brainGraph (version 2.7.3)

IndividualContributions: Approaches to estimate individual network contribution

Description

loo calculates the individual contribution to group network data for each subject in each group using a "leave-one-out" approach. The residuals of a single subject are excluded, and a correlation matrix is created. This is compared to the original correlation matrix using the Mantel test.

aop calculates the individual contribution using an "add-one-patient" approach. The residuals of a single patient are added to those of a control group, and a correlation matrix is created. This is repeated for all individual patients and each patient group.

The summary method prints the group/region-wise means and standard deviations.

The plot method is only valid for regional contribution estimates, and plots the average regional contribution for each vertex/region.

Usage

loo(resids, corrs, level = c("global", "regional"))
aop(resids, corr.mat, level = c("global", "regional"),
  control.value = 1)
# S3 method for IC
summary(object, region = NULL, ...)
# S3 method for IC
plot(x, plot.type = c("mean", "smooth", "boxplot"),
  region = NULL, ...)

Arguments

resids

An object of class brainGraph_resids (the output from get.resid)

corrs

List of lists of correlation matrices (as output by corr.matrix).

level

Character string; the level at which you want to calculate contributions (either global or regional)

corr.mat

Numeric; correlation matrix of the control group

control.value

Integer or character string specifying the control group (default: 1)

object

A IC object

region

Character vector of regions to plot; default is to plot for all regions

...

Unused

x

A IC object

plot.type

Character string indicating the type of plot; the default is to plot the mean (along with standard errors)

Value

A data.table with columns for

Study.ID

Subject identifier

Group

Group membership

region

If level='regional'

IC,RC

The value of the individual/regional contributions

References

Saggar M., Hosseini S.M.H., Buno J.L., Quintin E., Raman M.M., Kesler S.R., Reiss A.L. (2015) Estimating individual contributions from group-based structural correlations networks. NeuroImage, 120:274-284. doi:10.1016/j.neuroimage.2015.07.006

See Also

Other Structural covariance network functions: Bootstrapping, Residuals, brainGraph_permute, corr.matrix, import_scn, plot_volumetric

Other Group analysis functions: Bootstrapping, GLM, MediationAnalysis, NBS, brainGraph_permute, mtpc

Examples

# NOT RUN {
IC <- loo(resids.all, corrs)
RC <- loo(resids.all, corrs, level='regional')
# }
# NOT RUN {
IC <- aop(resids.all, corrs[[1]]$R)
RC <- aop(resids.all, corrs[[1]]$R, level='regional')
# }