Applies the Connectome-based Predictive Modeling approach to neural data. This method predicts a behavioral statistic using neural connectivity from the sample. Please cite Finn et al., 2015; Rosenberg et al., 2016; Shen et al., 2017
cpmIV(neuralarray, bstat, covar, thresh = 0.01, method = c("mean", "sum"),
model = c("linear", "quadratic", "cubic"), corr = c("pearson",
"spearman"), shen = FALSE, cores, progBar = TRUE)Array from convertConnBrainMat function
Behavioral statistic for each participant with neural data (a vector)
Covariates to be included in predicting relevant edges (time consuming). Must be input as a list() (see examples)
Sets an alpha threshold for edge weights to be retained. Defaults to .01
Use "mean" or "sum" of edge strengths in the positive and negative connectomes. Defaults to "mean"
Regression model to use for fitting the data. Defaults to "linear"
Correlation method for assessing the relatonship between the behavioral measure and edges between ROIs. Defaults to "pearson". Set to "spearman" for non-linear or monotonic associations
Are ROIs from Shen et al. 2013 atlas? Defaults to FALSE. Set to TRUE for canonical networks plot
Number of computer processing cores to use when performing covariate analyses. Defaults to n - 1 total number of cores. Set to any number between 1 and maxmimum amount of cores on your computer
Should progress bar be displayed? Defaults to TRUE. Set to FALSE for no progress bar
Returns a list containing a matrix (correlation coefficient (r), p-value (p), Bayes Factor (BF), mean absolute error (mae), root mean square error (rmse)). The list also contains the positive (posMask) and negative (negMask) masks, which can be visualized here: http://bisweb.yale.edu/build/connviewer.html
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