Using Static and Dynamic connectivity analyses of Eloquent brain tissue to assess surgical risk in patients with intractable seizures

Clinical Center

Investigators

Abstract

We have completed the review of 41 healthy individuals who were recruited as part of the epilepsy seizure protocol and completed the same analyses on a larger cohort on 150 healthy volunteers using the Human Connectome Project's publicly available datasets. We have now completed statistical analyses of the static and dynamic connectivity for both task related and resting state auditory language related networks for 41 healthy control subjects, recruited via NINDS epilepsy protocol, controlled for the level difficulty and who are matched for educational level and age. We have also confirmed our preliminary findings using the static and dynamic connectivity network analyses for 150 healthy adults from the HCP database. We have found: 1. We also found gender and age interaction effects such that gender effects were more pronounced in younger subjects but that age-related differences over take gender-related differences as subjects age, such older healthy adults do not show gender related differences. We interpret these results to indicate that age is the driving factor in the performance of language related brain networks over time. These results were successfully presented at the Organization of Human Brain Mapping Conference in June 2022. We are currently writing up the manuscript describing these findings. 2. We also found possible possible racial differences in the correlation strengths of language mediated networks that were not correlated to number of years of education nor was it predictive of their performance on behavioral tasks. However, we think that more research is needed, using larger more diversified cohorts, to assess the validity of these findings. This abstract was successfully submitted and accepted for presentation at the Society for Neuroscience meeting in Nov 2022. Future work: We are completing the analyses of a larger more racially diversified cohort of healthy adults subjects, controlled for years of education, gender to evaluate this potential finding. Significance: We think that if these broad variables are confirmed in larger cohort analyses, they may need to be included computational modelling for improved performance prediction of network performance status post surgical intervention for patients.