nipy/heudiconv v0.9.0

Halchenko, Yaroslav; Goncalves, Mathias; Castello, Matteo Visconti di Oleggio; Ghosh, Samik; Salo, Taylor; Hanke, Michael; Dae,; Velasco, Pablo; Kent, James; Brett, Matthew; Amlien, Inge K.; Gorgolewski, Chris; Lukas, Darren; Markiewicz, Chris; Tilley, Steven; Kaczmarzyk, Jakub; Stadler, Jörg; Kim, Sin; Kahn, Ari E.; Poldrack, Benjamin; Melo, Bruno Manoel Rezende de; Braun, Henry; Pellman, John; Lurie, Daniel J.; lee, john; Wagner, Adina; Feingold, Franklin; Carlin, Johan D.; Samuels, Kalle; Szczepanik, Michał

doi:10.5281/zenodo.4390433

Cited by 10 publications

(9 citation statements)

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“…This request was limited to 3 Tesla (3T) scans, as the department deployed a harmonized MPRAGE T1-weighted (T1w) sequence for routine brain MRIs across its 3T clinical scanners beginning in 2008 (see Supplementary Materials). The scans were organized into BIDS format (Gorgolowski et al 2016) using heudiconv (Halchenko et al 2020) and filtered according to their metadata using CuBIDS (Covitz et al 2022) to isolate non-contrast high-resolution T1w scans (<=1×1×1mm; N=444 scans), then were manually graded by two raters to remove low-quality scans (e.g, scans with significant motion artifact) (Rosen et al 2018; Bedford et al 2022) (N=372 scans). An overview of data curation and quality control is provided in Figure 1 with complete details in Supplemental Materials.…”

Section: Methodsmentioning

confidence: 99%

“…The scans were converted from DICOM format to NIFTI format using the tool heudiconv (version 0.9.0) (Halchenko et al 2020) and organized according to the Brain Imaging Data Structure (BIDS) standard (Gorgolowski et al 2016) using the CuBIDS tool developed by Covitz et al (Covitz et al 2022). CuBIDs was then used to filter the data based on parameters in the metadata.…”

Section: Supplementary Materialsmentioning

confidence: 99%

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Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

Schabdach

Schmitt

Sotardi

et al. 2023

Preprint

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Background: Brain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs. Purpose: To develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies. Materials and Methods: We curated a set of clinical MRI Scans with Limited Imaging Pathology (SLIP) - so-called "clinical controls" - from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs. Results: The curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts. Conclusion: Brain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

Schabdach

Schmitt

Sotardi

et al. 2023

Preprint

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“…All sMRI (T1w-MP-RAGE), dWI (diffusion weighted imaging) and rsFMRI (10 minutes EPI for XXY and XYY; 5 minutes and 15 seconds EPI for T21) data were gathered using one MR750 3-Tesla scanner (General Electric, MA) for XXY and XYY and another GE 3-Tesla scanner for T21 with identical sequences within each case-control aneuploidy cohort ( Supplementary Materials ). For each cohort, T1w MRI and rs-fMRI scans were converted from DICOM to Nifti and organized according to the Brain Imaging Data Structure 31 (BIDS) using heudiconv 32 . Each imaging modality was preprocessed as summarized below, with full methods in Supplemental Materials .…”

Section: Methodsmentioning

confidence: 99%

The variegation of human brain vulnerability to rare genetic disorders and convergence with behaviorally defined disorders

Levitis

Liu

Whitman

et al. 2022

Preprint

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Diverse gene dosage disorders (GDDs) increase risk for psychiatric impairment, but characterization of GDD effects on the human brain has so far been piecemeal and lacked simultaneous analysis of multiple brain features across different GDDs. Here, through multimodal neuroimaging of 3 aneuploidy syndromes (XXY, XYY, trisomy 21), we reveal considerable diversity in cortical changes across GDDs and imaging-derived phenotypes (IDPs). This variegation of IDP change underlines the limitations of studying GDD effects unimodally. Integration across all IDP maps reveals highly distinct architectures of cortical change in each GDD, along with partial coalescence onto a common spatial axis of cortical vulnerability. This common axis shows strong alignment with shared cortical changes in behaviorally defined psychiatric disorders, and is enriched for specific molecular and cellular signatures - offering a high-priority target for future translational research.

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“…Various BIDS conversion command-line tools 3 to support researchers have been made available, ranging from institute- or study-specific solutions, to community developed software, and from poorly documented tools to more advanced packages with programmatic interfaces. Among these, many use the well-known dcm2niix converter ( Li et al, 2016 ) under the hood to perform the actual data conversion, such as the popular HeuDiConv ( Halchenko et al, 2020 ), dcm2bids, 4 and the related bidskit 5 tools. HeuDiConv is a powerful tool but requires Python programming skills, albeit basic, and its rule-base heuristics design has a relatively steep learning curve and requires technical knowledge about the data.…”

Section: Introductionmentioning

confidence: 99%

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Zwiers

Moia

Oostenveld

2022

Front. Neuroinform.

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Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.

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nipy/heudiconv v0.9.0

Cited by 10 publications

References 0 publications

Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

The variegation of human brain vulnerability to rare genetic disorders and convergence with behaviorally defined disorders

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

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