NEUROTRANSMITTER TRANSPORTER/RECEPTOR CO-EXPRESSION SHARES ORGANIZATIONAL TRAITS WITH BRAIN STRUCTURE AND FUNCTION
Bibliographic record
Abstract
Abstract The relationship between brain areas based on neurotransmitter receptor and transporter molecule expression patterns may provide a link between brain structure and its function. Here, we studied the organization of the receptome, a measure of regional neurotransmitter receptor/transporter molecule (NTRM) similarity, derived from in vivo PET imaging studies of 19 different receptors and transporters. Nonlinear dimensionality reduction revealed three main spatial gradients of receptor similarity in the cortex. The first gradient differentiated the somato-motor network from the remaining cortex. The second gradient spanned between temporo-occipital and frontal anchors, differentiating visual and limbic networks from attention and control networks, and the third receptome gradient was anchored between the occipital and temporal cortices. In subcortical structures, the receptome delineated a striato-thalamic axis, separating functional communities. Moreover, we observed similar organizational principles underlying receptome differentiation in cortex and subcortex, indicating a link between subcortical and cortical NTRM patterning. Overall, we found that the cortical receptome shared key organizational traits with brain structure and function. Node-level correspondence of receptor similarity to functional, microstructural, and diffusion MRI-based measures decreased along a primary-to-transmodal gradient. Compared to primary and paralimbic regions, we observed higher receptomic diversification in unimodal and heteromodal regions, possibly supporting functional flexibility. In sum, we show how receptor similarity may form an additional organizational layer of human brain architecture, bridging 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".