IC‐P‐070: Contrasting default mode abnormalities in Alzheimer's disease revealed by task and passive rest
Bibliographic record
Abstract
Evidence suggests there is dysfunction of the default-modenetwork (DMN) in Alzheimer's disease (AD). Much of this work relies analysis of intrinsic connectivity at rest. Other studies examine task-related deactivation in AD patients, and differences are also attributed to DMN dysfunction. While the connectivity of the DMN is robust, it may not be identically instantiated across different states. Sensitivity to group differences may also depend on the state in which it is defined. To date, no work has compared the nature of DMN differences identified in AD from a passive-rest setting compared to a task-baseline setting. We hypothesized that differences in the nature of thesetwo states would lead to contrasting group differences. We scanned a sample of 20 patients with mild AD and 16 matched controls during a simple block-design task with interleaved fixation baseline, and a subsequent resting state run. Both groups showed excellent task performance. Group Independent Component Analysis decomposed task and resting fMRI data into components. The DMN was identified from rest as comprising posterior cingulate/precuneus, lateral parietal, medial prefrontal and medial temporal regions. The DMN was identified from task-data including the same set of regions, showing highest co-activation during baseline fixation blocks. Cross-correlation confirmed spatial similarity between these components (r = 0.85). Dual-regression wasused to compare groups in strength of passive-DMN and task-DMN separately. There was a dissociation in the group differences found in the two DMN states. In the passive-DMN patients showed reduced co-activation of the left hippocampus (p < 0.05, FWE corrected). In contrast the task-DMN showed significantly reduced posterior cingulate/precuneus co-activation in patients. We identified two DMN components from passive rest and task-runs. While both included the same major regions, there were contrasts in their group differences. Examining disruptions of the DMN in AD may depend on the state being examined. The free nature of passive-rest may increase sensitivity to group differences in the MTL. The preparatory nature of fixation baseline may predispose to posterior midline differences due to continuous attention. Taken together, these findings suggest that a comprehensive picture of DMN disruption in AD requires the analysis of multiple states.
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) | 0.002 | 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".