Dynamic network features of functional and structural brain networks support visual working memory in aging adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this work, we investigated how the relationship between structural connectivity and the dynamics of functional connectivity changes with age to benefit cognitive ability. Visual working memory (VWM) is an important brain function that allows us to maintain a mental representation of the world around us, but its capacity and precision peak by around 20 years old and decrease steadily throughout the rest of our lives. This research examined the functional brain network dynamics associated with VWM throughout the lifespan and found that Default Mode Network and Fronto-Parietal Network states were more well represented in individuals with better VWM. Furthermore, transitions from the Visual/Somatomotor Network state to the Attention Network state were more well represented in older adults, and a network control theory simulation demonstrated that structural connectivity differences supporting this transition were associated with better VWM, especially in middle-aged individuals. The structural connectivity of regions from all states was important for supporting this transition in younger adults, while regions within the Visual/Somatomotor and Attention Network states were more important in older adults. These findings demonstrate that structural connectivity supports flexible, functional dynamics that allow for better VWM with age and may lead to important interventions to uphold healthy VWM throughout the lifespan.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it