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Evaluating statistical tests for leave-one-out ISC analysis

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Evaluating statistical tests for leave-one-out ISC analysis

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This repository contains code for evaluating how well different nonparametric statistical tests control false positive rates (FPRs) for leave-one-out intersubject correlation (ISC) analysis.

References

  • Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248-259. https://doi.org/10.1016/j.neuroimage.2016.05.023

  • Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303(5664), 1634-1640. https://doi.org/10.1126/science.1089506

  • Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. Social Cognitive and Affective Neuroscience, 14(6), 667-685. https://doi.org/10.1093/scan/nsz037

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