Objective  To explore the differences in the robustness of structural covariance networks among populations with different circadian rhythm preferences, and to constructing brain structural covariance networks based on gray matter volume and cortical thickness. Methods  Morphological metrics of gray matter from early chronotype and late chronotype volunteers was extracted by using the CAT12 toolbox. Structural covariance networks based on gray matter volume and cortical thickness were constructed according to the LPBA40 atlas and Desikan-Killiany atlas. Graph theoretical parameters were calculated and the resilience of structural covariance networks against deliberate attacks were assessed by utilizing the GAT toolbox to observe the network’s robustness. Results  In the structural covariance network constructed based on gray matter volume, when using betweenness centrality as the target of deliberate attacks and network size as the measure of network robustness, the structural covariance network of early chronotype volunteers exhibits greater robustness than that of late chronotype volunteers (P<0.05).When using node degree as the target of deliberate attacks and network size as the evaluation metric for network robustness, the structural covariance network robustness of early chronotype volunteers showed no statistically significant difference compared with the late chronotype volunteers (P>0.05).In the structural covariance network constructed based on cortical thickness, when using betweenness centrality or node degree as the targets of deliberate attacks, and network size as the evaluation metric for network robustness, the structural covariance network robustness of early chronotype volunteers was weaker than that of late chronotype volunteers (P<0.05). Conclusion  The covariance relationships among brain gray matter morphology vary among populations with different circadian rhythm preferences, and the differences in structural covariance network robustness may be one of the manifestations, which provides a new understanding of the neuroanatomical features related to circadian rhythm.