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Frontal White Matter Changes and Craving Recovery in Inpatients With Heroin Use Disorder

2024· article· en· W4405515272 on OpenAlexfundno aff
Pierre‐Olivier Gaudreault, Sarah G. King, Yuefeng Huang, Ahmet O. Ceceli, Greg Kronberg, Nelly Alia‐Klein, Rita Z. Goldstein

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative HealthNational Institute on Drug AbuseNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsCravingHeroinMedicineOpioid use disorderPsychological interventionPsychiatryMoodPsychologyClinical psychologyInternal medicineOpioidAddictionDrug

Abstract

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Importance: Amidst an unprecedented opioid epidemic, identifying neurobiological correlates of change with medication-assisted treatment of heroin use disorder is imperative. White matter impairments in individuals with heroin use disorder (HUD) have been associated with drug craving, a reliable predictor of treatment outcomes; however, little is known about structural connectivity changes with inpatient treatment and abstinence in individuals with HUD. Objective: To assess white matter microstructure and associations with drug craving changes with inpatient treatment in individuals with HUD (effects of time and rescan compared with controls). Design, Setting, and Participants: This cohort study conducted from December 2020 to September 2022 included individuals recruited from urban inpatient treatment facilities treating HUD and surrounding communities in New York City. Participants with HUD were receiving medication-assisted treatment. Data were analyzed from October 2022 to March 2023. Intervention: Between scans, inpatient individuals with HUD continued treatment and related clinical interventions. Control participants were scanned at similar time intervals. Main Outcomes and Measures: Changes in white matter diffusion metrics (fractional anisotropy and mean, axial, and radial diffusivities) assessed voxelwise with general linear models in addition to baseline and cue-induced drug craving, and other clinical outcome variables (mood, sleep, affect, perceived stress, and therapy attendance). Results: Thirty-four individuals with HUD (mean [SD] age, 40.5 [11.0] years; 9 women [36%]; 3 Black [9%], 17 White [50%], 14 other race or ethnicity [41%]) and 25 control (mean [SD] age, 42.1 [9.0]; 7 women [21%]; 8 Black [32%], 10 White [40%], 7 other race or ethnicity [28%]) were included. Main voxelwise findings showed HUD-specific white matter microstructure changes (1 - P > .949), including increased fractional anisotropy and decreased mean and radial diffusivities, encompassing mostly frontal major callosal, projection, and association tracts. The increased fractional anisotropy (r = -0.72, P < .001, slope SE = 9.0 × 10-4) and decreased mean diffusivity (r = 0.69, P < .001, slope SE = 1.25 × 10-6) and/or radial diffusivity (r = 0.67, P < .001, slope SE = 1.16 × 10-6) in the genu and body of the corpus callosum and left anterior corona radiata in individuals with HUD correlated with a reduction in baseline craving (voxelwise 1 - P > .949). No other white matter correlations with outcome variables reached significance. Conclusions and Relevance: This cohort study of inpatients with HUD on medication-assisted treatment found whole-brain normalization of structural connectivity in frontal white matter pathways implicated in emotional regulation and top-down executive control. Observed associations with decreases in baseline craving further support the possibility of recovery, highlighting the relevance of these white matter markers to a major symptom of addiction, with implications for clinical outcome monitoring.

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)0.000
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 score0.066
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.320
Teacher spread0.278 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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