A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia
A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia
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DOI：
10.1016/j.neuroimage.2007.10.012
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发表时间：
2008-02-15
期刊：
影响因子：
5.7
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Demirci, Oguz;Clark, Vincent P.;Calhoun, Vince D.
精神分裂症的诊断主要根据行为症状。目前，尚未开发出定量的、基于生物学的诊断技术来识别精神分裂症患者。基于定量生物学数据将个体分为精神分裂症患者和健康对照组对于支持和完善精神病学诊断非常有意义。我们对在一项听觉奇怪任务中获得的 70 名受试者的功能磁共振成像激活图的独立成分分析 (ICA) 获得的各个成分应用了一种新颖的投影寻踪技术。该技术的有效性通过留一法进行了测试，检测性能在 80% 到 90% 之间变化。研究结果表明，所提出的数据缩减算法可以有效地将个体分为精神分裂症和健康对照组，并最终可能被证明作为一种有用的诊断工具。 (c) 2007 Elsevier Inc. 保留所有权利。
Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently, no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool. (c) 2007 Elsevier Inc. All rights reserved.