Combining CDP-choline and galantamine: Effects of a selective α7 nicotinic acetylcholine receptor agonist strategy on P50 sensory gating of speech sounds in healthy volunteers
Bibliographic record
Abstract
Background: Schizophrenia (SCZ) patients and relatives have deficits in early cortical sensory gating (SG) typically measured by suppression of electroencephalography-derived P50 event-related potentials (ERPs) in a conditioning-testing (S 1 –S 2 ) paradigm. Associated with alpha 7 nicotinic acetylcholine receptor (α7 nAChR) dysfunction and shown to be improved with nicotine and α7 nAChR agonists, SG has recently been shown to be improved in low P50 suppressing SCZ patients following acute CDP-choline treatment. Aims: This pilot study in healthy humans assessed the SG effects of an α7 nAChR strategy combining CDP-choline with galantamine, a positive allosteric modulator (PAM) of nAChRs, aimed at increasing and prolonging nicotinic receptor activity. Methods: The combined effect of CDP-choline (500 mg) and galantamine (16 mg) on speech P50 gating indices rP50 (S 2 /S 1 ) and dP50 (S 1 –S 2 ) was examined in 30 healthy participants stratified into low and high baseline P50 suppressors in a randomized, double-blind, placebo-controlled and counterbalanced design. Results: In low suppressors, CDP-choline/galantamine (vs. placebo) improved rP50 and dP50 gating, and reduced S 2 P50 amplitudes. No P50 gating effects were observed in high suppressors; however, CDP-choline/galantamine (vs. placebo) increased their S 2 P50 amplitudes. Conclusion: Findings from this pilot study with CDP-choline/galantamine in a healthy, SCZ-like surrogate deficient gating sample are consistent with the association of α7 nAChR mechanisms in SG impairment in SCZ and support further research trials with CDP-choline and galantamine targeting sensory processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".