9️⃣ Many thanks to participants from @ukbiobank.bsky.social @genscot.bsky.social and @edinunilbc.bsky.social, and to all co-authors.

8️⃣ In the supplement we also i) provide the largest analysis of meta-analytic subcortical volumetric-cognitive associations; ii) investigate the effect of smoothing tolerance and iii) will make the newly created maps openly accessible upon publication.

7️⃣ Together, we provide a compendium of cortex-wide and regional spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning.

6️⃣ In addition to calculating cortex-wide spatial correlations, we calculate spatial correlations within regions. These analyses show two important things: 1) the relative strength of correlations for different regions, and 2) the homogeneity of correlations among regions.

5️⃣ We also calculate correlations between g-morphometry and age-morphometry associations and find that areas of the brain most associated with g are also those that are most strongly associated with age: correlations range from r = -0.355 to -0.622.

4️⃣ The 33 neurobiological cortical profiles spatially covary along four major dimensions (accounting for 65.9% of the variance). Encouragingly, the largest dimension strongly recapitulates the principal gradient (unimodal ➡️ amodal brain regions).

3️⃣ Then, we bring together brain maps of 33 cortical characteristics (e.g., neurotransmitter receptor densities, gene expression, metabolism, and cytoarchitectural similarity) to quantify the neurobiological properties of the brain regions most implicated in cognitive differences

2️⃣ These associations (|β| range = < 0.001 to 0.17) show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18).

1️⃣ We run the largest vertex-wise meta-analysis of general cognitive function-morphometry associations (meta-analytic N = 38,379).