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Evaluation of Second-Level Inference in fMRI Analysis
We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Seco...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706870/ https://www.ncbi.nlm.nih.gov/pubmed/26819578 http://dx.doi.org/10.1155/2016/1068434 |
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author | Roels, Sanne P. Loeys, Tom Moerkerke, Beatrijs |
author_facet | Roels, Sanne P. Loeys, Tom Moerkerke, Beatrijs |
author_sort | Roels, Sanne P. |
collection | PubMed |
description | We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. |
format | Online Article Text |
id | pubmed-4706870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-47068702016-01-27 Evaluation of Second-Level Inference in fMRI Analysis Roels, Sanne P. Loeys, Tom Moerkerke, Beatrijs Comput Intell Neurosci Research Article We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. Hindawi Publishing Corporation 2016 2015-12-27 /pmc/articles/PMC4706870/ /pubmed/26819578 http://dx.doi.org/10.1155/2016/1068434 Text en Copyright © 2016 Sanne P. Roels et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Roels, Sanne P. Loeys, Tom Moerkerke, Beatrijs Evaluation of Second-Level Inference in fMRI Analysis |
title | Evaluation of Second-Level Inference in fMRI Analysis |
title_full | Evaluation of Second-Level Inference in fMRI Analysis |
title_fullStr | Evaluation of Second-Level Inference in fMRI Analysis |
title_full_unstemmed | Evaluation of Second-Level Inference in fMRI Analysis |
title_short | Evaluation of Second-Level Inference in fMRI Analysis |
title_sort | evaluation of second-level inference in fmri analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706870/ https://www.ncbi.nlm.nih.gov/pubmed/26819578 http://dx.doi.org/10.1155/2016/1068434 |
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