Won Hwa Kim

Multi-resolution Brain Connectivity Analysis (MBCA) Toolbox


Multi-resolution Brain Connectivity Analysis (MBCA) toolbox is a tool implemented in Matlab to decompose brain connectivity into multiple resolutional view. It uses WMD framework together with Line graph to derive Wavelet Connectivity Singature (WaCS) and carry multi-variate analysis.



1. Install SGWT toolbox.
2. Download MBCA toolbox.
3. Type "addpath('/mbca_toolbox')" in the Matlab command line.


Overview of the Framework

1. Transform a graph to a line graph.
2. Perform wavelet transform on the line graph to obtain WaCS.
3. Transform back to the original graph.
4. Use Hotelling's T2 test or Multivariate General Linear Model (MGLM) for statistical analysis

Fig. Top row: Original graphs (G), Bottom row: Corresponding line graph (L(G)). Original graphs with vertices (red) and edges (yellow) with edge weights (thickness), and corresponding line graphs with vertices (yellow) with function (vertex size) and edges (red).

Group Analysis on AD vs Controls

Fig. Anatomical connectivity showing group differences between AD and controls, and corresponding hub regions.






This research is supported by NIH R01AG040396, NIH R01AG021155, NSF RI1116584, NSF RI1252725, the Wisconsin Partnership Proposal, UW ADRC, and UW ICTR (1UL1RR025011), NIH grants P30 AG010129, K01 AG030514, NIH R01 AG027161 and Waisman Core grant P30 HD003352-45.


1. D. Hammond et al, Wavelets on graphs via spectral graph theory, Applied and Computational Harmonic Analysis, 2011.
2. W. Kim et al, Multi-resolutional Brain Network Filtering and Analysis via Wavelets on Non-Euclidean Space, MICCAI, 2013.