Toward data-driven surgical planning: multimodal mapping correlates of aphasic surgical deficits
Bibliographic record
Abstract
OBJECTIVE: For patients with language-eloquent brain tumors, surgeons must maximize the extent of resection while minimizing neurological morbidity. Despite the extensive use of noninvasive brain mapping techniques, it remains unclear how to preoperatively localize brain regions essential for sustaining language function in a data-driven manner. Here the authors investigate the clinical utility and functional relevance of regions mapped by functional MRI (fMRI), transcranial magnetic stimulation (TMS), and diffusion tensor imaging (DTI). METHODS: This study included 71 consecutive patients undergoing resection of language-eloquent gliomas from July 1, 2017, to August 1, 2023. A nonlinear coregistration algorithm was used to overlay the postoperative resection cavity onto the preoperative MRI. The resection of preoperative cortical and subcortical localizations was correlated with long-term linguistic outcomes. Fiber tracts were localized by seeding DTI tractography with different cortical features: TMS points (TMS tracts), the fMRI blood oxygen level-dependent (BOLD) signal (fMRI tracts), and a peritumoral 1-cm grid (grid tracts). Multiple fractional anisotropy (FA) thresholds were used for each cortical seed structure. Fiber tracts were normalized to Montreal Neurological Institute space to identify group-level characteristics. RESULTS: The majority of patients were male (n = 42, 59%) and younger than 60 years of age (n = 46, 65%). Forty-one patients (58%) had high-grade gliomas, while 30 patients (42%) had low-grade gliomas. The authors found that the resection of preoperatively identified cortical language regions does not predict long-term aphasic decline. However, the resection of tracts localized by the TMS points and grid at the 75% normalized FA threshold significantly predicted language outcomes (OR 51, p < 0.001 and OR 5.0, p = 0.04, respectively). Tracts localized by the fMRI BOLD signal did not predict language outcomes at any FA threshold (OR 0.29-5.32, p = 0.42-0.94). Tracts localized by all three cortical seed structures recapitulated normative patterns of subcortical connectivity by colocalizing with and sharing group-level features with language-associated normative tracts derived from the Human Connectome Project. Functional tracts that predicted linguistic outcomes (TMS and grid tracts) demonstrated shared features at a group-level distinct from the nonpredictive fMRI tracts. CONCLUSIONS: The resection of cortical language regions mapped preoperatively by fMRI and TMS did not predict aphasic surgical deficits. In contrast, resecting white matter tracts localized by cortical TMS points and a peritumoral cortical grid significantly predicted aphasic decline, while resecting tracts localized by the fMRI BOLD signal as seeds did not predict linguistic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".