Network Connectivity Maps Research Articles

Meta-Analysis of the Mechanisms Underlying COVID-19 Modulation of Parkinson's Disease.

Coronavirus disease-19 (COVID-19) is caused by the infection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The virus enters host cells through receptor-mediated endocytosis of angiotensin-converting enzyme-2 (ACE2), leading to systemic inflammation, also known as a "cytokine storm", and neuroinflammation. COVID-19's upstream regulator, interferon-gamma (IFNG), is downregulated upon the infection of SARS-CoV-2, which leads to the downregulation of ACE2. The neuroinflammation signaling pathway (NISP) can lead to neurodegenerative diseases, such as Parkinson's disease (PD), which is characterized by the formation of Lewy bodies made primarily of the α-synuclein protein encoded by the synuclein alpha (SNCA) gene. We hypothesize that COVID-19 may modulate PD progression through neuroinflammation induced by cytokine storms. This study aimed to elucidate the possible mechanisms and signaling pathways involved in COVID-19-triggered pathology associated with neurodegenerative diseases like PD. This study presents the analysis of the pathways involved in the downregulation of ACE2 following SARS-CoV-2 infection and its effect on PD progression. Through QIAGEN's Ingenuity Pathway Analysis (IPA), the study identified the NISP as a top-five canonical pathway/signaling pathway and SNCA as a top-five upstream regulator. Core Analysis was also conducted on the associated molecules between COVID-19 and SNCA to construct a network connectivity map. The Molecule Activity Predictor tool was used to simulate the infection of SARS-CoV-2 by downregulating IFNG, which leads to the predicted activation of SNCA, and subsequently PD, through a dataset of intermediary molecules. Downstream effect analysis was further used to quantify the downregulation of ACE2 on SNCA activation.

Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?

PurposeThis study aimed to investigate differences in functional connectivity (FC) among different resting state networks (RSN) in clinically non-progressive mild cognitive impairment (MCI) and Alzheimer's disease (AD). MethodsUsing 3T MRI acquired resting-state functional MRI (rs-fMRI), we attempted identification of different RSN using independent component analysis (ICA) in amnestic-MCI, convertors to early AD and age-matched cognitively normal healthy controls. Regions of interest (ROI) that showed significant differences in connectivity on group ICA were selected as seeds for seed-voxel analysis. Group differences in FC for each network-connectivity map were entered into a general linear model with age, gender and total intra-cranial volume (TIV) as covariates. ResultsIn this cross-sectional design 31 HC, 30 MCI and 30 MCI-convertors to early AD were evaluated. Seed-based analysis between AD and controls revealed reduced posterior connectivity within the default mode (DMN), dorsal attention (DAN) and antero-posterior connectivity with sensori-motor (SMN) networks. Reduced cerebellar connectivity of DMN and posterior connectivity within the frontal parietal network (FPN) separated AD from MCI. MCI-control comparisons revealed differences only on ICA. Positive correlation was observed between FC in DMN network clusters with verbal list-learning (r = 0.50) and recall scores (r = 0.51) in AD, the latter additionally demonstrating correlation with SMN clusters (r = 0.50). ConclusionsWidespread network hypo-connectivity is apparent in AD as opposed to MCI non-convertors in comparison to controls, along with positive correlation with memory scores. Reduced connectivity involving DMN and FPN helps to differentiate between AD and MCI-nonconvertors to dementia. Longitudinal studies are required into utility of these measures.

Networked Communication between Polymerase and Exonuclease Active Sites in Human Mitochondrial DNA Polymerase.

High fidelity human mitochondrial DNA polymerase (Pol γ) contains two active sites, a DNA polymerization site (pol) and a 3'-5' exonuclease site (exo) for proofreading. Although separated by 35 Å, coordination between the pol and exo sites is crucial to high fidelity replication. The biophysical mechanisms for this coordination are not completely understood. To understand the communication between the two active sites, we used a statistical-mechanical model of the protein ensemble to calculate the energetic landscape and local stability. We compared a series of structures of Pol γ, complexed with primer/template DNA, and either a nucleotide substrate or a series of nucleotide analogues, which are differentially incorporated and excised by pol and exo activity. Despite the nucleotide or its analogues being bound in the pol, Pol γ residue stability varied across the protein, particularly in the exo domain. This suggests that substrate presence in the pol can be "sensed" in the exo domain. Consistent with this hypothesis, in silico mutations made in one active site mutually perturbed the energetics of the other. To identify specific regions of the polymerase that contributed to this communication, we constructed an allosteric network connectivity map that further demonstrates specific pol-exo cooperativity. Thus, a cooperative network underlies energetic connectivity. We propose that Pol γ and other dual-function polymerases exploit an energetic coupling network that facilitates domain-domain communication to enhance discrimination between correct and incorrect nucleotides.

Multinode implementation of an extended Hodgkin–Huxley simulator

Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model’s computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.

Anomalous network architecture of the resting brain in children who stutter

PurposeWe combined a large longitudinal neuroimaging dataset that includes children who do and do not stutter and a whole-brain network analysis in order to examine the intra- and inter-network connectivity changes associated with stuttering. Additionally, we asked whether whole brain connectivity patterns observed at the initial year of scanning could predict persistent stuttering in later years. MethodsA total of 224 high-quality resting state fMRI scans collected from 84 children (42 stuttering, 42 controls) were entered into an independent component analysis (ICA), yielding a number of distinct network connectivity maps (“components”) as well as expression scores for each component that quantified the degree to which it is expressed for each child. These expression scores were compared between stuttering and control groups’ first scans. In a second analysis, we examined whether the components that were most predictive of stuttering status also predicted persistence in stuttering. ResultsStuttering status, as well as stuttering persistence, were associated with aberrant network connectivity involving the default mode network and its connectivity with attention, somatomotor, and frontoparietal networks. The results suggest developmental alterations in the balance of integration and segregation of large-scale neural networks that support proficient task performance including fluent speech motor control. ConclusionsThis study supports the view that stuttering is a complex neurodevelopmental disorder and provides comprehensive brain network maps that substantiate past theories emphasizing the importance of considering situational, emotional, attentional and linguistic factors in explaining the basis for stuttering onset, persistence, and recovery.

Global terrestrial water storage connectivity revealed using complex climate network analyses

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

Properties of metabolic graphs: biological organization or representation artifacts?

BackgroundStandard graphs, where each edge links two nodes, have been extensively used to represent the connectivity of metabolic networks. It is based on this representation that properties of metabolic networks, such as hierarchical and small-world structures, have been elucidated and null models have been proposed to derive biological organization hypotheses. However, these graphs provide a simplistic model of a metabolic network's connectivity map, since metabolic reactions often involve more than two reactants. In other words, this map is better represented as a hypergraph. Consequently, a question that naturally arises in this context is whether these properties truly reflect biological organization or are merely an artifact of the representation.ResultsIn this paper, we address this question by reanalyzing topological properties of the metabolic network of Escherichia coli under a hypergraph representation, as well as standard graph abstractions. We find that when clustering is properly defined for hypergraphs and subsequently used to analyze metabolic networks, the scaling of clustering, and thus the hierarchical structure hypothesis in metabolic networks, become unsupported. Moreover, we find that incorporating the distribution of reaction sizes into the null model further weakens the support for the scaling patterns.ConclusionsThese results combined suggest that the reported scaling of the clustering coefficients in the metabolic graphs and its specific power coefficient may be an artifact of the graph representation, and may not be supported when biochemical reactions are atomically treated as hyperedges. This study highlights the implications of the way a biological system is represented and the null model employed on the elucidated properties, along with their support, of the system.

The problem of medium access control in wireless sensor networks

In this article we revisit the problem of scheduled access through a detailed foray into the questions of energy consumption and throughput for MAC protocols in wireless sensor networks. We consider a static network model that rules out simultaneous transmission and reception by any sensor node and consequently requires partitioning of nodes into disjoint sets of transmitters and receivers at any time instant. Under the assumption of circular transmission (reception) ranges with sharp boundaries, a greedy receiver activation heuristic is developed relying on the network connectivity map to determine distinct receiver groups to be activated within disjoint time intervals. To conserve limited energy resources in sensor networks, the time allocation to each receiver group is based on the residual battery energy available at the respective transmitters. Upon activating each receiver group separately, the additional time-division mechanism of group TDMA is imposed to schedule transmissions interfering at the non-intended destinations within separate fractions of time in order to preserve the reliable feedback information. The two-layered time-division structure of receiver activation and group TDMA algorithms offers distributed and polynomial-time solutions (as required by autonomous sensor networks) to the problems of link scheduling as well as energy and throughput-efficient resource allocation in wireless access. The associated synchronization and overhead issues are not considered in this article.