<scp>GABA<sub>A</sub></scp> receptor deficits predict recovery in patients with disorders of consciousness: A preliminary multimodal [<sup>11</sup>C]Flumazenil <scp>PET</scp> and f<scp>MRI</scp> study
Bibliographic record
Abstract
OBJECTIVES: Disorders of consciousness (DoC)-that is, unresponsive wakefulness syndrome/vegetative state and minimally conscious state-are debilitating conditions for which no reliable markers of consciousness recovery have yet been identified. Evidence points to the GABAergic system being altered in DoC, making it a potential target as such a marker. EXPERIMENTAL DESIGN: In our preliminary study, we used [(11) C]Flumazenil positron emission tomography to establish global GABAA receptor binding potential values and the local-to-global (LTG) ratio of these for specific regions. These values were then compared between DoC patients and healthy controls. In addition, they were correlated with behavioral improvements for the patients between the time of scanning and 3 months later. Functional magnetic resonance imaging resting-state functional connectivity was also calculated and the same comparisons made. PRINCIPAL OBSERVATIONS: lobal GABAA receptor binding was reduced in DoC, as was the LTG ratio in specifically the supragenual anterior cingulate. Both of these measures correlated with behavioral improvement after 3 months. In contrast to these measures of GABAA receptor binding, functional connectivity did not correlate with behavioral improvement. CONCLUSIONS: Our preliminary findings point toward GABAA receptor binding being a marker of consciousness recovery in DoC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".