Planning of surgery involves the difficult balancing act between removing all areas likely to be affected by tumour infiltration and preserving function by removing as little functional tissue as possible.¹ The purpose of this work was to study the whole-brain connectivity of glioblastoma patients, paving the way for the development of an improved connectivity-based pre-surgical planning protocol. Therefore, the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox was used.²Nine patients with glioblastoma (5 left / 4 right hemisphere, all adjacent to the primary motor area, Figures 1 and 2) underwent simultaneous magnetic resonance imaging (MRI) and dynamic ¹⁸F-fluoro-ethyl-tyrosine (¹⁸F-FET) positron emission tomography (PET) scans. The control group comprised twenty-two healthy volunteers (only MRI was performed). Imaging data were acquired on a hybrid MR-PET scanner, consisting of a 3T Siemens scanner with a BrainPET insert.³ A birdcage transmit coil and an 8-element receive array were used for radiofrequency transmit and signal receive, respectively. The MRI protocol included volumetric T1-weighted (T1-w) MPRAGE (1x1x1 mm³), diffusion tensor imaging (DTI) (dir=30, b-value=800 s/mm², 2 averages, 2x2x2 mm³) and contrast enhanced volumetric T1-w MPRAGE (1x1x1 mm³) sequences. The amino acid ¹⁸F-FET was produced via nucleophilic ¹⁸F-fluorination with a specific radioactivity of more than 200 GBq/μmol.⁴ Time activity curves of FET uptake were generated and mean and maximum tumour-to-brain ratios were determined by region-of-interest (ROI) analysis. Tumour volumes in FET-PET images were calculated from images containing the integrated intensity (20–60 min) using threshold-based volume-of-interest analyses that included voxels with a tumour-to-brain ratio of at least 1.6. This cutoff was based on a biopsy-controlled study in cerebral gliomas in that a lesion-to-brain ratio of 1.6 best separated tumour from peritumour tissue.⁵ The MIBCA toolbox was used to automatically pre-process MR-PET data (including brain parcellation) and to derive imaging and connectivity metrics from the multimodal data, that is, cortical thickness from contrast enhanced T1-w data; mean diffusivity (MD), fractional anisotropy (FA), node degree, clustering coefficient and pairwise ROI fibre tracking from DTI data; and standardized uptake value (SUV) from PET data. Differences in whole-brain connectivity between patients and controls were analyzed. Mean and standard deviation (SD) values were obtained for the control data, and latter used to threshold patient data in order to constrain the results to significant differences. To do so, intervals with the mean and triple SD were obtained in order to threshold patient data (by verifying which ROIs of each metric fall off the range). Results were visualized in a connectogram and both structural connectivity and metrics were studied in regions surrounding lesions (to the extent of the present oedema), identified by increased uptake in FET-PET.In Table 1 an overview of the information obtained from the connectograms concerning the affected metrics is presented, divided by peritumour and more distant regions. All patients had regions in which one or more parameters included in the connectogram deviated by more than 3 SD from the mean value based on data from healthy volunteers. For most patients, the number of affected regions is higher in the contralateral, as compared to the ipsilateral hemisphere. In particular, patient E showed regions with more than two affected metrics only for non-peritumour regions. Connectograms with thresholded data can be found in Figures 3 and 4. Data presented in the connectograms concerning the distribution of fibres suggest an increase of connections, particularly in the lesion side, more evident in patients with the tumour in the right hemisphere (Figure 4, patients G and H). Findings concerning changes in metric values, particularly increased FA values, may be related to fibre packing caused by the tumour mass effect. Changes found in more distant areas may result from structural re-organization in response to the presence of the tumour.The use of multimodal imaging in this study allows the integration of different type of data that result in a richer characterization of brain connectivity. Reported results suggest that tumour infiltration may alter both local and more distant structural connections. Future work will focus on the inclusion of a larger patient group so as to increase robustness of these findings as well as diffusion kurtosis information to study its influence on the identification of connectivity changes produced by tumours. Ultimately, the goal of the approach taken in this study is to investigate if and how connectivity studies can help pre-surgical planning. For instance, by studying specific brain networks in order to determine the best anatomical approach to tumour resection and influence the process of decision-making during surgery, regarding the prevention of sequelae.