GLIOMA-06 THE GLIOMA CONNECTOME PROJECT – UPDATE

Abstract

Abstract The ability to define specific fiber tracts in individual patients before, during and following surgery represents a major advance in neurosurgical oncology. The Human Connectome Project (Glasser, 2016) generated insights into the organization of critically important “hubs” and pathways (edges) defining 7 major networks. There is a paradigm shift in our view of neurological functions from an outdated “modular” concept to a network perspective (Fox, 2018). Connectomics affects all branches of clinical neuroscience, including neuropsychiatry, stroke, trauma, neurodegenerative diseases, and especially glioblastoma. We first developed individualized maps of white matter (WM) pathways for a connectivity-based paradigm for neurosurgical planning (Tunç, 2016). We reviewed the principles and the potential applications of connectomics in the neurosurgical treatment of gliomas (Henderson, 2020). Working with key thought leaders, experienced neurosurgeons, and scientific partners (Omniscient Neurotechnology; Michael Sughrue), we developed the Glioma Connectome Project protocol, accruing the first patients at the University of Pennsylvania (UPCC#22321), funded by the StacheStrong, and now accruing patients nationwide: U. Miami (PI, Michael Ivan), Northwestern (PI, Matthew Tate), U. of Nebraska (Michele Aizenberg), Henry Ford Hospital (Ian Lee), University of Texas (Yoshua Esquenazi), and U. Pittsburgh (Costas Hadjipanayis). The specific aims of the GCP include 1) validation of connectomic metrics (global efficiency, FWHM, betweenness centrality, etc.); 2) cognitive correlates using the NIH Toolbox; 3) mapping the trajectory of recovery; 4) the effects of treatment at the level of WM fiber tracts; 5) re-imagining surgical approaches and 6) developing biomarkers for “virtual trials” to assess response to therapy. In the past 2.5 years since the inception of the GCP, we have overcome numerous technical and organizational challenges to develop a federated system to harmonize “big data”. The scientific and practical lessons learned will be highlighted.