# Graph Theory Analysis of Brain MRI Data

## Documentation for package ‘brainGraph’ version 2.7.3

- DESCRIPTION file.

## Help Pages

A B C D E F G H I L M N P R S V W X misc

## -- A --

AAL | Coordinates for data from the AAL-based atlases
aal116 | Coordinates for data from the AAL-based atlases
aal2.120 | Coordinates for data from the AAL-based atlases
aal2.94 | Coordinates for data from the AAL-based atlases
aal90 | Coordinates for data from the AAL-based atlases
analysis_random_graphs | Perform an analysis with random graphs for brain MRI data
aop | Approaches to estimate individual network contribution
apply_thresholds | Threshold additional set of matrices

## -- B --

bg_to_mediate | Mediation analysis with brain graph measures as mediator variables
Bootstrapping | Bootstrapping for global graph measures
brainGraph_boot | Bootstrapping for global graph measures
brainGraph_GLM | Fit linear models at each vertex of a graph
brainGraph_GLM_design | Create a design matrix for linear model analysis
brainGraph_GLM_fit_f | Fit linear models for t contrasts
brainGraph_GLM_fit_t | Fit linear models for t contrasts
brainGraph_mediate | Mediation analysis with brain graph measures as mediator variables
brainGraph_permute | Permutation test for group difference of graph measures
brainsuite | Coordinates for data from BrainSuite atlas

## -- C --

centr_betw_comm | Calculate communicability betweenness centrality
centr_lev | Calculate a vertex's leverage centrality
coeff_var | Calculate coefficient of variation
communicability | Calculate communicability
contract_brainGraph | Contract graph vertices based on brain lobe and hemisphere
cor.diff.test | Calculate the p-value for differences in correlation coefficients
corr.matrix | Calculate correlation matrix and threshold
CountEdges | Count number of edges of a brain graph
count_homologous | Count number of edges of a brain graph
count_inter | Count number of edges of a brain graph
count_interlobar | Count number of edges of a brain graph
craddock200 | Coordinates for data from the Craddock200 atlas
create_mats | Create connection matrices for tractography or fMRI data

## -- D --

DataTables | Create a data table with graph global and vertex measures
destrieux | Coordinates for data from Freesurfer atlases
destrieux.scgm | Coordinates for data from Freesurfer atlases
dk | Coordinates for data from Freesurfer atlases
dk.scgm | Coordinates for data from Freesurfer atlases
dkt | Coordinates for data from Freesurfer atlases
dkt.scgm | Coordinates for data from Freesurfer atlases
dosenbach160 | Coordinates for data from the Dosenbach160 atlas

## -- E --

edge_asymmetry | Calculate an asymmetry index based on edge counts
edge_spatial_dist | Calculate Euclidean distance of edges and vertices
efficiency | Calculate graph global, local, or nodal efficiency
Extract.brainGraph_resids | Linear model residuals in structural covariance networks

## -- F --

FreesurferAtlases | Coordinates for data from Freesurfer atlases

## -- G --

gateway_coeff | Gateway coefficient, participation coefficient, and within-mod degree z-score
get.resid | Linear model residuals in structural covariance networks
GLM | Fit linear models at each vertex of a graph
GLMdesign | Create a design matrix for linear model analysis
GLMfit | Fit linear models for t contrasts
GraphDistances | Calculate Euclidean distance of edges and vertices
graph_attr_dt | Create a data table with graph global and vertex measures

## -- H --

hoa112 | Coordinates for data from Harvard-Oxford atlas
hubness | Calculate vertex hubness

## -- I --

import_scn | Import data for structural connectivity analysis
IndividualContributions | Approaches to estimate individual network contribution

## -- L --

loo | Approaches to estimate individual network contribution
lpba40 | Coordinates for data from the LONI probabilistic brain atlas

## -- M --

make_brainGraph | Create a brainGraph object
make_ego_brainGraph | Create a graph of the union of multiple vertex neighborhoods
make_empty_brainGraph | Create an empty graph with attributes for brainGraph
make_glm_brainGraph | Create a graph with GLM-specific attributes
make_intersection_brainGraph | Create the intersection of graphs based on a logical condition
make_mediate_brainGraph | Create a graph with mediation-specific attributes
make_nbs_brainGraph | Create a graph with NBS-specific attributes
MediationAnalysis | Mediation analysis with brain graph measures as mediator variables
mtpc | Multi-threshold permutation correction

## -- N --

NBS | Network-based statistic for brain MRI data

## -- P --

part_coeff | Gateway coefficient, participation coefficient, and within-mod degree z-score
plot.bg_GLM | Fit linear models at each vertex of a graph
plot.brainGraph | Plot a brain graph with a specific spatial layout
plot.brainGraph_boot | Bootstrapping for global graph measures
plot.brainGraph_GLM | Plot a graph with results from brainGraph_GLM
plot.brainGraph_mediate | Plot a graph with results from a mediation analysis
plot.brainGraph_mtpc | Plot a graph with results from MTPC
plot.brainGraph_NBS | Plot a graph with results from the network-based statistic
plot.brainGraph_permute | Permutation test for group difference of graph measures
plot.brainGraph_resids | Linear model residuals in structural covariance networks
plot.IC | Approaches to estimate individual network contribution
plot.mtpc | Multi-threshold permutation correction
plot_brainGraph | Plot a brain graph with a specific spatial layout
plot_brainGraph_gui | GUI for plotting graphs overlaid on an MNI152 image or in a circle.
plot_brainGraph_list | Write PNG files for a list of graphs
plot_brainGraph_multi | Save PNG of three views of a brain graph
plot_corr_mat | Plot a correlation matrix
plot_global | Plot global graph measures across densities
plot_rich_norm | Plot normalized rich club coefficients against degree threshold
plot_vertex_measures | Plot vertex-level graph measures at a single density or threshold
plot_volumetric | Plot group distributions of volumetric measures for a given brain region

## -- R --

RandomGraphs | Perform an analysis with random graphs for brain MRI data
Residuals | Linear model residuals in structural covariance networks
RichClub | Rich club calculations
rich_club_all | Rich club calculations
rich_club_attrs | Assign graph attributes based on rich-club analysis
rich_club_coeff | Rich club calculations
rich_club_norm | Rich club calculations
rich_core | Rich club calculations
robustness | Analysis of network robustness

## -- S --

set_brainGraph_attr | Set graph, vertex, and edge attributes common in MRI analyses
sim.rand.graph.clust | Perform an analysis with random graphs for brain MRI data
sim.rand.graph.par | Perform an analysis with random graphs for brain MRI data
small.world | Calculate graph small-worldness
summary.bg_GLM | Fit linear models at each vertex of a graph
summary.bg_mediate | Mediation analysis with brain graph measures as mediator variables
summary.brainGraph | Create a brainGraph object
summary.brainGraph_boot | Bootstrapping for global graph measures
summary.brainGraph_permute | Permutation test for group difference of graph measures
summary.brainGraph_resids | Linear model residuals in structural covariance networks
summary.IC | Approaches to estimate individual network contribution
summary.mtpc | Multi-threshold permutation correction
summary.NBS | Network-based statistic for brain MRI data
symmetrize_array | Create a symmetric matrix
symmetrize_mats | Create a symmetric matrix
s_core | Calculate the s-core of a network

## -- V --

VertexRoles | Gateway coefficient, participation coefficient, and within-mod degree z-score
vertex_attr_dt | Create a data table with graph global and vertex measures
vertex_spatial_dist | Calculate Euclidean distance of edges and vertices
vulnerability | Calculate graph vulnerability

## -- W --

within_module_deg_z_score | Gateway coefficient, participation coefficient, and within-mod degree z-score
write_brainnet | Write files to be used for visualization with BrainNet Viewer

## -- X --

xfm.weights | Transform edge weights

## -- misc --

[.brainGraph_resids | Linear model residuals in structural covariance networks