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Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Background Methods Results Conclusions Many studies show that both callous‐unemotional (CU) traits (e.g., low empathy, lack of guilt) and cognitive difficulties increase risk for externalizing psychopathology across development. However, other work suggests that some aggression (e.g., relational, proactive) may rely on intact cognitive function, which could vary based on the presence of CU traits. Moreover, no prior research has adequately accounted for common risk factors shared by CU traits, cognitive difficulties, and externalizing problems, which confounds conclusions that can be drawn about their purported relationships. The current study addressed these knowledge gaps by leveraging rigorous propensity matching methods to isolate associations between CU traits and different dimensions of cognitive function and externalizing problems.Associations between CU traits, cognitive functioning, and externalizing outcomes were tested within dimensional (n = 11,868) and propensity‐matched group‐based (n = 1,224) models using data from the Adolescent Brain Cognitive Development Study®, with rigorous statistical control for shared sociodemographic risk factors. Cross‐sectional outcomes were parent‐reported symptoms of conduct disorder (CD), oppositional defiant disorder (ODD), and attention deficit hyperactivity disorder (ADHD). Longitudinal outcomes were child‐reported overt and relational aggression.CU traits were uniquely related to more parent‐reported CD, ODD, ADHD symptoms, as well as more child‐reported aggressive behaviors. Effects of cognitive difficulties were domain specific and were not consistent across dimensional and propensity matched models. There was minimal evidence for divergent associations between CU traits and externalizing outcomes as a function of cognition (i.e., no moderation).Rigorous control for sociodemographic factors within propensity‐matched models establish CU traits as a robust and unique risk factor for externalizing psychopathology, over and above difficulties with cognitive functioning. [ABSTRACT FROM AUTHOR]

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q‐space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q‐space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS‐DSI in post‐mortem or non‐human data. At present, the capacity for CS‐DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter‐scan reliability of 6 different CS‐DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS‐DSI images. This allowed us to compare the accuracy and inter‐scan reliability of derived measures of white matter structure (bundle segmentation, voxel‐wise scalar maps) produced by the CS‐DSI and the full DSI schemes. We found that CS‐DSI estimates of both bundle segmentations and voxel‐wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS‐DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS‐DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS‐DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications. [ABSTRACT FROM AUTHOR]

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed "FlywheelTools," a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv , for heuristic-driven curation of data into BIDS, and flaudit , which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform. [ABSTRACT FROM AUTHOR]

• Multi-shell imaging sequences may improve sensitivity to developmental effects. • Models that leverage multi-shell information are often less sensitive to the confounding effects of motion. • Multi-shell sequences and models that leverage this data may be of particular utility for studying the developing brain. Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12–30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment. [ABSTRACT FROM AUTHOR]