Abstract #0143

A Graph Theory based Pipeline for Intraoperative Resting-State fMRI Analysis

Nicolas Weilguny^1,2,3, Roxane Licandro¹, Matthias Tomschik³, Gregor Kasprian⁴, Karl Rössler^3,5, Christian Dorfer³, and Gilbert Hangel^2,3,5,6

¹Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ³Department of Neurosurgery, Medical University of Vienna, Vienna, Austria, ⁴Department of Radiodiagnostics, Medical University of Vienna, Vienna, Austria, ⁵Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria, ⁶Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

Synopsis

Keywords: Epilepsy, Analysis/Processing, Intraoperative, Graph Theory, Surgery, resting-state fMRI

Motivation: Immediate changes in functional connectivity during brain surgery in epilepsy patients are worth investigating in order to improve surgical planning and prediction of surgical outcomes.

Goal(s): We want to investigate changes in brain functionality during surgery using resting-state fMRI.

Approach: We correlate the mean time series of cortical ROIs to compare functional connectivity differences between pre- and post-surgery scans for each patient and calculate global network measures for group comparisons.

Results: Our approach enables visualization of functional connectivity before and after surgery, providing a valuable tool for investigating functional changes during the procedure.

Impact: We demonstrated the feasibility of pre- and post-surgery data preprocessing, which enables intraoperative fMRI analysis using graph theory, allowing investigation of functional changes during brain surgery. This approach may lead to future improvements in neurosurgical outcomes.

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