Connectivity Architecture Research Articles

3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors. First, a three-dimensional (3D) dense connectivity architecture is used to build the backbone for feature reuse. Second, we design a new feature pyramid module using 3D atrous convolutional layers and add this module to the end of the backbone to fuse multiscale contexts. Finally, a 3D deep supervision mechanism is equipped with the network to promote training. On the multimodal brain tumor image segmentation benchmark (BRATS) datasets, our method achieves Dice similarity coefficient values of 0.87, 0.72, and 0.70 on the BRATS 2013 Challenge, 0.84, 0.70, and 0.61 on the BRATS 2013 LeaderBoard, 0.83, 0.70, and 0.62 on the BRATS 2015 Testing, 0.8642, 0.7738, and 0.7525 on the BRATS 2018 Validation in terms of whole tumors, tumor cores, and enhancing cores, respectively. Compared to the published state-of-the-art methods, the proposed method achieves promising accuracy and fast processing, demonstrating good potential for clinical medicine.

Internet of Things: A Secure Cloud-based MANET Mobility Model

Connected devices such as Smart Home Automation Gateway, Smart Air Conditioners, Smart Hubs, Smart Thermostat, Color Changing Smart Lights, Smart Mobile phones, Smart Watches and Smart Tablets, etc. are omnipresent in our everyday lives and are becoming a valuable tool with wireless networking features using different wireless protocols commonly used. Access points allow interactions between users within an Internet of Things ecosystem infrastructure. These smart devices are automatically connected, and a network is formed by themselves. However, there are many challenges throughout this established network of its own for secure communication. Security has been perceived as a popular barrier to adopting the cloud model of internet realism. The storage and resource management may be in the cloud environment is a distributed structure that places the world in a raised situation with many concerns over its weaknesses, security risks and difficulties. Different participation parties have broadened those issues depending on the viewpoint and goal of each party. The author primarily addresses the causes of challenges and difficulties related to security, reliability, privacy and availability of services from the Cloud point of view. Connectivity Security has been identified as one of cloud computing's most critical issues where resolving such an issue would result in constant growth in the use and popularity of cloud computation. The purpose of this study is to build a mobile ad hoc network mobility model framework using cloud computing to provide secure Internet of Things communication between smart devices. The major contribution relates a new methodology to ensure secure communication with the 5G network of smart devices using the internet. The approach uses a desired study's accurate and effective simulation and can be applied in an Internet of Things structure. This research would create a new connectivity architecture to address the problem of secure communication between smart devices in 5G networking.

Towards deep learning for connectome mapping: A block decomposition framework

We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated into conventional connectome mapping pipelines to enhance accuracy. Our framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block’s internal connectivity architecture. We develop a block stitching algorithm to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. To evaluate our block decomposition and stitching (BDS) framework independent of CNN performance, we first map each block’s internal connectivity using conventional streamline tractography. Performance is evaluated using simulated diffusion MRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, we find that our block decomposition and stitching steps per se can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20–30%. Moreover, we demonstrate that our framework can improve the strength of structure-function coupling between in vivo diffusion and functional MRI data. We find that structural brain networks mapped with deep learning correlate more strongly with functional brain networks (r ​= ​0.45) than those mapped with conventional tractography (r ​= ​0.36). In conclusion, our BDS framework not only enables connectome mapping with deep learning, but its two constituent steps can be straightforwardly incorporated as part of conventional connectome mapping pipelines to enhance accuracy.

Early-life trauma endophenotypes and brain circuit-gene expression relationships in functional neurological (conversion) disorder.

Functional neurological (conversion) disorder (FND) is a neuropsychiatric condition whereby individuals present with sensorimotor symptoms incompatible with other neurological disorders. Early-life maltreatment (ELM) is a risk factor for developing FND, yet few studies have investigated brain network-trauma relationships in this population. In this neuroimaging–gene expression study, we used two graph theory approaches to elucidate ELM subtype effects on resting-state functional connectivity architecture in 30 patients with motor FND. 21 individuals with comparable depression, anxiety and ELM scores were used as psychiatric controls. Thereafter, we compared trauma endophenotypes in FND with regional-differences in transcriptional gene expression as measured by the Allen Human Brain Atlas (AHBA). In FND patients only, we found that early-life physical abuse severity, and to a lesser extent physical neglect, correlated with corticolimbic weighted-degree functional connectivity. Connectivity profiles influenced by physical abuse occurred in limbic (amygdalar-hippocampal), paralimbic (cingulo-insular and ventromedial prefrontal) and cognitive control (ventrolateral prefrontal) areas, as well as in sensorimotor and visual cortices. These findings held adjusting for individual-differences in depression/anxiety, PTSD and motor phenotypes. In FND, physical abuse also correlated with amygdala and insula coupling to motor cortices. In exploratory analyses, physical abuse correlated connectivity maps overlapped with the AHBA spatial expression of 3 gene-clusters: i) neuronal morphogenesis and synaptic transmission genes in limbic/paralimbic areas; ii) locomotory behavior and neuronal generation genes in left-lateralized structures; and iii) nervous system development and cell motility genes in right-lateralized structures. These circuit-specific architectural profiles related to individual differences in childhood physical abuse burden advance our understanding of the pathophysiology of FND.

A New Approach to the Experimental Study of Large Ensembles of Radioengineering Oscillators with Complex Couplings

In this paper, we propose a new approach that allows experimentally investigating complex dynamics in large ensembles of coupled radioengineering oscillators. The approach is used to construct an analog-to-digital experimental setup for studying ensembles of oscillators with delayed feedback that implements the ability to specify an arbitrary architecture and various types of connections between oscillators. The possibility of reconstructing the complex topology of connections and parameters of all oscillators by their experimental time series is demonstrated.

Differences in the major fiber-tracts of people with congenital and acquired blindness.

In order to better understand how our visual system processes information, we must understand the underlying brain connectivity architecture, and how it can get reorganized under visual deprivation. The full extent to which visual development and visual loss affect connectivity is not well known. To investigate the effect of the onset of blindness on structural connectivity both at the whole-brain voxel-wise level and at the level of all major white-matter tracts, we applied two complementary Diffusion-Tension Imaging (DTI) methods, TBSS and AFQ. Diffusion-weighted brain images were collected from three groups of participants: congenitally blind (CB), acquired blind (AB), and fully sighted controls. The differences between these groups were evaluated on a voxel-wise scale with Tract-Based Spatial Statistics (TBSS) method, and on larger-scale with Automated Fiber Quantification (AFQ), a method that allows for between-group comparisons at the level of the major fiber tracts. TBSS revealed that both blind groups tended to have higher FA than sighted controls in the central structures of the brain. AFQ revealed that, where the three groups differed, congenitally blind participants tended to be more similar to sighted controls than to those participants who had acquired blindness later in life. These differences were specifically manifested in the left uncinated fasciculus, the right corticospinal fasciculus, and the left superior longitudinal fasciculus, areas broadly associated with a range of higher-level cognitive systems.

Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age.

We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4–85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.

Combining Parsing Information With Joint Structure for Human Pose Estimation

Recently, with the growing popularity of Convolutional Neural Networks (CNN), major progress has been made in human pose estimation. However, due to the lack of consideration of human body structure, the current methods still suffer from occlusion, complex background, and large pose variations. Human parsing, as a highly relevant task, can provide useful semantic information of body parts for pose estimation. In this paper, we propose a novel convolutional network, which consists of a pose encoder and a parsing encoder in parallel, to combine parsing information with body structure information for effectively assisting human pose estimation in unconstrained environment. The pose encoder can extract discriminative features that contain rich structure information by incorporating a multi-branch parallel module and a hierarchical connection architecture into hourglass network. On the other hand, the parsing encoder can obtain valuable parsing information that serves as extra constraints to help improve the performance of pose estimation. Then, the generated pose features and parsing features will be integrated together for heatmap prediction. In addition, we apply a joint classification loss for preserving the structural consistency between keypoints and body parts. By correctly localizing the joints in their corresponding body parts, the network can better optimize the spatial relationship between different keypoints, thus reducing the errors of estimation. It is worth mentioning that the proposed modules can be introduced into most of the existing methods. The experimental results on the extended PASCAL-Person-Part and LSP datasets demonstrate the effectiveness of our model, and the performance can be greatly improved by utilizing parsing information.

Internet of Things: A Secure Cloud-based MANET Mobility Model

Connected devices such as Smart Home Automation Gateway, Smart Air Conditioners, Smart Hubs, Smart Thermostat, Color Changing Smart Lights, Smart Mobile phones, Smart Watches and Smart Tablets, etc. are omnipresent in our everyday lives and are becoming a valuable tool with wireless networking features using different wireless protocols commonly used. Access points allow interactions between users within an Internet of Things ecosystem infrastructure. These smart devices are automatically connected, and a network is formed by themselves. However, there are many challenges throughout this established network of its own for secure communication. Security has been perceived as a popular barrier to adopting the cloud model of internet realism. The storage and resource management may be in the cloud environment is a distributed structure that places the world in a raised situation with many concerns over its weaknesses, security risks and difficulties. Different participation parties have broadened those issues depending on the viewpoint and goal of each party. The author primarily addresses the causes of challenges and difficulties related to security, reliability, privacy and availability of services from the Cloud point of view. Connectivity Security has been identified as one of cloud computing's most critical issues where resolving such an issue would result in constant growth in the use and popularity of cloud computation. The purpose of this study is to build a mobile ad hoc network mobility model framework using cloud computing to provide secure Internet of Things communication between smart devices. The major contribution relates a new methodology to ensure secure communication with the 5G network of smart devices using the internet. The approach uses a desired study's accurate and effective simulation and can be applied in an Internet of Things structure. This research would create a new connectivity architecture to address the problem of secure communication between smart devices in 5G networking.

Variable functional connectivity architecture of the preterm human brain: Impact of developmental cortical expansion and maturation

Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.

A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants.

SummaryMore than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. In an attempt to solve this mystery, rather than studying the acute neurochemical effects of antidepressants, here we propose focusing on the early changes in the brain functional connectome using traditional statistics and machine learning approaches. Capitalizing on three independent datasets (n = 1,261) and recent developments in data and network science, we identified a specific connectome fingerprint that predates and predicts response to monoaminergic antidepressants. The discovered fingerprint appears to generalize to antidepressants with differing mechanism of action. We also established a consensus whole-brain hierarchical connectivity architecture and provided a set of model-based features engineering approaches suitable for identifying connectomic signatures of brain function in health and disease.

Synchronous patterns and intermittency in a network induced by the rewiring of connections and coupling.

The connection architecture plays an important role in the synchronization of networks, where the presence of local and nonlocal connection structures are found in many systems, such as the neural ones. Here, we consider a network composed of chaotic bursting oscillators coupled through a Watts-Strogatz-small-world topology. The influence of coupling strength and rewiring of connections is studied when the network topology is varied from regular to small-world to random. In this scenario, we show two distinct nonstationary transitions to phase synchronization: one induced by the increase in coupling strength and another resulting from the change from local connections to nonlocal ones. Besides this, there are regions in the parameter space where the network depicts a coexistence of different bursting frequencies where nonstationary zig-zag fronts are observed. Regarding the analyses, we consider two distinct methodological approaches: one based on the phase association to the bursting activity where the Kuramoto order parameter is used and another based on recurrence quantification analysis where just a time series of the network mean field is required.

Design of parallel-connected polymer tandem solar cells using efficient low bandgap PTB7-Th:PC71BM blend

Parallel-connected tandem cells adopting a highly efficient donor polymer, PTB7-Th, combined with acceptor fullerene PC71BM as the back sub-cell was introduced to further improve the performance of polymer solar cells. Design of the device architecture was investigated using modeling and simulation methods based on the transfer matrix formalism. To optimize the device structure, detailed analysis of the effect of active layer thickness, different device structure, and transparent Ag intermediated electrode on the short-circuit current density has been studied. It was found the long-wavelength absorption in the top-illuminated ITO-free back cell was significantly enhanced due to the resonant microcavity effect, leading to an efficient utilization of the incident light and increased photocurrent. Giving these advantages, the power conversion efficiency of the parallel homo-tandem cell was estimated to be ~ 11%, which was ~ 15% higher than that of a single cell of PTB7-Th. Moreover, the maximum achievable current density and the corresponding optimum active layer thickness of the sub-cells varied a little as the thickness of ultrathin Ag layer was changed, indicating that parallel connection architecture provided more freedom in the design and optimization for high-performance tandem solar cells.

Seamless connectivity architecture and methods for IoT and wearable devices

Wearable and Internet of Things (IoT) devices have the potential to improve lifestyle, personalize receiving treatments or introduce assisted living for elderly people. However, service delivery depends on maintaining and troubleshooting device connectivity to smartphones, where user engagement and technology proficiency represent a possible barrier that prevents a wider adoption, especially in the elderly and disabled population. Low-cost and low-power wearable and IoT devices face challenges when operating out of range of known home networks or pared devices. We propose an architecture and methods to provide seamless connectivity (Se-Co) between devices and wireless networks while maintaining low-power, low-cost and standards compatibility. Through Se-Co, the devices connect without user interaction both in home and in unknown roaming networks while maintaining anonymity, privacy and security. Roaming networks approve data limited connectivity to unknown devices that are able to provide a valid anonymized certificate of compliance and no harm through a home provider. Se-Co enables shifting data processing, such as pattern processing using artificial intelligence, from a wearable device or smartphone towards the cloud. The proposed Se-Co architecture could provide solutions to increase usability of wearable devices and improve their wider adoption, while keeping low the costs of devices, development and services.

Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning.

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders.

Macroscale white matter pathways are the infrastructure for large-scale communication in the human brain and a prerequisite for healthy brain function. Disruptions in the brain's connectivity architecture play an important role in many psychiatric and neurological brain disorders. Here we show that connections important for global communication and network integration are particularly vulnerable to brain alterations across multiple brain disorders. We report on a cross-disorder connectome study comprising in total 1,033 patients and 1,154 matched controls across 8 psychiatric and 4 neurological disorders. We extracted disorder connectome fingerprints for each of these 12 disorders and combined them into a 'cross-disorder disconnectivity involvement map' describing the level of cross-disorder involvement of each white matter pathway of the human brain network. Network analysis revealed connections central to global network communication and integration to display high disturbance across disorders, suggesting a general cross-disorder involvement and the importance of these pathways in normal function.

Development of a transcallosal tractography template and its application to dementia

Understanding the architecture of transcallosal connections would allow for more specific assessments of neurodegeneration across many fields of neuroscience, neurology, and psychiatry. To map these connections, we conducted probabilistic tractography in 100 Human Connectome Project subjects in 32 cortical areas using novel post-processing algorithms to create a spatially precise Trancallosal Tract Template (TCATT). We found robust transcallosal tracts in all 32 regions, and a topographical analysis in the corpus callosum largely agreed with well-established subdivisions of the corpus callosum. We then obtained diffusion MRI data from a cohort of patients with Alzheimer's disease (AD) and another with progressive supranuclear palsy (PSP) and used a two-compartment model to calculate free-water corrected fractional anisotropy (FAT) and free-water (FW) within the TCATT. These metrics were used to determine between-group differences and to determine which subset of tracts was best associated with cognitive function (Montreal Cognitive Assessment (MoCA)). In AD, we found robust between-group differences in FW (31/32 TCATT tracts) in the absence of between-group differences in FAT. FW in the inferior temporal gyrus TCATT tract was most associated with MoCA scores in AD. In PSP, there were widespread differences in both FAT and FW, and MoCA was predicted by FAT in the inferior frontal pars triangularis, preSMA, and medial frontal gyrus TCATT tracts as well as FW in the inferior frontal pars opercularis TCATT tract. The TCATT improves spatial localization of corpus callosum measurements to enhance the evaluation of treatment effects, as well as the monitoring of brain microstructure in relation to cognitive dysfunction and disease progression. Here, we have shown its direct relevance in capturing between-group differences and associating it with the MoCA in AD and PSP.

Video Quality-Aware Traffic Offloading System for Video Streaming Services Over 5G Networks With Dual Connectivity

This paper presents a video quality-aware traffic offloading system that provides seamless video streaming services to users by offloading part of the macrobase station traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of the video quality by allocating the radio resource of the macrobase station. The data flow is split into two parts toward the macrobase station and small cell AP, and the users simultaneously receive their data from both. In the proposed system, fountain code is employed to achieve dual connectivity enhancement by overcoming frequent packet loss in error-prone mobile networks and practical issues in the dual connectivity architecture. Furthermore, software-defined networking (SDN) technology is adopted not only to quickly react to time-varying network status but also to control network resources efficiently. The proposed resource allocation process is designed to require a low running time complexity. The proposed system is implemented using C/C++, Java, and a well-known open source, ONOS. The experimental results verify that the proposed system can achieve much better performance than other offloading algorithms.

Spatial modeling of brain connectivity data via latent distance models with nodes clustering

Brain network data—measuring structural interconnections among brain regions of interest—are increasingly collected for multiple individuals. Moreover, recent analyses provide additional information on the brain regions under study. These predictors typically include the three‐dimensional anatomical coordinates of the regions, and their membership to hemispheres and lobes. Although recent studies have explored the spatial effects underlying brain networks, there is still a lack of statistical analyses on the net connectivity structure which is not explained by the physical proximity of the brain regions. We answer this question via a predictor‐dependent latent space model for replicated brain network data which provides a meaningful representation for the net connectivity architecture via a set of latent positions having a mixture of Gaussians prior. This model allows for flexible inference on brain network patterns which are not explained by the anatomical structure, and facilitates clustering among brain regions according to local similarities in the latent space. Our findings offer novel insights on wiring mechanisms among subsets of brain regions which interestingly departs from the anatomical proximity structure.

Dopaminergic Circuits in Reward and Aversion

The mesocorticolimbic dopamine (DA) system, which is composed of DA neurons in the ventral tegmental area (VTA) projecting to nucleus accumbens and medial prefrontal cortex, has been intensively studied because of its importance in reward processing, drug addiction and other neuropsychiatric diseases. Importantly, while VTA DA neurons were thought to form a homogeneous cell population, recent research has demonstrated a much greater diversity of VTA DA cell type and function. Accordingly, VTA DA neurons encode much more than reward and also contribute to negative affect such as aversion and social defeat stress. How VTA DA neurons could mediate both reward and aversion is currently unknown and an important goal of my research. In my presentation, I will discuss work from the lab that has elucidated the circuit architecture and function of the reciprocal connectivity in the mesolimbic DA system (Yang et al., 2018; Neuron). Moreover, I will discuss recent data that demonstrate the identification of a subcircuit within the mesolimbic DA system that contributes to the encoding of conditioned and unconditioned aversive stimuli (de Jong et al., Neuron, in press). Collectively, our work suggest that we need to develop a new perspective on the DA circuitry that will guide future treatment strategies for addiction and other neuropsychiatric disorders where dysfunction of the neural systems underlying motivated behaviors have been strongly implicated.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.