Clemens, Béla

Emri, Miklós

Kis, Sándor Attila

Opposits, Gábor

Spisák, Tamás

Magnetic Resonance Materials in Physics Biology and Medicine

University of Debrecen Electronic Archive

BrainCON: software tool for graph theory based multimodal brain
      connectivity analysis and visualization
T. Spisák 1, G. Opposits 1, S. A. Kis 1, B. Clemens 2, M. Emri 1
1 Department of Nuclear Medicine, Medical and Health Science Center, University of Debrecen, 
Debrecen, Hungary
2 Department of Neurology, Kenézy Hospital Ltd., Debrecen, Hungary
PURPOSE OF THE SOFTWARE
Graph theory based structural and functional brain connectivity analysis is a novel method providing 
new insights into the dynamics and complexity of the brain by modeling it's regional interactions [1]. 
Due to the heterogeneity and dynamic development of the applied mathematical models and analysis 
techniques the software support of this field is still poorly accomplished [2].
Our  purpose  was  to  develop  a  user  friendly  software  system  dedicated  for  the  analysis  and 
visualization of multimodal brain connectivity data based on EEG, fMRI and DTI data. 
METHODS/IMPLEMENTATION/HARDWARE REQUIREMENTS
The software system has modular architecture which provides the opportunity to rapidly follow the 
latest improvements of connectivity analysis and visualization methods by incremental development. 
Reconstruction of brain networks is modality dependent and can be performed with various state-of-
the-art software tools, eg. BrainLOC (www.minipetct.com/brainloc) [3], Matlab and R for fMRI, FSL or 
Matlab softwares for DTI and NeuroGuide for EEG-LORETA data. These software tools can be easily 
fitted into the processing pipeline of the system. The resulting connectivity matrices can be displayed 
and thresholded interactively. Various interchangeable components are present for global (eg. small-
worldness), modular (eg. community detection, modularity scores) and nodal (eg. various hub-scores) 
analysis of binary and weighted graphs in both individual and population level [4]. Cost-integration [5] 
technique was implemented to solve the problem of thresholding networks. Interpreting the results is 
aided by real-time 2D and 3D “galss brain” visualization techniques and various plots.
The  program  is  built  upon  the  MultiModal  Medical  Imaging  (M3I)  software  library  system 
(www.minipetct.com/m3i) and runs on Windows 7 and Windows Xp operation systems and various 
Linux  distributions  (www.minipetct.com/braincon).  The  hardware  requirements  of  the  application 
match the current average PC configurations used in medical image analysis.
The software system was implemented mainly in C++ and partly in R.
FEATURES ILLUSTRATED AT THE EXHIBIT
At the exhibit fMRI, EEG-LORETA and DTI based connectivity data analysis is demonstrated.
Different methods (like binary and weighted analysis) are evaluated on the same data, connectivity 
patterns and hub-scores corresponding to brain regions are visualized in 3D.
REFERENCES
[1] Sporns, O., The human connectome: a complex network,  Annals of the New York Academy of 
Sciences, 1224(1):109-125, 2011.
[2] Leergaard, T.B. and Hilgetag, C.C. and Sporns, O., Mapping the Connectome: Multi-Level Analysis 
of Brain Connectivity, Frontiers in Neuroinformatics, 6, 2012.
[3] Spisák, T., Koselák, M., Opposits, G., Kis, SA., Trón, L., Jakab, A.,
Berényi, E., Emri, M., Region management toolkit for atlas-space image processing, MAGMA 24 
(S1):543, 2011.
[4] Rubinov, M. and Sporns, O., Complex network measures of brain connectivity: uses and 
interpretations, Neuroimage, 52(3):1059-1069, 2010.
[5] Ginestet, C.E. and Nichols, T.E. and Bullmore, E.T. and Simmons, A., Brain network analysis: 
separating cost from topology using cost-integration, PloS one, 6(7):e21570, 2011.


BrainCON: software tool for graph theory based multimodal brain  connectivity analysis and visualization

https://dea.lib.unideb.hu/dea/bitstream/2437/150923/1/postfile_up_esmrmb12-braincon-st.pdf

BrainCON: software tool for graph theory based multimodal brain connectivity analysis and visualization

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