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Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects

2018· article· en· W2895898846 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Journal of Psychiatry · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersDavid Geffen School of Medicine, University of California, Los AngelesNational Center for Complementary and Integrative HealthNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismMelbourne School of Psychological SciencesNational Health and Medical Research CouncilLaureate Institute for Brain Research, University of TulsaIllawarra Health and Medical Research InstituteMax-Planck-Institut für Kognitions- und NeurowissenschaftenUniversity of Cape TownJanssen Research and DevelopmentRadboud Universitair Medisch CentrumRadboud UniversiteitUniversitat de BarcelonaVrije Universiteit AmsterdamNational Institute of Mental HealthUniversity of Colorado BoulderQIMR Berghofer Medical Research InstituteUniversiteit UtrechtZonMwSchool of MedicineUniversity of VermontUniversiteit van AmsterdamMonash UniversityMedical Research CouncilUniversity of WollongongNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of Southern CaliforniaAustralian Catholic UniversityUniversiteit LeidenUniversity of OregonUniversity of California, San DiegoYale UniversityKing's College LondonVeterans Affairs San Diego Healthcare SystemMcGill UniversityUniversity of RochesterNational Institutes of HealthSan Diego State University
KeywordsBrain sizeCannabisSubstance dependenceOrbitofrontal cortexAlcohol dependenceNeuroimagingInsulaCocaine dependenceVoxel-based morphometrySubstance abuseBrain morphometryMedicinePsychologyAddictionMagnetic resonance imagingPsychiatryNeuroscienceAlcoholPrefrontal cortexCognitionWhite matterChemistryRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Although lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes. METHOD: Brain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings. RESULTS: Lower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects. CONCLUSIONS: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

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.

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 score0.011
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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