Reproducible brain PET data analysis: easier said than done.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2024-09-30 eCollection Date: 2024-01-01 DOI:10.3389/fninf.2024.1420315
Maryam Naseri, Sreekrishna Ramakrishnapillai, Owen T Carmichael
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Abstract

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

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可重复的脑 PET 数据分析:说起来容易做起来难。
最近,脑结构磁共振成像(MRI)和功能磁共振成像(MRI)研究界将大量精力集中在解决可重复性危机上,而本文认为脑正电子发射断层扫描(PET)研究的基础更加脆弱,落后于解决 MRI 可重复性问题的努力。我们首先考察了当前有助于神经成像数据分析可重复性的各种因素,包括科学标准、分析计划预注册、数据和代码共享、容器化工作流程和标准化处理管道。然后,我们将重点关注这些因素在脑 MRI 和脑 PET 之间的现状差异。为了证明进一步开发这些可重复性因素将对脑 PET 研究产生的积极影响,我们介绍了一个案例研究,该案例说明了一家实验室在试图复制社区标准脑 PET 处理管道时所面临的诸多挑战。我们确定了脑 PET 社区可以提高可重复性的关键领域,包括 PET 专用期刊之间更严格的报告政策、数据存储库、容器化分析工具和标准化处理管道。此外,还讨论了其他解决方案,如强制预注册、数据共享、将代码可用性作为资助条件、在线论坛和标准化报告模板等。在脑 PET 研究界加强这些可重复性因素有可能释放脑 PET 研究的全部潜力,推动其走向更有影响力的未来。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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