P.050 Spike source localizations between the three non-REM sleep stages: resemblances to wakefulness and distinctions from REM sleep
Bibliographic record
Abstract
Background: Sleep-wake states (SWS) affect the expression of interictal epileptiform discharges (“spikes”), which affects resultant source localization calculations used in epilepsy evaluation. We hypothesize that spike localizations from non-REM sleep 1-3 are most concordant with one another. Methods: We used Standardized low-resolution brain electromagnetic tomography (sLORETA) in Curry 8 software to calculate source localization voxels of spikes in N1-3, REM, or wakefulness (W). We assessed voxel concordance between N1-N2-N3/N1-N2-W/N1-N3-W/N2-N3-W/REM-N1-N2/REM-N1-N3/REM-N2-N3/REM-N1-W/REM-N2-W/REM-N3-W. We classified concordances into those containing and not containing a SWS (e.g. N1 vs. not-N1 = N1-N2-N3/N1-N2-W/N1-N3-W/REM-N1-N2/REM-N1-N3/REM-N1-W vs. REM-N2-W/REM-N3-W/REM-N2-N3/N2-N3-W) for comparison. Results: Concordances did not differ for N1-3 or W. However, concordances with REM were lower than those without REM as a fraction of source localization space (median 32.1% vs. 56.1%, p<0.001) and cortical grey matter (median 20.4% vs. 27.3%, p=0.003). Conclusions: As expected, source localizations from spikes in N1, N2, and N3 did not significantly differ from one another because these three states are constituent members of non-REM sleep. Surprisingly, however, source localizations derived from awake spikes – not a constituent of non-REM sleep – also did not differ. In contrast, REM was most different by reproducibly exhibiting the least three-way concordance. These findings reinforce the unique localizing ability of REM sleep.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.003 |
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".