The length of the thalamo-cortical white matter fibers brings insight into sex differences in sleep spindle frequency
Bibliographic record
Abstract
Abstract Sleep spindles (SS) are crucial to brain functions like memory and learning. SS characteristics result from the propagation of nerve impulses along white matter (WM) projections underlying an intricate loop between the thalamus and the cortex. SS amplitude and density have been associated with WM diffusion microarchitecture but physiological mechanisms underlying individual and sex-related variations in SS frequency are unknown. Here, we tested a model of traveling signals along the thalamo-cortico-thalamic projections to explain individual differences in spindle frequency. We predicted the presence of a relationship between the length of the thalamo-cortical WM bundles and a specific characteristic of this functional network, SS frequency. Thirty young participants underwent a polysomnographic recording and a 3T MRI including a diffusion sequence. The length of WM fiber bundles between the thalamus and the frontal cortex was derived from probabilistic tractography computed through constrained spherical deconvolution. Longer WM fiber bundles between the thalamus and specific regions of the frontal cortex (rostral middle frontal gyrus and anterior and middle part of the superior frontal gyrus) were associated with slower SS frequency. Moreover, the length of these WM fiber bundles statistically mediated the sex-related differences in SS frequency. By providing a neuroanatomical marker of individual and sex-related differences in SS frequency, this study is the first to highlight the association between the anatomy of a specific brain network and a specific functional characteristic of this network, the frequency of oscillations produced during 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 | 0.000 | 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) | 0.003 | 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".