Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data
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Published:2022-03-07
Issue:5
Volume:2021
Page:
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ISSN:
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Container-title:Aperture Neuro
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language:
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Short-container-title:Aperture Neuro
Author:
Chen Gang1, Taylor Paul A.1, Stoddard Joel2, Cox Robert W.1, Bandettini Peter A.3, Pessoa Luiz4
Affiliation:
1. Scientific and Statistical Computing Core, NIMH, National Institutes of Health, Bethesda, MD, USA 2. Department of Psychiatry, University of Colorado, Aurora, CO, USA 3. Section on Functional Imaging Methods, NIMH, National Institutes of Health, Bethesda, MD, USA 4. Department of Psychology, Department of Electrical and Computer Engineering, and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
Abstract
Neuroimaging relies on separate statistical inferences at tens of thousands of spatial locations. Such massively univariate analysis typically requires an adjustment for multiple testing in an attempt to maintain the family-wise error rate at a nominal level of 5%. First, we examine three sources of substantial information loss that are associated with the common practice under the massively univariate framework: (a) the hierarchical data structures (spatial units and trials) are not well maintained in the modeling process; (b) the adjustment for multiple testing leads to an artificial step of strict thresholding; (c) information is excessively reduced during both modeling and result reporting. These sources of information loss have far-reaching impacts on result interpretability as well as reproducibility in neuroimaging. Second, to improve inference efficiency, predictive accuracy, and generalizability, we propose a Bayesian multilevel modeling framework that closely characterizes the data hierarchies across spatial units and experimental trials. Rather than analyzing the data in a way that first creates multiplicity and then resorts to a post hoc solution to address them, we suggest directly incorporating the cross-space information into one single model under the Bayesian framework (so there is no multiplicity issue). Third, regardless of the modeling framework one adopts, we make four actionable suggestions to alleviate information waste and to improve reproducibility: (1) model data hierarchies, (2) quantify effects, (3) abandon strict dichotomization, and (4) report full results. We provide examples for all of these points using both demo and real studies, including the recent Neuroimaging Analysis Replication and Prediction Study (NARPS).
Publisher
Organization for Human Brain Mapping
Subject
Law,Philosophy,General Medicine,Sociology and Political Science,Law,Plant Science,Soil Science,Agronomy and Crop Science,Philosophy,Law,Political Science and International Relations,Tourism, Leisure and Hospitality Management,Sociology and Political Science,Environmental Science (miscellaneous),Tourism, Leisure and Hospitality Management,Geography, Planning and Development,Tourism, Leisure and Hospitality Management,Geography, Planning and Development
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