Neural mechanisms underlying cognitive changes in individuals with alcohol use disorder: insights from resting-state functional magnetic resonance imaging
Bibliographic record
Abstract
Background: Alcohol use disorder (AUD) is associated with cognitive impairment and disruptions in brain function. This study aimed to investigate the neural mechanisms underlying these cognitive changes. This study employed multiple analytical approaches to analyze resting-state functional magnetic resonance imaging (RS-fMRI) data. Methods: In total, 30 individuals with AUD and 29 healthy control (HC) individuals were enrolled in the study. All the participants completed the Montreal Cognitive Assessment (MoCA) and underwent RS-fMRI scans. Neural activity was assessed using the amplitude of low-frequency fluctuation (ALFF) method, and a seed-based functional connectivity (FC) analysis was then conducted to examine network interactions. Results: Compared to the HC group, the AUD group had significantly lower total MoCA scores, particularly in terms of visuospatial/executive function, attention, and orientation (P<0.05). In the AUD group, there was a significant increase in ALFF in the right inferior frontal gyrus and bilateral parahippocampal gyrus, and a significant decrease in ALFF in the right middle frontal gyrus (P<0.05). A positive correlation was found between the ALFF values and total MoCA scores in the right inferior frontal gyrus of the AUD group (P<0.05). Additionally, there was enhanced self-interactive FC between the medial and lateral regions of the right inferior frontal gyrus (different subregions within the same brain area), and a positive correlation between the FC values and attention scores in the group (P<0.05). Conclusions: The AUD patients demonstrated significant resting-state functional alterations across multiple brain regions, and dysregulation in the right inferior frontal gyrus emerged as a potential neural substrate for their cognitive deficits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".