Comparison of Neural Tracking and Spectral Entropy in Patients with Disorders of Consciousness
Bibliographic record
Abstract
Abstract Objectives This study aims to explore the brain responses of patients with disorders of consciousness (DoC) to natural speech. Specifically, it focuses on a key characteristic of natural speech: the speech envelope. To achieve this, we employed two distinct measures. The first evaluates how effectively the patients’ brain activity “tracks” the speech envelope, called neural tracking. The second assesses the complexity of the brain’s responses to the speech stimulus, measured through spectral entropy. These two measures are then compared in their association with the patient’s clinical diagnosis and their level of behavioral responsiveness, both of which were assessed using the Coma Recovery Scale-Revised (CRS-R). Design Four patients with DoC participated in this study, during which their brain activity was recorded using electroen-cephalography (EEG). At the same time, they listened to a narrated story in both Dutch and Swedish. Additionally, EEG baseline recordings were collected. We employed a backward modeling approach to evaluate the speech envelope’s neural tracking. This technique involves training a model to map the relationship between EEG signals and the corresponding speech envelope. Once the model is trained, it can use unseen EEG data to reconstruct the speech envelope, which is then compared to the original speech envelope to assess how effectively the patient processed the auditory stimulus. For the behavioral assessment, we recalculated the CRS-R score of each patient into the CSR-R index, a more meaningful score that utilizes all the information contained in the CRS-R instead of only the highest scores on each subscale. Results Our findings revealed positive correlations between spectral entropy and the CRS-R index, which were more pronounced during the listening conditions than the baseline. While neural tracking of the speech envelope did not correlate with the CRS-R index, it did exhibit a positive association with CRS-R diagnoses, indicating that patients with better clinical diagnoses demonstrated higher levels of neural tracking. Additionally, we identified an interaction effect between spectral entropy and neural tracking. Specifically, higher levels of neural tracking were associated with a stronger positive relationship between spectral entropy and the CRS-R index. In contrast, when neural tracking was lower, this relationship disappeared. Conclusion This study demonstrated the potential of neural tracking and spectral entropy as complementary tools to investigate patients with DoC. Spectral entropy proved valuable for assessing behavioral responsiveness, while neural tracking shows promise in assessing the DoC diagnosis. Terms : disorders of consciousness (DoC), neural tracking, speech envelope, spectral entropy
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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 | 0.003 |
| 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".