Impact of averaging fNIRS regional coherence data when monitoring people with long term post-concussion symptoms
Bibliographic record
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS), with its measure of delta hemoglobin concentration, has shown promise as a monitoring tool for the functional assessment of neurological disorders and brain injury. Analysis of fNIRS data often involves averaging data from several channel pairs in a region. Although this greatly reduces the processing time, it is uncertain how it affects the ability to detect changes post injury. Aim: We aimed to determine how averaging data within regions impacts the ability to differentiate between post-concussion and healthy controls. Approach: We compared interhemispheric coherence data from 16 channel pairs across the left and right dorsolateral prefrontal cortex during a task and a rest period. We compared the statistical power for differentiating groups that was obtained when undertaking no averaging, vs. averaging data from 2, 4, or 8 source detector pairs. Results: Coherence was significantly reduced in the concussion group compared with controls when no averaging was undertaken. Averaging all 8 channel pairs before undertaking the coherence analysis resulted in no group differences. Conclusions: Averaging between fiber pairs may eliminate the ability to detect group differences. It is proposed that even adjacent fiber pairs may have unique information, so averaging must be done with caution when monitoring brain disorders or injury.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".