Local structure-function relationships in human brain networks across the lifespan
Bibliographic record
Abstract
The human connectome is the set of physical pathways linking brain regions to one another. Empirical and in silico studies have demonstrated that the structure of this network helps shape patterns of functional coupling between brain regions. To better understand this link between structure and function, a growing number of studies have derived geometric, dynamic, and topological predictors from structural connectivity in order to make predictions about correlation structure. These studies, however, have typically focused on global (whole-brain) predictions using a restricted set of predictors. Here, we investigate a wide range of predictors and shift focus onto predictions of local (regional) patterns of functional coupling. We show that, globally, no individual predictor performs well and, that even the best predictors are largely driven by their ability to predict functional coupling between directly connected regions. We then use the same predictors to make predictions of local coupling and find marked improvement. Notably, the most predictable local FC is linked to sensorimotor regions, which are best predicted by measures based on topological similarity, mean first passage times of random walkers, and the brain’s embedding in Euclidean space. We then show that by combining the predictive power of more than one predictor using multi-linear models, we can further improve local predictions. Finally, we investigate how global and local structure-function coupling differences across the human lifespan. We find that, globally, the magnitude of coupling decreases with biological age, which is paralleled by an increase in the number of multi-step pathways. We also show that, locally, structure-function coupling is preserved in higher order cognitive systems, but preferentially decreases with age in sensorimotor systems. Our results illuminate the heterogeneous landscape of structure-function coupling across the cerebral cortex and help clarify its differences with age.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".