Optical Coherent Tomography as a Potential Biomarker for Logopenic Variant Primary Progressive Aphasia: A Cross‐Sectional Prospective Study
Bibliographic record
Abstract
BACKGROUND: The logopenic variant of Primary Progressive Aphasia (lvPPA) is a neurodegenerative disorder affecting primarily language functions. In 86% of lvPPA cases, the underlying pathology is amyloidopathy, as seen in Alzheimer's disease (AD). Since the retina is considered an extension of the brain, recent research has explored optical coherence tomography (OCT) and OCT-angiography (OCT-A) as non-invasive biomarkers in AD. However, their potential in lvPPA remains unexplored. The aim of this study was to compare retinal findings in lvPPA patients and healthy controls using OCT and OCT-A. METHODS: We conducted a cross-sectional study recruiting participants with lvPPA (diagnostic criteria by Gorno-Tempini, 2011) and healthy controls matched for sex and age. Participants were excluded if they had preexisting neurological or eye conditions. An extensive ophthalmological assessment was conducted to rule out eye diseases. OCT/OCTA imaging was then performed for all participants. For lvPPA patients, the Clinical Dementia Rating scale and a lumbar puncture were performed to assess disease stage and underlying pathology. RESULTS: Ten lvPPA patients and eleven controls were enrolled. All lvPPA patients had amyloidopathy confirmed by lumbar puncture. Mean CDR global score was 0.55 ± 0.16 (indicating mild dementia). Retinal nerve fiber layer (RNFL) in the temporal region was significantly thinner in the lvPPA group compared to controls (63.1 ± 3.3 μm vs 75.6 ± 3.2 μm, p = 0.013). Foveal avascular zone (FAZ) circularity was also significantly lower in the lvPPA group (0.69 ± 0.02 vs 0.77 ± 0.02, p = 0.002). CONCLUSIONS: Our findings suggest decreased RNFL thickness and reduced FAZ circularity in lvPPA. Decreased RNFL thickness reflects neuronal degeneration and its underlying mechanisms include retinal amyloid accumulation or retrograde degeneration. This suggests that amyloid-induced brain atrophy leads to a lack of trophic factors, resulting in thinning of the RNFL while reduced FAZ circularity could signal early vascular alterations induced by amyloid. Altogether, these findings suggest that OCT and OCT-A could serve as valuable biomarkers for lvPPA, thus enhancing early diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".