Neural Activation in Risky Decision-Making Tasks in Healthy Older Adults: A Meta-Analysis of fMRI Data
Bibliographic record
Abstract
Decision making is a complex cognitive phenomenon commonly used in everyday life. Studies have shown differences in behavioral strategies in risky decision-making tasks over the course of aging. The development of functional neuroimaging has gradually allowed the exploration of the neurofunctional bases of these behaviors. The purpose of our study was to carry out a meta-analysis on the neural networks underlying risky decision making in healthy older adults. Following the PRISMA guidelines, we systematically searched for fMRI studies of decision making in older adults using risky decision-making tasks. To perform the quantitative meta-analysis, we used the revised version of the activation likelihood estimation (ALE) algorithm. A total of 620 references were selected for initial screening. Among these, five studies with a total of 98 cognitively normal older participants (mean age: 69.5 years) were included. The meta-analysis yielded two clusters. Main activations were found in the right insula, bilateral dorsolateral prefrontal cortex (dlPFC) and left orbitofrontal cortex (OFC). Despite the limited number of studies included, our meta-analysis highlights the crucial involvement of circuits associated with both emotion regulation and the decision to act. However, in contrast to the literature on young adults, our results indicate a different pattern of hemispheric lateralization in older participants. These activations can be used as a minimum pattern of activation in the risky decision-making tasks of healthy older subjects.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".