Functional and structural neuroimaging studies of delayed reward discounting in addiction: A systematic review.
Bibliographic record
Abstract
Given the robust behavioral association between delayed reward discounting (DRD) and addictive behavior, there is an expanding literature investigating the neural correlates of this relationship. The objective of this systematic review was to characterize and critically appraise the existing literature examining the neural correlates of DRD in individuals exhibiting addictive behavior using functional and structural MRI (fMRI/MRI) and to do so through the lens of the neural networks implicated in addiction. Using a systematic search strategy, 20 studies were identified, with 12 focusing on task fMRI, 4 focusing on functional connectivity fMRI, and 4 focusing on structural MRI. Behaviorally, significantly steeper DRD was present in individuals with addictive disorders across studies, reproducing earlier findings. Among individuals with addictive disorders, there was substantial evidence of greater neural activity in the executive control network during choices for larger-delayed rewards (delayed gratification) relative to choices for smaller-immediate rewards (immediate gratification), particularly in dorsolateral prefrontal cortex, as well as moderate evidence of greater recruitment of the default mode, salience, and reward valuation networks during larger-delayed choices. In functional connectivity fMRI studies, there was moderate evidence for greater connectivity between the executive control, salience, and default mode networks in individuals exhibiting addictive behavior. Structural MRI studies reported highly heterogeneous findings and no consistent conclusions could be drawn. As a whole, this review suggests consistent differences in neural activation and connectivity relating to DRD in individuals with addictive disorders. It also reveals heterogeneity of methods and findings in this line of inquiry. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".