Motor Processing in Children With Cochlear Implants as Assessed by Functional Near-Infrared Spectroscopy
Bibliographic record
Abstract
Auditory-motor and visual-motor networks are often coupled in daily activities, such as when listening to music and dancing; but these networks are known to be highly malleable as a function of sensory input. Thus, congenital deafness may modify neural activities within the connections between the motor, auditory, and visual cortices. Here, we investigated whether the cortical responses of children with cochlear implants (CI) to a simple and repetitive motor task would differ from that of children with typical hearing (TH) and we sought to understand whether this response related to their language development. Participants were 75 school-aged children, including 50 with CI (with varying language abilities) and 25 controls with TH. We used functional near-infrared spectroscopy (fNIRS) to record cortical responses over the whole brain, as children squeezed the back triggers of a joystick that vibrated or not with the squeeze. Motor cortex activity was reflected by an increase in oxygenated hemoglobin concentration (HbO) and a decrease in deoxygenated hemoglobin concentration (HbR) in all children, irrespective of their hearing status. Unexpectedly, the visual cortex (supposedly an irrelevant region) was deactivated in this task, particularly for children with CI who had good language skills when compared to those with CI who had language delays. Presence or absence of vibrotactile feedback made no difference in cortical activation. These findings support the potential of fNIRS to examine cognitive functions related to language in children with CI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".