讯作者：
Hu X