Connectivity Changes Following Episodic Future Thinking in Alcohol Use Disorder
Bibliographic record
Abstract
Introduction: Recent addiction and obesity-related research suggests that episodic future thinking (EFT) can serve as a promising intervention to promote healthy decision-making. We used data from a pilot study to investigate the acute neural effects of EFT in alcohol use disorder (AUD). Because of the limitations of those data, we additionally used data from a previously published functional MRI (fMRI) study in which participants had not received any intervention for their AUD. Methods: In an out-of-scanner, guided interview, participants ( n = 24; median age = 37.3 years; median AUDIT = 22.5) generated scenarios and cues about their future (EFT intervention, n = 15) or recent past (control episodic thinking [CET] control intervention, n = 9). Then, they performed both resting-state and task-based (delay discounting [DD]) fMRI. We used nodes from the default mode network and salience networks as well as the hippocampus to perform seed-based analyses of the resting-state data. The results then guided psychophysiological interaction analyses in the DD task. In addition, we used data from a larger, previously reported study as a “no intervention” group of AUD participants ( n = 50; median age = 43.3; median Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) alcohol dependence score = 7) to reproduce and aid in interpreting our key findings. Results: EFT, but not CET, participants showed statistically improved DD rates—a behavioral marker for addiction. Resting-state analyses of the left hippocampus revealed connectivity differences in the frontal poles. The directionality of this difference suggested that EFT may reduce a hypo-connectivity relationship between these regions in AUD. We also found resting-state connectivity differences between the salience network and the right dorsolateral prefrontal cortex (R DLPFC), which then led us to discover R-to-L DLPFC psychophysiological interaction differences during DD. Moreover, the resting-state salience-to-DLPFC functional connectivity showed an inverse relationship to DD rate while hyperconnectivity between left and right DLPFC reflected slower reaction times during DD trials. Discussion: These findings suggest that previously noted benefits of EFT such as the improved DD replicated here might coincide with changes in neural connectivity patterns in AUD. The alterations in connectivity highlight potential mechanisms underlying the effectiveness of EFT in improving decision-making in AUD. Understanding these neural effects may contribute to the further development of targeted interventions for AUD and related disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".