Comparison of Brain Connectivity Networks Using Local Structure Analysis
Brain connectivity datasets are usually represented as networks in which nodes represent brain regions and links represent anatomical tracts or functional associations. Measuring similarity or dissimilarity among brain networks is useful for exploring connectivity relationships within individual subjects, or between groups of subjects under different conditions or with different characteristics. Several approaches based on graph theory have already been proposed to address this issue. They are mainly based on vertex or edge attributes, and most of them ignore the spatial location of the nodes or the spatial structure of the network. However, the spatial information is a crucial factor in the analysis of brain networks. In this paper, we introduce an approach for comparing brain functional networks, in particular EEG coherence networks, using their local structure. The method builds on an existing approach that partitions a multichannel EEG coherence network into data-driven regions of interest called functional units. The proposed method compares EEG coherence networks using the earth mover’s distance (EMD) between the distributions of functional units. It accounts for the connectivity, spatial character and local structure at the same time. The new method is first evaluated using synthetic networks, and it shows higher ability to detect and measure dissimilarity between coherence networks compared with a previous method. Next, the method is applied to real functional brain networks for quantification of inter-subject variability during a so-called oddball experiment.
KeywordsBrain connectivity networks Graph comparison Earth mover’s distance EEG
C. Ji acknowledges the China Scholarship Council (Grant number: 201406240159) for financial support.
- 1.Bullmore, E., Sporns, O.: Erratum: complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(4), 312–312 (2009). https://doi.org/10.1038/nrn2618
- 2.ten Caat, M.: Fumaplab: multichannel EEG Matlab toolbox (2008). http://www.cs.rug.nl/~roe/software/FuMapLab/FuMapLab0-2.tgz
- 3.ten Caat, M., Maurits, N., Roerdink, J.: Data-driven visualization and group analysis of multichannel EEG coherence with functional units. IEEE Trans. Vis. Comput. Graph. 14(4), 756–771 (2008). https://doi.org/10.1109/tvcg.2008.21
- 4.Costa, L.d.F., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: a survey of measurements. Adv. Phys. 56(1), 167–242 (2007)Google Scholar
- 5.Crippa, A., Maurits, N.M., Lorist, M.M., Roerdink, J.B.T.M.: Graph averaging as a means to compare multichannel EEG coherence networks and its application to the study of mental fatigue and neurodegenerative disease. Comput. Graph. 35(2), 265–274 (2011). https://doi.org/10.1016/j.cag.2010.12.008
- 6.Dantzig, G.B.: Application of the simplex method to a transportation problem - Ch XXIII (1951). http://cowles.econ.yale.edu/P/cm/m13/m13-23.pdf
- 7.Halliday, D., Rosenberg, J., Amjad, A., Breeze, P., Conway, B., Farmer, S.: A framework for the analysis of mixed time series/point process data—theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Prog. Biophys. Mol. Biol. 64(2), 237–278 (1995). https://doi.org/10.1016/s0079-6107(96)00009-0
- 8.Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8(4), 194–208 (1999). https://doi.org/10.1002/(SICI)1097-0193(1999)8:4194::AID-HBM43.0.CO;2-C
- 9.Luck, S.J.: An Introduction to the Event-related Potential Technique. MIT Press (2005). https://books.google.nl/books?id=r-BqAAAAMAAJ
- 10.Maurits, N.M., Scheeringa, R., van der Hoeven, J.H., de Jong, R.: EEG coherence obtained from an auditory oddball task increases with age. J. Clin. Neurophysiol. 23(5), 395–403 (2006). https://doi.org/10.1097/01.wnp.0000219410.97922.4e
- 12.Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010). https://doi.org/10.1016/j.neuroimage.2009.10.003
- 13.Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. Sixth International Conference on Computer Vision, pp. 59–66 (1998). https://doi.org/10.1109/ICCV.1998.710701