Brain activation detection by modified neighborhood one-class SVM on fMRI data
Brain activation detection by modified neighborhood one-class SVM on fMRI data
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DOI：
10.1016/j.bspc.2017.08.021
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Xiaoyan Tang;Weiming Zeng;Yuhu Shi;Le Zhao
中科院分区：
文献类型：
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作者：
Xiaoyan Tang;Weiming Zeng;Yuhu Shi;Le Zhao
单类支持向量机（OC-SVM）是一种数据驱动的机器学习方法，已被应用于大脑激活检测的新技术。一些研究人员已经获得了积极的初步结果，使用OC-SVM。然而，现有的算法要么过于复杂，要么过于简化，其性能需要进一步提高。在这项研究中，一个改进的邻域一类支持向量机（MNOC-SVM）算法，提出了检测功能磁共振成像（fMRI）数据的大脑功能激活。该方法基于两个基本假设：（a）对于任务相关的fMRI数据，只有少数体素的时间序列与特定的功能活动或功能区域相关，这些体素应被识别为激活体素，即，离群值相反，对于静息态fMRI数据，只有少量体素与任何静息态功能网络无关。这些体素应该被视为非激活体素，即，离群值(b)接近的体素具有类似的激活或非激活状态。为了提高检测精度，我们将以下特征应用于每个体素：每个体素及其26个相邻体素之间的RV系数（或者对于大脑边缘的体素，小于26个），孤立体素的标志和孤立区域的标志。对于任务相关和静息状态的fMRI数据，我们的MNOC-SVM方法有效地检测到激活的功能区在整个大脑。
The one-class support vector machine (OC-SVM) is a data-driven machine learning method that has been applied as a novel technique for brain activation detection. Several researchers have obtained positive preliminary results using OC-SVMs. Nevertheless, existing algorithms are either too complicated or oversimplified and their performance needs to be further improved. In this study, a modified neighborhood one-class support vector machine (MNOC-SVM) algorithm is proposed to detect brain functional activation on functional magnetic resonance imaging (fMRI) data. This method is based on two basic assumptions: (a) For task-related fMRI data, time series of only a few voxels are related to a particular functional activity or functional area, and these voxels should be identified as activated voxels, i.e., the outliers. In contrast, for resting-state fMRI data, only a small number of voxels are unrelated to any resting-state functional networks. These voxels should instead be taken as non-activated voxels, i.e., the outliers. (b) Close voxels have similarly activated or non-activated states. To improve detection accuracy, we apply the following features to each voxel: the RV coefficient between each voxel and its 26 neighborhood voxels (or fewer than 26 for voxels on the edge of the brain), a flag for isolated voxels and a flag for isolated areas. For both task-related and resting-state fMRI data, our MNOC-SVM method effectively detects activated functional areas in the whole brain.