Background Microscopic vascular events, such as neovascularization and neurovascular uncoupling, are common in cerebral glioma. Mapping the cerebrovascular network remodeling at the macroscopic level may provide an alternative approach to assess hemodynamic dysregulation in patients with glioma. Purpose To investigate cerebrovascular dynamics and their relevance to tumor aggressiveness by using time-shift analysis (TSA) of the systemic low-frequency oscillation (sLFO) of the resting-state blood oxygenation level-dependent signal and a decision tree model. Materials and Methods In this retrospective study, 96 patients with histologically confirmed cerebral glioma were consecutively included (March 2012 to February 2017). TSA was performed to quantify the temporal properties of sLFO signals. Alteration in the time-shift properties was assessed in the tumor region and the contralesional hemisphere relative to the brains of healthy controls by using the Mann-Whitney U test. A decision tree model based on time-shift features was developed to predict the World Health Organization (WHO) glioma grade. Results A total of 88 patients with glioma (WHO grade II, 45; grade III, 21; grade IV, 22; mean age, 42 years; age range, 20-73 years; 51 men) and 40 healthy individuals from the 1000 Functional Connectomes Project (mean age, 32 years; age range, 24-49 years; 19 men) were included. The sLFO of the brain tissues was characterized by increased time shift in the tumor region and enhanced correlation with the global reference signal in the contralesional hemisphere compared with healthy brains. The proportion of tumor voxels with negative correlation to the reference signal significantly increased with the glioma malignancy grade. The decision tree model achieved an accuracy of 91% (80 of 88 patients) in predicting the glioma malignancy grade at the individual level (P = .004) based on the time-shift features. Conclusion Gliomas induced grade-specific cerebrovascular dysregulation in the entire brain, with altered time-shift features of systemic low-frequency oscillation signals. © RSNA, 2022 Online supplemental material is available for this article.