Cholinergic Neurons in Nucleus Subputaminalis in Primary Progressive Aphasia
Bibliographic record
Abstract
BACKGROUND: Primary Progressive Aphasia (PPA) is a syndrome characterized by an isolated impairment of language function at disease onset. The cholinergic system is implicated in language function and cholinergic deficits are seen in the brains of individuals with PPA. One major source of cholinergic innervation of the cerebral cortex is the nucleus basalis of Meynert (NBM) within which lies the nucleus subputaminalis (NSP). This nucleus is postulated to be involved in language function. We compared the abundance of cholinergic neurons in the NBM and NSP of controls and individuals with PPA. Also explored was whether the individuals presenting 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 (ChAT) immunohistochemical staining labeled cholinergic neurons were quantified using Neurolucida software. RESULTS: In comparison to matched controls, PPA showed reduction of cholinergic neurons in the NBM (t(8) = 4.04, p = 0.0037; Cohen's effect size value d = 2.62) and the NSP (t(6) = 4.62, p = 0.0042; Cohen's d effect size d = 2.92). The average percent of cholinergic neuronal loss was relatively higher in the NSP (64.7%) compared to the NBM (47.7%). CONCLUSION: Regardless of underlying pathology, all cases presenting with PPA showed a marked loss of cholinergic neurons in the NSP, providing further evidence for the importance of this nucleus in language function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".