Human brain hubs(provincial and connector) identification using centrality measures

Abstract

Human Brain can be studied and analysed using neuroimages viz. MRI(Magnetic Resonance Imaging) / fMRI(Functional Magnetic Resonance Imaging) / PET(Positron Emission Tomography) / EEG(Electroencephalography) / MEG(Magnetoencephalography), which brings out the hidden information from it. Connectomes are graph like structure which represents complex brain network connectivity which are of two type's mainly structural connectivity and functional connectivity. Connectivity can be anatomical and functional properties of the brain. Nodes are voxels or Regions of Interest whereas edges are fibre bundles, temporal correlation between regions, in structural connectome and functional connectome respectively. This work focuses on functional connectivity analysis of brain network obtained through RS- fMRI images for identification of important regions in the human brain using image processing and graph theoretical approaches. The RS-fMRI images are obtained from 1000 Functional connectomes project and preprocessed using image processing techniques. Then the image is parcellated using AAL(Automated Anatomical Labeling) atlas and binary matrix is obtained. The graph is constructed from the derived matrix that exhibits functional connectivity between ROIs(Region of Interest). The various centrality measures (degree centrality, eigenvector centrality, betweenness centrality and closeness centrality) are used to identify the ROIs that act as provincial and/or connector hubs. The prominent provincial hubs are Rolandic Operculum, Thalamus, Insula, Hippocampus, Olfactory and connector hubs are Insula, Putamen, Occipital superior gyrus, Parietal Superior gyrus and Supramarginal gyrus. This work highlights the key regions in human brain which is involved in massive communication and information flow within the network.