A Quantitative Study of Network Robustness in Resting-State fMRI in Young and Elder Adults
Bibliographic record
Abstract
Brain connectivity analysis has shown great promise in understanding how aging affects functional connectivity; however, an explanatory framework to study healthy aging in terms of network efficiency is still missing. Here, we study network robustness, i.e., resilience to perturbations, in resting-state functional connectivity networks (rs-fMRI) in young and elder subjects. We apply analytic measures of network communication efficiency in the human brain to investigate the compensatory mechanisms elicited in aging. Specifically, we quantify the effect of "lesioning" (node canceling) of either single regions of interest (ROI) or whole networks on global connectivity metrics (i.e., efficiency). We find that young individuals are more resilient than old ones to random "lesioning" of brain areas; global network efficiency is over 3 times lower in older subjects relative to younger subjects. On the other hand, the "lesioning" of central and limbic structures in young subjects yield a larger efficiency loss than in older individuals. Overall, our study shows a more idiosyncratic response to specific brain network "lesioning" in elder compared to young subjects, and that young adults are more resilient to random deletion of single nodes compared to old adults.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".