Social network analysis for social neuroscientists

A framework for integrating neural development and social networks in adolescence

Adolescence is a developmental period characterized by increasingly complex and influential peer contexts. Concurrently, developmental changes in neural circuits, particularly those related to social cognition, affective salience, and cognitive control, contribute to individuals' social interactions and behaviors. However, while adolescents' behaviors and overall outcomes are influenced by the entirety of their social environments, insights from developmental and social neuroscience often come from studies of individual relationships or specific social actors. By capturing information about both adolescents' individual relations and their larger social contexts, social network analysis offers a powerful opportunity to enhance our understanding of how social factors interact with adolescent development. In this review, we highlight the relevant features of adolescent social and neural development that should be considered when integrating social network analysis and neuroimaging methods. We focus on broad themes of adolescent development, including identity formation, peer sensitivity, and the pursuit of social goals, that serve as potential mechanisms for the relations between neural processes and social network features. With these factors in mind, we review the current research and propose future applications of these methods and theories.

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.

Social cognition in hyperkinetic movement disorders: a systematic review

Numerous lines of research indicate that our social brain involves a network of cortical and subcortical brain regions that are responsible for sensing and controlling body movements. However, it remains unclear whether movement disorders have a systematic impact on social cognition. To address this question, we conducted a systematic review examining the influence of hyperkinetic movement disorders (including Huntington disease, Tourette syndrome, dystonia, and essential tremor) on social cognition. Following the PRISMA guidelines and registering the protocol in the PROSPERO database (CRD42022327459), we analyzed 50 published studies focusing on theory of mind (ToM), social perception, and empathy. The results from these studies provide evidence of impairments in ToM and social perception in all hyperkinetic movement disorders, particularly during the recognition of negative emotions. Additionally, individuals with Huntington's Disease and Tourette syndrome exhibit empathy disorders. These findings support the functional role of subcortical structures (such as the basal ganglia and cerebellum), which are primarily responsible for movement disorders, in deficits related to social cognition.

Increased interbrain synchronization and neural efficiency of the frontal cortex to enhance human coordinative behavior: A combined hyper-tES and fNIRS study

Coordination is crucial for individuals to achieve common goals; however, the causal relationship between coordination behavior and neural activity has not yet been explored. Interbrain synchronization (IBS) and neural efficiency in cortical areas associated with the mirror neuron system (MNS) are considered two potential brain mechanisms. In the present study, we attempted to clarify how the two mechanisms facilitate coordination using hypertranscranial electrical stimulation (hyper-tES). A total of 124 healthy young adults were randomly divided into three groups (the hyper-tACS, hyper-tDCS and sham groups) and underwent modulation of the right inferior frontal gyrus (IFG) during functional near-infrared spectroscopy (fNIRS). Increased IBS of the PFC or neural efficiency of the right IFG (related to the MNS) was accompanied by greater coordination behavior; IBS had longer-lasting effects on behavior. Our findings highlight the importance of IBS and neural efficiency of the frontal cortex for coordination and suggest potential interventions to improve coordination in different temporal windows.

The Impact of Global Collaboration on the Academic Focus of the West African College of Surgeons: Are Worldwide Efforts Aligning with Local Priorities?

BACKGROUND

Global collaboration has the potential to induce a shift in research focus away from the priorities of those in low- and low-middle-income countries (LICs and LMICs). This study quantified international collaboration among surgery publications by Fellows of the West African College of Surgeons (WACS) and investigated if collaboration with upper-middle-income and high-income countries (UMICs and HICs) decreases the homophily of research focus.

METHODS

Publications by WACS surgery Fellows from 1960 to 2019 were characterized as local WACS publications, collaborative publications without UMIC/HIC participation, or collaborative publications with UMIC/HIC participation. Research topics were determined for each publication, and topic percentages were compared between collaboration groups.

RESULTS

We analyzed 5065 publications. Most (3690 publications, 73%) were local WACS publications, while 742 (15%) were collaborative publications with UMIC/HIC participation and 633 (12%) were collaborative publications without UMIC/HIC participation. UMIC/HIC collaborations contributed to 49% of the increase (378 out of 766 publications) from 2000 to 2019. Topic homophily was significantly lower between local WACS publications and collaborations with UMIC/HIC participation (differed in nine research topics) than it was between local WACS publications and collaborations without UMIC/HIC participation (differed in two research topics).

CONCLUSIONS

Publications without international collaboration comprise most WACS research, but the rate of UMIC/HIC collaborations is rapidly increasing. We found that UMIC/HIC collaborations decreased the homophily of topic focus in WACS publications, indicating that global collaborations need to have greater emphasis on the priorities of those in LICs and LMICs.

Social network position, trust behavior, and neural activity in young adolescents

Our social interactions take place within numerous social networks, in which our relationships with others define our position within these networks. In this study, we examined how the centrality of positions within social networks was associated with trust behavior and neural activity in 49 adolescents (M_age = 12.8 years, SD_age = 0.4 years). The participants played a trust game with a cartoon animation as a partner, which showed adaptive behavior in response to the participant and was generally untrustworthy. Social network positions were obtained in secondary school classrooms where the participants and their classmates reported on who their friends were. Using social network analysis, a score was calculated that indicated the centrality of everyone's position within the friendship network. The results showed that more central social network positions were associated with higher levels of initial trust behavior, although no evidence was found for a relationship between network position and the adaptation of trust behavior. The results of the functional MRI analyses showed that the centrality of the network positions was positively associated with caudate activity when making trust decisions. Furthermore, the adolescents with more central network positions also showed stronger increases of caudate activity when the partner's return was processed compared to the adolescents with less central network positions. The current study provides initial evidence that social network positions in friendship networks relate to socio-cognitive behavior and neural activity in adolescents.

Using Network Science and Psycholinguistic Megastudies to Examine the Dimensions of Phonological Similarity

Network science was used to examine different dimensions of phonological similarity in English. Data from a phonological associate task and an identification of words in noise task were used to create a phonological association network and a misperception network. These networks were compared to a network formed by a computational metric widely used to assess phonological similarity (i.e., one-phoneme metric). The phonological association network and the misperception network were topographically more similar to each other than either were to the one-phoneme metric network, but there were several network features in common between the one-phoneme metric network and the phonological association network. To assess the influence of network structure on processing, we compared the influence of degree (i.e., neighborhood density) from each of the networks on visual and auditory lexical decision reaction times obtained from two psycholinguistic megastudies. The effect of degree differed across network types and tasks. We discuss the use of each approach to determine phonological similarity and a possible direction forward for language research through the use of multiplex networks.

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

The increase of the aging population in China and the rise of the concept of healthy aging have accelerated the transformation and upgrading of the traditional elderly nursing pattern. Nevertheless, there is a critical limitation existing in the current situation of China's elderly care, i.e., the medical institutions do not support elderly nursing and the elderly nursing institutions do not facilitate access to medical care. To eliminate the adverse impact of this issue, twelve ministries and commissions of the Chinese government have jointly issued a document, i.e., the Several Opinions on Further Promoting the Development of Combining the Healthcare with the Elderly care (SOFPDCHE), to provide guidance from the government level for further promoting the integration of elderly healthcare and elderly nursing. Under this background, this paper constructs a healthcare-nursing information collaboration network (HnICN) based on the SOFPDCHE, proposing three novel strategies to explore the different roles and collaboration relationships of relevant government departments and public organizations in this integration process, i.e., the node identification strategy (NIS), the local adjacency subgroup strategy (LASS), and the information collaboration effect measurement strategy (ICEMS). Furthermore, this paper retrieves 484 valid policy documents related to "the integration of elderly healthcare and elderly nursing" as data samples on the official websites of 12 sponsored ministries and commissions, and finally confirms 22 government departments and public organizations as the network nodes based on these obtained documents, such as the National Health Commission of the People's Republic of China (NHC), the Ministry of Industry and Information Technology of the People's Republic of China (MIIT), and the National Working Commission on Aging (NWCA). In terms of the collaboration effect, the results of all node-pairs in the HnICN are significantly different, where the collaboration effect between the NHC and MIIT is best and that between the NATCM and MIIT is second best, which are 84.572% and 20.275%, respectively. This study provides the quantifiable results of the information collaboration degree between different government agencies and forms the optimization scheme for the current collaboration status based on these results, which play a positive role in integrating elderly healthcare and elderly nursing and eventually achieving healthy aging.

White matter connectivity in brain networks supporting social and affective processing predicts real-world social network characteristics

Human behavior is embedded in social networks. Certain characteristics of the positions that people occupy within these networks appear to be stable within individuals. Such traits likely stem in part from individual differences in how people tend to think and behave, which may be driven by individual differences in the neuroanatomy supporting socio-affective processing. To investigate this possibility, we reconstructed the full social networks of three graduate student cohorts (N = 275; N = 279; N = 285), a subset of whom (N = 112) underwent diffusion magnetic resonance imaging. Although no single tract in isolation appears to be necessary or sufficient to predict social network characteristics, distributed patterns of white matter microstructural integrity in brain networks supporting social and affective processing predict eigenvector centrality (how well-connected someone is to well-connected others) and brokerage (how much one connects otherwise unconnected others). Thus, where individuals sit in their real-world social networks is reflected in their structural brain networks. More broadly, these results suggest that the application of data-driven methods to neuroimaging data can be a promising approach to investigate how brains shape and are shaped by individuals' positions in their real-world social networks.

Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis

Background

This study aimed to analyze the landscape of publications on bariatric metabolic surgery through machine learning and help experts and scholars from various disciplines better understand bariatric metabolic surgery's hot topics and trends.

Methods

In January 2021, publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Bariatric Surgery' from 1946 to 2020 were downloaded. Python was used to extract publication dates, abstracts, and research topics from the metadata of publications for bibliometric evaluation. Descriptive statistical analysis, social network analysis (SNA), and topic modeling with latent Dirichlet allocation (LDA) were used to reveal bariatric metabolic surgery publication growth trends, landscape, and research topics.

Results

A total of 21,798 records of bariatric metabolic surgery-related literature data were collected from PubMed. The number of publications indexed to bariatric metabolic surgery had expanded rapidly. Obesity Surgery and Surgery for Obesity and Related Diseases are currently the most published journals in bariatric metabolic surgery. The bariatric metabolic surgery research mainly included five topics: bariatric surgery intervention, clinical case management, basic research, body contour, and surgical risk study.

Conclusion

Despite a rapid increase in bariatric metabolic surgery-related publications, few studies were still on quality of life, psychological status, and long-term follow-up. In addition, basic research has gradually increased, but the mechanism of bariatric metabolic surgery remains to be further studied. It is predicted that the above research fields may become potential hot topics in the future.

Adipokines in Sleep Disturbance and Metabolic Dysfunction: Insights from Network Analysis

Adipokines are a growing group of secreted proteins that play important roles in obesity, sleep disturbance, and metabolic derangements. Due to the complex interplay between adipokines, sleep, and metabolic regulation, an integrated approach is required to better understand the significance of adipokines in these processes. In the present study, we created and analyzed a network of six adipokines and their molecular partners involved in sleep disturbance and metabolic dysregulation. This network represents information flow from regulatory factors, adipokines, and physiologic pathways to disease processes in metabolic dysfunction. Analyses using network metrics revealed that obesity and obstructive sleep apnea were major drivers for the sleep associated metabolic dysregulation. Two adipokines, leptin and adiponectin, were found to have higher degrees than other adipokines, indicating their central roles in the network. These adipokines signal through major metabolic pathways such as insulin signaling, inflammation, food intake, and energy expenditure, and exert their functions in cardiovascular, reproductive, and autoimmune diseases. Leptin, AMP activated protein kinase (AMPK), and fatty acid oxidation were found to have global influence in the network and represent potentially important interventional targets for metabolic and sleep disorders. These findings underscore the great potential of using network based approaches to identify new insights and pharmaceutical targets in metabolic and sleep disorders.

Analysis of OFDI Industry Linkage Network Based on Grey Incidence: Taking the Jiangsu Manufacturing Industry as an Example

Based on the outward direct investment data of each manufacturing industry segment in Jiangsu Province from 2000 to 2020, this paper establishes a correlation network by constructing a grey incidence model with the average value of absolute grey incidence as the threshold. We further analyze the relationship between each manufacturing industry segment in Jiangsu Province in the process of outward direct investment from two perspectives, namely, point and surface. The study shows that from the perspective of each node, the correlation coefficient between equipment manufacturing and other industries is significantly higher, i.e., the influence of equipment manufacturing on other industries is significantly greater. Chemical raw materials and chemical products manufacturing, general equipment manufacturing, special equipment manufacturing, and transportation equipment manufacturing are the important nodes in the network. From the perspective of the network as a whole, the Jiangsu manufacturing OFDI affiliation network is not concentrated. Still, it has small-world characteristics, which are conducive to disseminating information. In contrast, the close nature of the industry has more commonalities, leading to it being more easily divided into the same module in the network block model analysis.

Networks of necessity: Simulating COVID-19 mitigation strategies for disabled people and their caregivers

A major strategy to prevent the spread of COVID-19 is the limiting of in-person contacts. However, limiting contacts is impractical or impossible for the many disabled people who do not live in care facilities but still require caregivers to assist them with activities of daily living. We seek to determine which interventions can best prevent infections of disabled people and their caregivers. To accomplish this, we simulate COVID-19 transmission with a compartmental model that includes susceptible, exposed, asymptomatic, symptomatically ill, hospitalized, and removed/recovered individuals. The networks on which we simulate disease spread incorporate heterogeneity in the risk levels of different types of interactions, time-dependent lockdown and reopening measures, and interaction distributions for four different groups (caregivers, disabled people, essential workers, and the general population). Of these groups, we find that the probability of becoming infected is largest for caregivers and second largest for disabled people. Consistent with this finding, our analysis of network structure illustrates that caregivers have the largest modal eigenvector centrality of the four groups. We find that two interventions-contact-limiting by all groups and mask-wearing by disabled people and caregivers-most reduce the number of infections in disabled and caregiver populations. We also test which group of people spreads COVID-19 most readily by seeding infections in a subset of each group and comparing the total number of infections as the disease spreads. We find that caregivers are the most potent spreaders of COVID-19, particularly to other caregivers and to disabled people. We test where to use limited infection-blocking vaccine doses most effectively and find that (1) vaccinating caregivers better protects disabled people from infection than vaccinating the general population or essential workers and that (2) vaccinating caregivers protects disabled people from infection about as effectively as vaccinating disabled people themselves. Our results highlight the potential effectiveness of mask-wearing, contact-limiting throughout society, and strategic vaccination for limiting the exposure of disabled people and their caregivers to COVID-19.

In-degree centrality in a social network is linked to coordinated neural activity

Convergent processing of the world may be a factor that contributes to social connectedness. We use neuroimaging and network analysis to investigate the association between the social-network position (as measured by in-degree centrality) of first-year university students and their neural similarity while watching naturalistic audio-visual stimuli (specifically, videos). There were 119 students in the social-network study; 63 of them participated in the neuroimaging study. We show that more central individuals had similar neural responses to their peers and to each other in brain regions that are associated with high-level interpretations and social cognition (e.g., in the default mode network), whereas less-central individuals exhibited more variable responses. Self-reported enjoyment of and interest in stimuli followed a similar pattern, but accounting for these data did not change our main results. These findings show that neural processing of external stimuli is similar in highly-central individuals but is idiosyncratic in less-central individuals.

Convergent processing of external stimuli may contribute to social connectedness. Here the authors show that people with high in-degree centrality in a social network have similar neural responses to their peers and to each other and that less-central individuals have idiosyncratic responses.

Understanding the Dynamics of the Developing Adolescent Brain Through Team Science

One of the major goals for research on adolescent development is to identify the optimal conditions for adolescents to grow up in a complex social world and to understand individual differences in these trajectories. Based on influential theoretical and empirical work in this field, achieving this goal requires a detailed understanding of the social context in which neural and behavioral development takes place, along with longitudinal measurements at multiple levels (e.g., genetic, hormonal, neural, behavioral). In this perspectives article, we highlight the promising role of team science in achieving this goal. To illustrate our point, we describe meso (peer relations) and micro (social learning) approaches to understand social development in adolescence as crucial aspects of adolescent mental health. Finally, we provide an overview of how our team has extended our collaborations beyond scientific partners to multiple societal partners for the purpose of informing and including policymakers, education and health professionals, as well as adolescents themselves when conducting and communicating research.

Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants-who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied-that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.

An Adaptive Routing Algorithm Based on Relation Tree in DTN

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

Cognitive maps of social features enable flexible inference in social networks

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people's ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.

Being the Gatekeeper: How Thinking about Sharing Affects Neural Encoding of Information

Information transmission in a society depends on individuals' intention to share or not. Yet, little is known about whether being the gatekeeper shapes the brain's processing of incoming information. Here, we examine how thinking about sharing affects neural encoding of information, and whether this effect is moderated by the person's real-life social network position. In an functional magnetic resonance imaging study, participants rated abstracts of news articles on how much they wanted to read for themselves (read) or-as information gatekeepers-to share with a specific other (narrowcast) or to post on their social media feed (broadcast). In all conditions, consistent spatial blood oxygen level-dependent patterns associated with news articles were observed across participants in brain regions involved in perceptual and language processing as well as higher-order processes. However, when thinking about sharing, encoding consistency decreased in higher-order processing areas (e.g., default mode network), suggesting that the gatekeeper role involves more individualized processing in the brain, that is, person- and context-specific. Moreover, participants whose social networks had high ego-betweenness centrality (i.e., more likely to be information gatekeeper in real life) showed more individualized encoding when thinking about broadcasting. This study reveals how gatekeeping shapes our brain's processing of incoming information.

Computational methods in social neuroscience: recent advances, new tools and future directions

Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.