References
==========

The methods implemented in ConnInfPy draw on the following works. Grouped by
the pipeline stage they underpin (see :doc:`approach`).

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.)