cpmEV: Connectome-based Predictive Modeling--External Validation

Description

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

Usage

cpmEV(train_na, train_b, valid_na, valid_b, thresh = 0.01,
  overlap = FALSE, progBar = TRUE)

Arguments

train_na

Training dataset (an array from convertConnBrainMat function)

train_b

Behavioral statistic for each participant for the training neural data (a vector)

valid_na

Validation dataset (an array from convertConnBrainMat function)

valid_b

Behavioral statistic for each participant for the validation neural data (a vector)

thresh

Sets an alpha threshold for edge weights to be retained. Defaults to .01

overlap

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)

progBar

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

Value

Returns a list containing:

results

A matrix contaning: r coefficient (r), p-value (p-value), mean absolute error (mae), root mean square error (rmse)

posMask

Positive connectivity for input in BioImage Suite Connectivity Viewer

negMask

Negative connectivity for input in BioImage Suite Connectivity Viewer

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

Wei, T. & Simko, V.(2017). R package "corrplot": Visualization of a correlation matrix (Version 0.84). Available from https://github.com/taiyun/corrplot