Novel Application of Connectomics to the Surgical Management of Pediatric Arteriovenous Malformations

Authors:
Publisher website
Google Scholar
Abstract
Introduction: The emergence of connectomics in neurosurgery has allowed for construction of detailed maps of white matter connections, incorporating both structural and functional connectivity patterns. The advantage of mapping cerebral vascular lesions to guide surgical approach shows great potential. We aim to identify the clinical utility of connectomics for the surgical treatment of pediatric arteriovenous malformations (AVMs). Case Presentation: We present two illustrative cases of the application of connectomics to the management of cerebral AVM in a 9-year-old and 8-year-old female. Using magnetic resonance anatomic and diffusion tensor imaging, a machine learning algorithm generated patient-specific representations of the corticospinal tract for the first patient, and the optic radiations for the second patient. The default mode network and language network were also examined for each patient. The imaging output served as an adjunct to guide operative decision-making. It assisted with selection of the superior parietal lobule as the operative corridor for the first case. Furthermore, it alerted the surgeon to white matter tracts in close proximity to the AVM nidus during resection. Finally, it aided in risk versus benefit analysis regarding treatment approach, such as craniotomy for resection for the first patient versus radiosurgery for the second patient. Both patients had favorable neurologic outcomes at the available follow-up period. Conclusion: Use of the software integrated well with clinical workflow. The output was used for planning and overlaid on the intraoperative neuro-navigation system. It improved visualization of eloquent regions, especially those networks not visible on standard anatomic imaging. Future studies will focus on expanding the cohort, conducting in pre- and postoperative connectomic analysis with correlation to clinical outcome measures, and incorporating functional magnetic resonance imaging.

This publication has 23 references indexed in Scilit: