An Initial Investigation of Disrupted Intracortical Myelin as a Novel Brain Marker of Alcohol Use Disorder
Bibliographic record
Abstract
Abstract: Although disruption of cortical gray matter and white matter tracts are well-established markers of alcohol use disorder (AUD), this is the first study to examine the specific role of intracortical myelin (ICM; i.e., highly myelinated gray matter in deeper cortical layers) in AUD. The current study used a 3T MRI sequence optimized for high intracortical contrast to examine patterns of ICM-related MRI signal in 30 individuals with AUD and 33 healthy social drinkers. Secondary aims included exploring continuous associations with alcohol problem severity and examining sex differences. Surface-based analytic techniques were used to quantify ICM-related MRI signal for a priori region of interest analyses (20 bilateral regions) and exploratory vertex-wise analyses (using Cohen’s d). Although the distribution of ICM-related signal was generally comparable between groups, the AUD group exhibited significantly (p<.05) greater ICM-related MRI signal in precuneus, ventromedial prefrontal cortex, posterior cingulate, middle anterior cingulate, middle/posterior insula, dorsolateral prefrontal cortex, and posterior cingulate, among other regions (Cohen’s d = .50-.75, indicating medium magnitude effects). Significant positive correlations between ICM signal and AUD severity were found in several frontal, parietal, cingulate, and temporal regions (rs .25-.34). No sex differences in ICM were observed. These findings provide initial proof-of-concept for examining ICM in relation to AUD. Understanding the pathophysiological mechanisms of these associations (e.g., neuroinflammation) and the clinical relevance of ICM is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".