AIN'T NO REST FOR THE BRAIN. Neuroimaging and neuroethics in dialogue for patients with disorders of consciousness
Bibliographic record
Abstract
The sheer amount of different opinions about what consciousness is highlights its multifaceted character. The clinical study of consciousness in coma survivors provides unique opportunities, not only to better comprehend normal conscious functions, but also to confront clinical and medico-ethical challenges. For example, pain in vegetative state/unresponsive wakefulness syndrome patients (VS/UWS; i.e. awaken, but unconscious) and patients in minimally conscious states (MCS; awaken, with fluctuating signs of awareness) cannot be communicated and needs to be inferred. Behaviorally, we developed the Nociception Coma Scale, a clinical tool which measures patients’ motor, verbal, visual, and facial responsiveness to noxious stimulation. Importantly, the absence of proof of a behavioral response cannot be taken as proof of absence of pain. Functional neuroimaging studies show that patients in VS/UWS exhibit no evidence of control-like brain activity, when painfully stimulated, in contrast to patients in MCS. Similarly, the majority of clinicians ascribe pain perception in MCS patients. Interestingly, their opinions appear less congruent with regards to pain perception in VS/UWS patients, due to personal and cultural differences. The imminent bias in clinical practice due to personal beliefs becomes more ethically salient in complex clinical scenarios, such as end-of-life decisions. Surveys among clinicians show that the majority agrees with treatment withdrawal for VS/UWS, but fewer respondents would do so for MCS patients. For the issue of pain in patients with disorders of consciousness, the more the respondents ascribed pain perception in these states the less they supported treatment withdraw from these patients. Such medico-ethical controversies require an objective and valid assessment of pain (and eventually of consciousness) in noncommunicating patients. Functional neuroimaging during “resting state” (eyes closed, no task performance) is an ideal paradigm to investigate residual cognition in noncommunicating patients, because it does not require sophisticated technical support or subjective input on patients’ behalf. With the ultimate intention to use this paradigm in patients, we first aimed to validate it in controls. We initially found that, in controls, fMRI “resting state” activity correlated with subjective reports of “external” (perception of the environment through the senses) or “internal” awareness (self-related mental processes). Then, using hypnosis, we showed that there was reduced fMRI connectivity in the “external network”, reflecting decreased sensory awareness. When more cerebral networks were tested, increased functional connectivity was observed for most of the studied networks (except the visual). These results indicate that resign state fMRI activity reflects, at least partially, ongoing conscious cognition, which changes under different conditions. Using the resting state paradigm in patients with disorders of consciousness, we vi showed intra- and inter-network connectivity breakdown in sensorysensorimotor and “higher-order” networks, possibly accounting for patients’ limited capacities for conscious cognition. We have further observed positive correlation between the Nociception Coma Scale scores and the pain-related (salience) network connectivity, potentially reflecting nociception-related processes in these patients, measured in the absence of an external stimulus. These results highlight the utility of resting state analyses in clinical settings, where short and simple setups are preferable to activation protocols with somatosensory, visual, and auditory stimulation devices. Especially for neuroimaging studies, it should be stressed that such experimental investigations tackle the necessary conditions supporting conscious processing. The sufficiency of the identified neural correlates accounting for conscious awareness remains to be identified via dynamic and causal information flow investigations. Importantly, the quest of subjectivity in non-communicating patients can be better understood by adopting an interdisciplinary biopsychosocial approach, combining basic neuroscience (bio), psychological-cognitive-emotional processing (psycho), and the influence of different socioeconomic, cultural, and technological factors (social).
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".