Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Introduction

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain.

Methods

To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules.

Results

To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.

Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis

Objective

Ischemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.

Method

Forty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).

Results

Most of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.

Conclusions

Both global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.

EEG Frontal Asymmetry in Dysthymia, Major Depressive Disorder and Euthymic Bipolar Disorder

In the last few decades, the incidence of mood disorders skyrocketed worldwide and has brought an increasing human and economic burden. Depending on the main symptoms and their evolution across time, they can be classified in several clinical subgroups. A few psychobiological indices have been extensively investigated as promising markers of mood disorders. Among these, frontal asymmetry measured at rest with quantitative EEG has represented the main available marker in recent years. Only a few studies so far attempted to distinguish the features and differences among diagnostic types of mood disorders by using this index. The present study measured frontal EEG asymmetry during a 5-min resting state in three samples of patients with bipolar disorder in a Euthymic phase (EBD, n = 17), major depressive disorder (MDD, n = 25) and persistent depressive disorder (PDD, n = 21), once termed dysthymia. We aimed to test the hypothesis that MDD and PDD lack the typical leftward asymmetry exhibited by normal as well as EBD patients, and that PDD shows greater clinical and neurophysiological impairments than MDD. Clinical scales revealed no symptoms in EBD, and significant larger anxiety and depression scores in PDD than in MDD patients. Relative beta (i.e., beta/alpha ratio) EEG asymmetry was measured from lateral frontal sites and results revealed the typical greater left than right frontal beta activity in EBD, as well as a lack of asymmetry in both MDD and PDD. The last two groups also had lower bilateral frontal beta activity in comparison with the EBD group. Results concerning group differences were interpreted by taking into account both the clinical and the neurophysiological domains.

Nanosystems, Edge Computing, and the Next Generation Computing Systems

It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.