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Graph Theory

Hussam Mahmoud, Akshat Chulahwat
Recent wildland urban interface fires have demonstrated the unrelenting destructive nature of these events and have called for an urgent need to address the problem. The Wildfire paradox reinforces the ideology that forest fires are inevitable and are actually beneficial; therefore focus should to be shifted towards minimizing potential losses to communities. This requires the development of vulnerability-based frameworks that can be used to provide holistic understanding of risk. In this study, we devise a probabilistic approach for quantifying community vulnerability to wildfires by applying concepts of graph theory...
June 18, 2018: Scientific Reports
Tengda Zhao, Virendra Mishra, Tina Jeon, Minhui Ouyang, Qinmu Peng, Lina Chalak, Jessica Lee Wisnowski, Roy Heyne, Nancy Rollins, Ni Shu, Hao Huang
During the 3rd trimester, large-scale neural circuits are formed in the human brain, resulting in a highly efficient and segregated connectome at birth. Despite recent findings identifying important preterm human brain network properties such as rich-club organization, how the structural network develops differentially across brain regions and among different types of connections in this period is not yet known. Here, using high resolution diffusion MRI of 77 preterm-born and full-term neonates scanned at 31...
June 15, 2018: NeuroImage
Speranza Sannino, Sebastiano Stramaglia, Lucas Lacasa, Daniele Marinazzo
Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience...
October 1, 2017: Network neuroscience
Stavros I Dimitriadis, María E López, Ricardo Bruña, Pablo Cuesta, Alberto Marcos, Fernando Maestú, Ernesto Pereda
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv)...
2018: Frontiers in Neuroscience
M Gilson, N E Kouvaris, G Deco, G Zamora-López
Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics...
May 2018: Physical Review. E
Sheng H Wang, Muriel Lobier, Felix Siebenhühner, Tuomas Puoliväli, Satu Palva, J Matias Palva
It has not been well documented that MEG/EEG functional connectivity graphs estimated with zero-lag-free interaction metrics are severely confounded by a multitude of spurious interactions (SI), i.e., the false-positive "ghosts" of true interactions [1], [2]. These SI are caused by the multivariate linear mixing between sources, and thus they pose a severe challenge to the validity of connectivity analysis. Due to the complex nature of signal mixing and the SI problem, there is a need to intuitively demonstrate how the SI are discovered and how they can be attenuated using a novel approach that we termed hyperedge bundling...
June 2018: Data in Brief
Tiffany C Ho, Emily L Dennis, Paul M Thompson, Ian H Gotlib
Exposure to stress, particularly in periods of rapid brain maturation such as adolescence, can profoundly influence developmental processes that undergird the organization of structural and functional brain networks and that may mediate the association between stressful experiences and maladaptive outcomes. While studies in translational developmental neuroscience often focus on how specific brain regions or targeted connections are altered by stress and psychiatric disease, the emerging field of network science may be especially valuable for elucidating the impact of stress on the intricate connectomics of the adolescent brain...
February 2018: Neurobiology of Stress
Shinya Nawata, Atsuto Maki, Takashi Hikihara
A power packet is a unit of electric power composed of a power pulse and an information tag. In Shannon's information theory, messages are represented by symbol sequences in a digitized manner. Referring to this formulation, we define symbols in power packetization as a minimum unit of power transferred by a tagged pulse. Here, power is digitized and quantized. In this paper, we consider packetized power in networks for a finite duration, giving symbols and their energies to the networks. A network structure is defined using a graph whose nodes represent routers, sources and destinations...
May 2018: Proceedings. Mathematical, Physical, and Engineering Sciences
Nicholas Gilbert, Rachel A Bernier, Vincent D Calhoun, Einat Brenner, Emily Grossner, Sarah M Rajtmajer, Frank G Hillary
Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain's subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity...
2018: PloS One
Anika Sierk, Judith K Daniels, Antje Manthey, Jelmer G Kok, Alexander Leemans, Michael Gaebler, Jan-Peter Lamke, Johann Kruschwitz, Henrik Walter
BACKGROUND: Depersonalization/derealization disorder (DPD) is a chronic and distressing condition characterized by detachment from oneself and/or the external world. Neuroimaging studies have associated DPD with structural and functional alterations in a variety of distinct brain regions. Such local neuronal changes might be mediated by altered interregional white matter connections. However, to our knowledge, no research on network characteristics in this patient population exists to date...
June 6, 2018: Journal of Psychiatry & Neuroscience: JPN
Running Niu, Du Lei, Fuqin Chen, Ying Chen, Xueling Suo, Lingjiang Li, Su Lui, Xiaoqi Huang, John A Sweeney, Qiyong Gong
Introduction: Disrupted topological organization of brain functional networks has been widely observed in posttraumatic stress disorder (PTSD). However, the topological organization of the brain grey matter (GM) network has not yet been investigated in pediatric PTSD who was more vulnerable to develop PTSD when exposed to stress. Materials and methods: Twenty two pediatric PTSD patients and 22 matched trauma-exposed controls who survived a massive earthquake (8...
2018: NeuroImage: Clinical
Peter N Taylor, Nishant Sinha, Yujiang Wang, Sjoerd B Vos, Jane de Tisi, Anna Miserocchi, Andrew W McEvoy, Gavin P Winston, John S Duncan
Background: Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. Methods: We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network...
2018: NeuroImage: Clinical
Silke Kreitz, Benito de Celis Alonso, Michael Uder, Andreas Hess
Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA)...
2018: Frontiers in Neuroscience
Chandan Shah, Jia Liu, Peilin Lv, Huaiqiang Sun, Yuan Xiao, Jieke Liu, Youjin Zhao, Wenjing Zhang, Li Yao, Qiyong Gong, Su Lui
Introduction: There are still uncertainties about the true nature of age related changes in topological properties of the brain functional network and its structural connectivity during various developmental stages. In this cross- sectional study, we investigated the effects of age and its relationship with regional nodal properties of the functional brain network and white matter integrity. Method: DTI and fMRI data were acquired from 458 healthy Chinese participants ranging from age 8 to 81 years. Tractography was conducted on the DTI data using FSL...
2018: Frontiers in Neuroscience
M Mnich, E Teutrine
We study feedback vertex sets (FVS) in tournaments, which are orientations of complete graphs. As our main result, we show that any tournament on n nodes has at most 1.5949 n minimal FVS. This significantly improves the previously best upper bound of 1.6667 n by Fomin et al. [STOC 2016] and 1.6740 n by Gaspers and Mnich [ J. Graph Theory 72 (1):72-89, 2013]. Our new upper bound almost matches the best-known lower bound of 21n/7≈1.5448n, due to Gaspers and Mnich. Our proof is algorithmic, and shows that all minimal FVS of tournaments can be enumerated in time O(1...
July 2018: Journal of Graph Theory
Carlos Vazquez-Hernandez, Antonio Loza, Esteban Peguero-Sanchez, Lorenzo Segovia, Rosa-Maria Gutierrez-Rios
BACKGROUND: Metabolic reactions are chemical transformations commonly catalyzed by enzymes. In recent years, the explosion of genomic data and individual experimental characterizations have contributed to the construction of databases and methodologies for the analysis of metabolic networks. Some methodologies based on graph theory organize compound networks into metabolic functional categories without preserving biochemical pathways. Other methods based on chemical group exchange and atom flow trace the conversion of substrates into products in detail, which is useful for inferring metabolic pathways...
May 30, 2018: BMC Systems Biology
Hua-Jun Chen, Qiu-Feng Chen, Zhe-Ting Yang, Hai-Bin Shi
A higher risk of cognitive impairments has been found after an overt hepatic encephalopathy (OHE) episode in cirrhotic patients. We investigated the effect of prior OHE episodes on the topological organization of the functional brain network and its association with the relevant cognitive impairments. Resting-state functional MRI data were acquired from 41 cirrhotic patients (19 with prior OHE (Prior-OHE) and 22 without (Non-Prior-OHE)) and 21 healthy controls (HC). A Psychometric Hepatic Encephalopathy Score (PHES) assessed cognition...
May 30, 2018: Brain Imaging and Behavior
K M Jemshi, Varun P Gopi, Swamidoss Issac Niwas
PURPOSE: Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer...
May 29, 2018: International Journal of Computer Assisted Radiology and Surgery
Jun Guan, Xiaoyu Xu, Shan Wu, Lizhi Xing
The input-output table is very comprehensive and detailed in describing the national economic systems with abundant economic relationships, which contain supply and demand information among various industrial sectors. The complex network, a theory, and method for measuring the structure of a complex system can depict the structural characteristics of the internal structure of the researched object by measuring the structural indicators of the social and economic systems, revealing the complex relationships between the inner hierarchies and the external economic functions...
2018: PloS One
P Reyes, M P Ortega-Merchan, A Rueda, F Uriza, Hernando Santamaria-García, N Rojas-Serrano, J Rodriguez-Santos, M C Velasco-Leon, J D Rodriguez-Parra, D E Mora-Diaz, D Matallana
Frontotemporal dementia (FTD) affects behavior, language, and personality. This study aims to explore functional connectivity changes in three FTD variants: behavioral (bvFTD), semantic (svPPA), and nonfluent variant (nfvPPA). Seventy-six patients diagnosed with FTD by international criteria and thirty-two controls were investigated. Functional connectivity from resting functional magnetic resonance imaging (fMRI) was estimated for the whole brain. Two types of analysis were done: network basic statistic and topological measures by graph theory...
2018: Behavioural Neurology
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