Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study

Pernet, Cyril; Latinus, Marianne; Nichols, Thomas E.; Rousselet, Guillaume A.

doi:10.1016/j.jneumeth.2014.08.003

Cited by 237 publications

(208 citation statements)

References 19 publications

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“…4A). Multiple‐comparison correction is required over time points and sensors/sources: this can be achieved using permutation testing (repeating the calculation with shuffled stimulus values) combined with the method of maximum statistics [Holmes et al, 1996; Nichols and Holmes, 2002], or cluster sum statistics [Maris and Oostenveld, 2007] possibly with threshold‐free cluster enhancement [Pernet et al, 2015; Smith and Nichols, 2009]. An advantage of this design is that, because each time point is analyzed separately, there is no assumption that the signal is stationary.…”

Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

282

298

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

282

298

View full text Add to dashboard Cite

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“…The use of t-sum as compared to other clustering statistics enabled us to take into account both the height (magnitude of the t values) and extent (number of contiguous time points) as a Bcluster mass^ (Pernet, Latinus, Nichols, & Rousselet, 2014). Overall, such a nonparametric permutation test is data driven, implying that no a priori definition of time windows is required; it also accounts for the paired nature of the pupillary data.…”

Section: Data Analytic Planmentioning

confidence: 99%

The regulation of positive and negative social feedback: A psychophysiological study

Vanderhasselt

Remue

et al. 2015

Cogn Affect Behav Neurosci

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Everyday social evaluations are psychologically potent and trigger self-reflective thoughts and feelings. The present study sought to examine the psychophysiological impact of such evaluations using eye tracking, pupillometry, and heart-rate variability. Fifty-nine healthy adult volunteers received rigged social feedback (criticism and praise) based on their photograph. Gaze data were collected to investigate processes of attentional deployment/allocation toward the self or the evaluator expressing criticism or praise. Whereas voluntary attention was directed to evaluators who expressed praise, attention was drawn to one's own picture after criticism. Pupil dilation and heart-rate variability were larger in response to criticism as compared to praise, suggesting a flexible and adaptive emotion regulatory effort in response to social information that triggers an affective response. Altogether, healthy individuals recruited more regulatory resources to cope with negative (as compared to positive) social feedback, and this processing of social feedback was associated with adjustments in self-focused attention.

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“…To resolve these issues, we adopted resampling techniques for null-hypothesis statistical testing, as is suggested in neuroimaging analysis with GLM or HLM (Pernet et al, 2015;Winkler et al, 2014). Nonparametric statistics using Monte Carlo simulation are ideal for both parameter estimation and hypothesis testing (Baayen et al, 2008;Kherad-Pajouh & Renaud, 2015).…”

Section: Linear Mixed Modelsmentioning

confidence: 99%

“…The bootstrap clustering approach is identical to the bootstrap procedure described by Pernet et al (2011;Pernet et al, 2015) if only a subject intercept is considered as the random effect. In addition, Algorithm 2 extents the philosophy and approach presented by Pernet et al (2011;Pernet et al, 2015) to nonhierarchical mixed-effect models.…”

Section: Algorithmmentioning

confidence: 99%

“…from the resulting model coefficients and the estimated covariance. Importantly, we also introduced a novel nonparametric statistical test based on resampling (permutation and bootstrap spatial clustering) to assess the statistical significance of the linear contrasts (Pernet, Latinus, Nichols, & Rousselet, 2015;Winkler, Ridgway, Webster, Smith, & Nichols, 2014).…”

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confidence: 99%

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iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

et al. 2016

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A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-ofthe-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple betweenand within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance.Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.Keywords Eye movement analysis . Statistical mapping . Linear mixed models Human beings constantly move the eyes to sample visual information of interest from the environment. Eye fixations deliver inputs with the highest resolution to the human visual cortex from the fovea, as well as blurry, low-spatial-frequency information from peripheral vision (Rayner, 1998). Thus, isolating statistically where and how long fixations are deployed to process visual information is of particular interest to behavioral researchers, psychologists, and neuroscientists. Moreover, fixation mapping has a wide range of practical applications in determining marketing strategies and the understanding of consumer behaviour (Duchowski, 2002).Conventional eye movement data analyses rely on the estimation of probabilities of occurrence of fixations and saccades (or their characteristics, such as duration or length) within predefined regions of interest (ROIs), which are at best defined a priori-but often also defined a posteriori, on the basis of data exploration, which inflates the Type I error rate.

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Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study

Cited by 237 publications

References 19 publications

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

The regulation of positive and negative social feedback: A psychophysiological study

iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

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