Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a practical approach.

OBJECTIVE: In this study, we are presenting our experience using resting state functional magnetic resonsce imaging (rs-fMRI) in preoperative planning. We performed goup analysis to demonestrate the effects of brain tumor on resting state networks (RSNs) METHODS: Thirty patients with supratentorial gliomas were included in the study. Preoperative rs-fMRI and structural MRI were performed in all cases. The rs-fMRI was preprocessed ( realignment, slice time correction, coregistration to structural images, normalization and smoothing). The structural images were segmented and normalized. Band filtering and denoising were applied to the functional images. Connectivity analysis was performed using seed based connectivity analysis (SCA) at single subject level and group level. Correlation algorism has been used with r>0.5.

RESULTS: RSNs could be detected in all patients. They showed similarity to the results of the task based fMRI, when task based fMRI was feasible. Detection of the networks was also possible in patients with neurological deficits, in whom task based fMRI was not possible. We could use SCA in patients under anesthesia. High level networks ( default mode, salience, and dorsal attention networks) were detectable but showed wide spectrum of spatial alterations and components disconnections.

CONCLUSION: Rs-fMRI is a feasible method for extended brain mapping. Diverse RSNs could be detected in patients with brain tumors and could be applied in preoperative planning. SCA was a robust and direct approach for data analysis and could answer specific clinically relevant questions. However, further studies are needed to validate the technique and its clinical impact.