Naming impairments evoked by focal cortical electrical stimulation in the ventral temporal cortex correlate with increased functional connectivity
Bibliographic record
Abstract
BACKGROUND: High-frequency cortical electrical stimulations (HF-CES) are the gold standard for presurgical functional mapping. In the dominant ventral temporal cortex (VTC) HF-CES can elicit transient naming impairment (eloquent sites), defining a basal temporal language area (BTLA). OBJECTIVE: Whether naming impairments induced by HF-CES within the VTC are related to a specific pattern of connectivity of the BTLA within the temporal lobe remains unknown. We addressed this issue by comparing the connectivity of eloquent and non-eloquent sites from the VTC using cortico-cortical evoked potentials (CCEP). METHODS: Low frequency cortical electrical stimulations (LF-CES) were used to evoke CCEP in nine individual brains explored with Stereo-Electroencephalography. We compared the connectivity of eloquent versus non eloquent sites within the VTC using Pearson's correlation matrix. RESULTS: Overall, within the VTC, eloquent sites were associated with increased functional connectivity compared to non-eloquent sites. Among the VTC structures, this pattern holds true for the inferior temporal gyrus and the parahippocampal gyrus while the fusiform gyrus specifically showed a high connectivity in both non eloquent and eloquent sites. CONCLUSIONS: Our findings suggest that the cognitive effects of focal HF-CES are related to the functional connectivity properties of the stimulated sites, and therefore to the disturbance of a wide cortical network. They further suggest that functional specialization of a cortical region emerges from its specific pattern of functional connectivity. Cortical electrical stimulation functional mapping protocols including LF coupled to HF-CES could provide valuable data characterizing both local and distant functional architecture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".