Age-related Differences in Response Inhibition Are Mediated by Frontoparietal White Matter but Not Functional Activity
Bibliographic record
Abstract
Healthy older adults often exhibit lower performance but increased functional recruitment of the frontoparietal control network during cognitive control tasks. According to the cortical disconnection hypothesis, age-related changes in the microstructural integrity of white matter may disrupt inter-regional neuronal communication, which in turn can impair behavioral performance. Here, we use fMRI and diffusion-weighted imaging to determine whether age-related differences in white matter microstructure contribute to frontoparietal over-recruitment and behavioral performance during a response inhibition (go/no-go) task in an adult life span sample (n = 145). Older and female participants were slower (go RTs) than younger and male participants, respectively. However, participants across all ages were equally accurate on the no-go trials, suggesting some participants may slow down on go trials to achieve high accuracy on no-go trials. Across the life span, functional recruitment of the frontoparietal network within the left and right hemispheres did not vary as a function of age, nor was it related to white matter fractional anisotropy (FA). In fact, only frontal FA and go RTs jointly mediated the association between age and no-go accuracy. Our results therefore suggest that frontal white matter cortical "disconnection" is an underlying driver of age-related differences in cognitive control, and white matter FA may not fully explain functional task-related activation in the frontoparietal network during the go/no-go task. Our findings add to the literature by demonstrating that white matter may be more important for certain cognitive processes in aging than task-related functional activation.
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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".