MétaCan
Menu
Back to cohort

Tracing the Neuroanatomical Profiles of Reward Pathways with Markers of Neuronal Activation

2004· review· en· W2319183168 on OpenAlexaff
Anne T. M. Konkle, Catherine Bielajew

Bibliographic record

VenueReviews in the Neurosciences · 2004
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNeuroscienceStimulationBrain stimulation rewardTracingPsychologyPremovement neuronal activityReward systemBiologyComputer scienceDopamineNucleus accumbens

Abstract

fetched live from OpenAlex

Functional neuroanatomical tools have played an important role in proposing which structures underlie brain stimulation reward circuitry. This review focuses on studies employing metabolic markers of neuronal and glial activation, including 2-deoxyglucose, cytochrome oxidase, and glycogen phosphorylase, and a marker of cellular activation, the immediate early gene c-fos. The principles underlying each method, their application to the study of brain stimulation reward, and their strengths and limitations are described. The usefulness of this strategy in identifying candidate structures, and the degree of overlap in the patterns of activation arising from different markers is addressed in detail. How these data have contributed to an understanding of the organization of reward circuitry and directed our thinking towards an alternative framework of neuronal arrangement is discussed in the final section.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.168
GPT teacher head0.398
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueReviews in the NeurosciencesSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207