Combined optical coherence tomography and electroretinography system for imaging neurovascular coupling in the human retina
Bibliographic record
Abstract
Significance: During their early stages of development, neurological and neurodegenerative diseases cause changes to the biological tissue's morphology, physiology and metabolism at the cellular level, and acute, transient changes in the local blood flow. The development of optical methods that can image and quantify such changes simultaneously and investigate the relationship among them (neurovascular coupling) in neural tissues can have a profound effect on furthering our understanding of neurodegeneration. Aim: Our aim is to develop an optical imaging platform for imaging and characterization of neurovascular coupling in the human retina with high spatial and temporal resolutions. Approach: , simultaneous structural, functional, and vascular imaging of the human retina and was integrated with a clinical electroretinography system. Image processing algorithms were developed to measure visually evoked physiological and blood flow changes in the living retina and explore neurovascular coupling in the healthy human retina. Results: Both intensity and optical path length changes were measured with optical coherence tomography from most major retinal layers (nerve fiber layer, plexiform layers, inner and outer segments of the photoreceptors, and the retinal pigmented epithelium) in response to a visual stimulation with a 4-ms single white light flash. The visual stimulus also caused fast transient changes in the retinal blood flow in the local blood vessels. The time courses of these changes were similar, and their magnitude was proportional to the intensity of the visual stimulus. Conclusions: We have developed an optical imaging modality for non-invasive probing of neurovascular coupling in the living human retina and demonstrated its utility and clinical potential in a pilot study on healthy subjects. This imaging platform could serve as a useful clinical research tool for investigation of potentially blinding retinal diseases, as well as neurodegenerative brain diseases that are expressed in the retina such as Alzheimer's and Parkinson's diseases.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".