Altered Density of Resting-State Long- and Short-Range Functional Connectivity in Patients with Moderate-to-Severe Obstructive Sleep Apnea
Bibliographic record
Abstract
Purpose: This study is to evaluate the altered number of functional connection (s) in patients with obstructive sleep apnea (OSA) by functional connectivity density (FCD), to investigate its relationship with cognitive function, and to explore whether these features could be used to distinguish OSA from healthy controls (HCs). Methods: Seventy-six OSA patients and 72 HCs were included in the analysis. All participants underwent resting-state functional magnetic resonance imaging scan. Subsequently, intergroup differences between long-and short-range FCD groups were obtained in the Matlab platform by using the degree centrality option with a 75 mm cutoff. The partial correlation analysis were used to assess the relationship between the altered FCD value and clinical assessments in OSA patients. The FCD values of the different brain regions were used as classification features to distinguish the two groups by support vector machine (SVM). Results: Compared to HCs, OSA patients had decreased long-range FCD in the right superior frontal gyrus (SFG), right precuneus, and left middle frontal gyrus (MFG). Simultaneously, increased long-range FCD in the right cingulate gyrus (CG). Meanwhile, the short-range FCD were decreased in the right postcentral gyrus (PoCG), right SFG, left MFG, and right CG. The short-range FCD values of the right PoCG were correlated with the Montreal Cognitive Assessment scores in OSA patients. SVM analysis showed that FCD in differential brain regions could differentiate OSA patients from HCs. Conclusion: Long- and short-range FCD values in different brain regions of OSA patients may be related to cognitive decline, and also be effective in distinguishing OSA patients from HCs. These findings provide new perspectives on neurocognition in OSA patients.
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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".