Properties Of Dynamic Networks Research Articles

Dynamical predictive coding with reservoir computing performs noise-robust multi-sensory speech recognition.

Multi-sensory integration is a perceptual process through which the brain synthesizes a unified perception by integrating inputs from multiple sensory modalities. A key issue is understanding how the brain performs multi-sensory integrations using a common neural basis in the cortex. A cortical model based on reservoir computing has been proposed to elucidate the role of recurrent connectivity among cortical neurons in this process. Reservoir computing is well-suited for time series processing, such as speech recognition. This inquiry focuses on extending a reservoir computing-based cortical model to encompass multi-sensory integration within the cortex. This research introduces a dynamical model of multi-sensory speech recognition, leveraging predictive coding combined with reservoir computing. Predictive coding offers a framework for the hierarchical structure of the cortex. The model integrates reliability weighting, derived from the computational theory of multi-sensory integration, to adapt to multi-sensory time series processing. The model addresses a multi-sensory speech recognition task, necessitating the management of complex time series. We observed that the reservoir effectively recognizes speech by extracting time-contextual information and weighting sensory inputs according to sensory noise. These findings indicate that the dynamic properties of recurrent networks are applicable to multi-sensory time series processing, positioning reservoir computing as a suitable model for multi-sensory integration.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BackgroundInternet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. MethodResting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. ResultsIGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. ConclusionThe dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Higher general intelligence is associated with stable, efficient, and typical dynamic functional brain connectivity patterns

Abstract General intelligence, referred to as g, is hypothesized to emerge from the capacity to dynamically and adaptively reorganize macroscale brain connectivity. Temporal reconfiguration can be assessed using dynamic functional connectivity (dFC), which captures the propensity of brain connectivity to transition between a recurring repertoire of distinct states. Conventional dFC metrics commonly focus on categorical state switching frequencies which do not fully assess individual variation in continuous connectivity reconfiguration. Here, we supplement frequency measures by quantifying within-state connectivity consistency, dissimilarity between connectivity across states, and conformity of individual connectivity to group-average state connectivity. We utilized resting-state functional magnetic resonance imaging (fMRI) data from the large-scale Human Connectome Project and applied data-driven multivariate Partial Least Squares Correlation to explore emergent associations between dynamic network properties and cognitive ability. Our findings reveal a positive association between g and the stable maintenance of states characterized by distinct connectivity between higher-order networks, efficient reconfiguration (i.e., minimal connectivity changes during transitions between similar states, large connectivity changes between dissimilar states), and ability to sustain connectivity close to group-average state connectivity. This hints at fundamental properties of brain–behavior organization, suggesting that general cognitive processing capacity may be supported by the ability to efficiently reconfigure between stable and population-typical connectivity patterns.

Treatment effects in epilepsy: a mathematical framework for understanding response over time.

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea.

To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.

Polymer time crystal: Mechanical activation of reversible bonds by low-amplitude high frequency excitations.

Reversible supramolecular bonds play an important role in materials science and in biological systems. The equilibrium between open and closed bonds and the association rate can be controlled thermally, chemically, by mechanical pulling, by ultrasound, or by catalysts. In practice, these intrinsic equilibrium methods either suffer from a limited range of tunability or may damage the material. Here, we present a nonequilibrium strategy that exploits the dissipative properties of the system to control and change the dynamic properties of sacrificial and reversible networks. We show theoretically and numerically how high-frequency mechanical oscillations of very low amplitude can open or close bonds. This mechanism indicates how reversible bonds could alleviate mechanical fatigue of materials especially at low temperatures where they are fragile. In another area, it suggests that the system can be actively modified by the application of ultrasound to induce gel-fluid transitions and to activate or deactivate adhesion properties.

Partitioning the contribution of bees with different traits and hoverflies to flower-visitor interaction networks

Insect pollinators are key to maintaining biodiversity and providing important ecosystem services. Among them, bee species (i.e., Apidae) and hoverflies (i.e., Syrphidae) are two of the most important and well-studied taxa worldwide. Yet, their relative contribution to the structural and dynamic properties of plant-pollinator interaction networks remains poorly understood. This is an important gap in knowledge given that these phylogenetically and functionally different groups might play different roles within their communities and respond in contrasting ways to anthropogenic perturbations.Here, we study the relative contribution of bee species (with different traits) and syrphids to maintain network properties (i.e., nestedness, network specialization (H2), and interaction diversity) and their influence on the temporal dynamics of these network metrics. To this end, we simulate species removals in community-wide flower-arthropod visitor interaction networks. These interactions were extensively sampled across four years in two different near-pristine temperate ecosystems in Japan.We found that bee species contribute significantly more than syrphids or any random combination of species to network nestedness, low specialization, and interaction diversity across months and years. In addition, bees also played a particularly important role in maintaining the observed temporal dynamics over time. Conversely, although syrphids were an abundant and species-rich group, they did not contribute so prominently to network metrics or temporal dynamics, with the exception of network complementary specialization, to which they contributed positively. In addition, smaller bee species and those that were active for longer periods were particularly important for interaction diversity and the number of interactions, respectively, and to maintain observed network temporal dynamics.Our results support that bee species, and especially the small ones, are cornerstone contributors to plant-visitor communities by shaping network properties and network temporal dynamics in natural ecosystems.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study.

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Fractal and first-passage properties of a class of self-similar networks.

A class of self-similar networks, obtained by recursively replacing each edge of the current network with a well-designed structure (generator) and known as edge-iteration networks, has garnered considerable attention owing to its role in presenting rich network models to mimic real objects with self-similar structures. The generator dominates the structural and dynamic properties of edge-iteration networks. However, the general relationships between these networks' structural and dynamic properties and their generators remain unclear. We study the fractal and first-passage properties, such as the fractal dimension, walk dimension, resistance exponent, spectral dimension, and global mean first-passage time, which is the mean time for a walker, starting from a randomly selected node and reaching the fixed target node for the first time. We disclose the properties of the generators that dominate the fractal and first-passage properties of general edge-iteration networks. A clear relationship between the fractal and first-passage properties of the edge-iteration networks and the related properties of the generators are presented. The upper and lower bounds of these quantities are also discussed. Thus, networks can be customized to meet the requirements of fractal and dynamic properties by selecting an appropriate generator and tuning their structural parameters. The results obtained here shed light on the design and optimization of network structures.

Detecting periodic time scales of changes in temporal networks

Abstract Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterizing for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here, we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency-based method identifies more easily periodic changes in network density, while the temporal event graph-based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple time scales in systems represented by temporal networks.

Temporal and topological properties of dynamic networks reflect disability in patients with neuromyelitis optica spectrum disorders

Approximately 36% of patients with neuromyelitis optica spectrum disorders (NMOSD) suffer from severe visual and motor disability (blindness or light perception or unable to walk) with abnormalities of whole-brain functional networks. However, it remains unclear how whole-brain functional networks and their dynamic properties are related to clinical disability in patients with NMOSD. Our study recruited 30 NMOSD patients (37.70 ± 11.99 years) and 45 healthy controls (HC, 41.84 ± 11.23 years). The independent component analysis, sliding-window approach and graph theory analysis were used to explore the static strength, time-varying and topological properties of large-scale functional networks and their associations with disability in NMOSD. Compared to HC, NMOSD patients showed significant alterations in dynamic networks rather than static networks. Specifically, NMOSD patients showed increased occurrence (fractional occupancy; P < 0.001) and more dwell times of the low-connectivity state (P < 0.001) with fewer transitions (P = 0.028) between states than HC, and higher fractional occupancy, increased dwell times of the low-connectivity state and lower transitions were related to more severe disability. Moreover, NMOSD patients exhibited altered small-worldness, decreased degree centrality and reduced clustering coefficients of hub nodes in dynamic networks, related to clinical disability. NMOSD patients exhibited higher occurrence and more dwell time in low-connectivity states, along with fewer transitions between states and decreased topological organizations, revealing the disrupted communication and coordination among brain networks over time. Our findings could provide new perspective to help us better understand the neuropathological mechanism of the clinical disability in NMOSD.

Altered Static and Dynamic Brain Functional Topological Organization in Patients With Dysthyroid Optic Neuropathy.

Dysthyroid optic neuropathy (DON) is a serious vision-threatening complication of thyroid-associated ophthalmopathy (TAO). Exploration of the underlying mechanisms of DON is critical for its timely clinical diagnosis. We hypothesized that TAO patients with DON may have altered brain functional networks. We aimed to explore the alterations of static and dynamic functional connectomes in patients with and without DON using resting-state functional magnetic resonance imaging with the graph theory method. A cross-sectional study was conducted at a grade A tertiary hospital with 66 TAO patients (28 DON and 38 non-DON) and 30 healthy controls (HCs). Main outcome measures included topological properties of functional networks. For static properties, DON patients exhibited lower global efficiency (Eg), local efficiency, normalized clustering coefficient, small-worldness (σ), and higher characteristic path length (Lp) than HCs. DON and non-DON patients both exhibited varying degrees of abnormalities in nodal properties. Meanwhile, compared with non-DON, DON patients exhibited abnormalities in nodal properties in the orbitofrontal cortex and visual network (VN). For dynamic properties, the DON group exhibited higher variance in Eg and Lp than non-DON and HC groups. A strengthened subnetwork with VN as the core was identified in the DON cohort. Significant correlations were found between network properties and clinical variables. For distinguishing DON, the combination of static and dynamic network properties exhibited optimal diagnostic performance. Functional network alterations were observed both in DON and non-DON patients, providing novel insights into the underlying neural mechanisms of disease. Functional network properties may be potential biomarkers for reflecting the progression of TAO from non-DON to DON.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Engineering a dual-loop molecular circuit with buffering capability to solve molecular information tasks.

Molecular circuits, as an effective strategy for implementing artificial biochemical networks, have been widely constructed to process molecular-level information tasks both in vivo and in vitro. However, the complex and diverse structures of molecular devices, along with inflexible signal output methods, pose significant challenges for molecular circuits to handle complex molecular information tasks. In response to the growing field of molecular circuits, we design an exonuclease-driven fan-out molecular device (FMD) with a programmable cascade approach capable of receiving uniform signal types and transmitting multifunctional signals. Combined with the buffering reaction proposed here, the approach expands the dynamic properties of biochemical networks. Unlike the conventional delay strategy, the buffering process not only withstands transient changes in transmission signals, but also delays the transmission of lossless signals. Furthermore, we construct a dual-loop molecular circuit with adjustable buffering modes, thereby enabling signal amplification, time delay, and a differentiated output. Finally, we develop a method to obtain the colorimetric output of dual pulse signals driven by a dual-loop molecular circuit with buffering and hence precisely classify multiple signals. This work promises programmable and multifunctional molecular circuits in nanomachines, molecular computing, and biomedical applications.

Exponential stability of stochastic complex networks with multi-weights driven by second-order process based on graph theory

&lt;abstract&gt;&lt;p&gt;Stochastic complex networks with multi-weights which were driven by Brownian motion were widely investigated by many researchers. However, Brownian motion is not suitable for the modeling of engineering issues by reason of its variance, which is infinite at any time. So, in this paper, a novel kind of stochastic complex network with multi-weights driven by second-order process is developed. To disclose how the weights and second-order process affect the dynamical properties of stochastic complex networks with multi-weights driven by the second-order process, we discuss exponential stability of the system. Two types of sufficient criteria are provided to ascertain exponential stability of the system on the basis of Kirchhoff's matrix tree theorem and the Lyapunov method. Finally, some numerical examples are given to verify the correctness and validity of our results.&lt;/p&gt;&lt;/abstract&gt;

SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification ,

Identification in interconnected systems requires the handling of phenomena that go beyond the classical open-loop and closed-loop type of identification problems. Over the last decade a comprehensive theory has been developed for addressing identification problems in linear dynamic networks, formulated in a module framework, where the network structure is characterized by a directed graph in which nodes are signals and links are transfer functions. The resulting methods and approaches have been collected in a MATLAB App and Toolbox, supported by an attractive graphical user interface that provides an interactive workflow for manipulating the structural properties of dynamic networks, applying basic network operations like immersion and module invariance testing, and for investigating network/module generic identifiability and selecting appropriate predictor model inputs and outputs. The workflow supports the allocation of external excitation signals (actuation) and measured node signals (sensing) so as to achieve generic identifiability and provide consistent estimation of target modules. The Toolbox includes algorithms for actual network simulation and identification.

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Exploring static and dynamic functional brain networks in adolescent depression using a co-produced novel irritability paradigm

Background Irritability is a core symptom of depression in adolescence and a risk factor for emotion regulation problems. However, its neural correlates are not well understood. Existing functional magnetic resonance imaging (fMRI) research on irritability typically overlooks its social context. Methods Here, we pilot a novel naturalistic fMRI paradigm targeting the social nature of irritability that was co-produced with young people (N = 88) and apply it in an independent sample of youth (N = 29, mean age 18.9 years, 77% female) with self-reported low mood that were aged 16 to 20 years. Participants were also fluent English speakers, free from MRI contraindications, and did not report a diagnosis of a neurological or neurodevelopmental condition. Our aim was to investigate whether graph theoretic and dynamical properties of functional brain networks differed between a resting state scan and our irritability paradigm. We also examined whether these brain features were associated with depressive symptoms and trait irritability. Results Using Leading Eigenvector Dynamics Analysis (LEiDA), we found that the dynamic properties of brain networks comprising default-mode and fronto-parietal regions differed significantly during the irritability paradigm compared to the rest condition. While no gross static topological differences were found between these two conditions, we found that some dynamic and topological features of emotion-related brain networks were related to trait irritability and depressive symptoms in our sample. Conclusions Although the current findings are preliminary due to the pilot nature of this study, this work showcases the feasibility of co-produced research in neuroimaging and lays a strong foundation for further study.

Effect of Plant Oil Derived Bio-Resin and Curing Temperature on Static and Dynamic Mechanical Properties of Epoxy Network

The depletion of petrochemical resources and greater worldwide environmental consciousness has led to a growing interest in polymers made from renewable resources during the last two decades. Hence, this work has attempted to reduce the dependence on petroleum-based epoxy by partially replacing it with epoxidized castor oil (ECO). The ECO was blended with epoxy at 10, 20, and 30% and cured with amine hardener. The effect of bio-resin on tensile, flexural, impact strength and dynamic mechanical properties were investigated. Further, the result of post-curing temperature on static and dynamic properties was examined. It was found that the addition of ECO up to 30% increased the impact strength. The 20% ECO sample showed balanced stiffness to toughness property and could be considered for semi-structural composite applications. The post-curing of samples at 150 °C showed better mechanical and dynamic properties except for impact strength.

Segmental dynamics of poly(tetrahydrofuran) cross-linked by click-chemistry: Single-chain nanoparticles vs cross-linked networks

Cross-linked poly(tetrahydrofuran) (PTHF) networks are useful as organic solvent/oil sorbents with high swelling capacity and mechanically robust solid polymer electrolyte membranes for lithium-ion batteries, as well as solid-state calcium ion batteries. Recently, the control of intra-vs intermolecular interactions has attracted significant interest to modulate the properties of dynamic networks involving non-covalent and dynamic covalent bonds. However, to the best of our knowledge, a comparison of the segmental dynamics in polymeric materials prepared exactly from the same precursor polymer through intra-vs intermolecular covalent bonds is still lacking. In this work, we report the dissimilar dynamic properties of PTHF-based materials prepared through intra-vs intermolecular click chemistry (i.e., films of PTHF-based single-chain nanoparticles (SCNPs) vs cross-linked networks). Particularly we observed remarkable similarities in the segmental relaxation time and glass transition temperature value but evident differences in the dynamic heterogeneities. These differences are attributed to the peculiar morphology of the intra-chain collapsed polymer chains (i.e., SCNPs).