Integration of network topological and connectivity properties for neuroimaging classification.
Integration of network topological and connectivity properties for neuroimaging classification.
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DOI：
10.1109/tbme.2013.2284195
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发表时间：
2014-02
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Jie B;Zhang D;Gao W;Wang Q;Wee CY;Shen D
神经影像技术的快速进步为探索人脑的结构和功能连接提供了一种有效且无创的方法。对轻度认知障碍（MCI）和阿尔茨海默病（AD）等神经退行性疾病患者大脑连接异常的定量测量也已被广泛报道，尤其是在群体水平上。最近，机器学习技术已应用于 AD 和 MCI 的研究，即从健康对照（HC）中识别出患有 AD/MCI 的个体。然而，大多数现有方法只关注使用连接网络的单个属性，尽管可以使用多个网络属性，例如局部连接性和全局拓扑属性。在本文中，通过采用基于多内核的方法，我们提出了一种新颖的基于连接的框架来集成连接网络的多个属性，以提高分类性能。具体来说，两种不同类型的内核（即基于向量的内核和图内核）用于量化网络的两个不同但互补的属性，即局部连通性和全局拓扑属性。然后，采用多核学习（MKL）技术融合这些异构核进行神经影像分类。我们在两个不同的数据集上测试了我们提出的方法的性能。首先，我们在 12 个 MCI 和 25 个 HC 受试者的功能连接网络上进行测试。结果表明，与仅使用一种网络属性的方法相比，我们的方法取得了显着的性能改进。具体来说，我们的方法实现了 91.9% 的分类准确率，比基于单一网络属性的方法提高了 10.8%。然后，我们在大量功能连接网络上测试了我们的性别分类方法，对 133 名婴儿在出生、1 岁和 2 岁时进行了扫描，结果也显示出非常有希望的结果。
Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results.