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Record W2120826122 · doi:10.2174/187152709788921708

Neuroimaging of Narcolepsy

2009· review· en· W2120826122 on OpenAlexaff
Thien Thanh Dang‐Vu, Martin Desseilles, Sophie Schwartz, Pierre Maquet

Bibliographic record

VenueCNS & Neurological Disorders - Drug Targets · 2009
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsNarcolepsySleep paralysisNeuroimagingCataplexyOrexinNeurosciencePsychologySleep (system call)ModafinilSleep disorderNeurologyMedicinePsychiatryCognitionNeuropeptideInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.350
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations19
Published2009
Admission routes1
Has abstractyes

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