Sources of Variation Influencing Concordance between Functional MRI and Direct Cortical Stimulation in Brain Tumor Surgery
Bibliographic record
Abstract
Object: Preoperative functional magnetic resonance imaging (fMRI) remains a promising method to aid in the surgical management of patients diagnosed with brain tumors. For patients that are candidates for awake craniotomies, surgical decisions can potentially be improved by fMRI but this depends on the level of concordance between preoperative brain maps and the maps provided by the gold standard intraoperative method, direct cortical stimulation (DCS). There have been numerous studies of the concordance between fMRI and DCS using sensitivity and specificity measures, however the results are variable across studies and the key factors influencing variability are not well understood. Thus, the present work addresses the influence of technical factors on fMRI and DCS concordance. Methods: Motor and language mapping data were collected for a group of glioma patients (n = 14) who underwent both preoperative fMRI and intraoperative DCS in an awake craniotomy procedure for tumor removal. Normative fMRI data were also acquired in a healthy control group (n = 12). The fMRI and DCS mapping data were co-registered; true positive (TP), true negative (TN), false positive (FP) and false negative (FN) occurrences were tabulated over the exposed brain surface. Sensitivity and specificity were measured for the total group, and the motor and language sub-groups. The influence of grid placement, fMRI statistical thresholding, and task standardization were assessed. Correlations between proportions of agreement and error were carefully scrutinized to evaluate concordance more in-depth. Results: Concordance was significantly better for motor versus language mapping. There was an inverse relationship between TP and TN with increasing statistical threshold, and FP dominated the total error. Sensitivity and specificity were reduced when tasks were not standardized across fMRI and DCS. Conclusions: Although the agreement between fMRI and DCS is good, variability is introduced by technical factors that can diminish the quality of patient data. Neurosurgeons should evaluate the usefulness of fMRI data while considering that a) discordance arises primarily from FP fMRI results; b) there is an inherent trade-off between sensitivity and specificity with fMRI statistical threshold; and c) best results are achieved using batteries of tasks that are standardized across both mapping methods.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".