Use of functional magnetic resonance imaging to assess cognition and consciousness in severe Guillain-Barré syndrome
Bibliographic record
Abstract
Objective: Functional neuroimaging may provide a viable means of assessment and communication in patients with Guillain-Barré Syndrome (GBS) mimicking the complete locked-in state. Functional neuroimaging has been used to assess residual cognitive function and has allowed for binary communication with other behaviourally non-responsive patients, such as those diagnosed with unresponsive wakefulness syndrome. We evaluated the potential application of functional neuroimaging using a clinical-grade scanner to determine if individuals with severe GBS retained auditory function, command following, and communication. Methods: Fourteen healthy participants and two GBS patients were asked to perform motor imagery and spatial navigation imagery tasks while being scanned using functional magnetic resonance imaging. The GBS patients were also asked to perform additional functional neuroimaging scans to attempt communication. Results: The motor imagery and spatial navigation task elicited significant activation in appropriate regions of interest for both GBS patients, indicating intact command following. Both patients were able to use the imagery technique to communicate in some instances. Patient 1 was able to use one of four communication tasks to answer a question correctly. Patient 2 was able to use three of seven communication tasks. However, two questions were incorrectly answered while a third was non-verifiable. Conclusions: GBS patients can respond using mental imagery and these responses can be detected using functional neuroimaging. Furthermore, these patients may also be able to use mental imagery to provide answers to 'yes' or 'no' questions in some instances. We argue that the most appropriate use of neuroimaging-based communication in these patients is to allow them to communicate wishes or preferences and assent to previously expressed decisions, rather than to facilitate decision-making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".