Near-Infrared Spectroscopy to Assess Covert Volitional Brain Activity in Intensive Care
Bibliographic record
Abstract
BACKGROUND: Detecting covert consciousness in unresponsive patients is challenging. Although functional magnetic resonance imaging and advanced electroencephalography paradigms can identify volitional brain activity, the limited accessibility of these technologies necessitates alternative approaches. Functional near-infrared spectroscopy may provide a portable solution in the intensive care unit. We assessed the feasibility of functional near-infrared spectroscopy with verbal motor commands to detect volitional brain activity in acute disorders of consciousness (DoC). METHODS: Functional near-infrared spectroscopy recordings and clinical assessments were obtained from 50 patients with DoC with acute brain injury, with data analyzed post hoc and visually at the bedside. Twenty healthy volunteers served as controls. RESULTS: After quality control, data from 19 controls and 36 patients were analyzed. Cortical activation was detected in 18 (96%) controls and 16 (44%) patients. Among 13 minimally conscious patients, volitional activity was found in 8 (62%), whereas 8 (35%) of 23 clinically unresponsive patients showed activation. Volitional brain activity in the latter was associated with higher odds of command following within a week, although it was not statistically significant (odds ratio 3.1, 95% confidence interval 0.7-15.8; p = 0.14). Visual bedside analysis showed high specificity (90%) but moderate agreement (κ = 0.4) with post hoc computational analysis. CONCLUSIONS: Functional near-infrared spectroscopy with motor commands can detect volitional brain activity in acute DoC, although data quality issues remain a limitation.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".