References

The methods implemented in ConnInfPy draw on the following works. Grouped by the pipeline stage they underpin (see How Inference Works).

Network-based statistics and enhancement

  • Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4), 1197–1207.

  • Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1), 83–98.

  • Baggio, H. C., Abos, A., Segura, B., et al. (2018). Statistical inference in brain graphs using threshold-free network-based statistics. Human Brain Mapping, 39(6), 2289–2302.

  • Noble, S., & Scheinost, D. (2020). The constrained network-based statistic: a new level of inference for neuroimaging. MICCAI 2020, LNCS 12267.

  • Hao, Z., Wang, P., Xia, X., Pan, Y., & Dou, W. (2024). Threshold-free network-oriented statistics in neuroscience. bioRxiv, 2024-01.

  • Vinokur, L., Smith, R. E., Dhollander, T., Vaughan, D., Jackson, G. D., & Connelly, A. (2023). Parameter Sensitivity of Network-Based Statistical Inference. Research Square (preprint). doi:10.21203/rs.3.rs-3081615/v1. (Reports up to 75-fold variation in detected edge counts across (E, H).)

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.

Permutation inference and the GLM

  • Freedman, D., & Lane, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business & Economic Statistics, 1(4), 292–298.

  • Anderson, M. J., & Legendre, P. (1999). An empirical comparison of permutation methods for tests of partial regression coefficients in a linear model. Journal of Statistical Computation and Simulation, 62(3), 271–303.

  • Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397.

  • Winkler, A. M., Ridgway, G. R., Douaud, G., Nichols, T. E., & Smith, S. M. (2016). Faster permutation inference in brain imaging. NeuroImage, 141, 502–516. (GPD tail acceleration.)

  • Phipson, B., & Smyth, G. K. (2010). Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, 9(1).

Multi-site harmonization

  • Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118–127. (ComBat.)

  • Fortin, J.-P., et al. (2018). Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167, 104–120.

  • Nygaard, V., Rødland, E. A., & Hovig, E. (2016). Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics, 17(1), 29–39. (The label-leak motivation for ComBat Strategy D.)