Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults.

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.

Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students.

OBJECTIVE: A holistic understanding of the naturalistic dynamics among physical activity, sleep, emotions, and purpose in life as part of a system reflecting wellness is key to promoting well-being. The main aim of this study was to examine the day-to-day dynamics within this wellness system. METHODS: Using self-reported emotions (happiness, sadness, anger, anxiousness) and physical activity periods collected twice per day, and daily reports of sleep and purpose in life via smartphone experience sampling, more than 28 days as college students ( n = 226 young adults; mean [standard deviation] = 20.2 [1.7] years) went about their daily lives, we examined day-to-day temporal and contemporaneous dynamics using multilevel vector autoregressive models that consider the network of wellness together. RESULTS: Network analyses revealed that higher physical activity on a given day predicted an increase of happiness the next day. Higher sleep quality on a given night predicted a decrease in negative emotions the next day, and higher purpose in life predicted decreased negative emotions up to 2 days later. Nodes with the highest centrality were sadness, anxiety, and happiness in the temporal network and purpose in life, anxiety, and anger in the contemporaneous network. CONCLUSIONS: Although the effects of sleep and physical activity on emotions and purpose in life may be shorter term, a sense of purpose in life is a critical component of wellness that can have slightly longer effects, bleeding into the next few days. High-arousal emotions and purpose in life are central to motivating people into action, which can lead to behavior change.

Altered neural flexibility in children with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.

The emergence of a functionally flexible brain during early infancy.

Adult brains are functionally flexible, a unique characteristic that is thought to contribute to cognitive flexibility. While tools to assess cognitive flexibility during early infancy are lacking, we aimed to assess the spatiotemporal developmental features of "neural flexibility" during the first 2 y of life. Fifty-two typically developing children 0 to 2 y old were longitudinally imaged up to seven times during natural sleep using resting-state functional MRI. Using a sliding window approach, MR-derived neural flexibility, a quantitative measure of the frequency at which brain regions change their allegiance from one functional module to another during a given time period, was used to evaluate the temporal emergence of neural flexibility during early infancy. Results showed that neural flexibility of whole brain, motor, and high-order brain functional networks/regions increased significantly with age, while visual regions exhibited a temporally stable pattern, suggesting spatially and temporally nonuniform developmental features of neural flexibility. Additionally, the neural flexibility of the primary visual network at 3 mo of age was significantly and negatively associated with cognitive ability evaluated at 5/6 y of age. The "flexible club," comprising brain regions with neural flexibility significantly higher than whole-brain neural flexibility, were consistent with brain regions known to govern cognitive flexibility in adults and exhibited unique characteristics when compared to the functional hub and diverse club regions. Thus, MR-derived neural flexibility has the potential to reveal the underlying neural substrates for developing a cognitively flexible brain during early infancy.

Optimizing Emergency Stroke Transport Strategies Using Physiological Models.

BACKGROUND AND PURPOSE: The criteria for choosing between drip and ship and mothership transport strategies in emergency stroke care is widely debated. Although existing data-driven probability models can inform transport decision-making at an epidemiological level, we propose a novel mathematical, physiologically derived framework that provides insight into how patient characteristics underlying infarct core growth influence these decisions. METHODS: We represent the physiology of time-dependent infarct core growth within an ischemic penumbra as an exponential function with consideration to rate-determining collateral blood flow. Monte Carlo methods generate distributions of infarct core volumes, which are translated to distributions of 90-day modified Rankin Scale scores. We apply the model to a stroke network that serves rural Bastrop County and urban Travis County by simulating transport strategies from thousands of potential patient pickup locations. In every pickup location, the simulation yields a distribution of outcomes corresponding to each transport strategy. A 2-sample Kolmogorov-Smirnov test and Student t test determine which transport strategy provides a significantly better probability of a good outcome for a given pickup location in each respective county (P<0.01). RESULTS: In Travis County, drip and ship provides significantly better probabilities of a good outcome in 24.0% of the pickup locations, while 59.8% favor mothership. In Bastrop County, 11.3% of the pickup locations favor drip and ship, while only 7.1% favor mothership. The remaining pickup locations in each county are not statistically significant in either direction. We also reveal how differing rates of infarct core growth, the application of bypass policies, and the use of large vessel occlusion field tests impact these results. CONCLUSIONS: Modeling stroke physiology enables the use of clinically relevant metrics for determining comparative significance between drip and ship and mothership in a given geography. This formalism can help understand and inform emergency medical service transport decision-making, as well as regional bypass policies.

Accrual of functional redundancy along the lifespan and its effects on cognition.

Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.

Positive psychological states and stress responses in caregivers of adults receiving an allogeneic bone marrow transplant: A study protocol.

AIMS: This protocol directs a study that aims to: (a) describe the caregiver's experience over 8-12 weeks after an index adult patient's allogeneic bone marrow transplant (BMT) for advanced cancer using a case-oriented approach and mixed methods, with qualitative methods in the foreground; and (b) explore networks of relationships among psycho-neurological symptoms, positive psychological states and caregiver health. DESIGN: Case-oriented longitudinal design using multiple data types and analytic approaches. METHODS: Data will be collected from 10-12 caregivers. The sample will be recruited from a large public hospital in the southeastern United States using maximum variation sampling (e.g., caregiver race/ethnicity, relationship to patient, age, education, and number of caregiving roles). Participants will be asked to complete weekly surveys, have their blood drawn bi-weekly and participate in an interview each month during the study period (~100 days). Aim 1 analysis will include directed content analysis and case-oriented visual analysis. Aim 2 analysis will include symptom network estimation of psycho-neurological symptoms, positive psychological states, and caregiver health. Institutional review board approval was obtained August 2018. DISCUSSION: Results will provide an in-depth description of caregivers' experiences in the 100 days after BMT. Findings will inform generation of hypotheses and identification of targets for interventions to improve caregiver's experiences after BMT. IMPACT: This in-depth multi-method longitudinal study to describe caregivers of adult patients receiving an allogeneic BMT is an essential step in understanding caregivers' complex responses to chronic stress and the role of positive psychological states. The results from this study will inform future research on chronic stress processes, intense caregiving, and intervention development.

Network Interconnectivity and Community Detection in HIV/Syphilis Contact Networks Among Men Who Have Sex With Men.

BACKGROUND: Despite persistent HIV and syphilis epidemics among men who have sex with men (MSM), the relationship between HIV and syphilis contact networks has not been well characterized. We aimed to measure interconnectivity between HIV and syphilis contact networks among MSM and identify network communities with heightened interconnectivity of the syphilis network with the HIV network. METHODS: Using contact-tracing data, we generated independent and combined HIV and syphilis networks for all MSM diagnosed with HIV or early syphilis, respectively, in North Carolina between 2015 and 2017. We treated the independent networks as layers and identified network communities, or groups of densely connected nodes, in the 2-layer network. We assessed interconnectivity by comparing the mean node degree among syphilis network members in the syphilis network alone versus the combined HIV/syphilis network, both overall and by network community. RESULTS: The syphilis network was interconnected with the HIV network, especially in network communities with younger median age, higher proportions of persons self-identifying as Black, non-Hispanic, and higher proportions of syphilis cases diagnosed at sexually transmitted disease clinics. CONCLUSIONS: Interconnected contact networks underlie HIV and syphilis epidemics among MSM, particularly among young, Black MSM. Intensified transmission prevention interventions within highly interconnected network communities may be particularly beneficial.

Care Coordination and Multispecialty Teams in the Care of Colorectal Cancer Patients.

OBJECTIVES: To estimate the association between provider and team experience and adherence to guidelines, survival, and utilization among colorectal cancer patients in North Carolina. SUBJECTS: The analysis cohort included 7295 patients diagnosed with incident stage II/III colorectal cancer between 2004 and 2013 who received surgery. METHODS: Primary outcomes included adherence to guidelines: consultation with a medical oncologist (stage III), receipt of adjuvant chemotherapy (stage III), and receipt of surveillance colonoscopy posttreatment. Secondary outcomes included 5-year overall survival, number of surveillance radiology studies, any unplanned hospitalization, and any emergency department visit. The primary predictors were measures of provider volume and patient sharing across surgeons and medical oncologists. Regression analyses adjusted for patient and provider characteristics. RESULTS: Patients whose surgeons shared >40% of their colorectal cancer patients in the previous year with a medical oncologist were (1) more likely to have had a consultation with a medical oncologist [marginal effect (ME)=13.3 percentage points, P-value<0.001], (2) less likely to receive a surveillance colonoscopy within 12 months (ME=3.5 percentage points, P-value=0.049), and (3) received more radiology studies (ME=0.254 studies, P-value=0.029). Patients whose surgeon and medical oncologist shared >20% of their colorectal cancer patients with each other in the previous year had a higher likelihood of receiving adjuvant chemotherapy (ME=11.5 percentage points, P-value<0.001) and surveillance colonoscopy within 12 months (ME=6.7 percentage points, P-value=0.030) and within 18 months (ME=6.2 percentage points, P-value=0.054). CONCLUSIONS: Our study shows that team experience is associated with patients' quality of care, survival, and utilization.

Kantian fractionalization predicts the conflict propensity of the international system.

Network science has spurred a reexamination of relational phenomena in political science, including the study of international conflict. We introduce a new direction to the study of conflict by showing that the multiplex fractionalization of the international system along three key dimensions is a powerful predictor of the propensity for violent interstate conflict. Even after controlling for well-established conflict indicators, our new measure contributes more to model fit for interstate conflict than all of the previously established measures combined. Moreover, joint democracy plays little, if any, role in predicting system stability, thus challenging perhaps the major empirical finding of the international relations literature. Lastly, the temporal variability of our measure with conflict is consistent with a causal relationship. Our results have real-world policy implications as changes in our fractionalization measure substantially aid the prediction of conflict up to 10 years into the future, allowing it to serve as an early warning sign of international instability.

RIGID GRAPH COMPRESSION: MOTIF-BASED RIGIDITY ANALYSIS FOR DISORDERED FIBER NETWORKS.

Using particle-scale models to accurately describe property enhancements and phase transitions in macroscopic behavior is a major engineering challenge in composite materials science. To address some of these challenges, we use the graph theoretic property of rigidity to model mechanical reinforcement in composites with stiff rod-like particles. We develop an efficient algorithmic approach called rigid graph compression (RGC) to describe the transition from floppy to rigid in disordered fiber networks ("rod-hinge systems"), which form the reinforcing phase in many composite systems. To establish RGC on a firm theoretical foundation, we adapt rigidity matroid theory to identify primitive topological network motifs that serve as rules for composing interacting rigid particles into larger rigid components. This approach is computationally efficient and stable, because RGC requires only topological information about rod interactions (encoded by a sparse unweighted network) rather than geometrical details such as rod locations or pairwise distances (as required in rigidity matroid theory). We conduct numerical experiments on simulated two-dimensional rod-hinge systems to demonstrate that RGC closely approximates the rigidity percolation threshold for such systems, through comparison with the pebble game algorithm (which is exact in two dimensions). Importantly, whereas the pebble game is derived from Laman's condition and is only valid in two dimensions, the RGC approach naturally extends to higher dimensions.

EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS.

Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.

Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation.

Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common-but not well understood-practice of thresholding pairwise-interaction data to obtain sparse network representations.

Spatiotemporal patterns and trends of Indian monsoonal rainfall extremes.

In this study, we provide a comprehensive analysis of trends in the extremes during the Indian summer monsoon (ISM) months (June to September) at different temporal and spatial scales. Our goal is to identify and quantify spatiotemporal patterns and trends that have emerged during the recent decades and may be associated with changing climatic conditions. Our analysis primarily relies on quantile regression that avoids making any subjective choices on spatial, temporal, or intensity pattern of extreme rainfall events. Our analysis divides the Indian monsoon region into climatic compartments that show different and partly opposing trends. These include strong trends towards intensified droughts in Northwest India, parts of Peninsular India, and Myanmar; in contrast, parts of Pakistan, Northwest Himalaya, and Central India show increased extreme daily rain intensity leading to higher flood vulnerability. Our analysis helps explain previously contradicting results of trends in average ISM rainfall.

Transitivity reinforcement in the coevolving voter model.

One of the fundamental structural properties of many networks is triangle closure. Whereas the influence of this transitivity on a variety of contagion dynamics has been previously explored, existing models of coevolving or adaptive network systems typically use rewiring rules that randomize away this important property, raising questions about their applicability. In contrast, we study here a modified coevolving voter model dynamics that explicitly reinforces and maintains such clustering. Carrying out numerical simulations for a variety of parameter settings, we establish that the transitions and dynamical states observed in coevolving voter model networks without clustering are altered by reinforcing transitivity in the model. We then use a semi-analytical framework in terms of approximate master equations to predict the dynamical behaviors of the model for a variety of parameter settings.

Climate Shocks and Migration: An Agent-Based Modeling Approach.

This is a study of migration responses to climate shocks. We construct an agent-based model that incorporates dynamic linkages between demographic behaviors, such as migration, marriage, and births, and agriculture and land use, which depend on rainfall patterns. The rules and parameterization of our model are empirically derived from qualitative and quantitative analyses of a well-studied demographic field site, Nang Rong district, Northeast Thailand. With this model, we simulate patterns of migration under four weather regimes in a rice economy: 1) a reference, 'normal' scenario; 2) seven years of unusually wet weather; 3) seven years of unusually dry weather; and 4) seven years of extremely variable weather. Results show relatively small impacts on migration. Experiments with the model show that existing high migration rates and strong selection factors, which are unaffected by climate change, are likely responsible for the weak migration response.

Resolving structural variability in network models and the brain.

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.

Graph fission in an evolving voter model.

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value α(c) which does not depend on u, with ρ ≈ u for α > α(c) and ρ ≈ 0 for α < α(c). In case (ii), the transition point α(c)(u) depends on the initial density u. For α > α(c)(u), ρ ≈ u, but for α < α(c)(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions.

Transient antibody-mucin interactions produce a dynamic molecular shield against viral invasion.

Given the difficulty in finding a cure for HIV/AIDS, a promising prevention strategy to reduce HIV transmission is to directly block infection at the portal of entry. The recent Thai RV144 trial offered the first evidence that an antibody-based vaccine may block heterosexual HIV transmission. Unfortunately, the underlying mechanism(s) for protection remain unclear. Here we theoretically examine a hypothesis that builds on our recent laboratory observation: virus-specific antibodies (Ab) can trap individual virions in cervicovaginal mucus (CVM), thereby reducing infection in vivo. Ab are known to have a weak-previously considered inconsequential-binding affinity with the mucin fibers that constitute CVM. However, multiple Ab can bind to the same virion at the same time, which markedly increases the overall Ab-mucin binding avidity, and creates an inheritable virion-mucin affinity. Our model takes into account biologically relevant length and timescales, while incorporating known HIV-Ab affinity and the respective diffusivities of viruses and Ab in semen and CVM. The model predicts that HIV-specific Ab in CVM leads to rapid formation and persistence of an HIV concentration front near the semen/CVM interface, far from the vaginal epithelium. Such an HIV concentration front minimizes the flux of HIV virions reaching target cells, and maximizes their elimination upon drainage of genital secretions. The robustness of the result implies that even exceedingly weak Ab-mucin affinity can markedly reduce the flux of virions reaching target cells. Beyond this specific application, the model developed here is adaptable to other pathogens, mucosal barriers, and geometries, as well as kinetic and diffusional effects, providing a tool for hypothesis testing and producing quantitative insights into the dynamics of immune-mediated protection.