Gradients in signal complexity of sleep-wake intracerebral EEG
Bibliographic record
Abstract
Spatial variation in the morphology of the electroencephalogram (EEG) over the head is classically described. Ultimately, location-dependent variation in EEG must arise from the cytoarchitectural and network structure of the portion of cortex sensed. In previous work, we demonstrated that over the lateral frontal lobe, sample entropy (SE) of intracerebral EEG (iEEG) over a subdural recording contact was predictive of that contact's connectivity to other contacts. In this work, we used a publicly available repository (the Montreal Neurological Institute Atlas; MNIA) of whole-brain normative iEEG to calculate SE over the entire cortical surface. SE was averaged region-wise and classified by the state of arousal (awake, N2, N3 and REM). SE averages were transformed to a linear scale between zero and unity, mapped to continuous color scale and overlaid on segmented cortical surface models, one for each sleep-wake state. Wake SE followed a rostro-caudal gradient (RCG), with high values anteriorly and a global minimum in the posterior cortex. Superimposed on the RCG were other gradients radiating away from primary somatic sensorimotor, visual and auditory regions to their association areas. All gradients were attenuated in deep (N3) sleep. In REM, the majority of the cortex exhibited wake-like SE, with the prominent exception of primary cortical sensory and motor areas. Normative human intracerebral EEG exhibits rich spatial structure - cortical gradients - in the distribution of SE. SE in the wake state tracks temporal processing hierarchies in cerebral cortex, concordant to the distribution of several other cortical attributes of structure (e.g., cortical thickness, myelin content). Sleep disrupts these gradients, with REM sleep bringing out unusual discordances between primary sensory and their association areas. Our results deepen the interpretation of EEG from conventional descriptors such as Berger bands to a spatial perspective related to cortical biology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".