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
cpmFP(session1, session2, progBar = TRUE)Array from convertConnBrainMat function
(first session)
Array from convertConnBrainMat function
(second session)
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