A MATLAB toolbox for multivariate analysis of brain networks

Abstract

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks

“…are included. This may be achieved by using regression techniques (e.g., by modifying regression tools provided in Bahrami et al, 2019a) for dynamic network analyses) or by capitalizing on new developments in the field of manifold learning, such as Uniform Manifold Approximation and Projection-UMAP (McInnes et al, 2018) which extends the capabilities already available in t-SNE. Future studies can investigate these possibilities.…”

Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity

“…For the scope of this study, we focused solely on the strength of the network connections. Negative correlations (connection strength) were set to 0 because the model includes graph features that are not applicable with negative connections (29). All connection strength results for each condition are provided in the online Supporting Information (Tables S1-S4), as well as a description of the probability findings (Supporting Information Tables S5-S8).…”

Analysis of brain subnetworks within the context of their whole‐brain networks

“…Nevertheless, as each subnetwork is mainly modeled via a binary variable and its interaction with a covariate of interest, and the contrast statements are estimated by using the already estimated residuals, the computational cost does not increase rapidly enough to make it nonapplicable for even larger numbers of subnetworks (or subnetwork components). Additionally, we will add GUIs to an already developed user‐friendly toolbox (Simpson et al, ) to make the application of this framework more accessible to those interested in using it. The appropriate data reduction methods already implemented in this toolbox, and its interface with strong statistical programming software packages such as SAS and R will make modeling even larger numbers of subnetworks feasible.…”

“…Connectivity matrices for the second group were obtained by applying population differences to C ijk,gr1 (details are presented in the following sections). The averages and standard deviations of the normal distributions and intervals for the uniform distribution were selected from results of modeling a real resting‐state fMRI data (Simpson et al, ; Simpson & Laurienti, ) to have more realistic simulations of brain connection strength. We recognize that these relatively simple simulations do not account for many properties of the brain networks or other sources of correlation.…”

“…This framework has been used in (Bahrami et al, ) to study the impacts of pesticide and nicotine on brain networks. Also, a user‐friendly toolbox has recently been released for the application of the original framework (Bahrami, Laurienti, & Simpson, ), as well as an extension that enables studying system‐level brain properties across multiple tasks (Simpson et al, ).…”