• Michael Hart, Stephen J Price, and John Suckling.
• Neurosurgery. 2015 Aug 1;62 Suppl 1:211.

IntroductionBrain mapping has undergone a paradigm shift from functional localization to focusing on complex network connectivity. Central to this has been the search for the connectome or the brain's wiring diagram. Modeling the effects of focal lesions using graph theory allows consideration of how important a region is to network function and the effects of its removal. Our aim is to determine the feasibility of applying connectomics to neurosurgery and determine the key topological characteristics of patients with real lesion.MethodsResting-state functional MRI at 3 T was performed with multi echo-independent component analysis preoperatively on 5 patients with glioblastoma in the right temporo-parieto-occipital region. Complex networks analysis was performed by parcellating the brain into an anatomically based 116 region template followed by wavelet-based decomposition of time series into correlation matrices that were subsequently thresholded and binarized into individual adjacency matrices.ResultsOur data set exhibited the key features of complex networks found in healthy controls including ubiquitous small world features of simultaneous network segregation and integration. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness and a core of highly connected and integrated hubs with disproportionate vulnerability. Real lesions produced both local and distant effects in terms of reduced connectivity, network fragmentation, as well as alterations to the topological core structure of hubs and robustness.ConclusionOur refined analysis pipeline confirms the feasibility of performing complex network analysis with graph theory in patients with real lesions and is a novel approach to preoperative brain mapping. Potential discrepancies between the effects of real and simulated lesions may allow identification of mechanisms behind network plasticity. Preoperative mapping of network hubs and robustness is a novel approach for understanding the mechanisms of how higher cognitive processes are affected by and recover from real lesions.

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