Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional functional connectivity network is essentially a low-order network with the assumption that the brain activity is static over the entire scanning period, ignoring temporal variations among the correlations derived from brain region pairs. To overcome this limitation, we proposed a new type of sparse functional connectivity network to precisely describe the relationship of temporal correlations among brain regions. Specifically, instead of using the simple pairwise Pearson’s correlation coefficient as connectivity, we first estimate the temporal low-order functional connectivity for each region pair based on an ULS Group constrained-UOLS regression algorithm, where a combination of ultra-least squares (ULS) criterion with a Group constrained topology structure detection algorithm is applied to detect the topology of functional connectivity networks, aided by an Ultra-Orthogonal Least Squares (UOLS) algorithm to estimate connectivity strength. Compared to the classical least squares criterion which only measures the discrepancy between the observed signals and the model prediction function, the ULS criterion takes into consideration the discrepancy between the weak derivatives of the observed signals and the model prediction function and thus avoids the overfitting problem. By using a similar approach, we then estimate the high-order functional connectivity from the low-order connectivity to characterize signal flows among the brain regions. We finally fuse the low-order and the high-order networks using two decision trees for MCI classification. Experimental results demonstrate the effectiveness of the proposed method on MCI classification.

中文翻译：

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融合ULS组约束的高阶和低阶稀疏功能连接网络以进行MCI分类。

从静止状态fMRI数据得出的功能连接网络已被用作识别健康老人的轻度认知障碍（MCI）的有效生物标志物。但是，传统的功能连接网络本质上是一个低阶网络，其前提是大脑活动在整个扫描期间都是静态的，而忽略了从大脑区域对得出的相关性之间的时间变化。为了克服此限制，我们提出了一种新型的稀疏功能连接网络，以精确描述大脑区域之间时间相关性的关系。具体而言，与其使用简单的成对皮尔逊相关系数作为连通性，不如说是 我们首先根据ULS组约束UOLS回归算法估算每个区域对的时间低阶功能连通性，其中将超最小二乘（ULS）准则与组约束拓扑结构检测算法结合使用以检测功能连接网络的拓扑结构，借助超正交最小二乘（UOLS）算法来估计连接强度。与仅测量观测信号和模型预测函数之间差异的经典最小二乘标准相比，ULS标准考虑了观测信号的弱导数与模型预测函数之间的差异，从而避免了过拟合问题。通过使用类似的方法，然后，我们从低阶连通性估计高阶功能连通性，以表征大脑区域之间的信号流。最后，我们使用两个决策树对MCI分类融合了低阶和高阶网络。实验结果证明了该方法对MCI分类的有效性。