Classification of ADHD children through multimodal magnetic resonance imaging.
Classification of ADHD children through multimodal magnetic resonance imaging.
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DOI：
10.3389/fnsys.2012.00063
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发表时间：
2012
影响因子：
3
通讯作者：
He H
中科院分区：
文献类型：
--
作者：
Dai D;Wang J;Hua J;He H
注意缺陷/多动障碍（ADHD）是学龄儿童最常见的疾病之一。迄今为止，ADHD的诊断主要是主观的，客观诊断方法的研究非常重要。尽管最近已经有很多研究试图利用大脑的结构和功能图像来进行诊断，但其中很少与多动症有关。本文介绍了一种基于ADHD患者脑成像特征的自动分类框架，详细介绍了特征提取、特征选择和分类器训练方法。使用不同特征的效果相互比较。此外，我们使用多核学习（MKL）集成多模态图像特征。我们的框架的性能已经在ADHD-200全球竞赛中得到验证，这是一个关于ADHD-200数据集的全球分类竞赛。在本次比赛中，我们使用静息状态功能连接（FC）特征的分类框架在比赛评分政策下的21个参与者中排名第6，在灵敏度和j统计量方面表现最好。
Attention deficit/hyperactivity disorder (ADHD) is one of the most common diseases in school-age children. To date, the diagnosis of ADHD is mainly subjective and studies of objective diagnostic method are of great importance. Although many efforts have been made recently to investigate the use of structural and functional brain images for the diagnosis purpose, few of them are related to ADHD. In this paper, we introduce an automatic classification framework based on brain imaging features of ADHD patients and present in detail the feature extraction, feature selection, and classifier training methods. The effects of using different features are compared against each other. In addition, we integrate multimodal image features using multi-kernel learning (MKL). The performance of our framework has been validated in the ADHD-200 Global Competition, which is a world-wide classification contest on the ADHD-200 datasets. In this competition, our classification framework using features of resting-state functional connectivity (FC) was ranked the 6th out of 21 participants under the competition scoring policy and performed the best in terms of sensitivity and J-statistic.