Effects of mild cognitive impairment on brain function during distracted driving
Bibliographic record
Abstract
Abstract Background Driving is an integral part of daily life. The complex, multi‐faceted nature of driving makes it vulnerable to the effects of pathological aging on the brain. Studies have shown that patients with mild cognitive impairment (MCI) maintain safe driving in routine circumstances, however there is limited examination of the effects of MCI in more challenging driving scenarios, such as distracted driving. In addition, little is known about how distraction may disrupt the brain networks that are involved in safe driving for patients with MCI. This is the first study to combine functional magnetic resonance imaging (fMRI) with an MRI‐compatible driving simulator to study the effect of distraction on driving‐related brain activity in healthy and MCI cohorts. Methods This study used fMRI and an MRI‐compatible driving simulator to measure brain activity during driving in 30 patients with MCI and 30 age and sex‐matched control participants. Patients were diagnosed using the criteria defined by the National Institute on Aging‐Alzheimer’s Association. In the simulator, participants were required to respond to a distracting true or false question while driving. Parametric maps of brain activity were calculated using one‐sample t‐tests, which were cluster‐size thresholded to adjust for multiple comparisons. Results Both groups displayed consistent recruitment of the bilateral frontal, parietal, temporal and occipital lobes during the distracting condition (Figure 1). Patients with MCI exhibited more extensive positive activation in the bilateral superior temporal lobes and the middle and inferior frontal lobes. Although patients with MCI did not commit more driving errors, they displayed impairment by not answering the true or false questions (p < 0.001) and by taking longer to complete turns (p < 0.001). Conclusions During distracted driving, patients with MCI significantly altered their behaviour to safely complete the driving task. The observed increased recruitment of the frontal lobes among patients may be reflective of the compensatory cognitive effort exerted to maintain task performance. MCI may result in changes in driving behaviour, which are exacerbated in challenging driving situations. These findings suggest that subtle changes in measures of driving behaviour may be signs of significant MCI‐related alterations in driving networks of the brain.
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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".