There is an increased emphasis on visualizing neuroimaging results in more intuitive ways. Common statistical tools for dissemination of these results, such as bar charts, lack the spatial dimension that is inherent in neuroimaging data. Here we present two packages for the statistical software R that integrate this spatial component. The ggseg and ggseg3d packages visualize predefined brain segmentations as 2D polygons and 3D meshes, respectively. Both packages are integrated with other well-established R packages, which allows great flexibility. In this Tutorial, we describe the main data and functions in the ggseg and ggseg3d packages for visualization of brain atlases. The highlighted functions are able to display brain-segmentation plots in R. Further, the accompanying ggsegExtra package includes a wider collection of atlases and is intended for community-based efforts to develop additional compatible atlases for ggseg and ggseg3d. Overall, the ggseg packages facilitate parcellation-based visualizations in R, improve and facilitate the dissemination of results, and increase the efficiency of workflows.

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The results support the existence of DM deficits in adults with ADHD, which are of similar magnitude as attention deficits. These findings warrant further examination of DM in adults with ADHD to improve the understanding of underlying neurocognitive mechanisms.

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

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