Smaller grey matter volume in the central olfactory system in mild cognitive impairment
Bibliographic record
Abstract
One of the major challenges in the diagnosis of Alzheimer's disease (AD) is to increase the specificity of the early diagnosis. While episodic memory impairment is a sensitive AD marker, other measures are needed to improve diagnostic specificity. A promising biomarker might be a cerebral atrophy of the central olfactory processing areas in the early stages of the disease since an impairment of olfactory identification is present at the clinical stage of AD. Our goal was therefore, (1) to evaluate the grey matter volume (GMV) of central olfactory processing regions in prodromal AD and (2) to assess its association with episodic memory. We included 34 cognitively normal healthy controls (HC), 92 individuals with subjective cognitive decline (SCD), and 40 with mild cognitive impairment (MCI). We performed regions of interest analysis (ROI) using two different approaches, allowing to extract GMV from (1) atlas-based anatomical ROIs and from (2) functional and non-functional subregions of these ROIs (olfactory ROIs and non-olfactory ROIs). Participants with MCI exhibited smaller olfactory ROIs GMV, including significant reductions in the piriform cortex, amygdala, entorhinal cortex, and left hippocampus compared to other groups (p ≤ 0.05, corrected). No significant effect was found regarding anatomical or non-olfactory ROIs GMV. The left hippocampus olfactory ROI GMV was correlated with episodic memory performance (p < 0.05 corrected). Limbic/medial-temporal olfactory processing areas are specifically atrophied at the MCI stage, and the degree of atrophy might predict cognitive decline in AD early stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".