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Record W4294938536 · doi:10.3389/fnins.2022.943512

Four decades of mapping and quantifying neuroreceptors at work in vivo by positron emission tomography

2022· article· en· W4294938536 on OpenAlexaff
Albert Gjedde, Dean F. Wong

Bibliographic record

VenueFrontiers in Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthStatens Naturvidenskabelige ForskningsradNational Institutes of HealthNational Science Foundation
KeywordsNeurochemicalPositron emission tomographyNeuroscienceComputer scienceNeuroimagingMedical physicsArtificial intelligenceData scienceCognitive sciencePsychologyMedicine

Abstract

fetched live from OpenAlex

Decryption of brain images is the basis for the necessary translation of the findings from imaging to information required to meet the demands of clinical intervention. Tools of brain imaging, therefore, must satisfy the conditions dictated by the needs for interpretation in terms of diagnosis and prognosis. In addition, the applications must serve as fundamental research tools that enable the understanding of new therapeutic drugs, including compounds as diverse as antipsychotics, antidepressants, anxiolytics, and drugs serving the relief of symptoms from neurochemical disorders as unrelated as multiple sclerosis, stroke, and dementia. Here we review and explain the kinetics of methods that enable researchers to describe the brain’s work and functions. We focus on methods invented by neurokineticists and expanded upon by practitioners during decades of experimental work and on the methods that are particularly useful to predict possible future approaches to the treatment of neurochemical disorders. We provide an overall description of the basic elements of kinetics and the underlying quantification methods, as well as the mathematics of modeling the recorded brain dynamics embedded in the images we obtain in vivo. The complex presentation to follow is necessary to justify the contribution of modeling to the development of methods and to support the specifications dictated by the proposed use in clinical settings. The quantification and kinetic modeling processes are equally essential to image reconstruction and labeling of brain regions of structural or functional interest. The procedures presented here are essential tools of scientific approaches to all conventional and novel forms of brain imaging. The foundations of the kinetic and quantitative methods are keys to the satisfaction of clinicians that actively engage in treating the neurochemical disorders of mammalian brains in the fields of neurology, neurosurgery, and neuropsychiatry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.007
Scholarly communication0.0030.006
Open science0.0020.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.031
GPT teacher head0.286
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2022
Admission routes1
Has abstractyes

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