Genetic, transcriptomic, metabolic, and neuropsychiatric underpinnings of cortical functional gradients
Bibliographic record
Abstract
Abstract The intrinsic functional organization of the human connectome can be captured along multiple spatial axes or gradients that differentiate sensory from association networks, visual from somatomotor networks, and control from default networks. These gradients demonstrate variability at the individual level, changing across the lifespan and varying across disorders. However, the biological basis of this variability is not fully understood. In this study, we integrated genetic, neuroimaging, and transcriptomic data using genome-wide association studies (GWAS) and twin-based heritability analysis of over 30,000 individuals to understand the contribution of genes to variability in functional organization gradients. Specifically, we identified five genetic loci involving 16 genes associated with the global organization of functional gradients that are enriched for lipid biosynthesis and energy metabolism. At the local regional level, we observe modest heritability scores across twin- and GWAS-based analyses of different samples, including adolescents, young adults, and middle-aged and older adults. Association areas are the most heritable. Importantly, the genes identified by GWAS do not overlap with those observed using post-mortem spatial transcriptomics. Lastly, while we did not observe genetic overlap between neuropsychiatric disorders and functional gradients, we found that regional gradient loadings are altered not only in neuropsychiatric disorders, but also in healthy individuals with polygenic risk scores for these disorders. Our findings highlight the complex interplay between genetic variation and intrinsic brain function. They offer new insights into the biological foundations of functional brain organization and its implications for neuropsychiatric vulnerability.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".