Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.

Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen
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引用次数: 11

Abstract

Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel "multi-frequency HON construction" method. Specifically, we construct not only multiple frequency-specific HONs (intra-spectrum HONs), but also a series of cross-frequency interaction-based HONs (inter-spectrum HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer's disease.

Abstract Image

构建多频高阶功能连接网络诊断轻度认知障碍。
基于静息状态功能磁共振成像(rs-fMRI)估计的人脑功能连接(FC)网络已成为基于成像的脑疾病诊断的一种有前途的工具。传统的低阶FC网络(LON)通常表征任意一对脑区之间的rs-fMRI信号的两两时间相关性。同时,高阶FC网络(HON)提供了另一种脑网络建模策略,表征了涉及多个脑区的低阶FC子网络之间更复杂的相互作用。然而,LON和HON通常都是在固定且相对较宽的频带内构建的,可能无法捕捉到病理性攻击引起的(敏感的)频率特异性FC变化。为了解决这一问题,我们提出了一种新的“多频HON构建”方法。具体而言,我们不仅构建了多个特定频率的HONs(频谱内HONs),而且基于在不同频段构建的低阶FC子网络构建了一系列基于交叉频率交互的HONs(频谱间HONs)。将这两种类型的hon与频率特异性lon一起用于基于复杂网络分析的特征提取,然后进行基于稀疏回归的特征选择,并使用支持向量机对轻度认知障碍(MCI)患者和正常衰老受试者进行分类。与以往的方法相比,我们提出的方法在阿尔茨海默病的早期诊断中达到了最好的诊断准确率。
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