Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer\'s disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology.
To study the relationship of regional and global network properties by using graph theory analysis and glutamatergic (Glx) neuromodulation in the ApoE isoforms.
Prospective.
Eighty-four cognitively normal adults (26 ApoE4 and 58 non-ApoE4 carriers).
Gradient-echo echo-planar and point resolved spectroscopy sequence at 3 T.
Glx concentration in bilateral hippocampi were processed with jMRUI (4.0), and graph theory metrics (global: γ, λ, small-worldness in whole brain; regional: nodal clustering coefficient (Ci ) and nodal characteristic path length (Li )) in top 20% highly connected hubs of subgroups (low-risk: non-ApoE4; high-risk: APOE4) were calculated and compared.
Two-sample t test was used to compare metrics between subgroups. Correlations between regional properties and Glx by Pearson\'s partial correlation with false discovery rate correction.
Significant differences (P < 0.05) in Ci between subgroups were found in hubs of left inferior frontal, bilateral inferior temporal, and bilateral precentral gyri, right parahippocampus, and bilateral precuneus. In addition, there was a significant correlation between Glx in the left hippocampus and Ci in inferior frontal gyrus (r = -0.537, P = 0.024), right inferior temporal (r = -0.478, P = 0.043), right parahippocampus (r = -0.629, P = 0.016), left precentral (r = -0.581, P = 0.022), right precentral (r = -0.651, P = 0.003), left precuneus (r = -0.545, P = 0.024), and right precuneus (r = -0.567, P = 0.022); and Li in left precuneus (r = 0.575, P = 0.032) and right precuneus (r = 0.586, P = 0.032) in the high-risk group, but not in the low-risk group.
Our results suggested that healthy ApoE4 carriers exhibit poorer local interconnectivity. Moreover, the close relationship between glutamate and small-world network properties in ApoE4 carriers might reflect a compensatory response to the impaired network efficiency.
2 TECHNICAL EFFICACY: Stage 3.

This resting-state functional magnetic resonance imaging (fMRI) study determined the functional connectivity (FC) changes and topologic property alterations of the brain functional network provoked by a strong desire to void in healthy adults using a graph theory analysis (GTA).
Thirty-four healthy, right-handed subjects filled their bladders by drinking water. The subjects were scanned under an empty bladder and a strong desire to void states. The Pearson\'s correlation coefficients were calculated among 90 brain regions in the automated anatomical labeling (AAL) atlas to construct the brain functional network. A paired t test (P < .05, after false discovery rate [FDR] correction) was used to detect significant differences in the FC, topologic properties (small-world parameters [gamma, sigma], Cp, Lp, Eglob, Eloc, and Enodal) between the two states in all subjects.
Both the two states showed small-world network properties. The clustering coefficient (Cp) and local efficiency (Eloc) in the whole brain network decreased, while the FC within the default mode network (DMN) increased during the strong desire to void compared with the empty bladder state. Moreover, an increased nodal efficiency (Enodal) was detected in the basal ganglia (BG), DMN, sensorimotor-related network (SMN), and visual network (VN).
We detected FC changes and topologic property alterations in brain functional networks caused by a strong desire to void in healthy and suggest that the micturition control may be a process dominated by DMN and coordinated by multiple sub-networks (such as, BG, SMN, and VN), which could serve as a baseline for understanding the pathologic process underlying bladder dysfunction and be useful to improve targeted therapy in the future.

This study proposes an integrated model based on small world network (SWN) and multi-agent system (MAS) for simulating epidemic spatiotemporal transmission. In this model, MAS represents the process of spatiotemporal interactions among individuals, and SWN describes the social relation network among agents. The model is composed of agent attribute definitions, agent movement rules, neighborhoods, construction of social relation network among agents and state transition rules. The construction of social relation network and agent state transition rules is essential for implementing the proposed model. The decay effects of infection \"memory\", distance and social relation between agents are introduced into the model, which are unavailable in traditional models. The proposed model is used to simulate the transmission process of flu in Guangzhou City based on the swarm software platform. The integration model has better performance than the traditional SEIR model and the pure MAS based epidemic model. This model has been applied to the simulation of the transmission of epidemics in real geographical environment. The simulation can provide useful information for the understanding, prediction and control of the transmission of epidemics.

The artificial neural network has the ability of the information processing and storage, good adaptability, strong learning function, association function and fault tolerance function. The research on the artificial neural network is mostly focused on the dynamic properties due to fact that the applications of artificial neural networks are related to its dynamic properties. At present, the researches on the neural network are based on the hierarchical network which can not simulate the real neural network. As a high level of abstraction of real complex systems, the small world network has the properties of biological neural networks. In the study, the small world network was constructed and the optimal parameter of the small word network was chosen based on the complex network theory firstly. And then based on the regulation mechanism of the synaptic plasticity and the topology of the small world network, the small world neural network was constructed and dynamic properties of the neural network were analyzed from the three aspects of the firing properties, dynamic properties of synaptic weights and complex network properties. The experimental results showed that with the increase of the time, the firing patterns of excitatory and inhibitory neurons in the small world neural network didn\'t change and the firing time of the neurons tended to synchronize; the synaptic weights between the neurons decreased sharply and eventually tended to be steady; the connections in the neural network were weakened and the efficiency of the information transmission was reduced, but the small world attribute was stable. The dynamic properties of the small world neural network vary with time, and the dynamic properties can also interact with each other: the firing synchronization of the neural network can affect the distribution of synaptic weights to the minimum, and then the dynamic changes of the synaptic weights can affect the complex network properties of the small world neural network.
人工神经网络具有大规模的信息处理和存储能力、良好的自适应性以及很强的学习功能、联想功能和容错功能。动态特性的研究一直是人工神经网络理论研究的重点，主要原因在于人工神经网络的应用都与网络的动态特性有关。目前，神经网络的研究主要是基于层级网络，其拓扑不能模拟真实生物神经网络。小世界网络作为大量真实复杂系统的高度抽象，具有生物神经网络特性。本研究首先构建了小世界网络并基于复杂网路理论选择出适合于小世界网络的最佳参数，进而基于突触可塑性调节机制和小世界网络拓扑构建了小世界神经网络，并从放电特性、突触权重动态特性和复杂网络特性三个方面分析了小世界神经网络的动态特性。实验结果表明：随着时间的增加，小世界神经网络的兴奋性与抑制性神经元放电模式没有改变且神经元的放电时间趋于同步；小世界神经网络中各神经元间的突触权重急剧减小最终趋于稳定；网络的连接减弱且信息传递效率降低，但小世界属性较为稳定。小世界神经网络的动态特性随时间而变化且相互影响：网络的放电同步特性可影响突触权重趋于最小值分布，进而突触权重的动态变化也可影响复杂网络特性。.

Ensuring patient comfort and compliance by emphasizing reduced time frame for image acquisition, without compromising image quality is the key aspect with functional MRI examination. Multiband resting state fMRI (MB-rsfMRI) is a fairly new technique that potentially shortens MR image acquisition time by providing increased number of time points. The study aims to compare signal characteristics as well as the functional connectivity using conventional resting-state fMRI (rsfMRI) with that of MB-rsfMRI technique.
9 healthy volunteers have prospectively undergone conventional resting-state fMRI and Multiband rsfMRI scanning technique in a 3T GE scanner (Discovery MR750w™). We compared the temporal SNR (tSNR) of conventional rs-fMRI with that of MB-rsfMRI. We looked at the language network connectivity and small world network characteristics from graph theoretical measures to compare the two techniques.
We computed the tSNR of conventional resting-state fMRI (rsfMRI) and MB-rsfMRI technique. A strong positive correlation was seen between graph theoretical measures from MB-rsfMRI and conventional rsfMRI (Pearson Correlation, r = 0.99). Both techniques showed similar small world network characteristics in healthy controls.
The present study demonstrates negligible differences between the conventional-rsfMRI and MB-rsfMRI acquisitions on the computed graph theoretic measures. Accordingly current analysis proves that MB-rs-fMRI may be used as a time reducing acquisition technique that enables mapping of functional connectivity with similar outcome as conventional rs-fMRI in healthy subjects.

Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.

Schizophrenia is a condition marked by a disrupted brain functional network. In schizophrenia, the brain network is characterized by reduced distributed information processing efficiency; however, the correlation between information processing efficiency and the symptomatology of schizophrenia remains unclear. Few studies have examined path length efficiencies in schizophrenia. In this study, we examined small-world network metrics computed from resting state functional magnetic resonance imaging data collected from 49 patients with schizophrenia and 28 healthy people. We calculated brain network efficiency using graph theoretical analysis of the networks of brain areas, as defined by the Automated Anatomical Labeling parcellation scheme, and investigated efficiency correlations by using the 5-factor model of psychopathology, which considers the various domains of schizophrenic symptoms and might also consider discrete pathogenetic processes. The global efficiency of the resting schizophrenic brains was lower than that of the healthy controls, but local efficiency did not differ between the groups. The severity of psychopathology, negative symptoms, and depression and anxiety symptoms were correlated with global efficiency in schizophrenic brains. The severity of psychopathology was correlated with increased network efficiency from short-range connections, but not networks from long-range connections. Our findings indicate that schizophrenic psychopathology is correlated with brain network information processing efficiency.

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on \"support vector machines\" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed childhood psychiatric disorder. Disrupted sustained attention is one of the most significant behavioral impairments in this disorder. We mapped systems-level topological properties of the neural network responsible for sustained attention during a visual sustained task, on the premise that strong associations between anomalies in network features and clinical measures of ADHD would emerge.
METHODS: Graph theoretic techniques (GTT) and bivariate network-based statistics (NBS) were applied to fMRI data from 22 children with ADHD combined-type and 22 age-matched neurotypicals, to evaluate the topological and nodal-pairing features in the functional brain networks. Correlation testing for relationships between network properties and clinical measures were then performed.
RESULTS: The visual attention network showed significantly reduced local-efficiency and nodal-efficiency in frontal and occipital regions in ADHD. Measures of degree and between-centrality pointed to hyper-functioning in anterior cingulate cortex and hypo-functioning in orbito-frontal, middle-occipital, superior-temporal, supra-central, and supra-marginal gyri in ADHD. NBS demonstrated significantly reduced pair-wise connectivity in an inner-network, encompassing right parietal and temporal lobes and left occipital lobe, in the ADHD group.
CONCLUSIONS: These data suggest that atypical topological features of the visual attention network contribute to classic ADHD symptomatology, and may underlie the inattentiveness and hyperactivity/impulsivity that are characteristics of this syndrome.