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
基本信息
- 批准号:10516830
- 负责人:
- 金额:$ 150.4万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAdolescenceAdolescentAdoptedAdoptionAgreementArchitectureBRAIN initiativeBase of the BrainBiological MarkersBrainBrain imagingChildhoodCognitiveCollectionCommunitiesComputer softwareConsensusDataData SetDevelopmentEnsureFunctional Magnetic Resonance ImagingGoalsGraphHealthHeterogeneityIndividualLeadLiteratureMachine LearningMapsMeasuresMethodsModelingPhenotypePopulationPositioning AttributeReportingReproducibilityReproducibility of ResultsSamplingScientistSignal TransductionSiteSoftware ToolsSourceTechniquesTestingTrainingUnited States National Institutes of HealthValidationVariantWorkanalysis pipelinebasebiomarker discoveryclinically relevantcognitive developmentconnectomedata analysis pipelinedesignfitnessflexibilityimprovedmachine learning classifiermachine learning frameworkneuroimagingopen datapredictive markerpreventsegmentation algorithmsuccesstoolvector
项目摘要
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
项目成果
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