Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

• Published in: Network neuroscience (Cambridge, Mass.) Mass.), 2021, Vol.5 (3), p.783-797
• Main Authors: Zhao, Kun, Zheng, Qiang, Che, Tongtong, Dyrba, Martin, Li, Qiongling, Ding, Yanhui, Zheng, Yuanjie, Liu, Yong, Li, Shuyu
• Format: Article
• Language: English
• Subjects: Biological basis; Biological properties; Biomarkers; Brain; Cognitive ability; Correlation coefficients; Covariance; Datasets; Gene expression; Magnetic resonance imaging; Medical imaging; Morphology; Network analysis; Neuroimaging; Radiomics; Regional radiomics similarity networks; Reproducibility; Similarity; Small-world; Substantia grisea; Thickness
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00200

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression ( = 0.29, < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI. Gray matter volume and cortical thickness are some of the most popular brain morphological measures of structural magnetic resonance imaging (sMRI). These patterns are important for understanding complex brain cognitive function. However, most of the studies typically analyze single/several anatomical regions independently without considering associations among brain regions. The structural covariance network (SCN) is often used to reconstruct the brain structural network from sMRI and is commonly used to measure the association between regions in the human brain with morphological similarity. However, most of the individual SCNs have been constructed by a unitary marker such as gray volume/cortical thickness with hyposensitivity. We develop a novel, reliable and biologically plausible brain network to understand human morphological covariance based on sMRI.