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Background: Extra-axial cerebrospinal fluid (eaCSF) refers to the CSF in the subarachnoid spaces that surrounds the brain parenchyma. Benign enlargement of the subarachnoid space (BESS), a condition marked by increased eaCSF thickness, has been associated with macrocephaly and may be associated with subdural collections. However, diagnosis of BESS is complicated by the lack of age-specific normative data which hinders rigorous investigation of its clinical associations. Growth charts of eaCSF could shed light on normal CSF dynamics while also providing a normative benchmark to assist the diagnosis of BESS and other associated conditions., Methods: We accessed clinically-acquired T1w MRI scans from 1226 pediatric patients to form a clinical control cohort. Nine scans from subjects with a diagnosis of BESS from a board-certified pediatric neuroradiologist were also reviewed. SynthSeg was used to segment each T1w scan into various tissue types, including eaCSF. Growth charts of eaCSF were modeled using the clinical control cohort. The confirmed BESS cases were then benchmarked against these charts to test the performance of eaCSF growth charts., Results: eaCSF thickness varied nonlinearly with age, steadily decreasing from birth to two years, then trending upwards in early adolescence. Seven of the nine patients with a clinical diagnosis of BESS were above the 97.5 th percentile for their age for at least one eaCSF measure. Centile scores were able to distinguish BESS cases from controls with an area under curve (AUC) greater than 0.95., Discussion: eaCSF thickness evolves in a dynamic pattern throughout childhood and adolescence. Patients with BESS can be differentiated from clinical controls using computational measurements of eaCSF thickness paired with normative modeling. Our findings demonstrate the feasibility of computational extraction of eaCSF with a potential point of clinical relevance, delineation of BESS diagnosis. Enhanced understanding of normative eaCSF is critical in further investigations its clinical associations.

Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons., Competing Interests: J.S., R.A.I.B., and A.F.A.-B. hold shares in and are directors of Centile Bioscience Inc. A.F.A.-B. receives consulting income from Octave Bioscience. Other authors report no related funding support, financial or potential conflicts of interest., (© 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets., Competing Interests: Declaration of Competing Interest The authors of Curation of BIDS (CuBIDS) declare that they have no competing or conflicting interests., (Copyright © 2022. Published by Elsevier Inc.)

Data retention is a significant problem in the medical imaging domain. For example, resting-state functional magnetic resonance images (rs-fMRIs) are invaluable for studying neurodevelopment but are highly susceptible to corruption due to patient motion. The effects of patient motion can be reduced through post-acquisition techniques such as volume registration. Traditional volume registration minimizes the global differences between all volumes in the rs-fMRI sequence and a designated reference volume. We suggest using the spatiotemporal relationships between subsequent image volumes to inform the registration: they are used initialize each volume registration to reduce local differences between volumes while minimizing global differences. We apply both the traditional and novel registration methods to a set of healthy human neonatal rs-fMRIs with significant motion artifacts (N=17). Both methods impacted the mean and standard deviation of the image sequences' correlation ratio matrices similarly; however, the novel framework was more effective in meeting gold standard motion thresholds., (©2020 AMIA - All rights reserved.)