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Record W4309859459 · doi:10.1111/adb.13257

Disrupted functional connectivity of the brain reward system in substance use problems: A meta‐analysis of functional neuroimaging studies

2022· review· en· W4309859459 on OpenAlexaff
Jules R. Dugré, Pierre Orban, Stéphane Potvin

Bibliographic record

VenueAddiction Biology · 2022
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNeuroimagingFunctional neuroimagingNeuroscienceFunctional connectivityPsychologyMeta-analysisReward systemMedicineInternal medicine

Abstract

fetched live from OpenAlex

Extensive literature suggests that the brain reward system is crucial in understanding the neurobiology of substance use disorders. However, evidence of reliable deficits in functional connectivity across studies on substance use problems remains limited. Therefore, a voxel-wise seed-based meta-analysis using brain regions of the reward system as seeds of interest was conducted on 96 studies representing 5757 subjects with substance use problems. The ventromedial prefrontal cortex exhibited hyperconnectivity with the ventral striatum and hypoconnectivity with the amygdala and hippocampus. The executive striatum showed hyperconnectivity with the motor thalamus and dorsolateral prefrontal cortex and hypoconnectivity with the anterior cingulate cortex and anterior insula. Finally, the limbic striatum was found to be hyperconnected to the orbitofrontal cortex and hypoconnected to the precuneus compared with healthy subjects. The current study provided meta-analytical evidence of deficient functional connectivity between brain regions of the reward system and cortico-striato-thalamocortical loops in addiction. These results are consistent with deficits in motivation and habit formation occurring in addiction, and they highlight alterations in brain regions involved in socio-emotional processing and attention salience.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.309
GPT teacher head0.356
Teacher spread0.047 · 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.

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

Citations22
Published2022
Admission routes1
Has abstractyes

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