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Imaging of dopaminergic transmission in neuropsychiatric disorders

2001· article· en· W2314163331 on OpenAlexaff
Nicolaas Paul L.G. Verhoeff

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2001
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsDopaminergicDopamineNeurochemicalPositron emission tomographyDopamine transporterNeurotransmissionNeuroscienceDopamine receptorDopamine receptor D2Dopamine receptor D1ChemistryPostsynaptic potentialBiologyReceptorBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.321
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2001
Admission routes1
Has abstractyes

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