Multiscale structural gradients during childhood and adolescence.
<p><b>(A)</b> The matrices containing the structural features of geodesic distance, microstructural profile covariance, and diffusion MRI tractography were concatenated and transformed into an affinity matrix, followed by the diffusion map embedding algorithm. The first three gradi...
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2025
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| Summary: | <p><b>(A)</b> The matrices containing the structural features of geodesic distance, microstructural profile covariance, and diffusion MRI tractography were concatenated and transformed into an affinity matrix, followed by the diffusion map embedding algorithm. The first three gradients capture the largest proportion of the variance. The group-averaged gradients were projected onto the cortical surface and visually represented (right). <b>(B)</b> The global density map of the principal gradient for six age-specific groups. <b>(C)</b> Radar plot of the principal gradient for comparison between the 6–7-year-old group and other age-specific groups based on Yeo functional networks (left) [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002710#pbio.3002710.ref047" target="_blank">47</a>] and laminar differentiation parcellation (right) [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002710#pbio.3002710.ref048" target="_blank">48</a>]. <b>(D)</b> The first and third structural gradients mapped into a 2D gradient space for the 6- to 7-, 9-, and 12- to 13-year-old groups. The data underlying this figure can be found at <a href="https://zenodo.org/records/14874537" target="_blank">https://zenodo.org/records/14874537</a>.</p> |
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