Proposal of Brain Plasticity Index Based on Navigated Transcranial Magnetic Stimulation: Metric of Functional Displacement for Language Function
Bibliographic record
Abstract
OBJECTIVE: To identify a brain plasticity index (BPI) that accounts for the cortical variation in language function on the basis of preoperative/postoperative navigated transcranial magnetic stimulation (nTMS) mapping. METHODS: This was a prospective, monocentric study of preoperative/postoperative nTMS mapping. A Python-based open-source pipeline was created to objectively quantify BPI maps, calculated with 2-dimensional nearest-neighbor interpolation of nTMS points for the 2 hemispheres (right/left). BPI maps registered on the Montreal Neurological Institute neuro-anatomy-atlas. Tumoral segmentation was performed with the BraTS toolkit. RESULTS: ). A subanalysis of 7 high-grade (HGG) and 7 low-grade gliomas (LGG) was then performed. Quantitative analyses: Estimation of BPI values specific for Language function: for patients with HGG, BPI was greater in the left hemisphere (46.07 powered by Editorial Manager and ProduXion Manager from Aries Systems Corporation, in mm). Conversely, for patients with LGG, a greater BPI was found in the right hemisphere (57.58 mm). However, an overall greater BPI value was obtained for HGG (46.70 mm) than for LGG (28.19 mm). Qualitative analyses showed linguistic pathway reshaping from temporoparietal areas to the inferior frontal gyrus. The relationship between BPI and Mini-Mental State Examination showed that for HGG, the greater the BPI, the more difficult cognitive recovery was (R = -0.80). Coherently, for LGG the trend was the same (R = -0.17). The variation in different linguistic components at the Aachen Aphasia test documented that linguistic performance had an average greater value for patients with HGG than LGG, with comparable final recovery (90.67% vs. 90%). CONCLUSIONS: Identifying a subject-specific BPI may noninvasively define functional reshaping of corticosubcortical circuits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".