Distinct genetic underpinnings of inter-individual differences in the sensorimotor-association axis of cortical organisation
Bibliographic record
Abstract
In humans, many neurobiological features of the cortex — including gene expression patterns, microstructure, and functional connectivity — vary systematically along a sensorimotor-association (S-A) axis of brain organisation. To date, it is still poorly understood whether inter-individual differences in patterns of S-A axis capture these robust spatial relationships across neurobiological properties observed at the group-level. Here, we examine inter-individual differences in structural and functional properties of the S-A axis, namely cortical microstructure, geodesic distances, and the functional gradient, in a sample of young adults from the Human Connectome Project (N = 992, including 328 twins). We quantified heritable variation associated with inter-individual differences in the S-A axis, and assessed whether structural and functional properties that are highly spatially correlated at the group-level also share genetic underpinnings. To consider measurement errors in resting-state functional connectivity data and their impact on properties of the S-A axis, we used a multivariate twin design capable of disentangling individual-level variation in both intra- and inter-individual differences. After accounting for some of the intra-individual variation, we found average heritable individual differences in both the functional gradient ( h twin 2 = 57%), cortical microstructure ( h twin 2 = 43%), and geodesic distances ( h twin 2 = 34%). However, these genetic influences were mostly distinct and deviated from group-level patterns. In particular, we found no significant genetic correlation between the functional gradient and microstructure, while we found both positive and negative genetic associations between the functional gradient and geodesic distances. Our approach highlights the complexity of genetic contributions to brain organisation and may have potential implications for understanding cognitive variability within the S-A axis framework.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".