Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep
Bibliographic record
Abstract
In humans, some evidence suggests that there are two different types of spindles during sleep, which differ by their scalp topography and possibly some aspects of their regulation. To test for the existence of two different spindle types, we characterized the activity associated with slow (11-13 Hz) and fast (13-15 Hz) spindles, identified as discrete events during non-rapid eye movement sleep, in non-sleep-deprived human volunteers, using simultaneous electroencephalography and functional MRI. An activation pattern common to both spindle types involved the thalami, paralimbic areas (anterior cingulate and insular cortices), and superior temporal gyri. No thalamic difference was detected in the direct comparison between slow and fast spindles although some thalamic areas were preferentially activated in relation to either spindle type. Beyond the common activation pattern, the increases in cortical activity differed significantly between the two spindle types. Slow spindles were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. The recruitment of partially segregated cortical networks for slow and fast spindles further supports the existence of two spindle types during human non-rapid eye movement sleep, with potentially different functional significance.
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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 | 0.001 |
| 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) | 0.002 | 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".