Data-analytical stability of cluster-wise and peak-wise inference in fMRI data analysis

Roels, Sanne; Bossier, Han; Loeys, Tom; Moerkerke, Beatrijs

doi:10.1016/j.jneumeth.2014.10.024

Cited by 9 publications

(13 citation statements)

References 53 publications

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“…As this is an assumption crucial for the procedure presented here, we opt for peak inference rather than cluster inference. Moreover, problems with localisation and stability have been reported with cluster inference (Roels et al, 2014;Woo et al, 2014). However, when a user wants to infer power for cluster inference, this procedure on peaks can be used as a lower bound, as the power of cluster inference should be generally higher than peak inference .…”

Section: Discussionmentioning

confidence: 99%

Power and sample size calculations for fMRI studies based on the prevalence of active peaks

Durnez

Degryse

Moerkerke

et al. 2016

Preprint

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Highlights• The manuscript presents a method to calculate sample sizes for fMRI experiments • The power analysis is based on the estimation of the mixture distribution of null and active peaks • The methodology is validated with simulated and real data. AbstractMounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at www.neuropowertools.org.

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Section: Discussionmentioning

confidence: 99%

Power and sample size calculations for fMRI studies based on the prevalence of active peaks

Durnez

Degryse

Moerkerke

et al. 2016

Preprint

View full text Add to dashboard Cite

show abstract

“…The activation is then judged at the voxel level, rather than based on topological features. The selection of activated voxels can be viewed as a sequence of different phases [ 4 ]. For first-level analyses, Carp [ 5 ] demonstrated the large variation in the choices made in each of these different phases which impacts results.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is important to point out that we do not intend to investigate cluster-based testing which is fundamentally different from the approach taken here and relies on different topological assumptions. Instead, we focus on voxelwise testing (for an elaborate investigation of cluster-based testing, we refer to [ 4 ]).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Second-Level Inference in fMRI Analysis

Roels

Loeys

Moerkerke

2016

Computational Intelligence and Neuroscience

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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.

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“…We preferred voxelwise inference since topological features such as peaks are less stable both in number and spatial location (Roels et al, 2015), which makes them less than optimal to define spatially accurate fROIs. Another reason for choosing voxelwise inference is that we focused on the extent of the fROI to be defined.…”

Section: Discussionmentioning

confidence: 99%

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI

et al. 2017

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In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.

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Data-analytical stability of cluster-wise and peak-wise inference in fMRI data analysis

Cited by 9 publications

References 53 publications

Power and sample size calculations for fMRI studies based on the prevalence of active peaks

Power and sample size calculations for fMRI studies based on the prevalence of active peaks

Evaluation of Second-Level Inference in fMRI Analysis

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI

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