Cognition‐Associated Changes in Retinal Thickness Relate to Limbic and Temporal Cortical Atrophy in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Research links retinal changes to cognitive decline in Parkinson's disease (PD), paralleling findings in Alzheimer's, raising questions about specific cortical patterns of cognition-related retinal abnormalities in PD. OBJECTIVE: The study aimed to explore whether retinal thinning linked to cognitive decline could act as a potential biomarker for cerebral atrophy in PD. METHODS: Twenty seven patients with PD underwent cognitive and neurological assessments, along with retinal imaging using OCT and cerebral imaging using structural MRI. After identifying abnormal retinal layers associated with cognitive dysfunction through partial correlation analyses controlling for age-related effects, associations between these retinal layers and the parcellated cerebral gray matter were assessed using multiple comparison-corrected partial correlation analyses adjusted for age and gender. RESULTS: Significant positive correlations were found between cognitive impairment measured by MoCA and specific retinal layers (IPL, GCL, and RNFL). Of these, strong associations were observed between the IPL and GCL and cortical thickness in brain the temporal lobe and limbic cortex, with more detailed further analysis showing significant correlations particularly within the middle and posterior cingulate cortex in the limbic cortex and the middle and superior temporal gyrus in the temporal lobe. CONCLUSION: Correlations between retinal thinning, cognitive decline, and specific patterns of cortical atrophy in PD support a potential of retinal measurements as a biomarker for cognitive impairment linked to cerebral neurodegeneration.
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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".