Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substa...

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Bibliographic Details
Main Authors:	Geerligs, L. (Author), Maris, E. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2021
Subjects:	article brain Brain cluster analysis Cluster Analysis cluster inference controlled study data analysis Data Interpretation, Statistical diagnostic imaging false positive result false positives fMRI functional magnetic resonance imaging functional neuroimaging Functional Neuroimaging human Humans image processing Image Processing, Computer-Assisted Magnetic Resonance Imaging Models, Statistical nonparametric nuclear magnetic resonance imaging physiology procedures Random Allocation randomization randomized controlled trial sensitivity sensitivity and specificity Sensitivity and Specificity simulation statistical analysis statistical model statistics
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View Fulltext in Publisher

Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data

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