Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer’s disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.g., strong associations) between brain regions in subject groups. In this work, we propose a distribution-guided network thresholding learning (DNTL) method for FC network analysis in brain disorder identification with rs-fMRI. Specifically, for each connection of a pair of brain regions, we propose to determine its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNTL can adaptively yield an FC-specific threshold for each connection in an FC network, thus preserving diversity of temporal correlation among different brain regions. Experiment results on $ 365$ subjects from two datasets (i.e., ADNI and ADHD-200) suggest that the DNT method outperforms state-of-the-art methods in brain disorder identification with rs-fMRI data.

中文翻译：

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基于 fMRI 的脑疾病识别中功能连接分析的分布引导网络阈值

源自静息状态功能磁共振成像 (rs-fMRI) 的功能连接 (FC) 网络已广泛用于脑部疾病的自动识别，例如阿尔茨海默病 (AD) 和注意力缺陷多动障碍 (ADHD)。为了生成 FC 网络的紧凑表示，已经为 FC 网络分析设计了各种阈值方法。然而，这些研究通常使用预定义的阈值或连接百分比来阈值整个 FC 网络，因此忽略了受试者组中大脑区域之间时间相关性（例如，强关联）的多样性。在这项工作中，我们提出了一种分布引导的网络阈值学习 (DNTL) 方法，用于使用 rs-fMRI 进行脑疾病识别中的 FC 网络分析。具体来说，对于一对大脑区域的每个连接，我们建议根据受试者组（例如，患者和正常对照）之间的连接强度分布（即时间相关性）来确定其特定阈值。所提出的 DNTL 可以为 FC 网络中的每个连接自适应地产生一个特定于 FC 的阈值，从而保持不同大脑区域之间时间相关性的多样性。实验结果365 美元来自两个数据集（即 ADNI 和 ADHD-200）的受试者表明，DNT 方法在使用 rs-fMRI 数据进行脑部疾病识别方面优于最先进的方法。