School of Science and Technology, University of Camerino, Camerino, Italy; School of Mathematics Science, Liaocheng Univerisity, Liaocheng, China.
School of Mathematics Science, Liaocheng Univerisity, Liaocheng, China; School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
Comput Methods Programs Biomed. 2022 Oct;225:107082. doi: 10.1016/j.cmpb.2022.107082. Epub 2022 Aug 23.
Functional brain graph (FBG), by describing the interactions between different brain regions, provides an effective representation of fMRI data for identifying mild cognitive impairment (MCI), an early stage of Alzheimer's Disease (AD). Prior to the identification task, selecting features from the estimated FBG is a necessary step for reducing computational cost, alleviating the risk of overfitting, and finding potential biomarkers of brain diseases. In practice, either node-based features (e.g., local clustering coefficients) or edge-based features (e.g., adjacency weights) are generally considered in current studies. Despite their popularity, these schemes can only capture one granularity (node or edge) of information in the FBG, which might be insufficient for the classification task and the interpretation of the classification result.
To address this issue, in this paper, we propose to jointly select nodes and edges from the estimated FBGs. Specifically, we first assign the edges to different node groups. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and edges in the groups towards a better classification performance. Such a technique enables us to simultaneously locate discriminative brain regions, as well as connections between these brain regions, making the classification results more interpretable.
Experimental results show that the proposed method achieves better classification performance than state-of-the-art methods. Moreover, by exploring brain network "features" that contributed most to MCI identification, we discover potential biomarkers for MCI diagnosis.
A novel method for jointly selecting nodes and edges from the estimated functional brain graphs (FBGs) is proposed.
功能脑图（FBG）通过描述不同脑区之间的相互作用，为识别轻度认知障碍（MCI）——阿尔茨海默病（AD）的早期阶段——提供了 fMRI 数据的有效表示。在识别任务之前，从估计的 FBG 中选择特征是减少计算成本、缓解过拟合风险和寻找脑疾病潜在生物标志物的必要步骤。在实践中，当前的研究通常考虑基于节点的特征（例如，局部聚类系数）或基于边的特征（例如，邻接权重）。尽管这些方案很受欢迎，但它们只能捕获 FBG 中的一个粒度（节点或边）的信息，这对于分类任务和分类结果的解释可能是不够的。
为了解决这个问题，在本文中，我们提出了从估计的 FBG 中联合选择节点和边的方法。具体来说，我们首先将边分配到不同的节点组。然后，稀疏组最小绝对收缩和选择算子（sgLASSO）用于选择组（节点）和组内的边，以获得更好的分类性能。这种技术使我们能够同时定位有区别的脑区以及这些脑区之间的连接，从而使分类结果更具可解释性。
实验结果表明，所提出的方法比最先进的方法具有更好的分类性能。此外，通过探索对 MCI 识别贡献最大的脑网络“特征”，我们发现了 MCI 诊断的潜在生物标志物。
提出了一种从估计的功能脑图（FBG）中联合选择节点和边的新方法。
