A.03 Cholinergic Neurons in Nucleus Subputaminalis in Primary Progressive Aphasia
Bibliographic record
Abstract
Background: Primary Progressive Aphasia (PPA) involves an isolated impairment of language function at disease onset. The cholinergic system is implicated in language and cholinergic deficits are seen in brains of individuals with PPA. One major source of cholinergic innervation is the nucleus basalis of Meynert (NBM) within which lies the nucleus subputaminalis (NSP). We quantified cholinergic neurons in the NBM and NSP of PPA and controls. Also explored was whether individuals with PPA who subsequently developed different clinical and neuropathological profiles, showed similar cholinergic deficits in the NSP. Methods: Cytoarchitecture of the basal forebrain was studied using Nissl staining in control (n=5) and PPA (n=5) brains. Choline acetyltransferase immunohistochemical staining labelled cholinergic neurons, quantified using Neurolucida software. Results: Compared to matched controls, PPA showed reduction of cholinergic neurons in the NBM, t(4) = 4.224, p = 0.013; Cohen’s d=1.89 and the NSP, t(4) = 4.013, p = 0.016; Cohen’s d= 1.79. The average percent of cholinergic neuronal loss was higher in the NSP (64.66%) compared to NBM (17.66%). Conclusions: Regardless of underlying pathology, all cases presenting with PPA showed marked loss of cholinergic neurons in the NSP providing further evidence for the importance of this nucleus in language function.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".