Functional neuroimaging in disorders of consciousness: towards clinical implementation
Bibliographic record
Abstract
Functional neuroimaging has provided several new tools for improving both the diagnosis and prognosis in patients with disorders of consciousness. These tools are now being used to detect residual and covert awareness in behaviourally non-responsive patients with an acquired severe brain injury and predict which patients are likely to recover. Despite endorsement of advanced imaging by multiple clinical bodies, widespread implementation of imaging techniques such as functional MRI (fMRI), EEG and PET in both acute and prolonged disorders of consciousness patients has been hindered by perceived costs, technological barriers, and lack of expertise needed to acquire, interpret and implement these methods. In this review we provide a comprehensive overview of neuroimaging in disorders of consciousness, the different technical approaches employed (i.e. fMRI, EEG, PET), the imaging paradigms used (active, passive, resting state) and the types of inferences that have been made about residual cortical function based on those paradigms (e.g. perception, awareness, communication). Next, we outline how these barriers might be overcome, discuss which select patients stand to benefit the most from these neuroimaging techniques, and consider when, during their clinical trajectory, imaging tests are likely to be most useful. Moreover, we make recommendations that will help clinicians decide which advanced imaging technologies and protocols are likely to be most appropriate in any particular clinical case. Finally, we describe how these techniques can be implemented in routine clinical care to augment current clinical tools and outline future directions for the field as a whole.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".