Investigating individual variability in microstructural-functional coupling in the human cortex
Bibliographic record
Abstract
Abstract Understanding the relationship between the structural and functional architecture of the human brain remains a key question in neuroscience. In this regard variation in cortical myelin may provide key insights into the functional organization. Previous findings have demonstrated that regions sharing myeloarchitectonic features are also likely to be structurally and functionally connected. However, this association is not uniform for all regions. For example, the strength of the association, or ‘coupling’, between microstructure and function is regionally heterogeneous, with strong coupling in primary cortices but weaker coupling in higher order transmodal cortices. However, the bases of these observations have been typically made at the group level, leaving much to be understood regarding the individual-level behavioural relevance of microstructural-functional coupling variability. To examine this critical question, we apply a multivariate framework to a combination of high-resolution structural, diffusion, and functional magnetic resonance imaging (MRI) data in a sample of healthy young adults. We identify four distinct patterns of coupling variation that vary across individuals. Remarkably, we find that while microstructural-functional coupling is consistently strong in primary cortices, significant variation in transmodal cortices exists. Importantly, we identified coupling variability maps and their association with behaviour that demonstrate the existence of latent dimensions of variability related to inter-individual performance on cognitive tasks. These findings suggest that the existence of behaviourally relevant coupling variation is a key principle for brain organization.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".