Dopamine D1 receptor expression in prefrontal parvalbumin neurons influences distractibility across species
Bibliographic record
Abstract
Abstract Marmosets and macaques are common non-human primate models of cognition, yet marmosets appear more distractible and perform worse in cognitive tasks. The dorsolateral prefrontal cortex (dlPFC) is pivotal for sustained attention, and prior macaque research suggests that dopaminergic modulation and inhibitory parvalbumin (PV) neurons could contribute to distractibility. Thus, we compared the two species using a visual fixation task with distractors, performed molecular and anatomical analyses in dlPFC, and linked functional microcircuitry with cognitive performance using computational modeling. We found that marmosets are more distractible than macaques, and that marmoset dlPFC PV neurons contain higher levels of dopamine-1 receptor (D1R) transcripts and protein, similar to their levels in mice. The modeling indicated that higher D1R expression in marmoset dlPFC PV neurons may increase distractibility by making dlPFC microcircuits more vulnerable to disruptions of their task-related persistent activity, especially when dopamine is released in dlPFC in response to unexpected salient stimuli. Declaration of Interests The authors have nothing to declare. Author Contributions AFTA, MKPJ, TGI, and SFW designed the study; MKPJ collected and analyzed anatomical data; TGI designed the computational framework with guidance from RG and SFW; TGI performed the modeling simulations and data analysis; FM and SAM collected and analyzed transcriptomics data; GGB and DAL provided macaque brain tissue for transcriptomics performed by FM; SM and NS collected and analyzed phylogenetic transcriptomics dataset; JFM and ASN performed and analyzed the behavioral testing; IW and JMT provided the marmoset tissue for immunofluorescence; MKPJ, TGI, SFW, and AFTA wrote the first draft and all authors revised and edited subsequent drafts of the article.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".