Amnestic Mild Cognitive Impairment: Topological Reorganization of the Default-Mode Network
Bibliographic record
Abstract
PURPOSE: To investigate the topologic reorganization of the default-mode network (DMN) in patients with mild cognitive impairment (MCI) and whether, relative to healthy control subjects, patients with MCI would be more likely to show disrupted functional connectivity and altered topological configuration of the DMN during the memory task compared with that observed during the resting state. MATERIALS AND METHODS: This study was approved by the institutional review board of Beijing Normal University Imaging Center for Brain Research. Written informed consent was obtained from each participant. Healthy control subjects (n = 26) and patients with amnestic MCI (aMCI) (n = 25) performed an episodic memory task and also rested while undergoing functional magnetic resonance imaging. Task-induced deactivations were identified and parcellated into different regions associated with the DMN. Functional connectivity across all pairs of regions was computed to construct the DMN architecture. Graph theoretical approaches were used to characterize topological properties of this network. RESULTS: Patients with aMCI showed similar deactivation in the DMN to that observed in healthy control subjects (P > .05) but showed significantly decreased anterior-to-posterior functional connectivity only during the task (P < .05). Significant increases in local efficiency (P < .05), but not in global efficiency (P > .05), were observed in aMCI only during the task. Decreased functional connectivity was predictive of increased local efficiency (r = -0.35, P = .015). Significant correlations between these network measures and cognitive performance (P < .05) indicated their potential use as early markers to assess the risk of Alzheimer disease (AD). CONCLUSION: This study suggests the early onset functional reorganization of the DMN toward a nonoptimized regularity configuration in aMCI and expands the understanding of dynamic functional reorganization in brain networks along the continuum from normal aging to AD dementia.
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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 | 0.001 | 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".