How Do Neurotransmitter Pathways Contribute to Neuroimaging Phenotypes?
Bibliographic record
Abstract
Abstract Neuroimaging could accurately reflect human behaviour in health and disease, but the mechanism by which image-derived phenotypes correspond to neurotransmitter systems remains uncertain. Prior studies have explored spatial correlations between neuroimaging phenotypes and positron emission tomography radiotracers. However, the influence of neurotransmitters goes beyond the receptors/transporters, influencing a wider array of intracellular components as pivotal parts of neurotransmitter pathways. Here, we used unsupervised learning to understand how the brain maps of healthy function (i.e., magnetoencephalography frequency-specific power) and abnormal structure (i.e., disorder-specific cortical thickness) are closely anchored to underlying neurotransmitter pathways assessed by gene expression data. To do this, we used large-scale datasets of the Human Connectome Project (HCP), Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and Allen Human Brain Atlas (AHBA). We considered spatial and random gene null models to mitigate false positives. We replicate our analyses using different gene stability thresholds. This analytic approach paves the way for personalised medicine and advanced biomarkers.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".