Ruiwang Huang1, N Jon Shah1,2, Lars Hoemke1, Oleg Posnansky1, Karl Zilles1,3, Katrin Amunts1,4
1Institute of Neuroscience and Biophysics - Medicine, Research Centre Juelich, Juelich, Germany; 2Department of Neurology, RWTH Aachen University, Aachen, Germany; 3C. and O. Vogt Institute for Brain Research, Heinrich-Heine-University of Duesseldorf, Duesseldorf, Germany; 4Department of Psychiatry and Psychotherapy, RWTH Aachen University, Aachen, Germany
The identification and quantification of networks in the human brain is a key issue in neuroscience. Here, we construct an anatomical cortico-cortical connectivity (CCC) matrix based on diffusion-weighted imaging (DWI) and diffusion probabilistic tractography (DPT) in 14 subjects. The cortical networks corresponding to symmetric and antisymmetric CCC-matrices were constructed with respect to Brodmann areas (BA) as vertices. With the application of an unsupervised learning network approach on the cortical network, the cortex was partitioned into seven major subdivisions. Several highly connected cortical areas were detected from the CCC-network such as BA8, BA22 and BA42 as well as the insular and periinsular cortex.
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