The dissociation between command following and communication in disorders of consciousness: an fMRI study in healthy subjects
Bibliographic record
Abstract
Neuroimaging studies have identified a subgroup of patients with a Disorder of Consciousness (DOC) who, while being behaviorally non-responsive, are nevertheless able to follow commands by modulating their brain activity in motor imagery (MI) tasks. These techniques have even allowed for binary communication in a small number of DOC patients. However, the majority of patients who can follow commands are unable to use their responses to communicate. A similar dissociation between present command following (CF) and absent communication abilities has been reported in overt behavioral assessments. However, the neural correlates of this dissociation in both overt and covert modalities are unknown. Here, we used functional magnetic resonance imaging (fMRI) to explore the neural mechanisms underlying CF and selection of responses for binary communication using either executed or imagined movements. Fifteen healthy participants executed or imagined two different types of arm movements that were either pre-determined by the experimenters (CF) or decided by them (action selection, AS). Action selection involved greater activity in high-level associative areas in frontal and parietal regions than CF. Additionally, motor execution (ME), as compared to MI, activated contralateral motor cortex, while the opposite contrast revealed activation in the ipsilateral sensorimotor cortex and the left inferior frontal gyrus. Importantly, there was no interaction between the task (CF/AS) and modality (MI/ME). Our results suggest that the neural processes involved in following a motor command or selecting between two motor actions are not dependent on how the response is expressed (via ME/MI). They also suggest a potential neural basis for the distinction in cognitive abilities seen in DOC patients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".