Brain Connectivity and Laplacian Transformation in Profoundly Deaf Subjects
Bibliographic record
Abstract
Vibrotactile discrimination represents a viable alternative to training patients with profound bilateral deafness in language acquisition. However, more information is needed about the impact of vibrotactile discrimination training (VTDT) on the temporal dynamics of neural substrates involved, probably due to several limitations of traditional analysis methods while evaluating the brain's electrical activity. In the present study, a methodology based on the EEG analysis was implemented to localize brain electrical changes at a cortical level by using Laplacian operators to build spatiotemporal reconstructions in a realistic-geometry head model to compare the effect of VTDT in ten prelingual profoundly deaf and ten normal-hearing control subjects, while performing a vibrotactile discrimination task of pure tones. The VTDT prompted distinctive activation of specific temporoparietal zones in deaf individuals, concordant with previous studies on the perceptual integration of auditory-vibrotactile stimuli as a probable effect of a cross-modal interaction in auditory cortices. In brief, the results confirm the methodology's usefulness, which could be used further to study the neurofunctional dynamic underlying vibrotactile discrimination learning.
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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".