BRAPH 2.0: A Graph Theory Software for the Analysis of Multilayer Brain Connectivity

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Giovanni Volpe1, Mite Mijalkov2, Joana Pereira3


1University of Gothenburg, Gothenburg, Sweden, 2Karolinska Institutet, Huddinge, Uppland, 3Karolinska Institute, Stockholm, Stockholm

First Author:

Giovanni Volpe  
University of Gothenburg
Gothenburg, Sweden


Mite Mijalkov  
Karolinska Institutet
Huddinge, Uppland
Joana Pereira  
Karolinska Institute
Stockholm, Stockholm


The brain is a complex network that relies on the interaction between its various regions, known as the connectome (Sporn 2013). In the past decade, the organization of the human connectome has been studied on different imaging modalities such as structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data (Bullmore & Bassett 2011). However, the connectomes obtained from these modalities are often analyzed separately using a single network approach, despite growing evidence that they are not independent and often interact with each other in the same subjects (De Domenico 2017; Mandke et al. 2018).


Here we present the version 2.0 of the freeware MatLab-based software BRAPH - BRain Analysis using graPH theory (Mijalkov et al. 2017). BRAPH 2.0 represents the networks obtained from distinct imaging modalities as different layers and integrates them into a multilayer brain connectome. Each layer comprises a group of nodes representing important brain features such as mean cortical thickness, activation signals or glucose metabolism. These nodes are included in a supra-adjacency matrix with a block structure where diagonal blocks encode intra-layer connectivity and off-diagonal blocks encode inter-layer connectivity.


BRAPH allows correcting for differences in average connectivity between groups using network density or singular value decomposition. It computes a similarity index between layers and allows assessing core-periphery organization, where the core consists of a group of tightly connected nodes across layers and the periphery is made by the remaining weakly connected inter-layer nodes. Different multilayer measures can also be calculated based on network distance (characteristic path length, global efficiency, nodal efficiency), clustering (clustering coefficient, modularity) and hubness (degree, eigenvector centrality, participation coefficient, betweeness centrality). Finally, comparisons between groups or correlation analyses can be carried out using permutation testing.


The study of multilayer brain networks is a promising new field that can potentially provide new insights into the interaction between anatomical, functional and metabolic brain connectivity both in health and disease. BRAPH is the first object-oriented software that allows assessing multilayer brain connectivity in an automated way through command line or a user-friendly interface, which will increase the reproducibility of results across studies and the use of multilayer networks by the scientific community.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)
Methods Development
Multivariate Approaches 1

Neuroinformatics and Data Sharing:

Workflows 2
Informatics Other


Computational Neuroscience
Machine Learning
Positron Emission Tomography (PET)
Statistical Methods

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):


Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Structural MRI
Diffusion MRI

Provide references using author date format

Sporns O (2013). The human connectome: origins and challenges. Neuroimage 80, 53-61.

Bullmore ET, Bassett DS (2011). Brain graphs: graphical models of the human brain connectome. Ann Rev Clin Psychol 7, 113-40.

De Domenico M (2017). Multilayer modeling and analysis of human brain networks. Giga Science 6, gix004.

Mandke K, Meier J, Brookes MJ, O'dea RD, Van Mieghem P, Stam CJ, Hillebrand A, Tewarie P (2018). Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations. NeuroImage 166, 371-84.

Mijalkov M, Kakaei E, Pereira JB, Westman E, Volpe G (2017). Alzheimer's Disease Neuroimaging Initiative. BRAPH: a graph theory software for the analysis of brain connectivity. PloS one 12, e0178798.