NIMG-57. EMBEDDED GRAPH DERIVED FROM PSEUDO-RESTING-STATE FUNCTIONAL MRI CAN PREDICT COGNITIVE IMPAIRMENT IN GLIOMA

Abstract Resting-state brain network analyses are of great interest in studying the neurocognition and measuring the functional connectivity (FC) patterns of glioma patients. However, resting-state functional MRI (rs-fMRI) is not assessed in clinical routine due to the scan time and cost. The current study used pseudo-resting-state functional MRI (pseudo-rs-fMRI) derived from dynamic susceptibility contrast (DSC) perfusion MRI to predict cognitive impairment in glioma. 37 glioma patients were enrolled consecutively in the current study with DSC perfusion MRI acquired and neurocognition assessed, where 24 glioma patients also got rs-fMRI collected. The pseudo-rs-fMRI was created by voxel-wise subtracting the Gamma-variate modeled signal of contrast agent bolus from the original DSC perfusion signal. Following the pre-processing of pseudo-rs-fMRI and full rs-fMRI, the functional connectivity network (FCN) was established for each patient. Graph embedding was implemented to learn and extract features of each node in the binarized FCN, and the decision tree classification algorithm was applied to distinguish cognitive impairment. FCNs of 24 patients with DSC perfusion and rs-fMRI acquired were used to train the model, and FCNs of 13 patients with only DSC perfusion MRI acquired were used to test the model. The AUC (area under the receiver operating characteristic curve) was used to evaluate the model’s performance. The similarity and mean-square-difference between average FCNs extracted from rs-fMRI and pseudo-rs-fMRI were 0.6769 and 0.0263, respectively. Classifier trained using 24 FCNs of pseudo-rs-fMRI has an AUC of 0.750 in identifying cognitively impaired patients in the testing cohort. Combining FCNs of both rs-fMRI and pseudo-rs-fMRI to train the classifier resulted in a higher AUC of 0.833 in the testing cohort. To summary, DSC perfusion MRI-derived pseudo-rs-fMRI can be used to predict cognitive impairment in patients with gliomas. External validation would be required.