(G-243) Functional Connectivity of Lesional and Perilesional Tissue Before and After Resection of Brain Gliomas

Friday, October 13, 2023

3:30 PM – 4:30 PM PDT

Location: Exhibit Hall - Row G - Poster # 243

Presenting Author(s)

DM

Derek Madden

Research Assistant
Stephenson School of Biomedical Engineering, University of Oklahoma, United States

Co-Author(s)

TS

Tressie Stephens

Physical Therapist
The University of Oklahoma Health Sciences Center Department of Neurosurgery, United States

Primary Investigator(s)

ID

Ian Dunn

Neurosurgeon
The University of Oklahoma Health Sciences Center Department of Neurosurgery, United States

Last Author(s)

HY

Han Yuan

Associate Professor
Stephenson School of Biomedical Engineering/Institute for Biomedical Engineering, Science, and Technology, The University of Oklahoma, United States

Introduction::

Despite significant efforts to combat the disease, malignant gliomas have remained one of the deadliest forms of cancer. With a five-year survival rate of 5% and median overall survival of 12 to 16 months, glioblastoma has proven incredibly difficult to treat (Delgado-López & Corrales-García, 2016). Currently, surgical resection followed by chemo-radiation is the gold standard of treatment with the intention of surgically removing the glioma from the brain. One of the most influential variables regarding effectiveness of surgical resection is the extent of resection (EOR) – or the percentage of the tumor that is resected. A greater EOR is heavily associated with increased survival, but increased resection can also result in neurological morbidity (Rahman et al., 2017). In efforts to prevent this morbidity, techniques have been developed to map the brain and its function. However, the majority of these techniques are intraoperative methods that do not allow for preoperative planning (Sanai & Berger, 2018). To combat this dilemma, this study uses functional magnetic resonance imaging (fMRI) to investigate functional connectivity in and around brain gliomas both before and after surgical resection. With information on the changing functional connectivity around brain gliomas and subsequent outcomes of treatment, models will be developed for prediction of surgical outcomes that can be used in presurgical planning.

Materials and Methods::

MRI and fMRI images were obtained with a GE Signa 3T Architect scanner at the OU Medical Center Campus before surgery and again four weeks after surgical resection. Preprocessing of fMRI/MRI data was undertaken using Analysis of Functional NeuroImages (AFNI) software following prior published protocol (Yuan et al., 2014). Using high resolution anatomical images, the area comprising the glioma or resected area was defined using 3DSlicer. The area of interest was carefully traced on each slice of the volume to generate a mask encompassing the desired region. To include perilesional tissue, new masks were generated by dilating the previous masks by a value of 3 in AFNI. Further masks were generated that consisted of strictly perilesional tissue, the whole brain excluding the lesional/glioma area, and contralateral versions of all other masks.

To determine connectivity values among different regions, seeds were defined as 5 voxel-radius spheres centered at each voxel within the region of interest (ROI). The average BOLD time series of each seed was calculated and correlated with each voxel in the cavity-excluding whole-brain mask to generate connectivity values with the remainder of the brain. This process was repeated for perilesional and contralateral seeds, which allowed us to calculate connectivity differences with all remaining voxels of the brain. These differences were averaged for each seed, and then seed values were averaged to get overall connectivity differences between ROIs. Using the coordinates associated with each seed, maps were also generated displaying the magnitude of difference values.

Results, Conclusions, and Discussions:: In preliminary analysis, six subjects were used for examination. Within the six subjects, three groups were defined: major positive changes in connectivity (greater than 0.01), minor changes in connectivity (between -0.01 and 0.01), and major negative changes in connectivity (less than -0.01), based on our previous investigation of an independent pilot cohort. Four of the subjects fell into the major negative changes group, while one subject each fell into the remaining two groups. Based on prior studies examining connectivity using magnetoencephalography (Tarapore et al., 2012), it is predicted that more positive increases in connectivity will coincide with greater retention of brain function after surgical resection. This study is currently blinded to ensure unbiased tracing of resected area, so functional outcomes have not yet been compared to connectivity changes. With the complete data, the validity of our conclusion that more positive increases in connectivity relate to more positive functional outcomes will be examined alongside alternative models predicting functional outcomes of surgery.

Glioma remains a deadly disease with limited treatment options outside of surgical resection and adjuvant treatment. After resection of brain tissue, though, morbidity can occur that inhibits neurological function. Development of preoperative techniques could provide a massive step forward in mapping functional boundaries for a multitude of patients. Based on previous findings relating connectivity to surgical outcomes, in this study we have developed an fMRI-based analytic pipeline for measuring functional connectivity of lesional and contralateral regions before and after surgery as predictors of functional and survival outcomes. In this preliminary examination, large variability in functional connectivity changes was observed, ranging from large negative changes to large positive changes in connectivity after surgery. With more data, functional outcomes will be revealed to examine the validity of these longitudinal changes in prediction of surgical outcomes. Going forward, other aspects will also be included in the predictive model, including overall connectivity difference before surgery as well as comparison of connectivity differences of the lesional vs. perilesional tissue. In combination of the wide variety of variables, an accurate model is to be developed for prediction of surgical outcomes to be used in presurgical planning.

Acknowledgements (Optional): : This work was supported in part by NIH NIGMS P20GM135009, P20GM103447-24S1, NSF RII Track-4 2132182, and Undergraduate Research Opportunities Program at The University of Oklahoma

References (Optional): :

Delgado-López, P. D., & Corrales-García, E. M. (2016). Survival in glioblastoma: a review on the impact of treatment modalities. Clin Transl Oncol, 18(11), 1062-1071. https://doi.org/10.1007/s12094-016-1497-x

Rahman, M., Abbatematteo, J., De Leo, E. K., Kubilis, P. S., Vaziri, S., Bova, F., Sayour, E., Mitchell, D., & Quinones-Hinojosa, A. (2017). The effects of new or worsened postoperative neurological deficits on survival of patients with glioblastoma. J Neurosurg, 127(1), 123-131. https://doi.org/10.3171/2016.7.Jns16396

Sanai, N., & Berger, M. S. (2018). Surgical oncology for gliomas: the state of the art. Nat Rev Clin Oncol, 15(2), 112-125. https://doi.org/10.1038/nrclinonc.2017.171

Tarapore, P. E., Martino, J., Guggisberg, A. G., Owen, J., Honma, S. M., Findlay, A., Berger, M. S., Kirsch, H. E., & Nagarajan, S. S. (2012). Magnetoencephalographic imaging of resting-state functional connectivity predicts postsurgical neurological outcome in brain gliomas. Neurosurgery, 71(5), 1012-1022. https://doi.org/10.1227/NEU.0b013e31826d2b78

Yuan, H., Young, K. D., Phillips, R., Zotev, V., Misaki, M., & Bodurka, J. (2014). Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connect, 4(9), 690-701. https://doi.org/10.1089/brain.2014.0262