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.
Challenges associated with the allocation of limited resources to mitigate the impact of natural disasters inspire fundamentally new theoretical questions for dynamic decision making in coupled human ...and natural systems. Wildfires are one of several types of disaster phenomena, including oil spills and disease epidemics, where (1) the disaster evolves on the same timescale as the response effort, and (2) delays in response can lead to increased disaster severity and thus greater demand for resources. We introduce a minimal stochastic process to represent wildfire progression that nonetheless accurately captures the heavy tailed statistical distribution of fire sizes observed in nature. We then couple this model for fire spread to a series of response models that isolate fundamental tradeoffs both in the strength and timing of response and also in division of limited resources across multiple competing suppression efforts. Using this framework, we compute optimal strategies for decision making scenarios that arise in fire response policy.
We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on ...lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.
We numerically and theoretically study the macroscopic properties of dense, sheared granular materials. In this process we first consider an invariance in Newton's equations, explain how it leads to ...Bagnold's scaling, and discuss how it relates to the dynamics of granular temperature. Next we implement numerical simulations of granular materials in two different geometries--simple shear and flow down an incline--and show that measurements can be extrapolated from one geometry to the other. Then we observe nonaffine rearrangements of clusters of grains in response to shear strain and show that fundamental observations, which served as a basis for the shear transformation zone (STZ) theory of amorphous solids M. L. Falk and J. S. Langer, Phys. Rev. E. 57, 7192 (1998); M.R.S. Bull 25, 40 (2000), can be reproduced in granular materials. Finally we present constitutive equations for granular materials as proposed by Lemaître Phys. Rev. Lett. 89, 064303 (2002), based on the dynamics of granular temperature and STZ theory, and show that they match remarkably well with our numerical data from both geometries.
Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many ...cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies.
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.
•Dynamics of task-active brain communities are quantified in adults of varying ages.•Community number and node flexibility are positively associated with subject age.•Dynamics of intrinsic functional networks are differentially modulated by age.•No correspondence is found between age or brain dynamics and task performance.
The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the ...performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.
Purpose
Provide a direct, non‐invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper ...presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases.
Theory/Methods
MR micro‐Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k‐values to characterize texture—as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k‐value phase encode to the excited spins in the VOI, and acquiring each individual k‐value data point in a single excitation—providing motion immunity and extended acquisition time for maximizing signal‐to‐noise ratio. Additional k‐value measurements from the same tissue can be made to characterize the tissue texture in the VOI—there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue.
Results
Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented.
Conclusion
The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.
To describe the development, implementation, and validation of a radiology-administered protocol to obtain magnetic resonance imaging (MRI) in patients with cochlear implants and auditory brainstem ...implants without magnet removal.
Retrospective review and description of novel care pathway.
A radiology-administered protocol was designed based on careful input from the radiology safety committee and neurotology. Radiology technologist training modules, consent instructions, patient educational material, clinical audits, and other safeguards were implemented, with samples provided in this report. The primary outcomes measured included instances of magnet displacement during MRI and premature termination of MRI studies secondary to pain.
Between June 19, 2018, and October 12, 2021, 301 implanted ears underwent MRI without magnet removal, including 153 devices housing diametric MRI-conditional magnets, and 148 implants with conventional axial (i.e., nondiametric) magnets. Among cases with diametric MRI-conditional magnets, all studies were completed without magnet dislodgement or need to terminate imaging early due to pain. Among cases with conventional axial (nondiametric) magnets, 29 (19.6%) MRI studies were stopped prematurely secondary to pain or discomfort; the overall rate of this event was 9.6% (29 of 301) among the entire study cohort. In addition, 6.1% (9 of 148) experienced confirmed magnet displacement despite headwrap placement; the overall rate among all cases was 3.0% (9 of 301). Eight of these patients received successful external magnet reseating through manual pressure on the external scalp without surgery, and one required surgical replacement of the magnet in the operating room. There were no documented instances of hematoma, infection, device or magnet extrusion, internal device movement (i.e., gross receiver-stimulator migration), or device malfunction in this cohort related to MRI.
We present the successful implementation of a radiology-administered protocol designed to streamline care for cochlear implant and auditory brainstem implant recipients who require MRI and ease clinical demands for otolaryngology providers. Examples of resources developed, including a process map, radiology training modules, consent instructions, patient educational materials, clinical audit, and other procedural safety measures are provided so interested groups may consider adapting and implementing related measures according to need.
In the context of numerical simulations of elastodynamic ruptures, we compare friction laws, including the linear slip‐weakening (SW) law, the Dieterich‐Ruina (DR) law, and the free volume (FV) law. ...The FV law is based on microscopic physics, incorporating shear transformation zone (STZ) theory which describes local, nonaffine rearrangements within the granular fault gouge. A dynamic state variable models dilation and compaction of the gouge, and accounts for weakening and restrengthening in the FV law. The principal difference between the FV law and the DR law is associated with the characteristic length scale L. In the FV law, LFV grows with increasing slip rate, while in the DR law LDR is independent of slip rate. The length scale for friction is observed to vary with slip velocity in laboratory experiments with simulated fault gouge, suggesting that the FV law captures an essential feature of gouge‐filled faults. In simulations of spontaneous elastodynamic rupture, for equal energy dissipation the FV law produces ruptures with smaller nucleation lengths, lower peak slip velocities, and increased slip required for friction to fully weaken to steady sliding when compared to ruptures governed by the SW or DR laws. We also examine generalizations of the DR and FV laws that incorporate rapid velocity weakening. The rapid weakening laws produce self‐healing slip pulse ruptures for low initial shear loads. For parameters which produce identical net slip in the pulses of each rapid weakening friction law, the FV law exhibits a much shorter nucleation length, a larger slip‐weakening distance, and less frictional energy dissipation than corresponding ruptures obtained using the DR law.
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