Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Renton, Angela I.; Dao, Thanh Thuy; Abbott, David F.; Bollmann, Saskia; Campbell, Megan E. J.; Chang, Jeryn; Close, Thomas G.; Eckstein, Korbinian; Egan, Gary F.; Evas, Stefanie; Garner, Kelly; Garrido, Marta I.; Hannan, Anthony J.; Huber, Laurentius; Johnstone, Tom; Kaczmarzyk, Jakub; Kasper, Lars; Kuhlmann, Levin; Lou, Kexin; Lyons, Paris; Mattingley, Jason B.; Narayanan, A; Pestilli, Franco; Puce, Aina; Ribeiro, Fernanda L.; Rogasch, Nigel C.; Shaw, Thomas; Sowman, Paul F.; Spitz, Gershon; Stewart, Ashley; Sullivan, Ryan P.; White, David; Ye, Xincheng; Zhu, Judy D.; Narayanan, Aswin

doi:10.1101/2022.12.23.521691

Cited by 10 publications

(8 citation statements)

References 95 publications

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“…However, it is worth emphasizing that a neuroimaging tool can provide slightly different results in different environments. 18 Neuroimaging computations are conducted using floating point representations, where the precise order of instructions, subtle assumptions of those instructions and precision of each instruction can generate small rounding differences. As Kernighan and Plauger note 19 "Floating point numbers are like piles of sand; every time you move one you lose a little sand and pick up a little dirt".…”

Section: Discussionmentioning

confidence: 99%

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niimath and fslmaths: replication as a method to enhance popular neuroimaging tools

Rorden,

Webster,

Drake

et al. 2024

Aperture Neuro

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Neuroimaging involves the acquisition of extensive 3D images and 4D time series data to gain insights into brain structure and function. The analysis of such data necessitates both spatial and temporal processing. In this context, “fslmaths” has established itself as a foundational software tool within our field, facilitating domain-specific image processing. Here, we introduce “niimath,” a clone of fslmaths. While the term “clone” often carries negative connotations, we illustrate the merits of replicating widely-used tools, touching on aspects of licensing, performance optimization, and portability. For instance, our work enables the popular functions of fslmaths to be disseminated in various forms, such as a high-performance compiled R package known as “imbibe”, a Windows executable, and a WebAssembly plugin compatible with JavaScript. This versatility is demonstrated through our NiiVue live demo web page. This application allows ‘edge computing’ where image processing can be done with a zero-footprint tool that runs on any web device without requiring private data to be shared to the cloud. Furthermore, our efforts have contributed back to FSL, which has integrated the optimizations that we’ve developed. This synergy has enhanced the overall transparency, utility and efficiency of tools widely relied upon in the neuroimaging community.

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Section: Discussionmentioning

confidence: 99%

“…For example, the same version of FSL can generate numerically different results on different installations. 18 This can reflect different hardware (ARM vs x86 CPU), different instructions (e.g. a fused multiply-add instruction reduces the rounding error of computing two separate instructions), different coprocessor (e.g.…”

Section: Discussionmentioning

confidence: 99%

niimath and fslmaths: replication as a method to enhance popular neuroimaging tools

Rorden,

Webster,

Drake

et al. 2024

Aperture Neuro

Self Cite

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“…First, further pre-processing was performed on the HCP data using the MRtrix3 software package (http://www.mrtrix.org) on a flexible, light-weight, scalable, and out-of-the-box analysis environment called NeuroDesk (Renton et al, 2023). The pre-processing steps that we applied to the HCP data included, bias-field correction, followed by multi-shell multi-tissue constrained spherical deconvolution (CSD) (Wilson et al, 2021) to model the white matter, grey matter, and the cerebrospinal fluid using a maximum harmonic degree Lmax=8.…”

Section: Methodsmentioning

confidence: 99%

Structural connectivity predicts individual perceived stress

Amos,

Bamisile,

Jin

et al. 2023

Preprint

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Many previous studies have investigated the neural mechanisms of perceived stress using either task or resting-state functional connectomes. However, to date, the structural connectivity predictors of individual perceived stress remain unknown. In this study, using connectome-based predictive modeling with a leave-one-out cross-validation framework in a sample of 100 unrelated healthy young adults, we show that individual differences in perceived stress can be reliably predicted from their structural connectivity. The obtained results show that perceived stress could be predicted from the interaction of visual association, and motor and sub-cortical networks. This present work highlights that structural connectivity can be used to investigate the neural mechanism of PS in healthy populations.

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“…Another approach is to use programming languages such as Python or MATLAB to write more complex custom scripts. As an example, specialized libraries, including Nipype [189,190] and NeuroDesk [152] are available for image processing and analysis in Python.…”

Section: Setup Of Processing Pipelinesmentioning

confidence: 99%

A guide to advanced MRI processing for clinical glioma research

Clement,

Beun,

Arzanforoosh

et al. 2024

Preprint

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BackgroundTo date, multiple advanced magnetic resonance imaging (MRI) methods beyond conventional qualitative structural imaging for the diagnosis, prognosis, and treatment follow-up of glioma have demonstrated their utility for clinical studies. However, these methods often rely on complex off-scanner processing to yield the most information and to extract quantitative biomarkers, limiting their practical use for studies, as well as their clinical translation.While community-driven software solutions exist for these advanced MRI methods, many aspiring clinical researchers face challenges in acquiring the necessary knowledge to effectively apply these tools. This guide, an initiative of the Glioma MR imaging 2.0 network (GliMR), aims to provide an overview of existing solutions, communities, and repositories with the ultimate goal of enabling standardization, open science, and reproducible quantitative imaging studies of gliomas. Yet, most of the reviewed tools and approaches to image data analyses may also be used in the context of studies on diseases other than glioma.ContentThis guide summarizes the state-of-the-art processing software solutions and the repositories/communities for the following advanced MRI methods: DSC; DCE; ASL; diffusion MRI; relaxometry; MRF; MRS; CEST; SWI; QSM; MRE; and task-based and resting-state fMRI. For each of those, after a short introduction about the method and output parameters, the required and recommended image processing steps and quality control measures are described, and we point to further literature for more details. In addition, an overview of openly available software tools that provide these functionalities for MRI processing and exemplify workflows is given. Wherever possible, the readers are guided toward existing inventories, repositories, and communities, which offer not only a collection of these tools, but also more in-depth guidance. Each part concludes with an appraisal of the estimated required expertise and future development needs.ConclusionThis guide provides an extensive overview of the currently available processing tools that can help aspiring clinical researchers to obtain high-quality reproducible imaging data from advanced MRI scans of gliomas. While GliMR n is focused on glioma research, this guide will also be helpful for other clinical neuroimaging topics as general processing steps may not be specific to glioma only.

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Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Cited by 10 publications

References 95 publications

niimath and fslmaths: replication as a method to enhance popular neuroimaging tools

niimath and fslmaths: replication as a method to enhance popular neuroimaging tools

Structural connectivity predicts individual perceived stress

A guide to advanced MRI processing for clinical glioma research

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