P3‐197: AGE‐RELATED FUNCTIONAL CONNECTIVITY CHANGES: VARIATIONS IN MAGNITUDE AND SHIFTS FROM NEGATIVE TO POSITIVE CORRELATIONS
Bibliographic record
Abstract
Results of functional connectivity (FC) studies are often described as increases or decreases in FC and the interpretation can be challenging. For instance, “increases in FC” may not only represent stronger positive correlations but also decreased magnitude of negative correlations or even shifts from negative to positive correlations. Each of these may indicate different neurobiological substrates. In order to better characterize the age-related changes in FC we subclassified the types of FC increases and decreases in a sample of cognitively healthy adults. We extracted the resting-state fMRI time series from 278 brain regions of a sample of 13 young, 13 middle-aged and 7 elderly. Correlations between all 38503 pairs of regions were calculated as estimates of FC and tested for association with age using behavioral partial least squares. The connections that were significantly and reliably associated with age were classified in six types: 1) increases in magnitude of positive correlations; 2) decreases in magnitude of negative correlations; 3) shifts from negative to positive correlations; 4) increases in magnitude of negative correlations; 5) decreases in magnitude of positive correlation; 6) shifts from positive to negative correlations. 2009 connections were associated with age (permuted p=0.02; correlation [95% CI] = 0.79 [0.76-0.83]; |bootstrap ratio| ≥ 3). Most were characterized by age-related increases in magnitude of positive correlation. Shifts from negative (in the young) to positive correlation (in the old) were also very common. Fewer connections were characterized by decreased in magnitude of positive correlations (Figure). We could not find connections presenting reliable age-related increases in magnitude of anticorrelations. Figure (abstract). Changes in functional connectivity associated with age, classified by type
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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 | 0.002 |
| 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) | 0.009 | 0.001 |
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".