Improving the robustness of neuroimaging through exploitation of variability in processing pipelines

通过利用处理流程的可变性来提高神经影像的鲁棒性

• 批准号: 10516830
• 负责人: Gregory Kiar
• 金额: $ 150.4万
• 依托单位: CHILD MIND INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10516830
• 关键词: Address; Adolescence; Adolescent; Adopted; Adoption; Agreement; Architecture; BRAIN initiative; Base of the Brain; Biological Markers; Brain; Brain imaging; Childhood; Cognitive; Collection; Communities; Computer software; Consensus; Data; Data Set; Development; Ensure; Functional Magnetic Resonance Imaging; Goals; Graph; Health; Heterogeneity; Individual; Lead; Literature; Machine Learning; Maps; Measures; Methods; Modeling; Phenotype; Population; Positioning Attribute; Reporting; Reproducibility; Reproducibility of Results; Sampling; Scientist; Signal Transduction; Site; Software Tools; Source; Techniques; Testing; Training; United States National Institutes of Health; Validation; Variant; Work; analysis pipeline; base; biomarker discovery; clinically relevant; cognitive development; connectome; data analysis pipeline; design; fitness; flexibility; improved; machine learning classifier; machine learning framework; neuroimaging; open data; predictive marker; prevent; segmentation algorithm; success; tool; vector

ABSTRACT Reproducible findings are essential to scientific advancement. Unfortunately, when fields lack consensus standards for methods, or their implementations, reproducibility tends to be more of an ideal than a reality. Such is the case for functional neuroimaging analysis, where there is a sprawling and heterogeneous analytic space from which scientists can select tools, construct processing pipelines, and draw interpretations from their results. Recent demonstrations of disappointing levels of reproducibility for findings across labs, even when using the same datasets, have made the urgent need to overcome analytic heterogeneity clear. Differences in processing steps, parameters, and their software implementation have all been shown to bias results, limiting their comparability with one another. One solution that has emerged in the literature is the adoption of highly prescribed pipelines, such as the fMRIPrep and HCP Pipelines. While successful in restricting variability, the lack of ground truths or consensus processing components and parameters prevents such efforts from being a desirable long-term solution. An alternative strategy, which our team has successfully deployed to achieve robust results in the face of numerical instabilities, is to develop tools that ensemble results across a space of pipeline configurations (i.e., a range of components and parameters). Based on our prior work, we predict that such a strategy would not only improve the robustness of findings, but minimize biases arising from single pipeline selections that compromise the success of biomarker discovery efforts. We address this challenge by proposing a framework for characterizing, summarizing, and minimizing analytic biases in experimental findings. Building on prior work implementing independently developed pipelines (e.g., ABCD-HCP, CCS, fMRIPrep) within a common platform (i.e., the Configurable Pipeline for the Analysis of Connectomes; C-PAC), we will systematically vary their components to generate a broad space of pipelines (n=192). We will quantify the variability in full-brain functional connectivity matrices generated across configurations, and identify both the contribution of individual components (e.g., segmentation, spatial normalization) and the relationships between pipelines (Aim 1). We will construct robust estimates of functional connectivity by sampling the variability observed across pipelines (Aim 2), and improve the generalizability of brain-phenotype relationships through the extension of machine learning ensembling techniques (Aim 3). We will increase the accessibility of our approach by sampling the pipeline configuration space to identify a minimal set of representative pipelines. The strength of these techniques will be demonstrated by identifying generalizable brain-based biomarkers of cognitive and psychiatric wellness using the NIH ABCD Study dataset. This project will lead a shift in neuroimaging towards the capture and inclusion of dominant sources of variability in functional neuroimaging, and in doing so, help to carry functional neuroimaging out of the reproducibility crisis into an era of robustness. Consistent with the values of open science, all contributions will be made publicly and freely available.

摘要 可重复的发现对科学进步至关重要。不幸的是，当各个领域缺乏共识时 方法的标准，或其实施，可重复性往往更多的是一种理想，而不是现实。是这样的 是功能神经成像分析的情况，其中有一个杂乱无章的、不同种类的分析空间 科学家可以从中选择工具，建造加工管道，并从他们的结果中得出解释。 最近的演示表明，各个实验室的研究结果的重复性令人失望，即使在使用 同样的数据集，清楚地表明了克服分析异质性的迫切需要。加工中的差异 事实证明，步骤、参数及其软件实现都会对结果产生偏差，从而限制其 彼此的可比性。文献中出现的一种解决方案是采用高度 规定的管道，如FMRIPrep和HCP管道。虽然成功地限制了可变性，但 缺乏基本事实或协商一致的处理组件和参数使这种努力无法成为 可取的长期解决方案。一种替代战略，我们的团队已成功部署该战略以实现 在数字不稳定的情况下，结果是开发工具，将结果整合到一个管道空间中 配置(即一系列组件和参数)。根据我们之前的工作，我们预测这样一个 这一策略不仅可以提高调查结果的稳健性，还可以最大限度地减少因单一管道而产生的偏差 影响生物标记物发现工作成功的选择。我们通过提出以下建议来应对这一挑战 描述、总结和最小化实验结果中的分析偏差的框架。建房 在之前的工作中实施独立开发的管道(例如ABCD-HCP、CCS、fMRIPrep) 公共平台(即用于分析连接的可配置管道；C-PAC)，我们将 系统地改变它们的组件，以产生广阔的管道空间(n=192)。我们将量化 跨配置生成的全脑功能连接矩阵的可变性，并识别 单个组件的贡献(例如，分割、空间归一化)以及 管道(目标1)。我们将通过抽样可变性来构建功能连通性的稳健估计 跨管道观察(目标2)，并通过以下方式提高脑-表型关系的概括性 推广机器学习集成技术(目标3)。我们将增加我们方法的可及性 通过对流水线配置空间进行采样来识别最小的代表性流水线集合。优势所在 这些技术中的一项将通过识别可概括的基于大脑的认知和 使用NIH ABCD研究数据集的精神健康。该项目将引领神经成像领域的转变， 捕捉和纳入功能神经成像中的主要可变性来源，并在这样做的过程中，有助于 将功能神经成像从重复性危机中带到一个健壮的时代。与价值观一致 对于开放科学，所有的贡献都将公开和免费提供。