Brain white matter microstructural alterations in patients with diabetic retinopathy: an automated fiber-tract quantification study
Bibliographic record
Abstract
Background: Cognitive decline may occur in patients with diabetic retinopathy (DR), yet the mechanism underlying the relationship between cognitive decline and DR remains unclear. This study applied an automated fiber-tract quantification (AFQ) technique based on diffusion tensor imaging (DTI) to identify alterations in specific segments of brain white matter fiber tracts in patients with DR, and analyze their correlation with cognitive test scores and clinical biochemical indicators. Methods: A total of 19 patients with DR and 20 age-, sex-, and education-matched healthy controls (HCs) were included. Clinical and imaging data were prospectively collected. The AFQ technique was applied to track the whole brain white matter fiber tracts of each participant, and each fiber tract was segmented into 100 equidistant nodes. The fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion in 100 nodes of each fiber tract were calculated and compared between the two groups. Partial correlation analysis was performed to analyze the correlation between altered DTI metrics in segments of the fiber tracts and cognitive test scores, as well as clinical biochemical indicators in patients with DR. Results: Compared with the HC group, the DR group showed significantly reduced FA values in nodes 81-100, increased MD values in nodes 39-50, and reduced AD values in nodes 91-100 of the left cingulum cingulate (CGC) [P<0.05, false discovery rate (FDR) corrected], they also showed increased AD values in the left superior longitudinal fasciculus (SLF; nodes 1-23, 37-50, and 66-99), and the right SLF (nodes 1-36 and 79-100) (P<0.05, FDR corrected). Correlation analysis revealed a positive correlation between the FA values in nodes 82-98 of the left CGC and Montreal Cognitive Assessment scores (MoCA scores, r=0.760, P<0.05/P=0.021), and a positive correlation between the AD values in nodes 37-41 in the left SLF and glycated hemoglobin A1c (HbA1c) levels (r=0.559, P<0.05/P=0.039). Conclusions: Our findings demonstrated alterations in the white matter fiber tracts at the point-wise level in patients with DR using AFQ analysis. These alterations may be associated with cognitive impairment in DR. The AFQ technique can accurately detect the damage to the integrity of the brain white matter fiber tracts in patients with DR, and have high clinical application value in the diagnosis and evaluation of DR, which can deepen our understanding of brain white matter microstructural abnormalities in patients with DR.
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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) | 0.000 | 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".