Input permutation method to detect active voxels in fMRI study |
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Authors: | Sang H Lee Johan Lim DoHwan Park Bharat B Biswal Eva Petkova |
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Institution: | 1. The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;2. Department of Statistics, Seoul National University, Seoul 151-747, South Korea;3. Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;4. Department of Radiology, University of Medicine and Dentistry of New Jersey Medical School, Newark, NJ 07103, USA;5. Department of Child and Adolescent Psychiatry, School of Medicine, New York University, New York, NY 10016, USA |
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Abstract: | Correctly identifying voxels or regions of interest (ROI) that actively respond to a given stimulus is often an important objective/step in many functional magnetic resonance imaging (fMRI) studies. In this article, we study a nonparametric method to detect active voxels, which makes minimal assumption about the distribution of blood oxygen level-dependent (BOLD) signals. Our proposal has several interesting features. It uses time lagged correlation to take into account the delay in response to the stimulus, due to hemodynamic variations. We introduce an input permutation method (IPM), a type of block permutation method, to approximate the null distribution of the test statistic. Also, we propose to pool the permutation-derived statistics of preselected voxels for a better approximation to the null distribution. Finally, we control multiple testing error rate using the local false discovery rate (FDR) by Efron Correlation and large-scale simultaneous hypothesis testing. J Am Stat Assoc 102 (2007) 93–103] and Park et al. Estimation of empirical null using a mixture of normals and its use in local false discovery rate. Comput Stat Data Anal 55 (2011) 2421–2432] to select the active voxels. |
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Keywords: | False discovery rate fMRI Input permutation method Lagged correlation Time delay |
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