Abnormal Brain Network Topology During Non–rapid Eye Movement Sleep and Its Correlation With Cognitive Behavioral Abnormalities in Narcolepsy Type 1
Bibliographic record
Abstract
Objective: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) were applied to investigate the abnormalities in the topological characteristics of functional brain networks during non-rapid eye movement(NREM)sleep. And we investigated its relationship with cognitive abnormalities in patients with narcolepsy type 1 (NT1) disorder in the current study. Methods: The Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) and EEG-fMRI were applied in 25 patients with NT1 and 25 age-matched healthy controls. All subjects participated in a nocturnal video polysomnography(PSG)study, and total sleep time (TST), percentage of TST (%TST) for each sleep stage and arousal index were calculated. The Epworth Sleepiness Score (ESS) was used to measure the degree of daytime sleepiness. The EEG-fMRI study was performed simultaneously using a 3T MRI system and a 32-channel MRI-compatible EEG system during sleep. Visual scoring of EEG data was used for sleep staging. Cognitive function was assessed for all subjects using the MoCA-BJ. The fMRI data were applied to establish a whole-brain functional connectivity network for all subjects, and the topological characteristics of the whole-brain functional network were analyzed using a graph-theoretic approach. The topological parameters were compared between groups. Lastly, the correlation between topological parameters and the assessment scale using Montreal Cognition was analyzed. Results: The MoCA-BJ scores were lower in patients with NT1 than in normal controls. Whole-brain global efficiency during stage N2 sleep in patients with NT1 displayed significantly lower small-world properties than in normal controls. Whole-brain functional network global efficiency in patients with NT1 was significantly correlated with MoCA-BJ scores. Conclusion: The global efficiency of the functional brain network during stage N2 sleep in patients with NT1 and the correspondingly reduced small-world attributes were associated with cognitive impairment.
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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 | 0.001 | 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".