Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes
Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes is a dataset published in Nature Genetics (2023). On theSindex it has a DataRank of 1.2, placing it in the top 39.5% of the data-sharing corpus. It has been cited 79 times, with 58 citing works in its 1-hop citation network. Its calibrated FAIR score is 54/100.
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.644
From this paper's citation signal
Citation Network Contribution
0.606
From 32 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage AnalysesThe American Journal of Human Genetics200735,753 citationsDataRank 1.6
- A global reference for human genetic variationNature201519,823 citationsDataRank 11.1Top 19%
- The mutational constraint spectrum quantified from variation in 141,456 humansNature20209,958 citationsDataRank 1.4
- The organization of the human cerebral cortex estimated by intrinsic functional connectivityJournal of Neurophysiology20119,617 citationsDataRank 1.4
- Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median EstimatorGenetic Epidemiology20169,473 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 52% comes from its base citations and 48% from the citation network (32 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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