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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Samu, David (2013)
Languages: English
Types: Doctoral thesis
Subjects: QP0351
Systems neuroscience has recently unveiled numerous fundamental features of the macroscopic architecture of the human brain, the connectome, and we are beginning to understand how characteristics of brain dynamics emerge from the underlying anatomical connectivity. The current work utilises complex network analysis on a high-resolution structural connectivity of the human cortex to identify generic organisation principles, such as centralised, modular and hierarchical properties, as well as specific areas that are pivotal in shaping cortical dynamics and function.\ud \ud After confirming its small-world and modular architecture, we characterise the cortex’ multilevel modular hierarchy, which appears to be reasonably centralised towards the brain’s strong global structural core. The potential functional importance of the core and hub regions is assessed by various complex network metrics, such as integration measures, network vulnerability and motif spectrum analysis.\ud \ud Dynamics facilitated by the large-scale cortical topology is explored by simulating coupled oscillators on the anatomical connectivity. The results indicate that cortical connectivity appears to favour high dynamical complexity over high synchronizability. Taking the ability to entrain other brain regions as a proxy for the threat posed by a potential epileptic focus in a given region, we also show that epileptic foci in topologically more central areas should pose a higher epileptic threat than foci in more peripheral areas.\ud \ud To assess the influence of macroscopic brain anatomy in shaping global resting state dynamics on slower time scales, we compare empirically obtained functional connectivity data with data from simulating dynamics on the structural connectivity. Despite considerable micro-scale variability between the two functional connectivities, our simulations are able to approximate the profile of the empirical functional connectivity.\ud \ud Our results outline the combined characteristics a hierarchically modular and reasonably centralised macroscopic architecture of the human cerebral cortex, which, through these topological attributes, appears to facilitate highly complex dynamics and fundamentally shape brain function.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • B Summary analysis of various structural brain networks 217 B.1 List of analysed brain maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 B.2 Description of attributes and measures . . . . . . . . . . . . . . . . . . . . . . . 219
    • 2.1 Network hierarchy in anatomical space . . . . . . . . . . . . . . . . . . . . . . . 17
    • 2.2 Structural connectivity matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
    • 2.3 Visualization of structural connectivity . . . . . . . . . . . . . . . . . . . . . . . 20
    • 3.1 Average connectivity matrices of surrogate network groups . . . . . . . . . . . . 28
    • 3.2 Connection lengths in the cortical connectivity and its surrogate network groups . 30
    • 4.1 Distributions and correlations of integration measures . . . . . . . . . . . . . . . 39
    • 4.2 Integration measure results on projections . . . . . . . . . . . . . . . . . . . . . 40
    • 4.3 Distributions and correlations of segregation measures . . . . . . . . . . . . . . 44
    • 4.4 Segregation measure results on projections . . . . . . . . . . . . . . . . . . . . . 45
    • 4.5 Correlations of segregation and integration measures . . . . . . . . . . . . . . . 47
    • 4.6 Specialisation between integration and segregation . . . . . . . . . . . . . . . . 49
    • 4.7 Small-worldness evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
    • 5.12 Distributed and centralised modular architectures. . . . . . . . . . . . . . . . . . 84
    • 5.13 Local s-cores in anatomical space . . . . . . . . . . . . . . . . . . . . . . . . . 87
    • 5.14 Organisation of local s-cores in supermodules . . . . . . . . . . . . . . . . . . . 88
    • 6.1 Analysis of core hubs and module hubs . . . . . . . . . . . . . . . . . . . . . . 96
    • 6.2 Motif node types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
    • 6.3 Three-node motif results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
    • 6.4 Three-motif node fingerprints of the two hub and two non-hub groups . . . . . . 109
    • 6.5 Four-node motif results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
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