Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short‐Term Memory Binding Task
Bibliographic record
Abstract
Working memory, which tends to be the most vulnerable cognitive domain to aging, is thought to depend on a functional brain network for efficient communication. The dynamic communication within this network is represented by segregation and integration. This study aimed to investigate healthy aging by examining age effect on outcomes of graph theory analysis during the visual short-term memory binding (VSTMB) task. VSTMB tasks rely on the integration of visual features and are less sensitive to semantic and verbal strategies. Effects of age on neuropsychological test scores, along with the EEG graph-theoretical integration, segregation and global organization metrics in frequencies from delta to gamma band were investigated. Neuropsychological assessment showed low sensitivity as a measure of age-related changes. EEG results indicated that network architecture changed more effectively during middle age, while this effectiveness appears to vanish or show compensatory mechanisms in the elderly. These differences were further found to be related to cognitive domain scores. This study is the first to demonstrate differences in working memory network architecture across a broad age range.
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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".