Neuroimaging of Narcolepsy
Bibliographic record
Abstract
Neuroimaging techniques have refined the characterization of neural structures involved in the regulation of normal sleep-wake cycle in healthy humans. Yet brain imaging studies in patients with sleep disorders still remain scarce. In narcoleptic patients, structural and functional brain imaging studies have suggested the involvement of the hypothalamus in the pathophysiology of narcolepsy, plausibly consistent with an impairment of the hypocretin-orexin system. Some recent studies have further suggested that cataplexy, a key feature of the narcoleptic syndrome, might result from a dysfunction of the hypothalamus and its interactions with limbic structures. Other neuroimaging studies have focused on the assessment of neurotransmission and the effects of pharmacological treatment in narcoleptic patients. However, the neural correlates of some main symptoms of narcolepsy, such as sleep attacks, hypnagogic/hypnopompic hallucinations and sleep paralysis, are still unknown. In addition, the description of brain activity patterns during sleep in narcoleptic patients needs further investigation. Neuroimaging has proven to be a valuable tool for the study of sleep regulation and sleep disorders; its future developments will undoubtedly improve our understanding of the neural mechanisms underlying narcolepsy with cataplexy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".