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，当各领域缺乏共识时， 方法或其实现的标准，再现性往往是一种理想而不是现实。等 是功能性神经影像分析的情况，其中有一个蔓延和异构的分析空间 科学家可以从中选择工具，构建处理管道，并从他们的结果中得出解释。 最近的证据表明，即使使用 同样的数据集，已经使克服分析异质性的迫切需要变得清晰。处理上的差异 步骤、参数和它们的软件实现都被证明会使结果产生偏差， 彼此之间的相似性。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， 缺乏基本事实或一致性处理组件和参数阻止了这种努力成为一种 理想的长期解决方案。我们的团队已经成功部署了一种替代策略， 在面对数值不稳定性的结果，是开发工具，合奏结果在整个空间的管道 配置（即，一系列组件和参数）。根据我们之前的工作，我们预测这样的 这一战略不仅可以提高调查结果的可靠性，而且可以最大限度地减少单一渠道产生的偏差。 这些选择损害了生物标志物发现努力的成功。我们应对这一挑战的方法是， 描述、总结和最小化实验结果中的分析偏差的框架。建筑 关于实施独立开发的管道的先前工作（例如，ABCD-HCP、CCS、fMRIPrep）， 公共平台（即，用于连接体分析的可配置管道; C-PAC），我们将 系统地改变它们的成分，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，并确定 单个组分的贡献（例如，分割，空间归一化）以及 管道（目标1）。我们将通过对变异性进行采样来构造函数连通性的稳健估计 观察整个管道（目标2），并通过提高大脑表型关系的普遍性， 机器学习集成技术的扩展（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以识别代表性流水线的最小集合。强度 这些技术将通过识别认知和认知功能的可推广的基于大脑的生物标志物来证明。 使用NIH ABCD研究数据集的精神健康。该项目将导致神经成像的转变， 在功能性神经成像中捕获和包含变异性的主要来源，并在这样做时，有助于 将功能性神经影像学从再现性危机带入稳健性时代。与价值观一致 在开放科学的框架内，所有的贡献都将公开和免费提供。