Regional Cerebral Blood Flow during Wakeful Rest in Older Subjects with Mild to Severe Obstructive Sleep Apnea
Bibliographic record
Abstract
OBJECTIVES: To evaluate changes in regional cerebral blood flow (rCBF) during wakeful rest in older subjects with mild to severe obstructive sleep apnea (OSA) and healthy controls, and to identify markers of OSA severity that predict altered rCBF. DESIGN: High-resolution (99m)Tc-HMPAO SPECT imaging during wakeful rest. SETTING: Research sleep laboratory affiliated with a University hospital. PARTICIPANTS: Fifty untreated OSA patients aged between 55 and 85 years, divided into mild, moderate, and severe OSA, and 20 age-matched healthy controls. INTERVENTIONS: N/A. MEASUREMENTS: Using statistical parametric mapping, rCBF was compared between groups and correlated with clinical, respiratory, and sleep variables. RESULTS: Whereas no rCBF change was observed in mild and moderate groups, participants with severe OSA had reduced rCBF compared to controls in the left parietal lobules, left precentral gyrus, bilateral postcentral gyri, and right precuneus. Reduced rCBF in these regions and in areas of the bilateral frontal and left temporal cortex was associated with more hypopneas, snoring, hypoxemia, and sleepiness. Higher apnea, microarousal, and body mass indexes were correlated to increased rCBF in the basal ganglia, insula, and limbic system. CONCLUSIONS: While older individuals with severe obstructive sleep apnea (OSA) had hypoperfusion in the sensorimotor and parietal areas, respiratory variables and subjective sleepiness were correlated with extended regions of hypoperfusion in the lateral cortex. Interestingly, OSA severity, sleep fragmentation, and obesity correlated with increased perfusion in subcortical and medial cortical regions. Anomalies with such a distribution could result in cognitive deficits and reflect impaired vascular regulation, altered neuronal integrity, and/or undergoing neurodegenerative processes.
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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) | 0.000 | 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) | 0.001 | 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".