fMRI classification method with multiple feature fusion based on minimum spanning tree analysis
fMRI classification method with multiple feature fusion based on minimum spanning tree analysis
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基于最小生成树分析的多特征融合fMRI分类方法
DOI：
10.1016/j.pscychresns.2018.05.001
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发表时间：
2018-07-30
影响因子：
2.3
通讯作者：
Xiang, Jie
中科院分区：
文献类型：
--
作者：
Guo, Hao;Yan, Pengpeng;Xiang, Jie
静息态脑功能网络在脑疾病研究中得到了广泛的研究。传统的网络分析方法受到网络大小、密度和归一化差异的阻碍。最小生成树（MST）分析最近已被建议，以改善这些限制。此外，常见的MST分析方法涉及计算可量化的属性并选择这些属性作为分类中的特征。然而，这些方法的一个缺点是没有充分考虑网络的拓扑信息，限制了分类性能的进一步提高。为了解决这个问题，我们提出了一种新的方法，结合大脑区域和子图特征进行分类，利用两种特征类型来量化网络的两个属性。我们使用重性抑郁症（MDD）患者数据集实验验证了我们提出的方法。结果表明，MDD患者的MST更类似于随机网络，在边缘-皮层-纹状体-苍白球-丘脑（LCSPT）回路中的某些区域表现出显著差异，而LCSPT回路被认为是抑郁症的主要病理回路。此外，我们证明了这种新的分类方法可以有效地提高分类精度，并提供更好的可解释性。总体而言，目前的研究表明，不同形式的特征表示提供了互补的信息。
Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.