Mapping neuropeptide sigaling in the human brain
Bibliographic record
Abstract
Neuropeptides are functionally diverse signaling molecules in the brain, regulating a wide range of basal bodily and cognitive processes. Despite their importance, the distribution and function of neuropeptides in the human brain remains underexplored. Here we comprehensively map the organization of human whole-brain neuropeptide receptors across multiple levels of description; from molecular and cellular embedding to mesoscale connectivity and macroscale cognitive specialization. Using gene transcription as a proxy, we reconstruct a topographic cortical and subcortical atlas of neuropeptide receptors for 38 neuropeptide receptors, across 14 different neuropeptide families. We find that most neuropeptide receptors are highly expressed either in cortex or subcortex, delineating an anatomical cortical-subcortical gradient. Neuropeptides preferentially co-localize with metabotropic neurotransmitters, suggesting a system-wide correspondence between slow-acting molecular signaling mechanisms. Mapping neuropeptide receptors and their cognate ligands onto white-matter connectomes, we demonstrate that specific neuropeptides families shape electrophysiological and haemodynamic inter-regional connectivity. To investigate the behavioural consequences of distributed neuropeptide systems, we apply meta-analytic decoding to neuropeptide maps and show a gradient of functions, from sensory-cognitive to reward and bodily functions. Finally, evolutionary analysis indicates extended positive selection for neuropeptides in early mammals, suggesting that refinement of neuropeptides coincides with the emergence of neocortex and higher cognitive function. Collectively, these results show that the neuropeptide receptors are highly organized across the human brain and closely intertwined with multiple features of brain structure and 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".