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 205 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 205 healthy adults from the HCP database. We have tested 4 different computational models as to their ability to predict functional connectivity changes related to cognitive mental states. We have found: 1. We have completed the analyses of the dynamic functional connectivity for a large cohort of over 200 healthy adults and found differences in the dynamic performance of neural networks in rest vs task related conditions that may be used to explain differential patient performed under pathophysiologic processes that affect the human brain. We conclude that, dynamic FC is not interchangeable even though spatial region of interests may be. We have found that differences in dynamic network FC may be important in determining patient clinical outcomes and language recovery following CNS injury. This work will be presented at the Biomedical Engineering meeting in Oct 2025 and has been accepted from presentation at the Society for Neuroscience annual conference in Nov 2025. This work has resulted in three accepted manuscripts publications. 2. We have also completed analyses of fMRI times series data that revealed the inner organization of realtime information processing using a parsimonious reorganization of neural assets. This manuscript is currently under review at Proceedings of the National Academy of Science and this work has been accepted for presentation at the Society for Neuroscience meeting in Nov 2025. 3. We have assessed the performance of 4 different types of computational models in their ability to predict spatial functional connectivity changes that occur during an auditory language task. The manuscript is currently under review and NIH has submitted a provisional patent based on this work. Under going internal NIH review. Significance: We think that machine learning based simulations can be used to better understand functional neuronal organization of the human brain and its recovery following CNS surgery and other injuries and or interventions.