Assessing cognitive impairment in OSAHS patients through NODDI-based gray matter analysis
Bibliographic record
Abstract
BACKGROUND: This study employed neurite orientation dispersion and density imaging (NODDI) to investigate gray matter microstructural changes in obstructive sleep apnea hypopnea syndrome (OSAHS) patients with cognitive impairment, assessing early diagnostic potential. METHODS: The study comprised 23 OSAHS patients (OSA-NCI group), 43 OSAHS patients with experiencing cognitive impairment (OSA-CI group), and 15 healthy controls (HC group). Fractional anisotropy (FA), neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) in regions of interest (ROIs) were calculated. Correlations between these parameters and Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores were examined. Diagnostic effect was evaluated using receiver operating characteristic (ROC) curve analysis, and area under the curve (AUC) was calculated. RESULTS: Significant variations were observed in the NDI, ODI, Viso, and FA. Compared to HCs, the NDI and ODI in the OSA-NCI group decreased, while the Viso value increased. NDI and ODI showed slight increases in the OSA-CI group but remained below HC levels; Viso significantly increased. However, FA did not significantly differ. NDI, ODI, and Viso strongly correlated with MoCA scores in specific gray matter regions; FA showed weak correlations (r < 0.5). ROC analysis confirmed the effectiveness of the NODDI parameters, with average AUC values of 0.689 for the Viso value, 0.676 for the ODI, and 0.635 for the NDI; however, FA showed limited diagnostic utility (AUC 0.452). CONCLUSION: This study suggests the potential diagnostic utility of NODDI in detecting gray matter pathology in OSAHS patients with cognitive impairment, though further validation in larger, independent cohorts is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".