Connectomic Predictors of Outcome in Centromedian RNS and DBS for Generalized Epilepsy
Abstract number :
1.469
Submission category :
9. Surgery / 9C. All Ages
Year :
2024
Submission ID :
1123
Source :
www.aesnet.org
Presentation date :
12/7/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Pranav Nanda, MD – Massachusetts General Hospital
Gabriel Gonzalez-Escamilla, PhD – Johannes Gutenberg University of Mainz
Aaron Warren, PhD – Brigham and Women's Hospital, Harvard Medical School
Clemens Neudorfer, MD – Massachusetts General Hospital
Zachary Kons, MD – Massachusetts Eye and Ear
Nathaniel Sisterson, MD – Massachusetts General Hospital
Andreas Horn, MD, PhD – Massachusetts General Hospital
Mark Richardson, MD, PhD – Massachusetts General Hospital
Rationale: Although generalized epilepsy patients have traditionally not been considered surgical candidates, thalamic responsive neurostimulation (RNS) and deep brain stimulation (DBS) of the centromedian nucleus (CM) have yielded promising results. However, specific mechanisms of action and relevant brain networks are uncertain, and it therefore remains unclear how best to target these neuromodulatory interventions. Accordingly, we aimed to identify structural connectomic predictors of outcome in CM neurostimulation for generalized epilepsy and compare predictors for RNS and DBS in order to better understand these neuromodulatory techniques and guide their implementation.
Methods: 37 patients undergoing CM neurostimulation for generalized epilepsy were aggregated, including 11 patients undergoing RNS (MGH-UPMC), 9 patients undergoing DBS (Mainz-Madrid), and 17 patients undergoing DBS (ESTEL). Patient outcomes were recorded as percent seizure frequency reduction from baseline. Volumes of tissue activation (VTAs) were generated using Lead-DBS software. Normative fiber-tracks traversing VTAs were identified using a 64K track thalamic-specific connectome generated using 32 Human Connectome Project (HCP) subjects. Fiber-track predictivity of outcome was determined using fiber-filtering methodology, with internal validation using leave-one-out methods. Fiber-track predictivity of outcome was cross-validated by testing across patient cohorts. Top predictive fiber-tracks were then characterized anatomically.
Results: Normative structural connectivity (Figure 1A) significantly predicted outcomes using leave-one-out methods within each cohort (MGH-UPMC p=0.02, Mainz-Madrid p=0.02, ESTEL p=0.04) (Figure 1B). Normative structural connectivity was significantly cross-predictive between DBS cohorts (p< 0.02), but not between DBS and RNS cohorts (Figure 1C). Top predictive fiber-tracks were significantly more likely to traverse the CM for RNS (p< 0.0001) but not DBS (Figure 2A). Top predictive fiber-tracks were also significantly more likely to overlap ascending reticular activating system (ARAS) nuclei in all groups, most significantly the pontis oralis (Figure 2B).
Conclusions: Normative structural connectivity may predict outcomes of CM RNS and CM DBS for generalized epilepsy and may represent a tool for targeting. However, disparate predictive networks are implicated for CM RNS and CM DBS, indicating the possibility of varying mechanisms of action. While the CM itself is specifically involved in the predictive network of only CM RNS, both CM RNS and CM DBS involve ARAS nuclei. These findings suggest that structural connectivity may serve as a tool to target and program for CM RNS and CM DBS, hinting at different mechanisms of action while underscoring the shared importance of arousal pathways of the ARAS.
Funding: NA
Surgery