Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks
作者： S. M. Hadi Hosseini , Shelli R. Kesler
DOI: 10.1371/JOURNAL.PONE.0067354
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摘要: In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between regions to infer large-scale correlation networks. Recent evidence suggests that networks constructed this manner are inherently more clustered than random the same size and degree. Thus, null by randomizing topology not good choice for benchmarking small-world parameters these present report, we investigated influence on gray matter healthy individuals survivors acute lymphoblastic leukemia. Three types were studied: 1) randomization (TOP), 2) matched distributional properties observed covariance matrix (HQS), 3) generated from randomized input data (COR). The results revealed network only influences estimated parameters, it also between-group differences parameters. addition, at higher densities, direction group measures. Our suggest is quite crucial interpretation We argue none available models perfect estimation relative strengths weaknesses selected model should be carefully considered with respect obtained
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