Intrinsic functional connectivity strength of SuperAgers in the default mode and salience networks: Insights from ADNI
Bibliographic record
Abstract
There exists a group of older individuals who appear to be resistant to age-related memory decline. These "SuperAgers" have been shown to demonstrate preservation of cortical thickness and functional connectivity strength across the cortex which positively correlates with memory performance. Over the last decade, roughly 30 articles have been published regarding SuperAgers; however, to our knowledge, no replications of these studies have been published. The current study sought to conceptually replicate Zhang and colleagues' (2020) findings that SuperAgers demonstrate stronger intrinsic functional connectivity within the default mode (DMN) and salience networks (SN), and that connectivity strength within these networks correlates with memory performance. We identified 20 SuperAgers and 20 matched Normal Agers in the control cohort of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared the functional connectivity strength of the DMN and SN between these groups, and used the Rey Auditory Verbal Learning Test (RAVLT) to evaluate correlations between functional connectivity and memory performance. Our results did not replicate Zhang and colleagues' (2020) results, as we found negligible differences between SuperAgers and Normal Agers in the DMN and SN, and no significant correlations between functional connectivity and memory performance after accounting for multiple comparisons. More replications are needed to confirm existing work. In addition, more research with larger SuperAger samples and more consistent definitions of SuperAging is needed, so that we can better understand this remarkable group of older adults.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".