• Sofiane Mrah, Maxime Descoteaux, Michel Wager, Arnaud Boré, François Rheault, Bertrand Thirion, and Emmanuel Mandonnet.
• 1Department of Neurosurgery, Hôpital Lariboisière, AP-HP, Paris, France.
• J. Neurosurg. 2022 Nov 1; 137 (5): 132913371329-1337.

ObjectiveThe aim of this study was to predict set-shifting deterioration after resection of low-grade glioma.MethodsThe authors retrospectively analyzed a bicentric series of 102 patients who underwent surgery for low-grade glioma. The difference between the completion times of the Trail Making Test parts B and A (TMT B-A) was evaluated preoperatively and 3-4 months after surgery. High dimensionality of the information related to the surgical cavity topography was reduced to a small set of predictors in four different ways: 1) overlap between surgical cavity and each of the 122 cortical parcels composing Yeo's 17-network parcellation of the brain; 2) Tractotron: disconnection by the cavity of the major white matter bundles; 3) overlap between the surgical cavity and each of Yeo's networks; and 4) disconets: signature of structural disconnection by the cavity of each of Yeo's networks. A random forest algorithm was implemented to predict the postoperative change in the TMT B-A z-score.ResultsThe last two network-based approaches yielded significant accuracies in left-out subjects (area under the receiver operating characteristic curve [AUC] approximately equal to 0.8, p approximately equal to 0.001) and outperformed the two alternatives. In single tree hierarchical models, the degree of damage to Yeo corticocortical network 12 (CC 12) was a critical node: patients with damage to CC 12 higher than 7.5% (cortical overlap) or 7.2% (disconets) had much higher risk to deteriorate, establishing for the first time a causal link between damage to this network and impaired set-shifting.ConclusionsThe authors' results give strong support to the idea that network-level approaches are a powerful way to address the lesion-symptom mapping problem, enabling machine learning-powered individual outcome predictions.

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