Procedures in MNET Introduction Misun Yoon 1 , Bumhee Park 3 , Jong Doo Lee 1,2 , HaeJeong Park 2 1 Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea, 2 Department of Nuclear Medicine, Yonsei University College of Medicine , Seoul, Korea, 3 Department of Anesthesiology, David Geffen School of Medicine at UCLA, LA, California, USA [email protected], http://neuroimage.yonsei.ac.kr/mnet MNET: Multimodal network analysis toolbox for integrating structural-functional human brain connectome In recent years, network analyses of neuroimaging have played key roles in understanding la rgescale structural and functional human brain connectome. Besides, database of neuroima ging is getting bigger with accumulated and publicly shared data. Despite the importance of various analyses of brain network and lots of accessible data, there are lacks of simple and au tomatic pipeline for analyzing brain network based on graph theory, using multimodal imagi ng data with respect to the viewpoint of structuralfunctional integration. Taking all of these into consideration, here we propose a toolbox combining various modality connectivity met rics, graphtheoretical metrics, and visualizations of functional and structural connectivity u sing three different modalities; resting state functional MRI (rsfMRI), electroencephalograp hy (EEG), and diffusion tensor images (DTI). 뮤직비디오 (arousal약함) 뮤직비디오 Visualization Methods MNET offers various analysis methods based on the graph theory; node definition, edge defin ition, and topology for each modality (fMRI, EEG, and DTI). Node definition First step to explore brain network is to define unique and homogeneous nodes from the con tinuous medium of the cortex. For this purpose, MNET provides several advanced methods t o define network nodes from existing atlas map using spatial ICA (Kim et al., Human Brain Mapping 2013) or Anatomicalconstrained Hierarchical Modularity Optimization (Park et al., PLoS ONE 2013). rsfMRI / DTI / EEG Connectivity Edges can be defined functionally using correlation of fMRI or coherence of EEG time series among nodes. On top of that, existing standard preprocessing pipeline for resting state fMRI (e.g. regression of nuisance variables for physiological signal and motion effect, temporal ban dpass filtering) was also designed in MNET. DTI based network is constructed using fiber tr actography in DoDTI (http://neuroimage.yonsei.ac.kr/dodti ) and counting number, length a nd mean FA of fibers that pass through the nodes, based on predefined anatomical labels, w hich are registered to the individual space to obtain weighted adjacency matrix. Subnetworks and Topology We offered various analysis methods including univariate and multivariate approaches to rev eal edge and topological network (e.g. topological properties, graph ICA, modularity optimiz ation). For example, graph ICA can be used to decompose networks into edgesharing indepe ndent subnetworks (Park et al., PLoS ONE 2014). Finally, MNET provides methods for combi ne and analyze three network modalities. MNET can produce the results of functional and structural connectivity for each subject. Mo reover, using series of graph metrics and adjacency matrices, it could conduct group level st udy (e.g. general linear modeling including ANCOVA with additional demographic covariat es). MNET provided various interactive and informative visualization techniques; 3Dvisuali zation containing edge weights, node degree, and time series plot of each node, colored adja cency matrices, and hierarchical edge bundle display. It could be used for combining visuali zation of functionalstructural connectivity. Conclusion In this study, we presented a toolbox MNET, which has fully automated pipeline analysis sys tem of functional and structural connectivity from rsfMRI, M/EEG and DTI. This toolbox ca n cover from preparing process for graphtheoretical measurement to statistical analysis. M NET shows the relationship between structural and functional connectivity through compar ing their metrics and displaying them together. Also, the toolbox has high usability with bot h intuitive Graphic User Interface and commandbased running. MNET(Multimodal Network Analysis) MNET incorporates the network property calculation codes from the Brain Connectivity Toolbox developed by the Sporns group (http://www.brainconnectivitytoolbox.net), and it uses several UI control and other functions from the SPM software (the Wellcome Trust Centre for Neuroimaging, UCL: http://www.fil.ion.u cl.ac.uk/spm/). EDGES NODE Main Workspace NETWORK STATISTICS MULTIMODAL Reference Park, B., Kim, D.S., Park, H.J.*, 2014. Graph independent component analysis reveals repertoires of intrinsic network components in the human brain. PLoS One 9, e82873. Park, B., Ko, J.H., Lee, J.D., Park, H.J.*, 2013c. Evaluation of nodeinhomogeneity effects on the functional brain network properties using an ana tomyconstrained hierarchical brain parcellation. PLoS One 8, e74935. Kim, D.J., Park, B., Park, H.J. *, 2013. Functional connectivitybased identification of subdivisions of the basal ganglia and thalamus using multile vel independent component analysis of resting state fMRI. Hum Brain Mapp 34, 13711385.