The genetics of spatiotemporal variation in cortical thickness in youth
Data files
Oct 08, 2024 version files 139.96 MB
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GenLong.RData
134.74 MB
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MatrixStatistics.xlsx
5.22 MB
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README.md
3.05 KB
Abstract
Prior studies have shown strong genetic effects on cortical thickness (CT), structural covariance, and neurodevelopmental trajectories in childhood and adolescence. However, the importance of genetic factors on the induction of spatiotemporal variation during neurodevelopment remains poorly-understood. Here, we explore the genetics of maturational coupling by examining 308 MRI-derived regional CT measures in a longitudinal sample of 677 twins and family members. We find dynamic inter-regional genetic covariation in youth, with the emergence of regional subnetworks in late childhood and early adolescence. Three critical neurodevelopmental epochs in genetically-mediated maturational coupling were identified, with dramatic network strengthening near eleven years of age. These changes are associated with statistically-significant (empiric p-value <0.0001) increases in network strength as measured by average clustering coefficient and assortativity. We then identify genes from the Allen Human Brain Atlas with similar co-expression patterns to genetically-mediated structural covariation in children. This set was enriched for genes involved in potassium transport and dendrite formation. Genetically-mediated CT-CT covariance was also strongly correlated with expression patterns for genes located in cells of neuronal origin.
https://doi.org/10.5061/dryad.7h44j103r
Description of the data and file structure
Supplementary Data and Figures for "The Genetics of Spatiotemporal Variation in Cortical Thickness in Youth," in press at Communications Biology.
Files include:
GenLong.RData: an .RData file including several objects:
- n_annot: names of 308 cortical ROIs, MNI coordinates
- roipair: 42278 x 2 matrix of pairwise combinations of ROI-ROI models performed
- rG_2D: a 42278 x 124 matrix of genetic correlations for 308 measures of cortical thickness, estimated for ages 5-18
- rG_3D: rG_2D converted to a 308 x 308 x 124 3D genetic correlation matrix (i.e. a 308 x 308 genetic correlation matrix for 124 timepoints ages 5-18)
- NMF5: 308 x 308 NMF-derived matrix for age 5
- NMF10: 308 x 308 NMF-derived matrix for age 11
- NMF18: 308 x 308 NMF-derived matrix for age 18
MatrixStatistics.xlsx: Hypothesis tests for main genetic effects. File includes several 308 x 308 matrices of ROI-ROI associations. Specifically X2 and p-values for submodels testing for main effects of additive genetic covariance in cortical thickness and genetic change over the age range. The MNI locations of each ROI are also provided in a separate tab.
Supplemental Files (Zenodo):
Movie 1S.mpg/Movie 1S.mp4: Dynamic spatiotemporal genetic correlation matrix for 308 regional CT ROIs between 5 and 18 years of age. Sidebars indicate lobe and hemisphere of corresponding ROIs.
Movie 2S.mpg/Movie 2S.mp4: Axial view of a dynamic network model based on the spatiotemporal genetic correlation matrix. Edges (connections) are thresholded at rG=0.8. Nodes are located at the MNI coordinates of their corresponding ROI’s centroid. Nodes are color coded by lobe-hemisphere combination (see legend from Movie S1 or Figure 1), and node size is scaled by its degree.
Movie 3S.mpg/Movie 3S.mp4: Sagittal view of a dynamic network model based on the spatiotemporal genetic correlation matrix. Edges (connections) are thresholded at rG=0.8. Nodes are located at the MNI coordinates of their corresponding ROI’s centroid. Nodes are color coded by lobe-hemisphere combination (see legend from Movie S1 or Figure 1), and node size is scaled by its degree.
SupplementaryFigures.docx: Numerous supplementary figures and tables including results from PisCES clustering, differences in ROI degree at 5,11, and 18 years, examination of global network statistics over variations in network sparsity, a table listing the specific top and bottom 1% of genes identified with sPCA using a ROI-ROI genetic correlation matrix aligned to AHBA expression data, output from Medscape analysis and MCODE, results comparing cell-specific gene expression patterns with sPCA results, age distribution of the sample, simplified genetically-informative latent growth curve path diagram, and data used to determine NMF rank.