In Reply: Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation Using a New Tool for Planning Brain Resections
Bibliographic record
Abstract
To the Editor: We would like to thank the authors for their thoughtful and detailed letter,1 on our recent article,2 “Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation of a New Tool for Planning Brain Resections.” We appreciate their recognition of the potential and relevance of resting-state fMRI (rs-fMRI) in the preoperative planning of patients with gliomas. As the authors correctly pointed out, one of the key advantages of rs-fMRI lies in its applicability to patients who are not candidates for awake craniotomy or who cannot cooperate with task-based paradigms. We also agree that the individualized approach provided by rs-fMRI, as shown by the smaller distances between direct electric stimulation points and patient-specific networks compared with template-based analyses, may be a meaningful advancement in the functional mapping of eloquent areas. We particularly appreciate the authors' emphasis on the relevance of mapping networks beyond the traditional motor and language systems, such as the default mode, frontoparietal, and visuospatial networks. As highlighted in our study, and confirmed by other recent works,3-8 these networks play critical roles in neurocognitive outcomes and quality of life, and incorporating them into preoperative planning, and especially in a longitudinal follow-up over the natural history of disease, could optimize surgical strategies and postoperative recovery. Furthermore, the use of RestNeuMap-v2 has indeed expanded the possibilities for more comprehensive network mapping, and we are pleased to see the interest in its application. We fully agree with the authors that further studies are needed to validate and refine the integration of rs-fMRI into routine surgical protocols and to explore its impact across different tumor types and clinical settings. In fact, we propose ReStNeuMap as a complementary tool with respect of awake surgery with cortical and subcortical direct electrical stimulation and/or intraoperative monitoring, highlighting the advantage to have a reliable prevision for orienting mapping and surgical planning. To move in this direction and to facilitate the public use of this tool, we have made the new version of RestNeuMap available, which can be downloaded from this GitHub repository: https://github.com/CIMeC-MRI-Lab/ReStNeuMap. In conclusion, we are grateful for the supportive and constructive feedback and concur with the call for continued research into the role of rs-fMRI in neurosurgical planning. Together, these efforts can pave the way for more personalized, safer, and functionally preserving surgical interventions for patients with brain tumors.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".