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.

抽象的 可重复的发现对于科学进步至关重要。不幸的是，当领域缺乏共识时 方法或其实施的标准、可重复性往往更多的是一种理想而不是现实。这样的 功能神经影像分析就是这种情况，其中存在庞大且异构的分析空间 科学家可以从中选择工具、构建处理管道并从结果中得出解释。 最近的研究表明，即使在使用 相同的数据集，已经表明克服分析异质性的迫切需要。加工差异 步骤、参数及其软件实现都已被证明会产生偏差结果，限制了它们的 彼此之间的可比性。文献中出现的一种解决方案是采用高度 规定的管道，例如 fMRIPrep 和 HCP Pipelines。虽然成功地限制了变异性， 缺乏基本事实或共识处理组件和参数会阻碍此类努力成为 理想的长期解决方案。我们的团队已成功部署另一种策略，以实现稳健的目标 面对数值不稳定的结果，是开发在管道空间中集成结果的工具 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 策略不仅可以提高研究结果的稳健性，还可以最大限度地减少单一管道产生的偏差 影响生物标志物发现工作成功的选择。我们通过提议来应对这一挑战 用于表征、总结和最小化实验结果中的分析偏差的框架。建筑 之前在一个项目内实施独立开发的管道（例如 ABCD-HCP、CCS、fMRIPrep）的工作 通用平台（即用于连接组分析的可配置管道；C-PAC），我们将 系统地改变其组件以生成广阔的管道空间 (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 各个组成部分（例如分割、空间标准化）的贡献以及之间的关系 管道（目标 1）。我们将通过对变异性进行采样来构建功能连接的稳健估计 跨管道观察（目标 2），并通过 机器学习集成技术的扩展（目标 3）。我们将提高我们方法的可及性 通过对管道配置空间进行采样来识别一组最小的代表性管道。实力 这些技术将通过识别可推广的基于大脑的认知和认知生物标志物来证明。 使用 NIH ABCD 研究数据集的精神健康。该项目将引领神经影像学向 捕获并纳入功能神经影像中变异性的主要来源，这样做有助于 将功能神经影像技术从可重复性危机带入稳健时代。与价值观一致 开放科学的所有贡献都将公开且免费提供。