Abstract
Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition.
Highlights
Magnetic resonance imaging (MRI), and functional MRI are highly popular methods in the field of neuroscience
The commands listed are included in the SAMRI test suite, and monitored for continued quality assurance
The bru2bids workflow presented is a significant first step in rendering data in the Bruker ParaVision standard automatically interpretable for high-level analysis pipelines. This is done by repositing the ParaVision data according to the Brain Imaging Data Structure (BIDS) standard, which offers superior legibility, as well as integration with community analysis tools, with tools adapted from human functional MRI (fMRI)
Summary
Magnetic resonance imaging (MRI), and functional MRI (fMRI) are highly popular methods in the field of neuroscience. Their high tissue penetration makes them eminently suited for reporting features at the whole-brain level in vivo. High assay coverage is relevant for an organ as holistic in its function as the brain, as it facilitates the interrogation of sensitivity and regional specificity. FMRI methods rely on highly indirect measures of neuronal activity, and are susceptible to numerous confounding factors. In animal fMRI in particular, subject preparation, and cerebrovascular parameters (Schroeter et al, 2016) and anesthesia (Schlegel et al, 2015; Bukhari et al, 2018) are widely known drivers of result variability. In order to integrate data which may be strongly
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