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• W2097624672 abstract "A central question in modern neuroscience is how anatomical and functional connections between brain areas are organized to allow optimal information processing. In particular, both segregation and integration of information have to be dealt with in a single architecture of brain networks. There is strong evidence that synchronization of neural activity, both locally and between distant regions is a crucial code for functional interactions. However, a powerful theoretical framework to describe the structural and functional topology of system-wide brain networks has only become available with the discovery of ‘small-world’ and ‘scale-free’ networks in 1998 and 1999. There is now strong evidence that brain networks, ranging from simple nets of interconnected neurons up to macroscopic networks of brain areas display the typical features of complex systems: high clustering, short path lengths (both typical of ‘small-world’ networks), skewed degree distributions, presence of hubs, assortative mixing and the presence of modules. This has been demonstrated for anatomical and functional networks using neuroanatomical techniques, EEG, MEG and structural and functional MRI, in organisms ranging from C. elegans to man. In addition, network topology has been shown to be highly heritable, and very predictive of cognitive functioning. A short path length, which implies that from any area in the brain any other area can be reached in a small number of steps, is strongly correlated with IQ. Computational models are now beginning to reveal how the complex structure of adult brain networks could arise during development." @default.
• W2097624672 created "2016-06-24" @default.
• W2097624672 creator A5015546124 @default.
• W2097624672 date "2010-09-01" @default.
• W2097624672 modified "2023-10-07" @default.
• W2097624672 title "Characterization of anatomical and functional connectivity in the brain: A complex networks perspective" @default.
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• W2097624672 doi "https://doi.org/10.1016/j.ijpsycho.2010.06.024" @default.
• W2097624672 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20598763" @default.
• W2097624672 hasPublicationYear "2010" @default.
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