Altered Functional Brain Connectivity in Mild Cognitive Impairment during a Cognitively Complex Car Following Task
Bibliographic record
Abstract
Mild cognitive impairment (MCI) can affect multiple cognitive abilities, leading to difficulty in performing complex, cognitively demanding daily tasks, such as driving. This study combined driving simulation and functional magnetic resonance imaging (fMRI) to investigate brain function in individuals with MCI while they performed a car-following task. The behavioral driving performance of 24 patients with MCI and 20 healthy age-matched controls was compared during a simulated car-following task. Functional brain connectivity during driving was analyzed for a separate cohort of 15 patients with MCI and 15 controls. Individuals with MCI had minor difficulty with lane maintenance, exhibiting significantly increased variability in steering compared to controls. Patients with MCI also exhibited reduced connectivity between fronto-parietal regions, as well as between regions involved in cognitive control (medial frontal cortex) and regions important for visual processing (cuneus, angular gyrus, superior occipital cortex, inferior and superior parietal cortex). Greater difficulty in lane maintenance (i.e., increased steering variability and lane deviations) among individuals with MCI was further associated with increased connectivity between the posterior cingulate cortex (PCC) and inferior frontal gyrus, as well as increased intra-cerebellar connectivity. Thus, compared to cognitively healthy controls, patients with MCI showed reduced connectivity between regions involved in visual attention, visual processing, cognitive control, and performance monitoring. Greater difficulty with lane maintenance among patients with MCI may reflect failure to inhibit components of the default-mode network (PCC), leading to interference with task-relevant networks as well as alterations in cerebellum connectivity.
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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".