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Chen B, Hou G, Li A. Temporal local clustering coefficient uncovers the hidden pattern in temporal networks. Phys Rev E 2024; 109:064302. [PMID: 39020959 DOI: 10.1103/physreve.109.064302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 05/07/2024] [Indexed: 07/20/2024]
Abstract
Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.
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Affiliation(s)
| | - Guyu Hou
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, People's Republic of China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, People's Republic of China
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2
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Thng G, Shen X, Stolicyn A, Adams MJ, Yeung HW, Batziou V, Conole ELS, Buchanan CR, Lawrie SM, Bastin ME, McIntosh AM, Deary IJ, Tucker-Drob EM, Cox SR, Smith KM, Romaniuk L, Whalley HC. A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples. Psychol Med 2024:1-12. [PMID: 38497116 DOI: 10.1017/s0033291724000643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.
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Affiliation(s)
- Gladi Thng
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Hon Wah Yeung
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Venia Batziou
- Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Eleanor L S Conole
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
- Population Research Center and Center on Aging and Population Sciences, University of Texas, Austin, TX, USA
| | - Simon R Cox
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Keith M Smith
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - Liana Romaniuk
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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3
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Peterson M, Floris DL, Nielsen JA. Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580153. [PMID: 38405819 PMCID: PMC10888742 DOI: 10.1101/2024.02.13.580153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
One organizing principle of the human brain is hemispheric specialization, or the dominance of a specific function or cognitive process in one hemisphere or the other. Previously, Wang et al. (2014) identified networks putatively associated with language and attention as being specialized to the left and right hemispheres, respectively; and a dual-specialization of the executive control network. However, it remains unknown which networks are specialized when specialization is examined within individuals using a higher resolution parcellation, as well as which connections are contributing the most to a given network's specialization. In the present study, we estimated network specialization across three datasets using the autonomy index and a novel method of deconstructing network specialization. After examining the reliability of these methods as implemented on an individual level, we addressed two hypotheses. First, we hypothesized that the most specialized networks would include those associated with language, visuospatial attention, and executive control. Second, we hypothesized that within-network contributions to specialization would follow a within-between network gradient or a specialization gradient. We found that the majority of networks exhibited greater within-hemisphere connectivity than between-hemisphere connectivity. Among the most specialized networks were networks associated with language, attention, and executive control. Additionally, we found that the greatest network contributions were within-network, followed by those from specialized networks.
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Affiliation(s)
- Madeline Peterson
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jared A Nielsen
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
- Neuroscience Center, Brigham Young University, Provo, UT, 84604, USA
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4
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Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
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Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
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Alipour S, Di Marco N, Avalle M, Etta G, Cinelli M, Quattrociocchi W. The drivers of global news spreading patterns. Sci Rep 2024; 14:1519. [PMID: 38233568 PMCID: PMC10794245 DOI: 10.1038/s41598-024-52076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 01/12/2024] [Indexed: 01/19/2024] Open
Abstract
The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the GDELT database. Unlike previous research, we focus on the sequential mention of events across various countries, thus incorporating a temporal dimension into the analysis of news dissemination networks. Our results show a significant imbalance in online news spreading. We identify news superspreaders forming a tightly interconnected rich club, exerting significant influence on the global news agenda. To further investigate the mechanisms underlying news dissemination and the shaping of global public opinion, we model countries' interactions using a gravity model, incorporating economic, geographical, and cultural factors. Consistent with previous studies, we find that countries' GDP is one of the main drivers to shape the worldwide news agenda.
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6
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Li YL, Wu JJ, Li WK, Gao X, Wei D, Xue X, Hua XY, Zheng MX, Xu JG. Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors. Cereb Cortex 2024; 34:bhad439. [PMID: 37991271 DOI: 10.1093/cercor/bhad439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.
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Affiliation(s)
- Yu-Lin Li
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Wei-Kai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200233, China
| | - Dong Wei
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xin Xue
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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7
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Li MY, Zhang YT, Zhou WX. Temporal rich club phenomenon and its formation mechanisms. Phys Rev E 2024; 109:014126. [PMID: 38366487 DOI: 10.1103/physreve.109.014126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 01/03/2024] [Indexed: 02/18/2024]
Abstract
The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a different perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.
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Affiliation(s)
- Mu-Yao Li
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Yin-Ting Zhang
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Wei-Xing Zhou
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
- School of Mathematics, East China University of Science and Technology, Shanghai 200237, China
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8
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Nazari R, Salehi M. Early development of the functional brain network in newborns. Brain Struct Funct 2023; 228:1725-1739. [PMID: 37493690 DOI: 10.1007/s00429-023-02681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 07/06/2023] [Indexed: 07/27/2023]
Abstract
During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.
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Affiliation(s)
- Reza Nazari
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mostafa Salehi
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
- School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, P.O.Box 19395-5746, Iran.
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9
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Osadchiy V, Bal R, Mayer EA, Kunapuli R, Dong T, Vora P, Petrasek D, Liu C, Stains J, Gupta A. Machine learning model to predict obesity using gut metabolite and brain microstructure data. Sci Rep 2023; 13:5488. [PMID: 37016129 PMCID: PMC10073225 DOI: 10.1038/s41598-023-32713-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 03/31/2023] [Indexed: 04/06/2023] Open
Abstract
A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions may contribute to obesity pathogenesis. In this study, we use a machine learning approach to leverage the enormous amount of microstructural neuroimaging and fecal metabolomic data to better understand key drivers of the obese compared to overweight phenotype. Our findings reveal that although gut-derived factors play a role in this distinction, it is primarily brain-directed changes that differentiate obese from overweight individuals. Of the key gut metabolites that emerged from our model, many are likely at least in part derived or influenced by the gut-microbiota, including some amino-acid derivatives. Remarkably, key regions outside of the central nervous system extended reward network emerged as important differentiators, suggesting a role for previously unexplored neural pathways in the pathogenesis of obesity.
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Affiliation(s)
- Vadim Osadchiy
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- UCLA Microbiome Center, Los Angeles, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA
- Department of Urology, David Geffen School of Medicine, Los Angeles, USA
| | - Roshan Bal
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
| | - Emeran A Mayer
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- UCLA Microbiome Center, Los Angeles, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA
| | - Rama Kunapuli
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
| | - Tien Dong
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- UCLA Microbiome Center, Los Angeles, USA
| | - Priten Vora
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA
- Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Danny Petrasek
- Department of Mathematics, California Institute of Technology, Pasadena, USA
| | - Cathy Liu
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA
| | - Jean Stains
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA
- Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Arpana Gupta
- Vatche and Tamar Manoukian Division of Digestive Diseases, Los Angeles, USA.
- UCLA Microbiome Center, Los Angeles, USA.
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Los Angeles, USA.
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, CHS 42-210 MC737818, 10833 Le Conte Avenue, Los Angeles, CA, USA.
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10
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Pan L, Mai Z, Wang J, Ma N. Altered vigilant maintenance and reorganization of rich-clubs in functional brain networks after total sleep deprivation. Cereb Cortex 2023; 33:1140-1154. [PMID: 35332913 DOI: 10.1093/cercor/bhac126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Sleep deprivation strongly deteriorates the stability of vigilant maintenance. In previous neuroimaging studies of large-scale networks, neural variations in the resting state after sleep deprivation have been well documented, highlighting that large-scale networks implement efficient cognitive functions and attention regulation in a spatially hierarchical organization. However, alterations of neural networks during cognitive tasks have rarely been investigated. METHODS AND PURPOSES The present study used a within-participant design of 35 healthy right-handed adults and used task-based functional magnetic resonance imaging to examine the neural mechanism of attentional decline after sleep deprivation from the perspective of rich-club architecture during a psychomotor vigilance task. RESULTS We found that a significant decline in the hub disruption index was related to impaired vigilance due to sleep loss. The hierarchical rich-club architectures were reconstructed after sleep deprivation, especially in the default mode network and sensorimotor network. Notably, the relatively fast alert response compensation was correlated with the feeder organizational hierarchy that connects core (rich-club) and peripheral nodes. SIGNIFICANCES Our findings provide novel insights into understanding the relationship of alterations in vigilance and the hierarchical architectures of the human brain after sleep deprivation, emphasizing the significance of optimal collaboration between different functional hierarchies for regular attention maintenance.
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Affiliation(s)
- Leyao Pan
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, South China Normal University, Guangzhou, 510631, China.,Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Zifeng Mai
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, South China Normal University, Guangzhou, 510631, China.,Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Ning Ma
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, South China Normal University, Guangzhou, 510631, China
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11
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Higher-order rich-club phenomenon in collaborative research grant networks. Scientometrics 2023. [DOI: 10.1007/s11192-022-04621-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
AbstractModern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National Science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more impactful in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs.
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12
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Duncan NW, Shean R. Analysing the effectiveness of Twitter as an equitable community communication tool for international conferences. PeerJ 2023; 11:e15270. [PMID: 37180583 PMCID: PMC10174057 DOI: 10.7717/peerj.15270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/30/2023] [Indexed: 05/16/2023] Open
Abstract
Scientific conferences increasingly include online aspects. Some are moving to be entirely virtual whilst others are adopting hybrid models in which there are both in-person and virtual elements. This development of opportunities for people to attend conferences virtually has the potential to both reduce their environmental impact and to make access to them more equitable. An issue with virtual conference participation that has been raised, however, is that there is a reduction in informal communication between attendees. This is an important deficit as informal contacts play a significant role in both knowledge transmission and professional network development. One forum where some informal communication around conferences does occur is Twitter, with participation there being encouraged by some conferences. It is not clear, however, how effective Twitter is as a community communication tool in terms of equitable participation amongst conference attendees. To investigate this, we looked at Twitter usage surrounding four international conferences between 2010 and 2021. It was found that engagement with conference hashtags increased steadily over time, peaking in 2019. Users represented 9% of conference attendees and were primarily located in Europe and North America, communicating primarily in English (97% of tweets). Hub nodes within the interaction network were also primarily located in these regions. East Asia had fewer users than would be expected based on neuroscience publication numbers from that region. What users there were in East Asia were engaged with less than were users in other regions. It was found that the overall interaction network showed a rich-club structure, where users with more connections tend to interact more with others with similar connection numbers. Finally, it was found that users in Europe and North America tend to communicate with other users in their own regions whereas users in the rest of the world direct their interactions out of their region. These results suggest that although conference-related Twitter use has been successful to some degree in opening up access, there are some notable limitations in its usage that may mirror aspects of inequality inherent to in-person conferences. How to build equitable informal communication networks around virtual conferences remains a challenging question that requires further discussion.
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Affiliation(s)
- Niall W. Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Centre, TMU-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Russell Shean
- Center for Data Science, Washington State Department of Health, Tumwater, Washington, United States of America
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13
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Camina E, Guell F, Sepulcre J, Bernacer J. Hubs of belief networks across sociodemographic and ideological groups. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00964-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractBeliefs are essential components of the human mind, as they define personal identity, integration and adaptation to social groups. Most theoretical studies suggest that beliefs are organized as structured networks: the so-called belief system. According to these studies and their empirical implementation using graph-theoretical approaches, a belief is any proposition considered as true by the respondent. In a recent contribution, we introduced a novel operationalization: a proposition is a belief if (1) it is taken to be true; and (2) the subject declares to be willing to hold it even if irrefutable evidence were hypothetically argued against it. Here, we implement this operationalization using a graph theory approach to investigate the network organization of the belief system in a sample of 108 participants, as well as the differences between key ideological (left- vs. right-wingers) and sociodemographic features (younger vs. older, female vs. male). We identified a well-coordinated network of interlocked spiritual, prosocial and nature-related beliefs, which displays a dense core of 10 hub nodes. Moreover, we observed how specific social liberalist beliefs and transcendental or individualistic/prosocial viewpoints are articulated within left- and right-wingers networks or younger and older participants. Interestingly, we observed that females tend to engage in denser belief networks than male respondents. In conclusion, our research expands tangible scientific evidence of the belief system of humans through the network study of belief reports, which in turn opens innovative ways to study belief systems in social and clinical samples.
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14
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Guillaume P. The frequency and position of stable associations offset their transitivity in a diversity of vertebrate social networks. Ethology 2022. [DOI: 10.1111/eth.13335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Li S, Chen Y, Ren P, Li Z, Zhang J, Liang X. Highly connected and highly variable: A Core brain network during resting state supports Propofol-induced unconsciousness. Hum Brain Mapp 2022; 44:841-853. [PMID: 36217733 PMCID: PMC9842907 DOI: 10.1002/hbm.26103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/17/2022] [Accepted: 09/20/2022] [Indexed: 01/25/2023] Open
Abstract
Despite that leading theories of consciousness make diverging predictions for where and how neural activity gives rise to subjective experience, they all seem to partially agree that the neural correlates of consciousness (NCC) require globally integrated brain activity across a network of functionally specialized modules. However, it is not clear yet whether such functional configurations would be able to identify the NCC. We scanned resting-state fMRI data from 21 subjects during wakefulness, propofol-induced sedation, and anesthesia. Graph-theoretical analyses were conducted on awake fMRI data to search for the NCC candidates as brain regions that exhibit both high rich-clubness and high modular variability, which were found to locate in prefrontal and temporoparietal cortices. Another independent data set of 10 highly-sampled subjects was used to validate the NCC distribution at the individual level. Brain module-based dynamic analysis revealed two discrete reoccurring brain states, one of which was dominated by the NCC candidates (state 1), while the other state was predominately composed of primary sensory/motor regions (state 2). Moreover, state 1 appeared to be temporally more stable than state 2, suggesting that the identified NCC members could sustain conscious content as metastable network representations. Finally, we showed that the identified NCC was modulated in terms of functional connectedness and modular variability in response to the loss of consciousness induced by propofol anesthesia. This work offers a framework to search for neural correlates of consciousness by charting the brain network topology and provides new insights into understanding the roles of different regions in underpinning human consciousness.
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Affiliation(s)
- Siyang Li
- School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina,Laboratory for Space Environment and Physical SciencesHarbin Institute of TechnologyHarbinChina
| | - Yali Chen
- Department of Anesthesiology, Shanghai Cancer CenterFudan UniversityShanghaiChina
| | - Peng Ren
- School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina
| | - Zhipeng Li
- School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina,Laboratory for Space Environment and Physical SciencesHarbin Institute of TechnologyHarbinChina
| | - Jun Zhang
- Department of Anesthesiology, Shanghai Cancer CenterFudan UniversityShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Xia Liang
- School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina,Laboratory for Space Environment and Physical SciencesHarbin Institute of TechnologyHarbinChina
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16
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Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey. PLoS Comput Biol 2022; 18:e1010354. [PMID: 35984841 PMCID: PMC9432692 DOI: 10.1371/journal.pcbi.1010354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/31/2022] [Accepted: 07/03/2022] [Indexed: 11/19/2022] Open
Abstract
The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks. Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.
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17
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Antonello PC, Varley TF, Beggs J, Porcionatto M, Sporns O, Faber J. Self-organization of in vitro neuronal assemblies drives to complex network topology. eLife 2022; 11:74921. [PMID: 35708741 PMCID: PMC9203058 DOI: 10.7554/elife.74921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 06/01/2022] [Indexed: 12/17/2022] Open
Abstract
Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.
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Affiliation(s)
- Priscila C Antonello
- Department of Biochemistry - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Thomas F Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States.,Department of Informatics, Computing, and Engineering, Indiana University, Bloomington, United States
| | - John Beggs
- Department of Physics, Indiana University, Bloomington, United States
| | - Marimélia Porcionatto
- Department of Biochemistry - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States
| | - Jean Faber
- Department of Neurology and Neurosurgery - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
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18
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The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts. Brain Imaging Behav 2022; 16:2199-2219. [DOI: 10.1007/s11682-022-00689-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2022] [Indexed: 12/20/2022]
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19
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Semi-Local Integration Measure of Node Importance. MATHEMATICS 2022. [DOI: 10.3390/math10030405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Numerous centrality measures have been introduced as tools to determine the importance of nodes in complex networks, reflecting various network properties, including connectivity, survivability, and robustness. In this paper, we introduce Semi-Local Integration (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood. We illustrate SLI node importance differentiation among nodes in lexical networks and demonstrate its potential in natural language processing (NLP). In the NLP task of sense identification and sense structure analysis, the SLI centrality measure evaluates node integration and provides the necessary local resolution by differentiating the importance of nodes to a greater extent than standard centrality measures. This provides the relevant topological information about different subnetworks based on relatively local information, revealing the more complex sense structure. In addition, we show how the SLI measure can improve the results of sentiment analysis. The SLI measure has the potential to be used in various types of complex networks in different research areas.
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20
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Painter DT, Daniels BC, Laubichler MD. Innovations are disproportionately likely in the periphery of a scientific network. Theory Biosci 2021; 140:391-399. [PMID: 34773205 DOI: 10.1007/s12064-021-00359-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 10/31/2021] [Indexed: 11/26/2022]
Abstract
The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field's pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field's conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness-adaptability trade-offs that are similar to those found in other biosocial complex systems.
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Affiliation(s)
- Deryc T Painter
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA
| | - Bryan C Daniels
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA
| | - Manfred D Laubichler
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA.
- Max Planck Institute for the History of Science, Berlin, Germany.
- Santa Fe Institute, Santa Fe, New Mexico, USA.
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21
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Huang J, Kwan MP, Kan Z. The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data. Health Place 2021; 72:102694. [PMID: 34649210 PMCID: PMC8501223 DOI: 10.1016/j.healthplace.2021.102694] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/02/2021] [Accepted: 10/05/2021] [Indexed: 11/17/2022]
Abstract
Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.
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Affiliation(s)
- Jianwei Huang
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
| | - Mei-Po Kwan
- Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB, Utrecht, the Netherlands.
| | - Zihan Kan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
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22
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The Asymmetric Pattern of Population Mobility during the Spring Festival in the Yangtze River Delta Based on Complex Network Analysis: An Empirical Analysis of “Tencent Migration” Big Data. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10090582] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Population mobility patterns are an important reflection of the future distribution of migrant populations and the evolution trends of urbanization patterns. However, although research based on statistical data can reveal the pattern of population flow, it also shows a time lag. Most of the population flow network research based on location services data has failed to fully discuss the symmetry of directional outflows and inflows in the same place and the two-way symmetrical connections between places. This paper creatively proposes and constructs the concept and analysis framework of population flow asymmetry. We used the Yangtze River Delta (YRD) as a typical case and the results of our analysis reveal the temporal and spatial asymmetry of the population flow using complex network analysis methods based on the Spring Festival (SF) population migration big data. We found that the timing asymmetry manifested in such a way that the closer it was to the festival, the greater the scale and intensity of the population movement. This is a feature of the lack of scale and regional differences within China. The spatial asymmetry was manifested in three aspects, network, node, and link, and the core cities with administrative and economic hierarchical advantages dominated the asymmetric pattern of regional population mobility. In addition, distance and administrative boundaries are factors that cannot be ignored in population movements, and they were implicated in the degree of asymmetry by distance enhancement and administrative boundary blocking. The conclusions of this study can not only provide policy decision-making guidelines for population management and resource allocation in the YRD, but they can also provide a reference value for achieving the goal of regional, high-quality, integrated development. Future research will further the discussion and management of socio-economic attributes in order to develop a more detailed and microscopic understanding of the mechanisms of population mobility patterns.
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23
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Coronel-Oliveros C, Castro S, Cofré R, Orio P. Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation. Front Comput Neurosci 2021; 15:687075. [PMID: 34335217 PMCID: PMC8316621 DOI: 10.3389/fncom.2021.687075] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/11/2021] [Indexed: 11/27/2022] Open
Abstract
The structural connectivity of human brain allows the coexistence of segregated and integrated states of activity. Neuromodulatory systems facilitate the transition between these functional states and recent computational studies have shown how an interplay between the noradrenergic and cholinergic systems define these transitions. However, there is still much to be known about the interaction between the structural connectivity and the effect of neuromodulation, and to what extent the connectome facilitates dynamic transitions. In this work, we use a whole brain model, based on the Jasen and Rit equations plus a human structural connectivity matrix, to find out which structural features of the human connectome network define the optimal neuromodulatory effects. We simulated the effect of the noradrenergic system as changes in filter gain, and studied its effects related to the global-, local-, and meso-scale features of the connectome. At the global-scale, we found that the ability of the network of transiting through a variety of dynamical states is disrupted by randomization of the connection weights. By simulating neuromodulation of partial subsets of nodes, we found that transitions between integrated and segregated states are more easily achieved when targeting nodes with greater connection strengths-local feature-or belonging to the rich club-meso-scale feature. Overall, our findings clarify how the network spatial features, at different levels, interact with neuromodulation to facilitate the switching between segregated and integrated brain states and to sustain a richer brain dynamics.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias, Mención Biofísica y Biología Computacional, Universidad de Valparaíso, Valparaíso, Chile
| | - Samy Castro
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Faculté de Psychologie, Université de Strasbourg, Strasbourg, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Université de Strasbourg, Strasbourg, France
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Patricio Orio
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Facultad de Ciencias, Instituto de Neurociencias, Universidad de Valparaíso, Valparaíso, Chile
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24
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Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, Aquino K, Hawi Z, Johnson B, Ball G, Klein M, Deco G, Franke B, Bellgrove MA, Fornito A. Genetic influences on hub connectivity of the human connectome. Nat Commun 2021; 12:4237. [PMID: 34244483 PMCID: PMC8271018 DOI: 10.1038/s41467-021-24306-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/03/2021] [Indexed: 02/06/2023] Open
Abstract
Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.
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Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
| | - Ben D Fulcher
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Kevin Aquino
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Ziarih Hawi
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Beth Johnson
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gustavo Deco
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Mark A Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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25
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Liu T, Yan Y, Ai J, Chen D, Wu J, Fang B, Yan T. Disrupted rich-club organization of brain structural networks in Parkinson's disease. Brain Struct Funct 2021; 226:2205-2217. [PMID: 34173868 DOI: 10.1007/s00429-021-02319-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 06/08/2021] [Indexed: 12/16/2022]
Abstract
Parkinson's disease (PD) can be considered as the dysfunction in segregation and integration of large-scale structural networks in the late stage of disease progression. However, the altered patterns in the early stage have not been extensively investigated, especially the altered structural rich-club patterns, which is proved powerful to detect the altered patterns of structural networks in Alzheimer's disease and schizophrenia. To this end, we investigated the rich-club organization of the structural networks derived from diffusion tensor imaging (DTI) data in the early stage of PD, and further investigated the relationship between rich-club organization and clinicopathological measures, including motor and non-motor scales and cerebrospinal fluid (CSF) biomarkers. Two datasets were included for validation in this study. The first one included 41 healthy controls (HC) and 64 PD patients from Parkinson's Disease Progression Marker Initiative (PPMI) dataset, and the second one included 24 HC and 26 PD patients. Results revealed that PD patients in early stage had disrupted rich-club organization, with abnormal connectivity strength between peripheral regions (two-sample t-test between PD and HC: p < 0.001), whereas connectivity strength between rich-club regions remained relatively stable (two-sample t-test between PD and HC: p = 0.108). The classification accuracies on three types of connections were 59.93%, 73.96% and 77.44% for rich-club, feeder and local connections. Furthermore, abnormal local and feeder connections showed significant correlation with poor clinical scales and CSF biomarkers. In summary, a selective disruption of non-rich-club connections here could be regarded as a potential marker in the early diagnosis of PD.
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Affiliation(s)
- Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jing Ai
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan.,Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Boyan Fang
- Beijing Rehabilitation Hospital Capital Medical University, Beijing, China.
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.
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Kumar S, Panda A. Identifying influential nodes in weighted complex networks using an improved WVoteRank approach. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02403-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Lou C, Cross AM, Peters L, Ansari D, Joanisse MF. Rich-club structure contributes to individual variance of reading skills via feeder connections in children with reading disabilities. Dev Cogn Neurosci 2021; 49:100957. [PMID: 33894677 PMCID: PMC8093404 DOI: 10.1016/j.dcn.2021.100957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/29/2021] [Accepted: 04/15/2021] [Indexed: 01/18/2023] Open
Abstract
The present work considers how connectome-wide differences in brain organization might distinguish good and poor readers. The connectome comprises a ‘rich-club’ organization in which a small number of hub regions play a focal role in assisting global communication across the whole brain. Prior work indicates that this rich-club structure is associated with typical and impaired cognitive function although no work so far has examined how this relates to skilled reading or its disorders. Here we investigated the rich-club structure of brain’s white matter connectome and its relationship to reading subskills in 64 children with and without reading disabilities. Among three types of white matter connections, the strength of feeder connections that connect hub and non-hub nodes was significantly correlated with word reading efficiency and phonemic decoding. Phonemic decoding was also positively correlated with connectivity between connectome-wide hubs and nodes within the left-hemisphere reading network, as well as the local efficiency of the reading network. Exploratory analyses also identified sex differences indicating these effects were stronger in girls. This work highlights the independent roles of connectome-wide structure and the more narrowly-defined reading network in understanding the neural bases of skilled and impaired reading in children.
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Affiliation(s)
- Chenglin Lou
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada.
| | - Alexandra M Cross
- Brain and Mind Institute, The University of Western Ontario, London, Canada; Health and Rehabilitation Sciences, The University of Western Ontario, London, Canada
| | - Lien Peters
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada
| | - Daniel Ansari
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Faculty of Education, The University of Western Ontario, London, Canada
| | - Marc F Joanisse
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Haskins Laboratories, New Haven, CT, USA
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Abstract
Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge transfer, business processes and e-commerce. In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks. This survey provides a comparative overview of these metrics, presents their strengths and limitations for analyzing the robustness of the Internet topology, and outlines a conceptual tool set in order to facilitate their future adoption by Internet research and practice but also other areas of network science.
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29
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Siddique AB, Biella P, Unterseher M, Albrectsen BR. Mycobiomes of Young Beech Trees Are Distinguished by Organ Rather Than by Habitat, and Community Analyses Suggest Competitive Interactions Among Twig Fungi. Front Microbiol 2021; 12:646302. [PMID: 33936005 PMCID: PMC8086555 DOI: 10.3389/fmicb.2021.646302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
Beech trees (Fagus sylvatica) are prominent keystone species of great economic and environmental value for central Europe, hosting a diverse mycobiome. The composition of endophyte communities may depend on tree health, plant organ or tissue, and growth habitat. To evaluate mycobiome communalities at local scales, buds, and twigs were sampled from two young healthy mountain beech stands in Bavaria, Germany, four kilometers apart. With Illumina high-throughput sequencing, we found 113 fungal taxa from 0.7 million high-quality reads that mainly consisted of Ascomycota (52%) and Basidiomycota (26%) taxa. Significant correlations between richness and diversity indices were observed (p < 0.05), and mycobiomes did not differ between habitats in the current study. Species richness and diversity were higher in twigs compared to spring buds, and the assemblages in twigs shared most similarities. Interaction network analyses revealed that twig-bound fungi shared similar numbers of (interaction) links with others, dominated by negative co-occurrences, suggesting that competitive exclusion may be the predominant ecological interaction in the highly connected twig mycobiome. Combining community and network analyses strengthened the evidence that plant organs may filter endophytic communities directly through colonization access and indirectly by facilitating competitive interactions between the fungi.
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Affiliation(s)
- Abu Bakar Siddique
- Department of Ecology and Environmental Sciences, Faculty of Science and Technology, Umeå University, Umeå, Sweden
| | - Paolo Biella
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
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30
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Wang L, Xu X, Kai Lau K, Li LSW, Kwun Wong Y, Yau C, Mak HKF, Hui ES. Relation between rich-club organization versus brain functions and functional recovery after acute ischemic stroke. Brain Res 2021; 1763:147441. [PMID: 33753065 DOI: 10.1016/j.brainres.2021.147441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/08/2021] [Accepted: 03/13/2021] [Indexed: 02/04/2023]
Abstract
Studies have shown the brain's rich-club organization may underpin brain function and be associated with various brain disorders. In this study, we aimed to investigate the relation between poststroke brain functions and functional recovery versus the rich-club organization of the structural brain network of patients after first-time acute ischemic stroke. A cohort of 16 acute ischemic stroke patients (11 males) was recruited. Structural brain networks were measured using diffusion tensor imaging within 1 week and at 1, 3 and 6 months after stroke. Motor impairment was assessed using the Upper-Extremity Fugl-Meyer motor scale and activities of daily living using the Barthel Index at the same time points as MRI. The rich-club regions that were stable over the course of stroke recovery included the bilateral dorsolateral superior frontal gyri, right supplementary motor area, and left median cingulate and paracingulate gyri. The network properties that correlated with poststroke brain functions were mainly the ratio between communication cost ratio and density ratio of rich-club, feeder and local connections. The recovery of both motor functions and activities of daily living were correlated with higher normalized rich club coefficients and a shorter length of local connections within a week after stroke. The communication cost ratio of feeder connections, the length of rich-club and local connections, and normalized rich club coefficients were found to be potential prognostic indicators of stroke recovery. Our results provide additional support to the notion that different types of network connections play different roles in brain functions as well as functional recovery.
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Affiliation(s)
- Lu Wang
- Department of Diagnostic Radiology, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Xiaopei Xu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Kui Kai Lau
- Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Leonard S W Li
- Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Yuen Kwun Wong
- Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Christina Yau
- Department of Occupational Therapy, Tung Wah Hospital, Hong Kong Special Administrative Region
| | - Henry K F Mak
- Department of Diagnostic Radiology, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region; State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Edward S Hui
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region.
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Vasilyeva E, Kozlov A, Alfaro-Bittner K, Musatov D, Raigorodskii AM, Perc M, Boccaletti S. Multilayer representation of collaboration networks with higher-order interactions. Sci Rep 2021; 11:5666. [PMID: 33707586 PMCID: PMC7970940 DOI: 10.1038/s41598-021-85133-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/19/2021] [Indexed: 12/04/2022] Open
Abstract
Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.
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Affiliation(s)
- E Vasilyeva
- Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia
- P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninsky Prosp., 119991, Moscow, Russia
| | - A Kozlov
- Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia
| | - K Alfaro-Bittner
- Unmanned Systems Research Institute, Northwestern Polytechnical University, Xi'an, 710072, China.
- Departamento de Física, Universidad Técnica Federico Santa María, Av. España 1680, Casilla 110V, Valparaíso, Chile.
| | - D Musatov
- Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia
- Russian Academy of National Economy and Public Administration, Pr. Vernadskogo, 84, 119606, Moscow, Russia
- Caucasus Mathematical Center, Adyghe State University, ul. Pervomaiskaya, 208, 385000, Maykop, Russia
| | - A M Raigorodskii
- Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia
- Caucasus Mathematical Center, Adyghe State University, ul. Pervomaiskaya, 208, 385000, Maykop, Russia
- Mechanics and Mathematics Faculty, Moscow State University, Leninskie Gory, 1, 119991, Moscow, Russia
- Institute of Mathematics and Computer Science, Buryat State University, ul. Ranzhurova, 5, 670000, Ulan-Ude, Russia
| | - M Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška Cesta 160, 2000, Maribor, Slovenia
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 404332, Taiwan
- Complexity Science Hub Vienna, Josefstädterstraße 39, 1080, Vienna, Austria
| | - S Boccaletti
- Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, 141701, Moscow, Russia
- Unmanned Systems Research Institute, Northwestern Polytechnical University, Xi'an, 710072, China
- CNR-Institute of Complex Systems, Via Madonna del Piano 10, 50019, Sesto Fiorentino, Italy
- Universidad Rey Juan Carlos, Calle Tulipán s/n, Móstoles, 28933, Madrid, Spain
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32
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Goekoop R, de Kleijn R. How higher goals are constructed and collapse under stress: A hierarchical Bayesian control systems perspective. Neurosci Biobehav Rev 2021; 123:257-285. [PMID: 33497783 DOI: 10.1016/j.neubiorev.2020.12.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 11/19/2020] [Accepted: 12/19/2020] [Indexed: 01/26/2023]
Abstract
In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.
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Affiliation(s)
- Rutger Goekoop
- Parnassia Group, PsyQ, Department of Anxiety Disorders, Early Detection and Intervention Team (EDIT), Netherlands.
| | - Roy de Kleijn
- Cognitive Psychology Unit, Leiden University, Netherlands
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33
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Kobayashi T, Génois M. Two types of densification scaling in the evolution of temporal networks. Phys Rev E 2020; 102:052302. [PMID: 33327108 DOI: 10.1103/physreve.102.052302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/18/2020] [Indexed: 11/07/2022]
Abstract
Many real-world social networks constantly change their global properties over time, such as the number of edges, size, and density. While temporal and local properties of social networks have been extensively studied, the origin of their dynamical nature is not yet well understood. Networks may grow or shrink if (a) the total population of nodes changes and/or (b) the chance of two nodes being connected varies over time. Here, we develop a method that allows us to classify the source of time-varying nature of temporal networks. In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally characterize the two dynamical classes. The model is fitted to the empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities.
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Affiliation(s)
- Teruyoshi Kobayashi
- Department of Economics, Kobe University, Kobe, Japan and Center for Computational Social Science, Kobe University, Kobe, Japan
| | - Mathieu Génois
- CNRS, CPT, Aix-Marseille Université, Université de Toulon, Marseille, France and GESIS, Leibniz Institute for the Social Sciences, Köln/Mannheim, Germany
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34
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Liu ZQ, Zheng YQ, Misic B. Network topology of the marmoset connectome. Netw Neurosci 2020; 4:1181-1196. [PMID: 33409435 PMCID: PMC7781610 DOI: 10.1162/netn_a_00159] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/21/2020] [Indexed: 12/11/2022] Open
Abstract
The brain is a complex network of interconnected and interacting neuronal populations. Global efforts to understand the emergence of behavior and the effect of perturbations depend on accurate reconstruction of white matter pathways, both in humans and in model organisms. An emerging animal model for next-generation applied neuroscience is the common marmoset (Callithrix jacchus). A recent open respository of retrograde and anterograde tract tracing presents an opportunity to systematically study the network architecture of the marmoset brain (Marmoset Brain Architecture Project; http://www.marmosetbrain.org). Here we comprehensively chart the topological organization of the mesoscale marmoset cortico-cortical connectome. The network possesses multiple nonrandom attributes that promote a balance between segregation and integration, including near-minimal path length, multiscale community structure, a connective core, a unique motif composition, and multiple cavities. Altogether, these structural attributes suggest a link between network architecture and function. Our findings are consistent with previous reports across a range of species, scales, and reconstruction technologies, suggesting a small set of organizational principles universal across phylogeny. Collectively, these results provide a foundation for future anatomical, functional, and behavioral studies in this model organism.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Ying-Qiu Zheng
- Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
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Notsu K, Wiratsudakul A, Mitoma S, Daous HE, Kaneko C, El-Khaiat HM, Norimine J, Sekiguchi S. Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis. Pathogens 2020; 9:pathogens9110903. [PMID: 33126749 PMCID: PMC7693104 DOI: 10.3390/pathogens9110903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/27/2020] [Accepted: 10/27/2020] [Indexed: 11/18/2022] Open
Abstract
The cattle industry is suffering economic losses caused by bovine leukemia virus (BLV) and enzootic bovine leukosis (EBL), the clinical condition associated with BLV infection. This pathogen spreads easily without detection by farmers and veterinarians due to the lack of obvious clinical signs. Cattle movement strongly contributes to the inter-farm transmission of BLV. This study quantified the farm-level risk of BLV introduction using a cattle movement analysis. A generalized linear mixed model predicting the proportion of BLV-infected cattle was constructed based on weighted in-degree centrality. Our results suggest a positive association between weighted in-degree centrality and the estimated number of introduced BLV-infected cattle. Remarkably, the introduction of approximately six cattle allowed at least one BLV-infected animal to be added to the farm in the worst-case scenario. These data suggest a high risk of BLV infection on farms with a high number of cattle being introduced. Our findings indicate the need to strengthen BLV control strategies, especially along the chain of cattle movement.
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Affiliation(s)
- Kosuke Notsu
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
| | - Anuwat Wiratsudakul
- Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom 73170, Thailand;
- The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Shuya Mitoma
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
| | - Hala El Daous
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
- Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt;
| | - Chiho Kaneko
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
| | - Heba M. El-Khaiat
- Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt;
| | - Junzo Norimine
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
- Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan
| | - Satoshi Sekiguchi
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
- Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan
- Correspondence: ; Tel.: +81-0985-58-7676
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36
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Gogaladze A, Raes N, Biesmeijer JC, Ionescu C, Pavel AB, Son MO, Gozak N, Anistratenko VV, Wesselingh FP. Social network analysis and the implications for Pontocaspian biodiversity conservation in Romania and Ukraine: A comparative study. PLoS One 2020; 15:e0221833. [PMID: 33095780 PMCID: PMC7584225 DOI: 10.1371/journal.pone.0221833] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/06/2020] [Indexed: 11/20/2022] Open
Abstract
Romania and Ukraine share the Black Sea coastline, the Danube Delta and associated habitats, which harbor the endemic, aquatic Pontocaspian biota. Currently, this biota is diminishing both in numbers of species and their abundance because of human activities, and its future persistence strongly depends on the adequacy of conservation measures. Romania and Ukraine have a common responsibility to address the conservation of Pontocaspian biodiversity. The two countries, however have different socio-political and legal conservation frameworks, which may result in differences in the social network structure of stakeholder institutions with different implications for Pontocaspian biodiversity conservation. Here, we study the social network structure of stakeholder organizations involved in conservation of Pontocaspian biodiversity in Romania and the implications of network structure for conservation outcomes. Then we compare the findings from Romania to an earlier similar study from Ukraine. We apply a mix of qualitative and quantitative social network analysis methods to combine the content and context of the interactions with relational measures. We show that Pontocaspian biodiversity plays a minor and mostly incidental role in the inter-organizational interactions in Romania. Furthermore, there is room for improvement in the network structure through e.g. more involvement of governmental and nongovernmental organizations and increased motivation of central stakeholders to initiate conservation actions. Social variables, such as lack of funding, hierarchical, non-inclusive system of conservation governance and continuous institutional reforms in the public sector are consequential for the network relations and structure. Social network of stakeholders in Ukraine is more connected and central stakeholders utilize their favorable positions. However, neither in Ukraine is the Pontocaspian biodiversity a driver of organizational interactions. Consequently, both networks translate into sub-optimal conservation actions and the roads to optimal conservation are different. We end with sketching out conservation implications and recommendations for improved national and cross-border conservation efforts.
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Affiliation(s)
- Aleksandre Gogaladze
- Naturalis Biodiversity Center, Leiden, The Netherlands
- Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands
| | - Niels Raes
- Naturalis Biodiversity Center, Leiden, The Netherlands
- NLBIF–Netherlands Biodiversity Information Facility, Leiden, The Netherlands
| | - Jacobus C. Biesmeijer
- Naturalis Biodiversity Center, Leiden, The Netherlands
- Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands
| | | | - Ana-Bianca Pavel
- Constanta Branch of the National Institute for Research and Development on Marine Geology and Geo-ecology–GeoEcoMar, Constanta, Romania
| | - Mikhail O. Son
- Institute of Marine Biology, National Academy of Sciences of Ukraine, Odessa, Ukraine
| | | | - Vitaliy V. Anistratenko
- Department of Invertebrate Fauna and Systematics, Schmalhausen Institute of Zoology, National Academy of Sciences of Ukraine, Kiev, Ukraine
| | - Frank P. Wesselingh
- Naturalis Biodiversity Center, Leiden, The Netherlands
- Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands
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Sa de Almeida J, Meskaldji DE, Loukas S, Lordier L, Gui L, Lazeyras F, Hüppi PS. Preterm birth leads to impaired rich-club organization and fronto-paralimbic/limbic structural connectivity in newborns. Neuroimage 2020; 225:117440. [PMID: 33039621 DOI: 10.1016/j.neuroimage.2020.117440] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/08/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
Prematurity disrupts brain development during a critical period of brain growth and organization and is known to be associated with an increased risk of neurodevelopmental impairments. Investigating whole-brain structural connectivity alterations accompanying preterm birth may provide a better comprehension of the neurobiological mechanisms related to the later neurocognitive deficits observed in this population. Using a connectome approach, we aimed to study the impact of prematurity on neonatal whole-brain structural network organization at term-equivalent age. In this cohort study, twenty-four very preterm infants at term-equivalent age (VPT-TEA) and fourteen full-term (FT) newborns underwent a brain MRI exam at term age, comprising T2-weighted imaging and diffusion MRI, used to reconstruct brain connectomes by applying probabilistic constrained spherical deconvolution whole-brain tractography. The topological properties of brain networks were quantified through a graph-theoretical approach. Furthermore, edge-wise connectivity strength was compared between groups. Overall, VPT-TEA infants' brain networks evidenced increased segregation and decreased integration capacity, revealed by an increased clustering coefficient, increased modularity, increased characteristic path length, decreased global efficiency and diminished rich-club coefficient. Furthermore, in comparison to FT, VPT-TEA infants had decreased connectivity strength in various cortico-cortical, cortico-subcortical and intra-subcortical networks, the majority of them being intra-hemispheric fronto-paralimbic and fronto-limbic. Inter-hemispheric connectivity was also decreased in VPT-TEA infants, namely through connections linking to the left precuneus or left dorsal cingulate gyrus - two regions that were found to be hubs in FT but not in VPT-TEA infants. Moreover, posterior regions from Default-Mode-Network (DMN), namely precuneus and posterior cingulate gyrus, had decreased structural connectivity in VPT-TEA group. Our finding that VPT-TEA infants' brain networks displayed increased modularity, weakened rich-club connectivity and diminished global efficiency compared to FT infants suggests a delayed transition from a local architecture, focused on short-range connections, to a more distributed architecture with efficient long-range connections in those infants. The disruption of connectivity in fronto-paralimbic/limbic and posterior DMN regions might underlie the behavioral and social cognition difficulties previously reported in the preterm population.
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Affiliation(s)
- Joana Sa de Almeida
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Djalel-Eddine Meskaldji
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland; Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Serafeim Loukas
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Lara Lordier
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Laura Gui
- Department of Radiology and Medical Informatics, Center of BioMedical Imaging (CIBM), University of Geneva, Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, Center of BioMedical Imaging (CIBM), University of Geneva, Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
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Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis. J Psychiatr Res 2020; 126:43-54. [PMID: 32416386 DOI: 10.1016/j.jpsychires.2020.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/21/2020] [Accepted: 04/27/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI. METHOD To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests. RESULT The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance. CONCLUSION The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.
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Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
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Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival. SUSTAINABILITY 2020. [DOI: 10.3390/su12083457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.
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Mondragón RJ. Estimating degree-degree correlation and network cores from the connectivity of high-degree nodes in complex networks. Sci Rep 2020; 10:5668. [PMID: 32221346 PMCID: PMC7101448 DOI: 10.1038/s41598-020-62523-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/10/2020] [Indexed: 01/22/2023] Open
Abstract
Many of the structural characteristics of a network depend on the connectivity with and within the hubs. These dependencies can be related to the degree of a node and the number of links that a node shares with nodes of higher degree. In here we revise and present new results showing how to construct network ensembles which give a good approximation to the degree-degree correlations, and hence to the projections of this correlation like the assortativity coefficient or the average neighbours degree. We present a new bound for the structural cut-off degree based on the connectivity within the hubs. Also we show that the connections with and within the hubs can be used to define different networks cores. Two of these cores are related to the spectral properties and walks of length one and two which contain at least on hub node, and they are related to the eigenvector centrality. We introduce a new centrality measured based on the connectivity with the hubs. In addition, as the ensembles and cores are related by the connectivity of the hubs, we show several examples how changes in the hubs linkage effects the degree-degree correlations and core properties.
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Affiliation(s)
- R J Mondragón
- School of Electronic Engineering and Computer Science, Queen Mary University of London Mile End Rd., London, E1 4NS, UK.
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da Cunha BR, MacCarron P, Passold JF, Dos Santos LW, Oliveira KA, Gleeson JP. Assessing police topological efficiency in a major sting operation on the dark web. Sci Rep 2020; 10:73. [PMID: 31919365 PMCID: PMC6952354 DOI: 10.1038/s41598-019-56704-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 10/17/2019] [Indexed: 11/19/2022] Open
Abstract
The networked nature of criminals using the dark web is poorly understood and infrequently studied, mostly due to a lack of data. Rarer still are studies on the topological effectiveness of police interventions. Between 2014 and 2016, the Brazilian Federal Police raided a child pornography ring acting inside the dark web. With these data, we build a topic-view network and compare network disruption strategies with the real police work. Only 7.4% of the forum users share relevant content, and the topological features of this core differ markedly from other clandestine networks. Approximately 60% of the core users need to be targeted to fully break the network connectivity, while the real effect of the arrests was similar to random failure. Despite this topological robustness, the overall "viewership network" was still well disrupted by the arrests, because only 10 users contributed to almost 1/3 of the total post views and 8 of these were apprehended. Moreover, the users who were arrested provided a total of 60% of the viewed content. These results indicate that for similar online systems, aiming at the users that concentrate the views may lead to more efficient police interventions than focusing on the overall connectivity.
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Affiliation(s)
- Bruno Requião da Cunha
- Rio Grande do Sul Superintendency, Federal Police, Porto Alegre, 90160-093, Brazil.
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland.
| | - Pádraig MacCarron
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
- Centre for Social Issues Research, University of Limerick, Limerick, V94 T9PX, Ireland
| | | | | | - Kleber A Oliveira
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
| | - James P Gleeson
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:152-162. [PMID: 31806486 DOI: 10.1016/j.bpsc.2019.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/06/2019] [Accepted: 09/07/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Graph theory applied to brain networks is an emerging approach to understanding the brain's topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear. METHODS We examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual's own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity. RESULTS Lower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD. CONCLUSIONS Results demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.
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Functional Connectome of the Fetal Brain. J Neurosci 2019; 39:9716-9724. [PMID: 31685648 PMCID: PMC6891066 DOI: 10.1523/jneurosci.2891-18.2019] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 08/22/2019] [Accepted: 10/01/2019] [Indexed: 01/05/2023] Open
Abstract
Large-scale functional connectome formation and reorganization is apparent in the second trimester of pregnancy, making it a crucial and vulnerable time window in connectome development. Here we identified which architectural principles of functional connectome organization are initiated before birth, and contrast those with topological characteristics observed in the mature adult brain. A sample of 105 pregnant women participated in human fetal resting-state fMRI studies (fetal gestational age between 20 and 40 weeks). Connectome analysis was used to analyze weighted network characteristics of fetal macroscale brain wiring. We identified efficient network attributes, common functional modules, and high overlap between the fetal and adult brain network. Our results indicate that key features of the functional connectome are present in the second and third trimesters of pregnancy. Understanding the organizational principles of fetal connectome organization may bring opportunities to develop markers for early detection of alterations of brain function.SIGNIFICANCE STATEMENT The fetal to neonatal period is well known as a critical stage in brain development. Rapid neurodevelopmental processes establish key functional neural circuits of the human brain. Prenatal risk factors may interfere with early trajectories of connectome formation and thereby shape future health outcomes. Recent advances in MRI have made it possible to examine fetal brain functional connectivity. In this study, we evaluate the network topography of normative functional network development during connectome genesis in utero Understanding the developmental trajectory of brain connectivity provides a basis for understanding how the prenatal period shapes future brain function and disease dysfunction.
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Nabulsi L, McPhilemy G, Kilmartin L, O'Hora D, O'Donoghue S, Forcellini G, Najt P, Ambati S, Costello L, Byrne F, McLoughlin J, Hallahan B, McDonald C, Cannon DM. Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis. Brain Connect 2019; 9:745-759. [PMID: 31591898 DOI: 10.1089/brain.2019.0667] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Well-established structural abnormalities, mostly involving the limbic system, have been associated with disorders of emotion regulation. Understanding the arrangement and connections of these regions with other functionally specialized cortico-subcortical subnetworks is key to understanding how the human brain's architecture underpins abnormalities of mood and emotion. We investigated topological patterns in bipolar disorder (BD) with the anatomically improved precision conferred by combining subject-specific parcellation/segmentation with nontensor-based tractograms derived using a high-angular resolution diffusion-weighted approach. Connectivity matrices were constructed using 34 cortical and 9 subcortical bilateral nodes (Desikan-Killiany), and edges that were weighted by fractional anisotropy and streamline count derived from deterministic tractography using constrained spherical deconvolution. Whole-brain and rich-club connectivity alongside a permutation-based statistical approach was used to investigate topological variance in predominantly euthymic BD relative to healthy volunteers. BP patients (n = 40) demonstrated impairments across whole-brain topological arrangements (density, degree, and efficiency), and a dysconnected subnetwork involving limbic and basal ganglia relative to controls (n = 45). Increased rich-club connectivity was most evident in females with BD, with frontolimbic and parieto-occipital nodes not members of BD rich-club. Increased centrality in females relative to males was driven by basal ganglia and fronto-temporo-limbic nodes. Our subject-specific cortico-subcortical nontensor-based connectome map presents a neuroanatomical model of BD dysconnectivity that differentially involves communication within and between emotion-regulatory and reward-related subsystems. Moreover, the female brain positions more dependence on nodes belonging to these two differently specialized subsystems for communication relative to males, which may confer increased susceptibility to processes dependent on integration of emotion and reward-related information.
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Affiliation(s)
- Leila Nabulsi
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Genevieve McPhilemy
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Liam Kilmartin
- College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
| | - Denis O'Hora
- School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Stefani O'Donoghue
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Giulia Forcellini
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland.,Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Pablo Najt
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Srinath Ambati
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Laura Costello
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Fintan Byrne
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - James McLoughlin
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Brian Hallahan
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
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Cui LB, Wei Y, Xi YB, Griffa A, De Lange SC, Kahn RS, Yin H, Van den Heuvel MP. Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia. Schizophr Bull 2019; 45:1291-1299. [PMID: 30926985 PMCID: PMC6811827 DOI: 10.1093/schbul/sbz014] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- School of Medical Psychology, Fourth Military Medical University, Xi’an, China
| | - Yongbin Wei
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Alessandra Griffa
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C De Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Martijn P Van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
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Cyclic motifs in the Sardex monetary network. Nat Hum Behav 2019; 2:822-829. [PMID: 31558815 DOI: 10.1038/s41562-018-0450-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 09/11/2018] [Indexed: 11/08/2022]
Abstract
From decentralized banking systems to digital community currencies, the way humans perceive and use money is changing1-3, thus creating novel opportunities for solving important economic and social problems. Here, we study Sardex, a fast-growing community currency in Sardinia (involving 1,477 businesses arrayed in a network with 48,170 transactions) using network analysis to shed light on its operation. Based on our experience with its day-to-day operations, we propose performance metrics tailored for Sardex but also to similar economic systems, introduce criteria for identifying prominent economic actors and investigate the interplay between network structure and economic robustness. Leveraging new methods for quantifying network 'cyclic density' and 'k-cycle centrality,' we show that geodesic transaction cycles, where money flows in a circle through the network, are prevalent and that certain nodes have a pivotal role in them. We analyse the transactions within cycles and find that the economic turnover of the involved firms is higher, and that excessive currency and debt accumulations are lower. We also measure a similar, but secondary, effect for nodes and edges that serve as intermediaries to many transactions. These metrics are strong indicators of the success of such mutual credit systems at individual and collective levels.
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