Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

作者：Liyuan Lin, Zhongyu Chang, Yu Zhang, Kaizhong Xue, Yingying Xie, Luli Wei, Xin Li, Zhen Zhao, Yun Luo, Haoyang Dong, Meng Liang, Huaigui Liu, Chunshui Yu, Wen Qin, Hao Ding · 发表于：NeuroImage · 年份：2024 · DOI：10.1016/j.neuroimage.2024.120688 · 被引用次数：3 · 研究领域：Functional Brain Connectivity Studies、Radiomics and Machine Learning in Medical Imaging、Advanced Neuroimaging Techniques and Applications

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarch...