Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently?
Bibliographic record
Abstract
Previous functional magnetic resonance imaging (fMRI) studies have shown that a subgroup of patients diagnosed as being in a vegetative state are aware and able to communicate by performing a motor imagery task in response to commands. Due to the fMRI's cost and accessibility, there is a need for exploring different imaging modalities that can be used at the bedside. A promising technique is functional near infrared spectroscopy (fNIRS) that has been successfully applied to measure brain oxygenation in humans. Due to the limited depth sensitivity of continuous-wave NIRS, time-resolved (TR) detection has been proposed as a way of enhancing the sensitivity to the brain, since late arriving photons have a higher probability of reaching the brain. The goal of this study was to assess the feasibility and sensitivity of TR fNIRS in detecting brain activity during motor imagery. Fifteen healthy subjects were recruited in this study, and the fNIRS results were validated using fMRI. The change in the statistical moments of the distribution of times of flight (number of photons, mean time of flight and variance) were calculated for each channel to determine the presence of brain activity. The results indicate up to an 86% agreement between fMRI and TR-fNIRS and the sensitivity ranging from 64 to 93% with the highest value determined for the mean time of flight. These promising results highlight the potential of TR-fNIRS as a portable brain computer interface for patients with disorder of consciousness.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".