Imaging of dopaminergic transmission in neuropsychiatric disorders
Bibliographic record
Abstract
The present review addresses recent advances in imaging dopaminergic neurotransmission in vivo. Radiotracer imaging with positron emission tomography and single-photon emission computed tomography can be used to measure pre-, post- and intrasynaptic aspects of dopaminergic transmission. The presynaptic sites can be labelled with radiotracers for the dopamine transporter or the synthetic enzyme aromatic L-amino acid decarboxylase. The postsynaptic sites can be labelled with radiotracers for the dopamine D1 receptor or the dopamine D2 receptor. Estimates of synaptic endogenous dopamine release are made indirectly by measurements of the displacement of receptor tracers by dopamine. Agents are used that either release (e.g. amphetamine) or deplete (e.g. α-methyl-paratyrosine, an inhibitor of tyrosine hydroxylase) dopamine tissue stores. Functional magnetic resonance imaging and positron emission tomography can provide measures of the effect of changes in dopaminergic transmission on neuronal function, as indexed by a change in regional cerebral blood flow, oxygen utilization or glucose metabolism. Magnetic resonance spectroscopy can provide measures of the effect of changes in dopaminergic transmission on the concentration of various neurochemical substances in cerebral tissue. Examples of recent applications of these imaging techniques in some neuropsychiatric diseases are provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".