Applies the Connectome-based Predictive Modeling approach to neural data. This method predicts a behavioral statistic using neural connectivity from the sample. Results may differ from Matlab results because of robust GLM methodology. This function is still in its testing phase. Please cite Finn et al., 2015; Rosenberg et al., 2016; Shen et al., 2017
cpmEV(train_na, train_b, valid_na, valid_b, thresh = 0.01, overlap = FALSE,
progBar = TRUE)Training dataset (an array from convertConnBrainMat function)
Behavioral statistic for each participant for the training neural data (a vector)
Validation dataset (an array from convertConnBrainMat function)
Behavioral statistic for each participant for the validation neural data (a vector)
Sets an alpha threshold for edge weights to be retained. Defaults to .01
Should leave-one-out cross-validation be used? Defaults to FALSE (use full dataset, no leave-one-out). Set to TRUE to select edges that appear in every leave-one-out cross-validation network (time consuming)
Should progress bar be displayed? Defaults to TRUE. Set to FALSE for no progress bar
Returns a list containing a matrix (r coefficient (r), p-value (p-value), Bayes Factor (BF), mean absolute error (mae), root mean square error (rmse)). The list also contains the positive (posMask) and negative (negMask) masks used
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664-1671.
Ly, A., Verhagen, A. J., & Wagenmakers, E.-J. (2016). Harold Jeffreys's default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. Journal of Mathematical Psychology, 72, 19-32.
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165-171.
Shen, X. Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506-518.
Wagenmakers, E. J., Verhagen, J., & Ly, A. (2016). How to quantify the evidence for the absence of a correlation. Behavior Research Methods, 48(2), 413-426.
Wei, T. & Simko, V.(2017). R package "corrplot": Visualization of a correlation matrix (Version 0.84). Available from https://github.com/taiyun/corrplot