Search

Quantifying factors that affect evacuation decision making remains a challenging task. Progress is crucial for developing predictive models of collective behavior and for designing effective policies to guide the action of populations during wildfires. We conduct a controlled behavioral experiment to probe factors influencing evacuation decision making in the face of an impending virtual wildfire. We consider competing factors that influence small groups and the community as a whole. Based on our data, we develop two distinct but complementary empirically-driven approaches to characterize individual and group evacuation decision making. Our first approach is a stochastic model that predicts evacuation of a population of individuals guided by the same decision-making strategy, which we define to be a continuous function of key experimental variables such as the likelihood of the disaster and the availability of resources. We extend this model to investigate strategy shifts leading to differences between individual and group behavior which manifest at the collective level. In our second approach, we characterize decision making of individuals and groups by incorporating variation in individual traits, group decision protocols, and time-dependent changes in experimental variables with logistic regression. By including personal identifying characteristics of each subject, we develop a model that can predict evacuation decision times with 85.0% accuracy. In parallel, we demonstrate that the social media activity of individual subjects, specifically their Facebook use, can be used to generate an alternative individual personality profile that leads to comparable prediction accuracy of 84.2%. Our results from both approaches demonstrate the importance of using a rate-based rather than threshold function to describe individual behavior, and of accounting for social influence and individual heterogeneity in modeling group decision making.

Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 μm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a ‘ratio metric’ determined from a few k -space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.

Quantifying the dynamics of collective human decision making is essential to optimizing communication systems, transportation networks, and action protocols for ensuring public safety in uncertain and risky situations. Representing human factors has been elusive since decision making is driven by a host of interacting factors such as time pressure, perceived risks, and social influence. We develop two complementary data-driven models to describe decision making in natural disaster scenarios based upon the observed behavior of subjects in a controlled behavioral experiment where participants must decide whether and when to evacuate from a virtual natural disaster. We first develop a rate-based model that quantifies an individual's decision-making strategy as a function of the reported disaster threat level, which we observe to be a primary influence of collective behavior, and use this model to simulate and predict evacuation at the population level. We investigate the effect of social influence on strategy adjustments that emerge in group contexts, comparing evacuation decisions made individually with those that are arrived at via group consensus or vote. We find that the accurate characterization of group behavior mandates the consideration of individual heterogeneity, leading to a second approach using an artificial neural network which incorporates more experimental parameters, including a measure of individual variation, to predict precise evacuation times. An alternative method of quantifying individual differences in the neural network uses participants' social media data as input to the model and achieves comparable prediction accuracy.

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism—hypergraph cardinality—we investigate individual variations in two separate, complementary data sets. The first data set (“multi-task”) consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set (“age-memory”), in which 95 individuals, aged 18–75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain., Author Summary Complex patterns of activity in each individual human brain generate the unique range of thoughts and behaviors that person experiences. Individual differences in ability, age, state of mind, and other characteristics are tied to differences in brain activity, but determination of the exact nature of these relationships has been limited by the intrinsic complexity of the brain. Here, we apply dynamic network theory to quantify fundamental features of individual neural activity. We represent functional connections between brain regions as a time varying network, and then identify groups of these interactions that exhibit similar behavior over time. The result of this construction is referred to as a hypergraph, and each grouping within the hypergraph is called a hyperedge. We find that the number of these hyperedges in an individual’s hypergraph is a trait-like metric, with considerable variation across the population of subjects, but remarkable consistency within each subject as they perform different tasks. We find a significant correspondence between this metric and the subject’s age, indicating that the dynamics of functional brain activity in older individuals tends to be more dynamically segregated. This new insight into age-related changes in the dynamics of cognitive processing expands our knowledge of the effects of age on brain function and confirms our methods as promising for quantifying and examining individual differences.

As humans age, cognition and behavior change significantly, along with associated brain function and organization. Aging has been shown to decrease variability in functional magnetic resonance imaging (fMRI) signals, and to affect the modular organization of human brain function. In this work, we use complex network analysis to investigate the dynamic community structure of large-scale brain function, asking how evolving communities interact with known brain systems, and how the dynamics of communities and brain systems are affected by age. We analyze dynamic networks derived from fMRI scans of 104 human subjects performing a word memory task, and determine the time-evolving modular structure of these networks by maximizing the multislice modularity, thereby identifying distinct communities, or sets of brain regions with strong intra-set functional coherence. To understand how community structure changes over time, we examine the number of communities as well as the flexibility, or the likelihood that brain regions will switch between communities. We find a significant positive correlation between age and both these measures: younger subjects tend to have less fragmented and more coherent communities, and their brain regions tend to change communities less often during the memory task. We characterize the relationship of community structure to known brain systems by the recruitment coefficient, or the probability of a brain region being grouped in the same community as other regions in the same system. We find that regions associated with cingulo-opercular, somatosensory, ventral attention, and subcortical circuits have a significantly higher recruitment coefficient in younger subjects. This indicates that the within-system functional coherence of these specific systems during the memory task declines with age. Such a correspondence does not exist for other systems (e.g. visual and default mode), whose recruitment coefficients remain relatively uniform across ages. These results confirm that the dynamics of functional community structure vary with age, and demonstrate methods for investigating how aging differentially impacts the functional organization of different brain systems.

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined "ground truth" communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change., Author Summary The human brain is a complex system in which the interactions of billions of neurons give rise to a fascinating range of behaviors. In response to its changing environment—for example, across situations involving rest, memory, focused attention, or learning—the brain dynamically switches between distinct patterns of activation. Despite the wealth of neuroimaging data available, the immense complexity of the brain makes the identification of fundamental principles guiding this task-based organization of neural activity a distinct challenge. We apply new techniques from dynamic network theory to describe the functional interactions between brain regions as an evolving network, allowing us to understand these time-dependent interactions in terms of organizing characteristics of the whole network. We examine patterns of neural activity during rest, an attention-demanding task, and two memory-demanding tasks. Using network science techniques, we identify groups of brain region interactions that change cohesively together over time, both across tasks and within individual tasks. By developing tools to analyze the size and spatial distributions of these groups, we quantify significant differences between brain network dynamics in different tasks. These tools provide a promising method for investigating how the changing brain network properties of individuals correspond to task performance.

Recently, extensive vortex lattice metastability was reported in MgB2 in connection with a second-order rotational phase transition. However, the mechanism responsible for these well-ordered metastable vortex lattice phases is not well understood. Using small-angle neutron scattering, we studied the vortex lattice in MgB2 as it was driven from a metastable to the ground state through a series of small changes in the applied magnetic field. Our results show that metastable vortex lattice domains persist in the presence of substantial vortex motion and directly demonstrate that the metastability is not due to vortex pinning. Instead, we propose that it is due to the jamming of counterrotated vortex lattice domains which prevents a rotation to the ground state orientation., 5 pages, 4 fgures

The superconductor TmNi${}_{2}$B${}_{2}$C possesses a significant fourfold basal plane anisotropy, leading to a square vortex lattice (VL) at intermediate fields. However, unlike other members of the borocarbide superconductors, the anisotropy in TmNi${}_{2}$B${}_{2}$C appears to decrease with increasing field, evident by a reentrance of the square VL phase. We have used small-angle neutron scattering measurements of the VL to study the field dependence of the anisotropy. Our results provide a direct, quantitative measurement of the decreasing anisotropy. We attribute this reduction of the basal plane anisotropy to the strong Pauli paramagnetic effects observed in TmNi${}_{2}$B${}_{2}$C and the resulting expansion of vortex cores near ${H}_{\mathrm{c}2}$.

The vortex lattice (VL) symmetry and orientation in clean type-II superconductors depends sensitively on the host material anisotropy, vortex density and temperature, frequently leading to rich phase diagrams. Typically, a well-ordered VL is taken to imply a ground-state configuration for the vortex-vortex interaction. Using neutron scattering we studied the VL in MgB(2) for a number of field-temperature histories, discovering an unprecedented degree of metastability in connection with a known, second-order rotation transition. This allows, for the first time, structural studies of a well-ordered, nonequilibrium VL. While the mechanism responsible for the longevity of the metastable states is not resolved, we speculate it is due to a jamming of VL domains, preventing a rotation to the ground-state orientation.

Spanning a broad range of physical systems, complex symmetry breaking is widely recognized as a hallmark of competing interactions. This is exemplified in superfluid 3He which has multiple thermodynamic phases with spin and orbital quantum numbers S = 1 and L = 1, that emerge on cooling from a nearly ferromagnetic Fermi liquid. The heavy fermion compound UPt3 exhibits similar behavior clearly manifest in its multiple superconducting phases. However, consensus as to its order parameter symmetry has remained elusive. Our small angle neutron scattering measurements indicate a linear temperature dependence of the London penetration depth characteristic of nodal structure of the order parameter. Our theoretical analysis is consistent with assignment of its symmetry to an L = 3 odd parity state for which one of the three thermodynamic phases in non-zero magnetic field is chiral.