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Record W4213223193 · doi:10.3389/fpsyt.2022.817302

Habenula as a Neural Substrate for Aggressive Behavior

2022· review· en· W4213223193 on OpenAlexaff
Flavia Venetucci Gouveia, George M. Ibrahim

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMonoaminergicHabenulaNeuromodulationNeuroscienceDeep brain stimulationPsychologyMedicineStimulationCentral nervous systemParkinson's diseaseDiseaseSerotonin

Abstract

fetched live from OpenAlex

Over the past decades, an ever growing body of literature has explored the anatomy, connections, and functions of the habenula (Hb). It has been postulated that the Hb plays a central role in the control of the monoaminergic system, thus influencing a wide range of behavioral responses, and participating in the pathophysiology of a number of psychiatric disorders and neuropsychiatric symptoms, such as aggressive behaviors. Aggressive behaviors are frequently accompanied by restlessness and agitation, and are commonly observed in patients with psychiatric disorders, intellectual disabilities, and neurodegenerative diseases of aging. Recently, the Hb has been explored as a new target for neuromodulation therapies, such as deep brain stimulation, with promising results. Here we review the anatomical organization of the habenula and discuss several distinct mechanisms by which the Hb is involved in the modulation of aggressive behaviors, and propose new investigations for the development of novel treatments targeting the habenula to reduce aggressive behaviors.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.357
Teacher spread0.313 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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