IEEE J Biomed Health Inform. 2023 Jan;27(1):215-226. doi: 10.1109/JBHI.2022.3212479. Epub 2023 Jan 4.
Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.
特征选择可以揭示生物机制尚不明确的精神障碍的生物标志物。虽然邻域粗糙集（NRS）已被应用于发现重要的稀疏特征，但由于缺乏特征评估指标和单个粒度下提供的不完整信息，它几乎从未被用于基于神经影像学的生物标志物识别中。在这里，我们提出了一种新的基于 NRS 的特征选择方法，并成功地使用功能磁共振成像（fMRI）数据识别了精神分裂症（SZ）的脑功能连接生物标志物。具体来说，我们开发了一种新的基于 NRS 的加权度量标准，结合信息熵来评估特征在区分不同组别的能力。受多粒度信息最大化理论的启发，我们还通过多粒度融合利用不同邻域大小的互补信息，以获得最具区分力和最稳定的特征。为了验证，我们使用三种公共组学数据集以及 393 名 SZ 患者和 429 名健康对照者的静息态 fMRI 数据，将我们的方法与六种流行的特征选择方法进行了比较。结果表明，我们的方法在组学数据（三种组学数据集的分类准确率分别为 100.0%、88.6%和 72.2%）和 fMRI 数据（主数据集的分类准确率为 93.9%，两个独立数据集的分类准确率分别为 76.3%和 83.8%）上均获得了更高的分类准确率。此外，我们的发现揭示了 SZ 的生物学意义上的潜在机制，特别是涉及丘脑与颞上回之间以及中央后回与距状回之间的连接。综上所述，我们提出了一种新的基于 NRS 的特征选择方法，该方法有望探索精神障碍的有效且稀疏的神经影像学生物标志物。
