The reliability of the N400 in single subjects: Implications for patients with disorders of consciousness
Bibliographic record
Abstract
Functional neuroimaging assessments of residual cognitive capacities, including those that support language, can improve diagnostic and prognostic accuracy in patients with disorders of consciousness. Due to the portability and relative inexpensiveness of electroencephalography, the N400 event-related potential component has been proposed as a clinically valid means to identify preserved linguistic function in non-communicative patients. Across three experiments, we show that changes in both stimuli and task demands significantly influence the probability of detecting statistically significant N400 effects - that is, the difference in N400 amplitudes caused by the experimental manipulation. In terms of task demands, passively heard linguistic stimuli were significantly less likely to elicit N400 effects than task-relevant stimuli. Due to the inability of the majority of patients with disorders of consciousness to follow task commands, the insensitivity of passive listening would impede the identification of residual language abilities even when such abilities exist. In terms of stimuli, passively heard normatively associated word pairs produced the highest detection rate of N400 effects (50% of the participants), compared with semantically-similar word pairs (0%) and high-cloze sentences (17%). This result is consistent with a prediction error account of N400 magnitude, with highly predictable targets leading to smaller N400 waves, and therefore larger N400 effects. Overall, our data indicate that non-repeating normatively associated word pairs provide the highest probability of detecting single-subject N400s during passive listening, and may thereby provide a clinically viable means of assessing residual linguistic function. We also show that more liberal analyses may further increase the detection-rate, but at the potential cost of increased false alarms.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".