Biology subjects

Close, T. G.

Publications and source records attributed to Close, T. G..

4 recordsLinked to original sources

bioRxiv2022

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

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

neuroscience↗

bioRxiv2018

Longitudinal dentate nuclei iron concentration and atrophy in Friedreich ataxia: IMAGE-FRDA

BackgroundFriedreich ataxia is a recessively inherited, progressive neurological disease characterised by impaired mitochondrial iron metabolism. The dentate nuclei of the cerebellum are characteristic sites of neurodegeneration in the disease, but little is known of the longitudinal progression of pathology in these structures.\n\nMethodsUsing in vivo magnetic resonance imaging, including quantitative susceptibility mapping, we investigated changes in iron concentration and volume in the dentate nuclei in individuals with Friedreich ataxia (n=20) and healthy controls (n=18) over a two-year period.\n\nResultsThe longitudinal rate of iron concentration was significantly elevated bilaterally in participants with Friedreich ataxia relative to healthy controls. Atrophy rates did not differ significantly between groups. Change in iron concentration and atrophy both correlated with baseline disease severity or duration, indicating sensitivity of these measures to disease stage. Moreover, atrophy was maximal in individuals early in the disease course, while the rate of iron concentration increased with disease progression.\n\nConclusionsProgressive dentate nuclei pathology is evident in vivo in Friedreich ataxia, and the rates of change of iron concentration and atrophy in these structures are sensitive to the disease stage. The findings are consistent with an increased rate of iron concentration and atrophy early in the disease, followed by iron accumulation and stable volume in later stages. This pattern suggests that iron dysregulation persists after loss of the vulnerable neurons in the dentate. The significant changes observed over a two-year period highlights the utility of quantitative susceptibility mapping as a longitudinal biomarker and staging tool.

neuroscience↗

bioRxiv2018

MR-PET head motion correction based on co-registration of multi-contrast MR images

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

bioinformatics↗

bioRxiv2018

A comprehensive framework to capture the arcana of neuroimaging analysis

Mastering the \"arcana of neuroimaging analysis\", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows.\n\nWe present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, \"end-to-end\" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

bioinformatics↗