The Difference Degree Test (DDT) is a two stage method to detect regions incident to a statistically significant number of differentially weighted edges (DWEs). In the phase, we select a data-adaptive threshold to identify the DWEs followed by a statistical test for the number of DWEs incident to each brain region. The key to our procedure the Hirscheberger-Qi-Steuer (Hirschberger et al., 2007) algorithm, which is a computationally efficient algorithm for generating random null networks that replicate statistical properties of the observed difference network.
Further details on the DDT, please see our paper (Higgins et al., 2019).
Store the correlation matrices in a cell array. All transformations will be performed by DDT. The covariate matrix must also be a matrix with the group membership variable as the first column. For categorical variables with k>2 levels, please create k-1 dummy variables.
function [D,pvals,vec,numDWE] = DDT(corrmats,covariate,adjust,method_null,M)
% INPUT
% corrmats: an array containing correlation matrices for all subjects
% covariate: design matrix (first column must be the group membership)
% adjust: binary indicator to adjust (=1) or not adjust (=0) for
% variables in covariate
% method_null: 'eDDT' (empirical threshold) or 'aDDT' (theoretical
% threshold
% M: number of HQS nulls to generate
%OUTPUT
% D: Difference network (ignore diagonal)
% pvals: p value for the test at each node
% vec: regions incident to significant num of DWEs
% numDWE: number of DWEs incident to each node
DDT is currently in version 0.0.1
This toolbox is licensed under the MIT License - see the LICENSE file for details
Please send comments and bug reports to: [email protected]
Higgins, I.A., Kundu, S., Choi, K.S., Mayberg, H.S. and Guo, Y. (2019). A difference degree test for comparing brain networks. Human brain mapping, 40(15):4518-4536.
Hirschberger, M., Qi, Y., and Steuer, R. E. (2007). Randomly generating portfolio-selection covariance matrices with specified distributional characteristics. European Journal of Operational Research, 177(3):1610–1625.