2 Into the gray zone: assessing residual cognitive function after serious brain injury
Bibliographic record
Abstract
Adrian M. Owen OBE, PhD is currently a Professor of Cognitive Neuroscience and Imaging in the Depts of Physiology & Pharmacology and Psychology at the University of Western Ontario, Canada. He also directs the Azrieli program in Brain, Mind, and Consciousness funded by the Canadian Institute for Advanced Research (CIFAR) and is on the Executive Committee of the CFREF funded initiative BrainsCAN at the University of Western Ontario. Dr. Owen was previously the Assistant Director of the Medical Research Council Cognition and Brain Sciences Unit at Cambridge University & the Canada Excellence Research Chair in Cognitive Neuroscience & Imaging at Western University. His research combines structural and functional neuroimaging with neuropsychological studies of brain-injured patients and has been published in many of the world’s leading scientific journals. He is best known for showing that functional neuroimaging can reveal conscious awareness in some patients who appear to be entirely vegetative and can even allow some of these individuals to communicate their thoughts and wishes to the outside world. These findings have attracted widespread media attention on TV, radio, in print and online and have been the subject of many TV and radio documentaries. He has published over 300 peer-reviewed articles and chapters and a best-selling popular science book ‘Into the Gray Zone: A Neuroscientist Explores the Border Between Life and Death. Dr. Owen was awarded an OBE) in the Queen’s Honors List, 2019, for services to scientific research. Abstract The thought of being ‘locked in’ following a brain injury or aware during general anaesthesia troubles us all because it awakens the old terror of being buried alive. But what does it mean to be awake, but entirely unable to respond and what can this tell us about consciousness itself? In recent years, rapid technological developments in the field of neuroimaging have provided a number of new methods for revealing thoughts, actions and intentions based solely on the pattern of activity that is observed in the brain. I will describe how we are using some of these methods, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), to detect covert conscious awareness in patients who are behaviourally entirely non-responsive (e.g. vegetative, comatose) and even to allow some of these individuals to communicate their wishes and thoughts. From this perspective, I will contrast those circumstances in which imaging data can be used to infer awareness in the absence of a reliable behavioural response, with those circumstances in which it cannot. This distinction is fundamental for understanding and interpreting patterns of brain activity in various states of consciousness (including vegetative state, coma, anaesthesia and sleep), and has profound implications for clinical care, diagnosis, prognosis, ethics and medical-legal decision-making after severe brain injury. It also sheds light on more basic scientific questions about how consciousness is measured and the neural representation of our own thoughts and intentions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".