Relevant Network Research Articles

Conceptualizing crisis brokerage: brokering crisis or brokers in crisis

ABSTRACT The contemporary world is facing multiple crises, from pandemics to wars, from climate change to forced migration. Many of them are closely interlinked and bring to the fore and enforce existing inequalities. Although these crises are often global in scale, they are dealt with differently in specific, local contexts. Regardless of nature and scope of the crisis, affected people often rely on brokers to create meaning, mitigate the disruptive and disturbing impacts, or find ways to cope with them and survive. While brokers play important roles as intermediaries and facilitators in a wide range of settings, their knowledge and skills are particularly relevant in rapidly emerging, volatile as well as continuing crisis settings, where established mechanisms to settle conflict or restore order no longer work. People with relevant knowledge, skills and networks become brokers to help those affected to navigate the crisis by mediating between clashing ‘worlds’, between the specific locality and forces or interventions meant to trigger or alleviate the crises, and to regain control. Through their centrality in processes of translation, information dissemination and connection of disparate actors, they are important agents of social change, impacting power dynamics. As introduction to a special issue, this article develops a conceptual framework for ‘Crisis Brokers’. It takes a critical look at the notion of crisis and analyses the phenomenon of brokerage in the context of crises. Time and temporality then serve as analytical tools that allow us to better understand the significance of brokerage in crises contexts. We are interested in how brokers respond to temporal aspects of crisis and how they can change ensuing temporalities. The framework allows us to explore ways in which crises create spaces for brokers to emerge and operate and to make novel contributions to both the crisis and brokerage literature in ‘times of crises’.

Borderline Personality Disorder and Loneliness: Broadening the Scope of Treatment for Social Rehabilitation.

Borderline personality disorder (BPD) has been described as a condition of intolerance of aloneness. This characteristic drives distinguishing criteria, such as frantic efforts to avoid abandonment. Both BPD and loneliness are linked with elevated mortality risk and multiple negative health outcomes. Psychodynamic theories of BPD emphasize fundamental impairment in attachment and interpersonal functioning. Empirical research demonstrates an association between BPD diagnosis and increased loneliness. Individuals with BPD experience higher levels of loneliness than the general population, and their social networks are systematically smaller, less diverse, and less satisfying. Differences in the subjective experience of loneliness persist when controlling for these relevant social network features, indicating that people with BPD experience more loneliness than others in the same objective social circumstances. According to patients with BPD, increased social connection is often a primary treatment goal and marker of satisfying recovery. There are, however, few evidence-based approaches that primarily target loneliness and building life structures that support durable connections with others. Therefore, loneliness persists as an intractable problem, often failing to remit alongside other symptoms, and few resources are routinely implemented to address this problem. In this article, we argue that loneliness is central to the symptomatic oscillations and subjective experiences of many patients with BPD. We propose that treatment extend beyond the overemphasized therapeutic alliance relationship to also promote socialization and group and vocational settings to enhance patients' social networks. Building larger social networks that rely less on exclusive caregiving and/or romantic relationships and more on role-bound identity building and community relationships would more directly target long-term identity diffusion and relational instability. Such interventions can harness nonclinical community resources, such as group treatment, vocational supports, and peer supports.

Semantic Integration Demands Modulate Large-Scale Network Interactions in the Brain.

The ability to integrate semantic information into the context of a sentence is essential for human communication. Several studies have shown that the predictability of a final keyword based on the sentence context influences semantic integration on the behavioral, neurophysiological, and neural level. However, the architecture of the underlying network interactions for semantic integration across the lifespan remains unclear. In this study, 32 healthy participants (30-75 years) performed an auditory cloze probability task during functional magnetic resonance imaging (fMRI), requiring lexical decisions on the sentence's final words. Semantic integration demands were implicitly modulated by presenting sentences with expected, unexpected, anomalous, or pseudoword endings. To elucidate network interactions supporting semantic integration, we combined univariate task-based fMRI analyses with seed-based connectivity and between-network connectivity analyses. Behavioral data revealed typical semantic integration effects, with increased integration demands being associated with longer response latencies and reduced accuracy. Univariate results demonstrated increased left frontal and temporal brain activity for sentences with higher integration demands. Between-network interactions highlighted the role of task-positive and default mode networks for sentence processing with increased semantic integration demands. Furthermore, increasing integration demands led to a higher number of behaviorally relevant network interactions, suggesting that the increased between-network coupling becomes more relevant for successful task performance as integration demands increase. Our findings elucidate the complex network interactions underlying semantic integration across the aging continuum. Stronger interactions between various task-positive and default mode networks correlated with more efficient processing of sentences with increased semantic integration demands. These results may inform future studies with healthy old and clinical populations.

Angiogenesis Dynamics: A Computational Model of Intravascular Flow Within a Structural Adaptive Vascular Network.

Understanding vascular development and the key factors involved in regulating angiogenesis-the growth of new blood vessels from pre-existing vasculature-is crucial for developing therapeutic approaches to promote wound healing. Computational techniques offer valuable insights into improving angiogenic strategies, leading to enhanced tissue regeneration and improved outcomes for chronic wound healing. While chorioallantoic membrane (CAM) models are widely used for examining fundamental mechanisms in vascular development, they lack quantification of essential parameters such as blood flow rate, intravascular pressure, and changes in vessel diameter. To address this limitation, the current study develops a novel two-dimensional mathematical model of angiogenesis, integrating discrete and continuous modelling approaches to capture intricate cellular interactions and provide detailed information about the capillary network's structure. The proposed hybrid meshless-based model simulates sprouting angiogenesis using the in vivo CAM system. The model successfully predicts the branching process with a total capillary volume fraction deviation of less than 15% compared to experimental data. Additionally, it implements blood flow through the capillary network and calculates the distribution of intravascular pressure and vessel wall shear stress. An adaptive network is introduced to consider capillary responses to hemodynamic and metabolic stimuli, reporting structural diameter changes across the generated vasculature network. The model demonstrates its robustness by verifying numerical outcomes, revealing statistically significant differences with deviations in key parameters, including diameter, wall shear stress (p < 0.05), circumferential wall stress, and metabolic stimuli (p < 0.01). With its strong predictive capability in simulating intravascular flow and its ability to provide both quantitative and qualitative assessments, this research enhances our understanding of angiogenesis by introducing a biologically relevant network that addresses the functional demands of the tissue.

The cellular and extracellular proteomic signature of human dopaminergic neurons carrying the LRRK2 G2019S mutation.

Extracellular vesicles are easily accessible in various biofluids and allow the assessment of disease-related changes in the proteome. This has made them a promising target for biomarker studies, especially in the field of neurodegeneration where access to diseased tissue is very limited. Genetic variants in the LRRK2 gene have been linked to both familial and sporadic forms of Parkinson's disease. With LRRK2 inhibitors entering clinical trials, there is an unmet need for biomarkers that reflect LRRK2-specific pathology and target engagement. In this study, we used induced pluripotent stem cells derived from a patient with Parkinson's disease carrying the LRRK2 G2019S mutation and an isogenic gene-corrected control to generate human dopaminergic neurons. We isolated extracellular vesicles and neuronal cell lysates and characterized their proteomic signature using data-independent acquisition proteomics. Then, we performed differential expression analysis to identify dysregulated proteins in the mutated line. We used Metascape and gene ontology enrichment analysis on the dysregulated proteomes to identify changes in associated functional networks. We identified 595 significantly differentially regulated proteins in extracellular vesicles and 3,205 in cell lysates. We visualized functionally relevant protein-protein interaction networks and identified key regulators within the dysregulated proteomes. Using gene ontology, we found a close association with biological processes relevant to neurodegeneration and Parkinson's disease. Finally, we focused on proteins that were dysregulated in both the extracellular and cellular proteomes. We provide a list of ten biomarker candidates that are functionally relevant to neurodegeneration and linked to LRRK2-associated pathology, for example, the sonic hedgehog signaling molecule, a protein that has tightly been linked to LRRK2-related disruption of cilia function. In conclusion, we characterized the cellular and extracellular proteome of dopaminergic neurons carrying the LRRK2 G2019S mutation and proposed an experimentally based list of biomarker candidates for future studies.

Differences in Network Functional Connectivity in Response to Sub-Symptomatic Exercise Between Elite Adult Athletes after Sport-Related Concussion and Healthy Matched Controls: A Pilot Study.

Resting-state electroencephalography (rsEEG) has developed as a method to explore functional network alterations related to sport-related concussion (SRC). Although exercise is an integral part of an athlete's return to sport (RTS) protocol, our understanding of the effects of exercise on (impaired) brain network activity in elite adult athletes is limited. However, this information may be beneficial to inform recovery and RTS progressions. Recording (128-channel) rsEEG datasets before and after a standardized moderate aerobic bike exercise test, this study aimed to explore functional connectivity patterns in whole brain and relevant functional networks in a group of elite adult athletes post-injury compared with healthy matched controls. The following networks were selected a priori: whole brain (68 regions of interest [ROIs]), default mode network (14 ROIs), central autonomic network (CAN, 24 ROIs), and visual network (8 ROIs). Twenty-one SRC athletes and 21 age-, sex-, sport type-, and skill level-matched healthy controls participated in this study. The SRC athletes were recruited during their RTS protocol (days since injury: 2-140 days). All athletes were able to achieve the exercise goal of reaching a moderate intensity (70% of their age-calculated maximum heart rate) while staying sub-symptomatic. Before and after exercise, functional connectivity was calculated by the phase locking value, in the alpha band (7-13 Hz). Mann-Whitney U and Wilcoxon signed rank tests were used to explore neurophysiological differences between and within groups, respectively. Whole-brain connectivity increased significantly from pre- to post-exercise within both groups (SRC: 0.264-0.284; p = 0.011 vs. controls: 0.253-0.257; p = 0.011). While CAN connectivity significantly increased only within the SRC group from pre-(0.298) to post-exercise (0.317; p = 0.003). Although all athletes reached their exercise goal without exacerbation of symptoms, the impact of exercise on the CAN appears to be greater for the SRC athletes, than matched healthy controls. The potential clinical significance of this finding is that it may have revealed an underlying mechanism for the cardiac autonomic alterations post-injury. This study merits further investigation into the CAN, as a network of interest more closely aligned with the clinical features (e.g., autonomic dysfunction) during athletes' RTS.

Rural physicians and social capital: the potential and promises of a rural health research capacity building program

BackgroundAccessible and contextually relevant healthcare research programs and networks for rural physicians are exceedingly rare, which hinders the development of social capital in an already isolating profession. This study aims to examine the impact of the Rural Health Research Capacity Building (RRCB) Program on enhancing cognitive, structural, and relational social capital through comprehensive research skills training, supported by professional teams and resources.MethodsThis study uses a mixed-methods approach with utilization of qualitative and quantitative data and pre-post quasi-experimental design. Data were collected prior and after completion of the program by means of surveys, focus group, and observation. Thirty-five rural physicians participated in this study from 2014 to 2021.ResultsThe results show a significant increase in cognitive (pre-program = 0.37 vs. post-program = 0.61, p < .001), structural (pre-program = 0.58 vs. post-program = 0.81, p < .001), and relational (pre-program = 0.49 vs. post-program 0.69, p < .001) components of social capital. Focus group discussions and observation data supported these findings, particularly highlighting that research capacity-building programs tailored to the needs of rural physicians can enhance collective values, improve the quality of relationships, and foster communities of research-focused practice.ConclusionsBeing equipped with a shared system of meanings and interpretations, research knowledge and resources, and a professional research network appears to play a critical role in enhancing social capital in rural health research. The RRCB program effectively improves social capital among rural and remote physicians.

LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data.

In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene. To overcome this limitation, a novel inference method, LBF-MI, has been proposed in this research. This two-phase method utilizes limited Boolean functions and multivariate mutual information to reconstruct a Boolean gene regulatory network from time-series gene expression data. Initially, Boolean functions are applied to determine the optimum solutions. In case of failure, multivariate mutual information is applied to obtain the optimum solutions. This research conducted a performance-comparison experiment between LBF-MI and three other methods: mutual information-based Boolean network inference, context likelihood relatedness, and relevance network. When examined on artificial as well as real-time-series gene expression data, the outcomes exhibited that the proposed LBF-MI method outperformed mutual information-based Boolean network inference, context likelihood relatedness, and relevance network on artificial datasets, and two real Escherichia coli datasets (E. coli gene regulatory network, and SOS response of E. coli regulatory network). LBF-MI's superior performance in gene regulatory network inference enables researchers to uncover the regulatory mechanisms and cellular behaviors of various organisms.

Effect of Water Networks On Ligand Binding: Computational Predictions vs Experiments.

Rational drug design focuses on the explanation and prediction of complex formation between therapeutic targets and small-molecule ligands. As a third and often overlooked interacting partner, water molecules play a critical role in the thermodynamics of protein-ligand binding, impacting both the entropy and enthalpy components of the binding free energy and by extension, on-target affinity and bioactivity. The community has realized the importance of binding site waters, as evidenced by the number of computational tools to predict the structure and thermodynamics of their networks. However, quantitative experimental characterization of relevant protein-ligand-water systems, and consequently the validation of these modeling methods, remains challenging. Here, we investigated the impact of solvent exchange from light (H2O) to heavy water (D2O) to provide complete thermodynamic profiling of these ternary systems. Utilizing the solvent isotope effects, we gain a deeper understanding of the energetic contributions of various components. Specifically, we conducted isothermal titration calorimetry experiments on trypsin with a series of p-substituted benzamidines, as well as carbonic anhydrase II (CAII) with a series of aromatic sulfonamides. Significant differences in binding enthalpies found between light vs heavy water indicate a substantial role of the binding site water network in protein-ligand binding. Next, we challenged two conceptually distinct modeling methods, the grid-based WaterFLAP and the molecular dynamics-based MobyWat, by predicting and scoring relevant water networks. The predicted water positions accurately reproduce those in available high-resolution X-ray and neutron diffraction structures of the relevant protein-ligand complexes. Estimated energetic contributions of the identified water networks were corroborated by the experimental thermodynamics data. Besides providing a direct validation for the predictive power of these methods, our findings confirmed the importance of considering binding site water networks in computational ligand design.

Retention of immigrant doctors and nurses in the medical labour markets of the Nordic countries: a scoping review.

In Nordic countries, the influx of immigrant doctors and nurses has been increasing since 2005, however retention remains a challenge. The aims of this scoping review were to examine the facilitators and barriers to the retention of immigrant doctors and nurses in the Nordic countries in order to inform future studies and interventions. A scoping review of peer-reviewed studies focusing on the retention of immigrant doctors and nurses in the Nordic countries was conducted using the framework developed by Arksey and O'Malley. In total, 37 studies were included. The analysis entailed both a numerical analysis to provide a descriptive view of frequencies and a thematic analysis utilising Pierre Bourdieu's concepts of field, capital, habitus and doxa. Facilitators for retaining immigrant doctors and nurses encompassed a sense of belonging, support from management and peers, collegiality, acknowledgement of diverse cultural competencies and the establishment of relevant professional networks. Barriers included unfamiliarity with local healthcare systems, discrimination, inadequate support in entry-level positions, poor psychosocial work environments, and limited professional networks. Few studies focused on the facilitators for retaining immigrant doctors or nurses, few examined the social and economic costs of immigration, and hardly any addressed the processes related to licencing, authorisation and accreditation. The majority of the included studies framed immigrant doctors and nurses as challenges to management or the medical labour market. The findings indicated a need for a more comprehensive and inclusive approach to health workforce management across the Nordic countries to accommodate the increasingly diverse workforce in terms of international migratory backgrounds.

Critical radicle length window governing loss of dehydration tolerance in germinated Perilla seeds: insights from physiological and transcriptomic analyses

BackgroundPerilla (Perilla frutescens L. Britt.) is an important oilseed and medicinal crop that frequently faces seasonal drought stress during seed germination, leading to a loss of dehydration tolerance (DT), which affects seed emergence and significantly reduces yield. DT has been successfully re-established for many species seeds. However, the physiological mechanisms and gene networks that regulate Perilla’s response to DT loss remain unclear.ResultsPhenotypic analysis determined that the window for DT in Perilla seeds occurs at radicle lengths of 0–4 mm. Integrating physiological and transcriptomic analyses revealed that the loss of DT promotes the production of reactive oxygen species (ROS) and regulates oxidase activity and gene expression. This implies that DT may influence seed germination by modulating ROS activity. Four radicle length (i.e., 0, 1, 3, and 4 mm) stages were analyzed, and 262 differentially expressed genes (DEGs) were identified that responded to DT. The majority of these genes were associated with epigenetics, cell function, and transport mechanisms. Analysis of expression data shows that desiccation inhibits the signaling network of genes encoding small secreted peptides (SSPs) and receptor-like protein kinases (RLKs). Finally, a relevant network diagram of DT response was proposed. Based on this information, we have revealed the metabolism regulation maps of the four main pathways involving these DEGs (i.e., metabolic pathways, cell cycle, plant hormone signal transduction, and motor proteins).ConclusionsIn conclusion, these findings deepen our understanding of gene network responses to DT during Perilla seed germination and provide potential target genes for the genetic improvement of drought resistance in this crop.

Neighborhoods, Networks, and HIV Care Among Men Who Have Sex With Men: Proposal for a Longitudinal Study.

The majority of people living with HIV in the United States are men who have sex with men (MSM), with race- and ethnicity-based disparities in HIV rates and care continuum. In order to uncover the neighborhood- and network-involved pathways that produce HIV care outcome disparities, systematic, theory-based investigation of the specific and intersecting neighborhood and social network characteristics that relate to the HIV care continuum must be engaged. Using socioecological and intersectional conceptual frameworks, we aim to identify individual-, neighborhood-, and network-level characteristics associated with HIV care continuum outcomes (viral suppression, retention in care, and antiretroviral adherence) among MSM living with HIV in New York City. In the longitudinal cohort study, we assess 3 neighborhoods of potential influence (residential, social, and health care access activity spaces) using Google Earth. We investigate the influence of neighborhood composition (eg, concentrated poverty and racial segregation) and four neighborhood-level characteristics domains: (1) community violence, physical disorder, and social disorganization (eg, crime rates and housing vacancy); (2) alcohol and other drug use; (3) social norms (eg, homophobia and HIV stigma); and (4) community resources (eg, social services and public transit access). We test theoretical pathways of influence, including stress or coping, stigma or resilience, and access to resources, across the different neighborhoods in which MSM live, socialize, and receive HIV care. At each visit, we locate each participant's reported activity spaces (ie, neighborhoods of potential influence) and collect individual-level data on relevant covariates (including perceptions of or exposure to neighborhoods) and social network inventory data on the composition, social support, and perceived social norms. The outcomes are HIV viral suppression, retention in care, and antiretroviral adherence. These data are combined with an existing, extensive geospatial database of relevant area characteristics. Spatial analysis and multilevel modeling are used to test the main theory-driven hypotheses and capture independent neighborhood-level and network-level effects and changes over time. The study began enrollment in March 2019 and concluded visits in December 2023, with a total of 327 participants enrolled. The median age was 44.1 (SD 11.5) years. Almost all participants self-identified as cisgender men (n=313, 98.1%) and as gay, homosexual, or bisexual (n=301, 94.4%). Overall, 192 (60.1%) participants identified as non-Hispanic Black, and 81 (25.3%) identified as Hispanic. Most (n=201, 63%) reported at least occasional difficulty in meeting basic needs (eg, rent and food) in the past 6 months. The mean number of years living with HIV was 15.4 (SD 10.1). This study will have direct implications for the design of multilevel interventions, addressing factors at the neighborhood, network, and individual levels. Results may inform urban planning and program design to improve HIV care outcomes for MSM, particularly for Black and Latino MSM living in urban areas. PRR1-10.2196/64358.

A noetic account of explanation in mathematics

Abstract We defend a noetic account of intramathematical explanation. On this view, a piece of mathematics is explanatory just in case it produces understanding of an appropriate type. We motivate the view by presenting some appealing features of noeticism. We then discuss and criticize the most prominent extant version of noeticism, due to Inglis and Mejía Ramos, which identifies explanatory understanding with the possession of well-organized cognitive schemas. Finally, we present a novel noetic account. On our view, explanatory understanding arises from meeting specific explanatory objectives. We defend a cluster-concept account of explanatory objectives and identify four important subfamilies within the relevant network of resemblance relations. The resulting view is objectivist (in the sense that it takes explanatory success to be a matter of observer-independent fact), broader in scope than why-question-based accounts, compatible with empirical findings on experts’ explanatory judgments, and capable of generalizing (with appropriate provisos) to scientific explanation as a whole. It thus fulfills Friedman’s half-century-old demand for a general and objectivist theory, which accounts for the link between explanation and understanding.

Symmetry and Complexity in Gene Association Networks Using the Generalized Correlation Coefficient

High-dimensional gene expression data cause challenges for traditional statistical tools, particularly when dealing with non-linear relationships and outliers. The present study addresses these challenges by employing a generalized correlation coefficient (GCC) that incorporates a flexibility parameter, allowing it to adapt to varying levels of symmetry and asymmetry in the data distribution. This adaptability is crucial for analyzing gene association networks, where the GCC demonstrates advantages over traditional measures such as Kendall, Pearson, and Spearman coefficients. We introduce two novel adaptations of this metric, enhancing its precision and broadening its applicability in the context of complex gene interactions. By applying the GCC to relevance networks, we show how different levels of the flexibility parameter reveal distinct patterns in gene interactions, capturing both linear and non-linear relationships. The maximum likelihood and Spearman-based estimators of the GCC offer a refined approach for disentangling the complexity of biological networks, with potential implications for precision medicine. Our methodology provides a powerful tool for constructing and interpreting relevance networks in biomedicine, supporting advancements in the understanding of biological interactions and healthcare research.

Optical Networking Gym: an open-source toolkit for resource assignment problems in optical networks

The dynamic provisioning of optical network services requires algorithms to find a suitable solution given the specific service requirements and the current network state. These algorithms are usually evaluated using a software simulator developed ad hoc, which may require different levels of detail depending on the problem addressed and how realistic the evaluation needs to be. Moreover, to demonstrate they are a significant contribution to the field, these new algorithms must be benchmarked against the best-performing previously proposed solutions. Due to the large set of parameters and their wide range of possible values, benchmarking algorithms from the literature is not straightforward and can quickly become challenging and time-consuming. This work introduces the Optical Networking Gym, an open-source toolkit that simplifies implementing optical resource assignment simulations and benchmarking new solutions against previously published algorithms. The toolkit provides environments modeling relevant optical networking scenarios, common algorithms for solving problems related to these scenarios, and a set of scripts to prepare and execute simulations for various use cases. Currently, four environments are available, with the possibility of increasing this number through contributions from the co-authors and the community. This paper describes the architecture, interface, environments, and scripts included with the toolkit. We adopt the quality of transmission (QoT)-aware dynamic resource allocation of optical services as the network scenario under examination. Three use cases highlight the toolkit’s modularity, flexibility, and performance. The toolkit allows researchers to streamline the process of developing simulation scenarios and algorithms, enhancing their ability to benchmark their algorithms.

Reverse engineering of feedforward cortical-Hippocampal microcircuits for modelling neural network function and dysfunction

Engineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuroengineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feedforward neural networks of primary cortical and hippocampal neurons, using a custom-designed multinodal microfluidic device with Tesla valve inspired microtunnels. By interfacing this device with nanoporous microelectrodes, we show that the reverse engineered multinodal neural networks exhibit capacity for both segregated and integrated functional activity, mimicking brain network dynamics. To advocate the broader applicability of our model system, we induced localized perturbations with amyloid beta to study the impact of pathology on network functionality. Additionally, we demonstrate long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimer’s-model mice and rats. Our results thus highlight the potential of our approach for reverse engineering of anatomically relevant multinodal neural networks to study dynamic structure-function relationships in both healthy and pathological conditions.

Harmony in the brain: A narrative review on the shared neural substrates of emotion regulation and creativity

The contribution of creativity in overall well-being through regulating emotions has sparkled research interest in employing artistic interventions recently for the improvement of mental health. Although the behavioural links between emotion regulation and creativity have been established, the neural networks reflecting these relations are yet to be investigated. In this mini review, we describe the neural underpinnings of all forms of creativity and of the emotion regulation strategies. Given the complexity of both of these constructs, we separate creativity in its various forms and report the regions and the neural networks involved. Similarly, we distinguish between the differential emotion regulation strategies and describe the relevant brain areas and networks. We then proceed to a critical exploration of common regions of interest and of neural pathways among these important functions. The studies included in this review point towards certain brain regions that are shared among creativity and affective control, such as the prefrontal cortex, the anterior cingulate cortex, the dorsolateral prefrontal cortex, the medial temporal lobe and the inferior parietal lobule. The common neural networks of emotion regulation and creativity mainly focus on the default mode, the executive control and the salience networks. We then suggest a shared neural mechanism that may underlie emotion regulation and creativity, involving both control and affective processing. Drawing on the limitations of the studies reviewed, directions for future research are suggested that could significantly inform the field.

Can Random Friends Seed More Buzz and Adoption? Leveraging the Friendship Paradox

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the relevant network structure, which is often unavailable. We examine the impact of WOM seeding strategies motivated by the friendship paradox, which can obtain more central nodes without knowing network structure. Higher degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore, whether friendship paradox–motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across village social networks in India. Counterfactuals show that the proposed strategies with limited seeds are about 13%–30% more effective in increasing adoption relative to random seeding. These strategies are also on average 5%–11% more effective than the firm’s leader seeding strategy. We also find these strategies are relatively more effective when we have fewer seeds. This paper was accepted by Juanjuan Zhang, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4991 .

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.