Intraoperative functional remapping unveils evolving patterns of cortical plasticity
Bibliographic record
Abstract
The efficiency with which the brain reorganizes following injury not only depends on the extent and the severity of the lesion, but also on its temporal features. It is established that diffuse low-grade gliomas (DLGG), brain tumours with a slow-growth rate, induce a compensatory modulation of the anatomo-functional architecture, making this kind of tumours an ideal lesion model to study the dynamics of neuroplasticity. Direct electrostimulation (DES) mapping is a well-tried procedure used during awake resection surgeries to identify and spare cortical epicentres which are critical for a range of functions. Because DLGG is a chronic disease, it inevitably relapses years after the initial surgery, and thus requires a second surgery to reduce tumour volume again. In this context, contrasting the cortical mappings obtained during two sequential neurosurgeries offers a unique opportunity to both identify and characterize the dynamic (i.e. re-evolving) patterns of cortical re-arrangements. Here, we capitalized on an unprecedented series of 101 DLGG patients who benefited from two DES-guided neurosurgeries usually spaced several years apart, resulting in a large DES dataset of 2082 cortical sites. All sites (either non-functional or associated with language, speech, motor, somatosensory and semantic processing) were recorded in Montreal Neurological Institute (MNI) space. Next, we used a multi-step approach to generate probabilistic neuroplasticity maps that reflected the dynamic rearrangements of cortical mappings from one surgery to another, both at the population and individual level. Voxel-wise neuroplasticity maps revealed regions with a relatively high potential of evolving reorganizations at the population level, including the supplementary motor area (SMA, Pmax = 0.63), the dorsolateral prefrontal cortex (dlPFC, Pmax = 0.61), the anterior ventral premotor cortex (vPMC, Pmax = 0.43) and the middle superior temporal gyrus (STG Pmax = 0.36). Parcel-wise neuroplasticity maps confirmed this potential for the dlPFC (Fisher's exact test, PFDR-corrected = 6.6 × 10-5), the anterior (PFDR-corrected = 0.0039) and the ventral precentral gyrus (PFDR-corrected = 0.0058). A series of clustering analyses revealed a topological migration of clusters, especially within the left dlPFC and STG (language sites); the left vPMC (speech arrest/dysarthria sites) and the right SMA (negative motor response sites). At the individual level, these dynamic changes were confirmed for the dlPFC (bilateral), the left vPMC and the anterior left STG (threshold free cluster enhancement, 5000 permutations, family-wise error-corrected). Taken as a whole, our results provide a critical insight into the dynamic potential of DLGG-induced continuing rearrangements of the cerebral cortex, with considerable implications for re-operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".