Sammba-MRI: A Library for Processing SmAll-MaMmal BrAin MRI Data in Python

Célestine, Marina; Nadkarni, Nachiket A.; Garin, Clément M.; Bougacha, Salma; Dhénain, Marc

doi:10.3389/fninf.2020.00024

Cited by 16 publications

(15 citation statements)

References 36 publications

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“…This leads to downstream variability in acquisition artefacts, anatomical contrast, and the extent of brain coverage. Recently, different rodent-adapted software pipelines were introduced for either mice and rats (Celestine et al, 2020; Diao et al, 2021; Ioanas et al, 2021; Lee et al, 2021), but a thorough validation of preprocessing quality across rodent acquisition sites and species is still missing. Additionally, following image processing, the stage of image analysis requires considerations regarding confound correction and data quality.…”

Section: Introductionmentioning

confidence: 99%

Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI

Desrosiers-Grégoire

Devenyi

Grandjean³

et al. 2022

Preprint

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Functional magnetic resonance imaging (fMRI) in rodents holds great potential for advancing our understanding of brain networks. However, rodent fMRI lags the human community with regards to the availability of image processing softwares to support reproducibility, a significant challenge facing neuroimaging. To address these shortcomings, we introduce RABIES, a novel pipeline designed along best practices for reproducibility and transparency handling processing stages from image registration to data quality assessment during analysis. First, we present a robust registration workflow validated across both acquisition sites and species. Leveraging insights from the human literature, we introduce a novel framework for data quality assessment of single scans and during group-level network analysis. This framework will support the development of standardized high-quality acquisition protocols and harmonization of findings between studies. Taken together, the RABIES software will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

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Section: Introductionmentioning

confidence: 99%

Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI

Desrosiers-Grégoire

Devenyi

Grandjean³

et al. 2022

Preprint

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“…Images acquired using each of the MRI modalities were co-registered and automatically segmented using an in-house python library (Sammba-MRI 57 , Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of the key role of white matter alteration in the pathogenesis of Huntington’s Disease

Pérot¹,

Célestine²,

Palombo

et al. 2021

Preprint

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Pathogenesis of the inherited neurodegenerative disorder Huntington s Disease (HD) is complex and progressive, with a long presymptomatic phase in which subtle changes occur in the brain of gene carriers up to 15 years before the onset of symptoms. Thus, there is a need of early, functional biomarker to better understand disease progression and to evaluate treatment efficacy far from onset. In particular, recent studies have shown that white matter may be affected early in HD. In this study, we scanned longitudinally Ki140CAG mice with structural MRI, Diffusion Tensor Imaging (DTI), Chemical Exchange Saturation Transfer of glutamate (gluCEST) and Magnetization Transfer (MT) imaging, in order to assess white matter integrity over the life of this very progressive mouse model. Our results show early defects of diffusion properties in the anterior part of the corpus callosum, preceding gluCEST defects in the same region (-10.8% at 8 months, -19% at 12 months) that spread to adjacent regions. At 12 months, frontal (-7.3%) and piriform (-16.7%) cortices showed reduced gluCEST, as well as the pallidum (-21.0%). MT imaging showed reduced signal in the septum (-21.7%) at 12 months. Cortical and striatal atrophy then appear at 18 months. Vulnerability of the striatum and motor cortex, combined with alterations of anterior corpus callosum, seems to point out the pivotal role of white matter, in the pathogenesis of HD and the pertinence of gluCEST and DTI as biomarkers in HD.

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“…The emerging field of Machine Learning Ops (MLOps) tackles the automation, provenance, performance, and other aspects of ML in a workflow-based form [92]. The ZenML Python library 16 provides a high-level API to machine learning tasks and tools, while offering workflow management features such as versioning, scheduling, and visualisation.…”

Section: Text-basedmentioning

confidence: 99%

“…Practitioners may use workflow management systems such as nipype to develop reproducible workflows focusing on particular domain processing tasks. For example, Celestine et al present a Python module 42 for performing pre-processing workflows such as DS file conversion and skull stripping for small mammal MRI brain data [16].…”

Section: Cs Nipypementioning

confidence: 99%

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Oakes¹,

Famelis²,

Sahraoui³

2022

Preprint

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Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution.To ground our conceptual framework in the stateof-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

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Sammba-MRI: A Library for Processing SmAll-MaMmal BrAin MRI Data in Python

Cited by 16 publications

References 36 publications

Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI

Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI

Identification of the key role of white matter alteration in the pathogenesis of Huntington’s Disease

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

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