Preoperative Brain Mapping Predicts Language Outcomes After Eloquent Tumor Resection

Abstract

When operating on gliomas near critical language regions, surgeons risk either leaving residual tumor or inducing permanent postoperative language deficits (PLDs). Despite the advent of intraoperative mapping techniques, subjective judgments frequently determine important surgical decisions. We aim to inform data-driven surgery by constructing a non-invasive mapping approach that quantitatively predicts the impact of individual surgical decisions on long-term language function. This study included 79 consecutive patients undergoing resection of language-eloquent gliomas. Patients underwent preoperative navigated transcranial magnetic stimulation (TMS) language mapping to identify language-positive sites (“TMS points”) and their associated white matter tracts (“TMS tracts”) as well as formal language evaluations pre-and postoperatively. The resection of regions identified by preoperative mapping was correlated with permanent postoperative language deficits (PLDs). Resected tract segments (RTS) were normalized to MNI space for comparison with normative data. The resection of TMS points did not predict PLDs. However, a TMS point subgroup defined by white matter connectivity significantly predicted PLDs (OR = 8.74, p < 0.01) and demonstrated a canonical distribution of cortical language sites at a group level. TMS tracts recapitulated normative patterns of white matter connectivity defined by the Human Connectome Project. Subcortical resection of TMS tracts predicted PLDs independently of cortical resection (OR = 60, p < 0.001). In patients with PLDs, RTS showed significantly stronger co-localization with normative language-associated tracts compared to RTS in patients without PLDs (p < 0.05). Resecting patient-specific co-localizations between TMS tracts and normative tracts in native space predicted PLDs with an accuracy of 94% (OR = 134, p < 0.001). Prospective application of this data in a patient with glioblastoma precisely predicted the results of intraoperative language mapping with direct subcortical stimulation. Long-term postoperative language deficits result from resecting patient-specific white matter segments. We integrate these findings into a personalized tool that uses TMS language mappings, diffusion tractography, and population-level connectivity to preoperatively predict the long-term linguistic impact of individual surgical decisions.