Sleep State Influences Early Sound Encoding at Cortical But Not Subcortical Levels
Bibliographic record
Abstract
In sleep, the brain balances protecting processes like memory consolidation with preserving responsiveness to significant external stimuli. Although reductions in higher-level auditory processes during deeper sleep have been described, the sleep-dependent changes across levels of auditory hierarchy, particularly as regards early sound representations, remain undefined. The frequency-following response (FFR) is an evoked auditory response that indexes neural encoding of sound periodicity. It is generated by neural populations in the brainstem, thalamus, and auditory cortex that phase-lock to periodic auditory stimuli and encode pitch information. The FFR's neural sources, which can be resolved using magnetoencephalography, allow evaluation of neural representation strength throughout the auditory neuraxis as a function of sleep state, as well as neural events like slow waves and sleep spindles that are hypothesized to attenuate acoustic processing as a means of preserving the sleep state. We recorded FFRs during a 2.5 h nap from 14 healthy male and female human adults to investigate how sleep depth and microarchitecture affect auditory encoding. We show that FFR strength is maintained across non-rapid eye movement sleep stages in subcortical nuclei, yet decreases in deeper sleep in the auditory cortex. FFR strength was not influenced by slow wave or spindle activity, but rather by reduced communication between the thalamus and cortex. This differentiation in sound representation across the auditory hierarchy suggests a means by which the brain might balance environmental monitoring with preserving critical restorative processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".