Applies the Connectome-based Predictive Modeling approach to neural data. This method identifies individuals based on their specific connectivity patterns. Please cite Finn et al., 2015; Rosenberg et al., 2016; Shen et al., 2017
cpmFPperm(session1, session2, iter = 1000, progBar = TRUE)Array from convertConnBrainMat function
(first session)
Array from convertConnBrainMat function
(second session)
Number of iterations to perform.
Defaults to 1000
Should progress bar be displayed?
Defaults to TRUE.
Set to FALSE for no progress bar
Returns a matrix containing the percentage and number of correctly identified subjects for sessions 1 and 2
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, 1664-1671. doi: 10.1038/nn.4135
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, 165-171. doi: 10.1038/nn.4179
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, 506-518. doi: 10.1038/nprot.2016.178