Functional Neuroimaging as an Assessment Tool in Critically Ill Patients
Bibliographic record
Abstract
OBJECTIVE: Little is known about residual cognitive function in the earliest stages of serious brain injury. Functional neuroimaging has yielded valuable diagnostic and prognostic information in chronic disorders of consciousness, such as the vegetative state (also termed unresponsive wakefulness syndrome). The objective of the current study was to determine if functional neuroimaging could be efficacious in the assessment of cognitive function in acute disorders of consciousness, such as coma, where decisions about the withdrawal of life-sustaining therapies are often made. METHODS: A hierarchical functional magnetic resonance imaging (fMRI) approach assessed sound perception, speech perception, language comprehension, and covert command following in 17 critically ill patients admitted to the intensive care unit (ICU). RESULTS: Preserved auditory function was observed in 15 patients (88%), whereas 5 (29%) also had preserved higher-order language comprehension. Notably, one patient could willfully modulate his brain activity when instructed to do so, suggesting a level of covert conscious awareness that was entirely inconsistent with his clinical diagnosis at the time of the scan. Across patients, a positive relationship was also observed between fMRI responsivity and the level of functional recovery, such that patients with the greatest functional recovery had neural responses most similar to those observed in healthy control participants. INTERPRETATION: These results suggest that fMRI may provide important diagnostic and prognostic information beyond standard clinical assessment in acutely unresponsive patients, which may aid discussions surrounding the continuation or removal of life-sustaining therapies during the early post-injury period. ANN NEUROL 2023;93:131-141.
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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 | 0.005 |
| 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 | 0.000 | 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".