Assessing awareness in severe Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background There is an urgent need to understand the nature of awareness in severe AD to ensure effective person‐centred care. Transcranial Magnetic Stimulation (TMS) combined with Electroencephalography (EEG), Event Related Potentials (ERPs) and functional Magnetic Resonance Imaging (fMRI) are robust techniques for assessing awareness in clinical populations who are unable to communicate. We present novel feasibility and preliminary data on using these methods to assess capacity for consciousness and contents of awareness in severe AD. Method a) TMS‐EEG was performed in 6 healthy older controls and 3 people with severe AD. The perturbational complexity index (PCIST) was calculated as a measure of capacity for conscious awareness. b) ERPs were recorded during a masked visual perception paradigm and analysed for the presence of visual awareness negativity and P400 components, previously identified as associated with conscious visual perception. c) An fMRI movie‐viewing task, validated in previous studies to demonstrate activation in a fronto‐parietal network during conscious engagement with the film was also conducted. Result a) Participants with severe AD demonstrated a PCIST around the previously identified threshold for consciousness, suggesting reduced capacity for consciousness. b) In response to viewing faces, two patients with severe AD provisionally demonstrated similar visual awareness negativity to healthy controls. c) Healthy controls and one person with severe AD revealed spatially, but not temporally similar activation in a fronto‐parietal network, suggesting differences in conscious engagement with the movie. Conclusion These biomarkers provide experimental approaches to assess awareness and improve understanding and care for people with severe AD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".