first of all, I'd like to thank the organizers for making it possible to present our data in these difficult times. Today I'm going to talk about our research project where we tested a novel resting state fMRI marker for disease burden in glauoma patients. The current imaging of glauoma patients includes, among other things, contrast-enhanced MRI, which gives us structural information about the tumor, and MR spectroscopy and PET imaging, which give us metabolic information about the tumor. It's a common feature of all these imaging modalities that they provide information only about the visible lesion, but they neglect the non-lesional brain. Furthermore, functional MRI is not routinely used to characterize the tumor. This led us to our research question. As you all know, glauoma is a systemic disease of the brain. One of the tall marks is widespread dissemination of tumor cells throughout the brain. These cells have been shown to form functional interconnected networks. We were wondering if these cells, which infiltrate the whole brain, if they interfere with global functional connectivity in the brain. This in turn led us to the question whether functional MRI could be used to quantify the disease burden caused by these glauoma cells in the whole brain. How can functional connectivity be determined using functional MRI? Resting state functional MRI records intrinsic fluctuations of oxygenated blood using the so-called blood oxygenation level dependent signal, or the BOLD signal. The presence of oxygenated blood is viewed as a marker for neuronal activation. During analysis of functional MRI data, the brain is divided into voxels and the connection strength between two voxels is determined by looking at the spatial and temporal correlation of the time course of the BOLD signal in these voxels. Hello everyone, my name is Veit Stockmann from the Department of Neurosurgery at the University of Munich. First of all, I would like to thank the organizers for making it possible to present our data in these difficult times. Today, I'm going to talk about our research project where we tested a novel resting state fMRI marker for disease burden in glauoma patients. The current imaging of glauoma patients includes, among other things, contrast-enhanced MRI, which gives us structural information about the tumor, and MR spectroscopy and PET imaging, which give us metabolic information about the tumor. It's a common feature of all these imaging modalities that they provide information only about the visible lesion, but they neglect the non-lesional brain. How can this signal then be used to build a standardized measure for abnormality of functional connectivity in glauoma patients? We looked at the connection strength of each patient voxel and compared it to a reference correlation matrix obtained from 1,000 healthy controls. The voxels, which had abnormal connectivity, were then counted and normalized to the number of voxels in their respective hemisphere. This resulted in an individualized measure for abnormality of functional connectivity in every patient, which we called the abnormality index. This is a real quick look at how we did the MRI scanning, the data processing, and the neurocognitive testing. The MRI scans were obtained on a Siemens Skyra 3-Tesla MRI machine. Three bold runs of six minutes length each were obtained. Standardized preprocessing of the raw functional and anatomical data was performed, and algorithms for calculating the abnormality index were coded in MATLAB. The abnormality index was calculated for a lesional and a non-lesional hemisphere separately. We also performed neuropsychological testing using the MOCA score, and a subgroup of patients also had PET scans. Individualized connectome maps were prepared by fusing anatomical and functional MRI data. These were the patients included in our study. 34 patients with the de novo glioma were included. 13 of these patients had grade 2 tumors, 6 had grade 3, and 15 had grade 4 tumors. 14 of those patients were found to harbor IDH mutations. These are our results. These are individualized connectome maps created by applying the abnormality index. On the left hand, you see some information about the abnormality index. On the left hand, you see some examples of patients with high grade tumors. As you can see, there is widespread damage to functional connectivity throughout the whole brain, even in small tumors, as shown here in the bottom row. In contrast, in patients with low grade tumors, we saw the damage to functional connectivity largely confined to the lesional hemisphere. Even large tumors did not cause widespread connectivity damage. This suggests that our measure for abnormal connectivity, the abnormality index, mirrors tumor biology independent of the size of the tumors. We also correlated the abnormality index with WHO grade, and here we found that both the lesional, which you can see on the left hand, and the contralesional hemisphere, which you can see on the right hand, had a statistically highly significant positive correlation between WHO grade and abnormality index. Healthy controls, which are not shown in this slide, had a significantly lower abnormality index than the patient groups, even much lower than patients with grade 2 tumors. Here we correlated the abnormality index with IDH mutation status, independent of histological diagnosis. The left two columns represent the patients with IDH wild type tumors. As you can see, these patients had a significantly higher abnormality in both the lesional and the contralesional hemisphere. An example for this is given on the right here. These are two tumors of roughly the same size. The IDH wild type tumor in the top row causes widespread damage to functional connectivity in both hemispheres, whereas the mutated tumor damages functional connectivity only in the lesional hemisphere. Neurocognitive performance was negatively correlated with the abnormality index with the strongest correlation seen in the lesional hemisphere. Again, this was indicative that the abnormality index is representative of the biology of glioma patients. We also correlated the abnormality index with metabolic information obtained by PET imaging. Tracer uptake was positively correlated with the abnormality index, again suggesting that the abnormality index mirrors tumor biology. In conclusion, we found that functional information obtained by resting state fMRI can be translated into an individualized measure for disease burden, which we have called the abnormality index. The abnormality index was significantly correlated with relevant clinical parameters, namely WHO grade, IDH mutation status, neurocognitive performance, and PET trace enhancement. Our individualized connectome maps furthermore show the potential of functional MRI to gain information beyond conventional structural MRI. These findings might open up the possibility of designing treatment concepts which are tailored to the individualized disease severity and burden, the disease distribution in the patient brain, and thus possibly making existing and future treatment regimens more effective. I would like to thank you all very much for listening and please email me with any questions you might have. Also, if you'd like to read up a little bit more about our data, a full paper was just published in Neuro-Oncology. Thank you again and

resting state fMRI

disease burden

glaucoma patients

functional MRI

abnormality index