High-order resting-state functional connectivity network for MCI classification.
High-order resting-state functional connectivity network for MCI classification.
复制标题
DOI：
10.1002/hbm.23240
复制
发表时间：
2016-09
影响因子：
4.8
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Chen X;Zhang H;Gao Y;Wee CY;Li G;Shen D;Alzheimer's Disease Neuroimaging Initiative
利用静息态功能磁共振成像（RS-fMRI）技术研究脑功能连接网络（FC）已成为一种有前途的神经退行性疾病诊断方法。然而，传统的FC网络本质上是低阶的，在这个意义上，只有大脑区域之间的相关性（在RS-fMRI时间序列方面）被考虑在内。从这种类型的大脑网络中获得的特征可能无法作为有效的疾病生物标志物。为了克服这一缺点，我们提出了新的高阶FC相关性的提取，其特征在于不同对大脑区域之间的低阶相关性如何相互作用。具体而言，对于每个大脑区域，首先在整个RS-fMRI时间序列上执行滑动窗口方法以生成多个短的重叠片段。对于每个片段，构建低阶FC网络，测量大脑区域之间的短期相关性。这些低阶网络（从所有部分获得）描述了短期FC沿着时间的动态，因此也形成了每对脑区的相关时间序列。为了克服维数灾难，我们进一步将相关时间序列根据其内在的共同模式分为少量不同的聚类。然后，计算不同聚类的相应平均相关时间序列之间的相关性，以表示不同脑区域对之间的高阶相关性。最后，结合低阶和高阶FC网络的特征，设计了一个模式分类器。实验结果验证了高阶FC网络在疾病诊断中的有效性。
Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis.