![]() ![]() ![]() Therefore, it is the best choice for small animal MRI researchers to publish data in as raw a form as possible. It is likely, above all in the effort of methodological comparison and improvement, that what is an artifact or outlier for the interrogation of a narrow hypothesis, may constitute a strong driver of the effect of another hypothesis. In such cases, it is vital that the shared entry and feature pool be as inclusive as possible. While valid rationales for both outlier filtering and data editing exist, these processes are best performed in a transparent and well-documented fashion, leaving the raw data untouched as an ultimate recourse.Īs published data is intended for reuse, it is reasonable to assume that it may be employed to explore hypotheses other than those under the constraints of which it was originally acquired. Raw data sharing increases transparency and reproducibility, as data can be assumed to be free from undocumented “fixes.” Such attempts at ex post facto data improvement may not just include data matrix manipulations, but also outlier (subject or session) filtering. In order to integrate data which may be thus strongly confounded-as well as in order to clarify the confounds themselves (Grandjean et al., 2019)-it is vital that data is shared in a raw state, i.e., having undergone no or as little processing as possible. In animal fMRI in particular, subject preparation, and more specifically cerebrovascular parameters (Schroeter et al., 2016) and anesthesia (Schlegel et al., 2015 Bukhari et al., 2018) are widely known drivers of result variability. Additionally, fMRI methods rely on highly indirect measures of neuronal activity, and are consequently susceptible to numerous confounding factors. However, MRI methods generate signal via nuclear spin polarization-which is commonly very weak-and characteristically posses low intrinsic sensitivity. High assay coverage is particularly relevant for an organ as holistic in its function as the brain, as it facilitates the interrogation of not only sensitivity but also regional specificity. Their high tissue penetration makes them eminently suited for reporting features at the whole-brain level in vivo. Magnetic resonance imaging (MRI), and functional MRI (fMRI) are highly popular methods in the field of neuroscience. ![]() 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. 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). ![]() Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Large-scale research integration is contingent on seamless access to data in standardized formats. ![]()
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