| The study of complex brain network is one of the most chanllenging researches andinternational frontiers, which crosses a wide range of morden sciences, such ascognitive neuronscience, physics, complex system/network, computer, braininfomatics and web intelligence, and it must bring a huge effect on the intelligentdevelopment of the human life, informatics, industry and health. The mainachievements of this area demonstrate that both structural and functional brainnetworks have a small-world topology and follow a power-law or truncatedpower-law degree distribution with hub, modular and hierarchical organizationalproperties. Such global and semi-global topological patterns may partly quantify andinterpret the fundamental orgniazational principles of cerebral cortex, that is, a pair ofantagonistic and coexisting mechanisms of functional segregation and integration.However, the knowledge of variability (plasticity) and low-level local connectionpatterns of brain organization is still lack, and the answers to these two problems maybe critical for understandings of the organizational and evolutional principles of brainneuronal network. This dissertation focuses on the topological structure of brainfunctional network and variability driven by cognitive task performance. Weemployed a rest-task-rest fMRI experimental paradigm and an activesemantic-matching task and a passive visual stimuli task, and investigated the externaltask effects on the intrinsic brain functional organization in topology, including theduration of task stimuli and rest following the task stimuli. Moreover, we developed anovel brain network model by hypergraph theories and investigated the topologicalfeatures by eigenvector centrality for both nodes and hyperedges. Last, we examinedthe core positions of three core intrinsic functional networks by computationalevidence and discussed their basic coordination mechanisms during basic externalvisual stimuli.This dissertation used positive and negative functional connectivity to constructtwo opposed and complementary brain functional networks, namelypositively-correlated brain functional network (PCBFN) and negatively-correlatedbrain functional network (NCBFN), which could be regarded as two significantlyfunctional network perspectives for investigating spontaneous neuronal activity andthe intrinsic functional architecture. In fact, the positive and negative functionalconnectivity may be a reflector of excitation and inhibition transmission betweenneurons. Here, we adopted Pearson’s correlation coefficient to measure theresting-state functional connectivity between regions and then constructed the twonetworks. And we found that only the PCBFN has a small-world topology but both of them follow a truncated power-law degree distribution. Moreover, the PCBFN couldbe regarded as constructed by the three-node-cycle motif while the NCBFN wasconstructed by the three-node-tree motif at resting states. In a word, the PCBFNreflects the global coupled synchronization while the NCBFN, in a manner, reflectslarge-scale functional segregation in the brain functional organization. They opposeand compete with each other and maintain a harmonous and optimized dynamicequilibrium, which exhibit the interaction between the functional segregation andintegration in the brain organization. Taken together, both the PCBFN and NCBFNare two significantly functional network perspectives to uncover the underlyingprinciples in the intrinsic functional architecture of spontaneous neuronal activity.How does the brain functional organization adaptively change itself in topologywhen assigned by a cognitive task? This dissertation adopted a rest-task-restexperimental paradigm and investigated the effect of an active cognitive task on theintrinsic functional organization of the human brain both during and after itsperformance. Here, we emphasized to observe the topological changes in thelarge-scale brain functional network during two sequential task-driven statesinvolving both on-task state and post-task resting state. Under three hierarchical levels,we found that (1) the brain functional organization has a robust large-scalesmall-world configuration, regardless of the behavioral state changing, while it variesadaptively in both local and global information efficiency due to a specific cognitivetask, meanwhile these variation also exhibited an adaptive modulation in informationdiversity over both local and global topologies;(2) The default mode network may beessentially engaged during both task and post-task processes with adaptively variedspatila patterns and nodal graph properties, and cannot be simply undertood by“suppression” or “deactivation”. On the whole, the present study provides furtherinsights into the robustness and plasticity of the brain intrinsic organization over states,which may be the basis of memory and learning in the brain.Network motif is a definition of local patterns in a network based on a profile of aspecific number of nodes and their connections, regarded as network building blocks.This dissertation extended the concept of motif by maximum cliques defined as“hyperedges” in brain functional networks with much flexibility, as a characterizationof elementary local spatial patterns or blocks in brain functional networks. Based onthe definition of hyperedge, a na ve brain hypergraph model was constructed duringrest, in order to investigagte the low-level modular structures in the brain functionalnetworks. We then adopted the eigenvector centrality of hypergraph to measure theimportance of information transmission of both nodes and hyperedges over the wholebrain hypergraph networks. Nine intrinsic hub hyperedges of functional connectivitywere identified, considered as the most important intrinsic information processingblocks (or units), and assembled into a compound structure as a core subsystem of the intrinsic brain organization.Finally, this disstertation investigated the effect of passive visual stimuli on theintrinsic functional organization of human brains in topology. We still used therest-task-rest fMRI experimental paradigm, in which a simple visual stimuli task(flashing checkerboard with8Hz) was designed, and used a weighted na vehypergraph model to investigate the changes of the large-scale intrinsic brainfunctional network in topology due to the sensory signal input. First, we examined thecore positions of central executive network (CEN), salience network (SN) and defaultmode network (DMN) by computational evidence from eigenvector centrality ofhypergraph, and these three core networks performed differently in coordinationbehaviors. Second, the passive visual stimuli evoked the deactivation of the defaultmode network, especially the precuneus areas. At the same moment, the default modenetwork (mainly the posterior part) formed most of the hyperedges in the whole brainfunctional organization, which may indicate that the default mode network is thefunctional core system during external visual stimuli input. Finally, the saliencenetwork only served as hub nodes of brain hypergraph functional networks during theresting state (eyes-closed) before the visual stimuli influence. And it initiatesmomentary control signal for regulating the competition and collaboration behaviorsbetween the central executive network and default mode network.The contributions of this disstertation include:1) we extend the brain functionalnetwork studies into a framework of task-dependent behavioral state development,that is, to study how the brain functional organization develops in terms of topologyacross a basic behavioral state development involving the pre-task resting state,on-task state and post-task resting state.2) We proposed a novel view of brainfunctional network studies based on hypergraph theories. This method extends themodel studies of brain networks, and may point a way for revealing the deeptopological principles of brain organization.3) We found that the basic modulationfashions of the CEN, SN and DMN, the three core intrisinc functional networks, whena basic visual sensory signal is transmitted into the brain. |
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