P3‐264: THE RELATIONSHIP BETWEEN FUNCTIONAL CONNECTIVITY CHANGES AND SELECTIVE ATTENTION DEFICITS IN ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Selective attention (SA) is affected by Alzheimer's disease (AD), yet neural correlates underlying these deficits remain elusive. The typical pattern of task-induced deactivation in the default mode network (DMN) is also altered in AD. We expect that a decrease in task-induced deactivation of the DMN may contribute to SA errors. We also expect that functional connectivity (FC) of areas related to errors of SA will differ between AD and healthy controls (HC). 11 AD and age-matched HC (mean age 77.3) participants underwent a version of the Stroop task using fMRI. Stimuli were neutral, congruent or incongruent colour words. Verbal responses identifying the ink colour were recorded. We contrasted neural activity preceding an incongruent error between HC and AD. Four regions of interest showing group differences served as seeds for the FC analyses. A t-test was done comparing AD and HC connectivity maps (p < .05 cluster corrected). Results were correlated with Stroop scores. We found slower reaction times and more errors for incongruent stimuli in the AD group compared to HC (p <.05). As for functional differences, there was decreased activation in a variety of Stroop related areas in HC, including the left anterior cingulated cortex (ACC) and precuneus. AD participants had greater activity in parietal and posterior areas, including the right lingual gyrus and superior and inferior parietal lobules. The AD group also had ACC and precuneus activity, but was lateralized to the right. FC increases in AD were between the left ACC and the right precentral gyrus and the left precuneus and bilateral middle occipital gyrus. Decreases were found between the left precuneus and parahippocampal gyrus. We also found correlations between FC and Stroop scores. The AD group showed more posterior DMN activity and the HC group showed more frontal activity preceding errors on the Stroop task, suggesting that the neural activity surrounding errors of SA differs between AD and HC. In AD, task related changes observed in the precuneus resulted in FC changes that correlated with poor performance on the Stroop task. These findings help identify neural correlates underlying SA deficits in AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.005 | 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".