Heritability of cortical morphology reflects a sensory-fugal plasticity gradient
Bibliographic record
Abstract
Abstract Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulam’s (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulam’s hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity. Highlights (up to 85 chars) Cortical areas vary in heritability. This is seen across structural measures. Heritability differences could be explained by plasticity, topography, or noise. We build a comprehensive model testing many explanations across 5 measures. Heritability is explained by noise and 1 st structural gradient reflecting plasticity. Heritability could be a method to study brain plasticity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".