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Evaluation of second-level inference in fMRI analysis

Sanne Roels (UGent) , Tom Loeys (UGent) and Beatrijs Moerkerke (UGent)
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Abstract
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.
Keywords
FALSE DISCOVERY RATE, GENERAL LINEAR-MODEL, STATISTICAL-ANALYSIS, RANDOM-FIELD, PERFORMANCE, STABILITY, RELIABILITY, PREDICTION, METRICS

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MLA
Roels, Sanne, Tom Loeys, and Beatrijs Moerkerke. “Evaluation of Second-level Inference in fMRI Analysis.” COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2016): n. pag. Print.
APA
Roels, S., Loeys, T., & Moerkerke, B. (2016). Evaluation of second-level inference in fMRI analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE.
Chicago author-date
Roels, Sanne, Tom Loeys, and Beatrijs Moerkerke. 2016. “Evaluation of Second-level Inference in fMRI Analysis.” Computational Intelligence and Neuroscience.
Chicago author-date (all authors)
Roels, Sanne, Tom Loeys, and Beatrijs Moerkerke. 2016. “Evaluation of Second-level Inference in fMRI Analysis.” Computational Intelligence and Neuroscience.
Vancouver
1.
Roels S, Loeys T, Moerkerke B. Evaluation of second-level inference in fMRI analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE. 2016;
IEEE
[1]
S. Roels, T. Loeys, and B. Moerkerke, “Evaluation of second-level inference in fMRI analysis,” COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016.
@article{7084884,
  abstract     = {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.},
  articleno    = {1068434},
  author       = {Roels, Sanne and Loeys, Tom and Moerkerke, Beatrijs},
  issn         = {1687-5265},
  journal      = {COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE},
  keywords     = {FALSE DISCOVERY RATE,GENERAL LINEAR-MODEL,STATISTICAL-ANALYSIS,RANDOM-FIELD,PERFORMANCE,STABILITY,RELIABILITY,PREDICTION,METRICS},
  language     = {eng},
  pages        = {22},
  title        = {Evaluation of second-level inference in fMRI analysis},
  url          = {http://dx.doi.org/10.1155/2016/1068434},
  year         = {2016},
}

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