Multimodal Network Analysis Research Articles

Matrix stiffness regulates the protein profile of extracellular vesicles of pancreatic cancer cell lines.

The fibrotic stroma characterizing pancreatic ductal adenocarcinoma (PDAC) derives from a progressive tissue rigidification, which induces epithelial mesenchymal transition and metastatic dissemination. The aim of this study was to investigate the influence of matrix stiffness on PDAC progression by analyzing the proteome of PDAC-derived extracellular vesicles (EVs). PDAC cell lines (mPDAC and KPC) were grown on synthetic supports with a stiffness close to non-tumor (NT) or tumor tissue (T), and the protein expression levels in cell-derived EVs were analyzed by a quantitative MSE label-free mass spectrometry approach. Our analysis figured out 15 differentially expressed proteins (DEPs) in mPDAC-EVs and 20 DEPs in KPC-EVs in response to matrix rigidification. Up-regulated proteins participate to the processes of metabolism, matrix remodeling, and immune response, altogether hallmarks of PDAC progression. A multimodal network analysis revealed that the majority of DEPs are strongly related to pancreatic cancer. Interestingly, among DEPs, 11 related genes (ACTB/ANXA7/C3/IGSF8/LAMC1/LGALS3/PCD6IP/SFN/TPM3/VARS/YWHAZ) for mPDAC-EVs and 9 (ACTB/ALDH2/GAPDH/HNRNPA2B/ITGA2/NEXN/PKM/RPN1/S100A6) for KPC-EVs were significantly overexpressed in tumor tissues according to gene expression profiling interaction analysis (GEPIA). Concerning the potential clinical relevance of these data, the cluster of ACTB, ITGA2, GAPDH and PKM genes displayed an adverse effect (p<0.05) on the overall survival of PDAC patients.

Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity

The brain is a complex system with functional and structural networks. Different neuroimaging methods have been developed to explore these networks, but each method has its own unique strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has gained interest, but understanding how the information derived from these modalities is related to each other remains an exciting open question. The multilayer network model has emerged as a promising approach to integrate different sources data. In this study, we investigated the hemodynamic and electrophysiological data captured by fNIRS and EEG to compare brain network topologies derived from each modality, examining how these topologies vary between resting state (RS) and task-related conditions. Additionally, we adopted the multilayer network model to integrate EEG and fNIRS data and evaluate the benefits of combining multiple modalities compared to using a single modality in capturing characteristic brain functioning.A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which depends on the modality and the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies

Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA-related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique. Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviors. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA.

Multi-mode Network Analysis under Differential Privacy

With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

Aberrant Multimodal Brain Networks in Patients with Anti-NMDA Receptor Encephalitis

Objective: To explore large-scale brain network alterations and examine their clinical and neuropsychological relevance in patients with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Methods: We enrolled 24 patients with anti-NMDAR encephalitis and 26 matched healthy controls (HCs) who underwent a comprehensive neuropsychological test battery and multimodal MRI scanning. Based on the multimodal MRI dataset, individual morphological, structural and functional brain networks were constructed and compared between the two groups at multiple levels. The associations with clinical/neuropsychological variables and the discriminant ability of significant alterations were further studied. Results: Compared with the HCs, patients with anti-NMDAR encephalitis exhibited cognitive deficits in verbal memory, spatial episodic memory and information processing speed. Multimodal network analysis revealed complicated network disruption patterns in the patients depending on the imaging modalities. Specifically, anti-NMDAR encephalitis profoundly affected morphological and structural networks, but subtle alterations were observed in functional networks. Intriguingly, decreased hierarchy and increased nodal centrality in the lateral orbital gyrus were convergently observed among the three types of networks in the patients. Moreover, the alterations, particularly those from structural networks, correlated well with clinical and neuropsychological variables of the patients and could distinguish the diseased individuals from the HCs with excellent performance (area under the curve = 0.933). Conclusions: The current study provides a comprehensive view of characteristic multimodal network dysfunction in anti-NMDAR encephalitis, which is crucial to establish new diagnostic biomarkers and promising therapeutic targets for the disease. Funding Statement: This work was supported by the National Natural Science Foundation of China (grant number 81922036, 81671764, 81571631, and 81401377), the ECTRIMS-MAGNIMS Fellowship from The European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) (YL) and the Beijing Natural Science fund (grant number 7162077). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved by the institutional review board of Xuanwu hospital, Capital Medical University, Beijing, China and written informed consent was obtained from each participant.

Imaging markers of disability in aquaporin-4 immunoglobulin G seropositive neuromyelitis optica: a graph theory study.

Neuromyelitis optica spectrum disorders lack imaging biomarkers associated with disease course and supporting prognosis. This complex and heterogeneous set of disorders affects many regions of the central nervous system, including the spinal cord and visual pathway. Here, we use graph theory-based multimodal network analysis to investigate hypothesis-free mixed networks and associations between clinical disease with neuroimaging markers in 40 aquaporin-4-immunoglobulin G antibody seropositive patients (age = 48.16 ± 14.3 years, female:male = 36:4) and 31 healthy controls (age = 45.92 ± 13.3 years, female:male = 24:7). Magnetic resonance imaging measures included total brain and deep grey matter volumes, cortical thickness and spinal cord atrophy. Optical coherence tomography measures of the retina and clinical measures comprised of clinical attack types and expanded disability status scale were also utilized. For multimodal network analysis, all measures were introduced as nodes and tested for directed connectivity from clinical attack types and disease duration to systematic imaging and clinical disability measures. Analysis of variance, with group interactions, gave weights and significance for each nodal association (hyperedges). Connectivity matrices from 80% and 95% F-distribution networks were analyzed and revealed the number of combined attack types and disease duration as the most connected nodes, directly affecting changes in several regions of the central nervous system. Subsequent multivariable regression models, including interaction effects with clinical parameters, identified associations between decreased nucleus accumbens (β = −0.85, P = 0.021) and caudate nucleus (β = −0.61, P = 0.011) volumes with higher combined attack type count and longer disease duration, respectively. We also confirmed previously reported associations between spinal cord atrophy with increased number of clinical myelitis attacks. Age was the most important factor associated with normalized brain volume, pallidum volume, cortical thickness and the expanded disability status scale score. The identified imaging biomarker candidates warrant further investigation in larger-scale studies. Graph theory-based multimodal networks allow for connectivity and interaction analysis, where this method may be applied in other complex heterogeneous disease investigations with different outcome measures.

Network Fundamental Diagram (NFD) and traffic signal control: first empirical evidences from the city of Santander

Abstract According to recent literature, the aggregate traffic conditions of an urban road network may be measured by an asymmetric inverse-U shaped diagram, called Network or Macroscopic Fundamental Diagram (NFD or MFD). The research on NFD was finalizes for applications connected to congestion control by means of gating, pricing schemes, multi-modal network analysis, freight vehicle routing. The control of urban road networks by means of NFD is a promising research area, where new methods and models are proposed to reduce traffic congestion and delay. The general objective of the research is to investigate if and in which measure the NFD profile (estimated by means of observed traffic data) changes according to the control strategy adopted for junction signals in an urban area. The first empirical evidences presented in this paper are related to a portion of Santander urban area, where a specific zone has been identified according to traffic characteristics and land uses. Data from traffic loops are collected and correlated with the signal control plans during a working day at link (flow-density diagrams) and network levels (NFD). Some preliminary considerations are derived from the empirical results. The cycle length with a fixed regulation plan does not influence the main traffic variables (flow, density) at link and network level, but these results cannot be generalized.

The Evolution of Neural Processes within the Scope of Multimodal Network Analysis

The development of new imaging technologies allows fascinating insights in the structural and functional networks of the human brain. One of the biggest challenges nowadays is the development of methods for combination of this both types of networks with the demanding goal to estimate the information processing underlying substructures individually. This fusion can contribute to an improved understanding of the time evolution of neural processes decisively. Several steps such as the selection of appropriate EEG-components, their sources localization and the combination with the white matter tractography are substantial for this multimodal network analysis. We introduce here a method for a fusion based subnetwork estimation and corresponding analysis of psychophysiological processes. Additionally we demonstrate the method functionality using real signals.

다중모드 회로망 분석을 이용한 광대역 혼 안테나의 효율적인 설계

This paper proposes the effective design procedure for a broadband, double-ridged horn antenna for evaluating the performance of the RF energy harvesting system with a multi-band rectenna. Using multi-mode network analysis, the higher-mode scattering parameters of the transition and horn were acquired and applied to the antenna design, respectively. As a result, the computing time could be reduced and the calculated VSWR(voltage standing wave ratio) of the antenna was very similar to the analyzed result using fully electromagnetic simulation. And there was also good agreement between the simulated and measured results. The designed broadband antenna has a bandwidth of 660~6360 MHz and 6~13.7 dBi peak radiation gain.

Multimodal Network Analysis and Vulnerability Assessment of U.S.–Canadian Trade in Lake Erie Corridor

Canada is the largest trading partner of the United States. Much of the flow of goods between the two countries is focused in the area between Detroit, Michigan, and Buffalo, New York: the Lake Erie corridor. A multimodal network model allows assessment of the vulnerability of these trade flows to disruptions at one or more of the major bridges and tunnels that are the border-crossing points. The model was calibrated against observed freight flows at these major border crossings for 2007 and then used in a series of experiments to assess the impact of various facility disruption scenarios.

Multi-mode network analysis for discontinuities in parallel-plate waveguides partially filled with multi chiral rods

The reflection and transmission characteristics of the guided modes in parallel-plate waveguides partially filled with one or multi chiral rods have been investigated by a method, which combines the multimode network theory with a rigorous mode matching procedure. The formulas of the reflection and transmission coefficient matrix are derived. The numerical results for different cases are presented and have indicated that the chirality parameters and the gometrical dimensions of the chiral rods have significant influence on the reflection and transmission characteristics of the guided modes.

Multimode network analysis for dielectric leaky-wave antennas consisting of multilayer periodic structure with arbitrary dielectric distributions

The multimode network method is used to analyze the radiation characteristics of the leaky-wave antenna consisting of multilayer dielectric periodic structure with arbitrary dielectric distributions in each layer. The eigenfunction of each periodic layer is determined by Floquet theorem; and the mode matching combining with generalized transverse resonant method are used to derive the dispersion equation of the leaky-wave antenna. The complex transmission constant is calculated by Newton iterative method and the radiation characteristics of the antenna are obtained. Some antenna structures are analyzed and useful guidelines for design of the leaky-wave antennas are suggested.

Multimode network analysis of planar transmission lines

An alternative approach for the full wave analysis of single or coupled planar transmission lines is presented. Following the proposed approach, a multiple (or single) transmission line structure is viewed as series of stacked (or single) transverse discontinuities in a parallel plate waveguide. As a consequence, both top and side coupled structures can be studied. Each individual discontinuity is described in terms of a multimode equivalent network representation. The complete cross section of the planar transmission line structure is then described in terms of a transverse equivalent network and a simple transverse resonance technique is then used to obtain the dispersion behavior, the modal field of the structure, and the characteristic impedances. The multimode equivalent network representations of the individual discontinuities used in this paper have been derived elsewhere and are given in terms of impedance or admittance matrices that are essentially independent from frequency. As a result, they need to be computed only once for each given geometry thus leading to codes that are computationally very efficient.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>