Applies the multi-threshold permutation correction (MTPC) method to perform inference in graph theory analyses of brain MRI data.
Plot the statistics from an MTPC analysis, along with the maximum permuted statistics. The output is similar to Figure 11 in Drakesmith et al. (2015).
mtpc(g.list, thresholds, covars, measure, contrasts, con.type = c("t",
"f"), outcome = NULL, con.name = NULL, level = c("vertex",
"graph"), clust.size = 3L, perm.method = c("freedmanLane",
"terBraak", "smith", "draperStoneman", "manly", "stillWhite"),
part.method = c("beckmann", "guttman", "ridgway"), N = 500L,
perms = NULL, alpha = 0.05, res.glm = NULL, long = TRUE, ...)# S3 method for mtpc
summary(object, contrast = NULL, digits = max(3L,
getOption("digits") - 2L), print.head = TRUE, ...)
# S3 method for mtpc
plot(x, contrast = 1L, region = NULL,
only.sig.regions = TRUE, show.null = TRUE, caption.stats = FALSE,
...)
# S3 method for mtpc
nobs(object, ...)
# S3 method for mtpc
terms(x, ...)
# S3 method for mtpc
formula(x, ...)
# S3 method for mtpc
labels(object, ...)
# S3 method for mtpc
case.names(object, ...)
# S3 method for mtpc
variable.names(object, ...)
# S3 method for mtpc
df.residual(object, ...)
# S3 method for mtpc
region.names(object)
# S3 method for mtpc
nregions(object)
An object of class mtpc with some input arguments plus the
following elements:
Design matrix, QR decomposition, and unscaled covariance matrix, if the design is the same across thresholds
The contrast matrix or list of matrices
Contrast names
Named integer vector of subjects with incomplete data
The atlas of the input graphs
The model rank and residual degrees of freedom
List with length equal to the number of thresholds; each
list element is the output from brainGraph_GLM
A data.table for all thresholds, combined from the outputs
of brainGraph_GLM
A data.table containing S.mtpc (the max. observed
statistic), tau.mtpc (the threshold of the max. observed
statistic), S.crit (the critical statistic value), and
A.crit (the critical AUC)
Numeric array with N columns and number of rows
equal to the number of thresholds. The 3rd dimension is for each
contrast. Each element of the array is the maximum statistic
for that permutation, threshold, and contrast combination.
Numeric matrix; the permutation set applied for all thresholds (each row is a separate permutation)
The plot method returns a trellis object or a list of
ggplot objects
A list of brainGraphList objects for all thresholds
Numeric vector of the thresholds applied to the raw connectivity matrices.
A data.table of covariates
Character string of the graph measure of interest
Numeric matrix (for T statistics) or list of matrices (for F statistics) specifying the contrast(s) of interest; if only one contrast is desired, you can supply a vector (for T statistics)
Character string; either 't' or 'f' (for t or
F-statistics). Default: 't'
Character string specifying the name of the outcome variable,
if it differs from the graph metric (measure)
Character vector of the contrast name(s); if contrasts
has row/list names, those will be used for reporting results
Character string; either vertex (default) or
graph
Integer indicating the size of “clusters” (i.e.,
consecutive thresholds for which the observed statistic exceeds the null)
(default: 3L)
Character string indicating the permutation method.
Default: 'freedmanLane'
Character string; the method of partitioning the design
matrix into covariates of interest and nuisance. Default: 'beckmann'
Integer; number of permutations to create. Default: 5e3
Matrix of permutations, if you would like to provide your own.
Default: NULL
Numeric; the significance level. Default: 0.05
A list of bg_GLM objects, as output by a previous run
of mtpc. Useful if you want to change the cluster size without
re-running all of the GLM's and permutations (default: NULL)
Logical indicating whether or not to return all permutation
results. Default: FALSE
Other arguments passed to brainGraph_GLM and/or
brainGraph_GLM_design
A mtpc object
Integer specifying the contrast to plot/summarize; defaults to showing results for all contrasts
Integer specifying the number of digits to display for P-values
Logical indicating whether or not to print only the first
and last 5 rows of the statistics tables (default: TRUE)
Character string specifying which region's results to
plot; only relevant if level='vertex'. Default: NULL
Logical indicating whether to plot only significant
regions (default: TRUE)
Logical indicating whether to plot points of the maximum null statistics (per permutation)
Logical indicating whether to print the MTPC statistics
in the caption of the plot. Default: FALSE
Christopher G. Watson, cgwatson@bu.edu
This is a multi-step procedure: (steps 3-4 are the time-consuming steps)
Apply thresholds \(\tau\) to the networks, and compute network metrics for all networks and thresholds. (already done beforehand)
Compute test statistics \(S_{obs}\) for each threshold. (done by
brainGraph_GLM)
Permute group assignments and compute test statistics for each
permutation and threshold. (done by brainGraph_GLM)
Build a null distribution of the maximum statistic across thresholds
(and across brain regions) for each permutation. (done by
brainGraph_GLM)
Determine the critical value, \(S_{crit}\) from the null distribution of maximum statistics.
Identify clusters where \(S_{obs} > S_{crit}\) and compute the AUC for these clusters (denoted \(A_{MTPC}\)).
Compute a critical AUC (\(A_{crit}\)) from the mean of the supra-critical AUC's for the permuted tests.
Reject \(H_0\) if \(A_{MTPC} > A_{crit}\).
Drakesmith, M. and Caeyenberghs, K. and Dutt, A. and Lewis, G. and David, A.S. and Jones, D.K. (2015) Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. NeuroImage, 118, 313--333. tools:::Rd_expr_doi("10.1016/j.neuroimage.2015.05.011")
Other Group analysis functions: Bootstrapping,
GLM, Mediation,
NBS, brainGraph_permute
Other GLM functions: GLM design,
GLM fits, GLM
if (FALSE) {
diffs.mtpc <- mtpc(g.list=g.norm, thresholds=thresholds, N=N,
covars=covars.dti, measure='E.nodal.wt', coding='effects',
contrasts=c(0, 0, 0, 0, -2), alt='greater',
binarize=c('Sex', 'Scanner'), con.name='Group 1 > Group 2')
sig.regions <- diffs.mtpc$DT[A.mtpc > A.crit]
}
if (FALSE) {
mtpcPlots <- plot(mtpc.diffs)
## Arrange plots into 3x3 grids
ml <- marrangeGrob(mtpcPlots, nrow=3, ncol=3)
ggsave('mtpc.pdf', ml)
}
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