cpmFP: Connectome-based Predictive Modeling-Fingerprinting

Description Usage Arguments Value Author(s) References

View source: R/cpmFP.R

Description

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

Usage

1
cpmFP(session1, session2, progBar = TRUE)

Arguments

session1

Array from convertConnBrainMat function (first session)

session2

Array from convertConnBrainMat function (second session)

progBar

Should progress bar be displayed? Defaults to TRUE. Set to FALSE for no progress bar

Value

Returns a matrix containing the percentage and number of correctly identified subjects for sessions 1 and 2

Author(s)

Alexander Christensen <[email protected]>

References

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


AlexChristensen/NetworkToolbox documentation built on Nov. 6, 2018, 2:54 a.m.