Search

Introduction: Major depressive disorder with suicidal ideation (MDD/SI+) is characterized by high prevalence, high recurrence rate, high disability rate and low response rate. There is an urgent need for clarifying the pathogenesis and developing novel treatment methods. Methods: Subjects were recruited for the collection of Magnetic Resonance Imaging data and clinical scales. Individual target-transcranial magnetic stimulation (IT-TMS) using Stanford Neuromodulation Therapy over individualized left dorsolateral prefrontal cortex was performed to treat MDD/SI+ with the ethical approval (KY20212218-C-1). GRETNA software was used to analyze brain network characteristics according to graph theory. Results: A total of 32 patients (aged 18-55) and 28 healthy controls (aged 20-51) had been recruited. Patients after IT-TMS treatment had significant reduction in suicidal ideation and depressive symptom. The functional network of all three groups conformed to small-world topology. There was a renormalization in topology structure after IT-TMS treatment. Decreased functional connectivity between right insula and left anterior cingulate gyrus correlated with improvement in Beck Scale for Suicide Ideation scores. Discussion: The current study highlights that MDD/SI+ patients in this cohort showed abnormal brain network connections compared to healthy controls, and that IT-TMS may exert its treatment effects by reducing spontaneous hyper-connectivity in the salience network and insula. [ABSTRACT FROM AUTHOR]

This functional magnetic resonance imaging (fMRI) study examined resting-state (RS) connectivity in adolescent and adult patients with anorexia nervosa (AN) using symptom provocation paradigms. Differential food reward mechanisms were investigated through separate assessments of responses to food images and low-caloric/high-caloric food consumption. Thirteen young (≤ 21 years) and seventeen adult (> 21 years) patients with AN and age-matched controls underwent two stimulus-driven fMRI sessions involving RS scans before and after the presentation of food-related stimuli and food consumption. Graph theory and machine learning were used for analyzing the fMRI and clinical data. Healthy controls (HCs) showed widespread developmental changes, while young participants with AN exhibited cerebellum differences for high-calorie food. Young individuals with AN displayed increased connectivity during the consumption of potato chips compared to zucchini, with no differences in adults with AN. Multiparametric machine learning accurately distinguished young individuals with AN from healthy controls based on RS connectivity following food visual stimulation ("anticipatory") and consumption ("consummatory"). This study highlights the differential food reward mechanisms and minimal developmental changes in RS connectivity from youth to adulthood in individuals with AN compared to healthy controls. Young individuals with AN demonstrated heightened reactivity to high-caloric foods, while adults showed decreased responsiveness, potentially due to desensitization. These findings shed light on aberrant eating behaviors in individuals with AN and contribute to our understanding of the chronicity of the disease. [ABSTRACT FROM AUTHOR]

Background: Obsessive-compulsive disorder (OCD) is characterized by alterations in brain connectivity, particularly within the default mode network (DMN) and the salience network (SN). Investigating these connectivity differences can provide a deeper understanding of the neural mechanisms underlying OCD. Methods: This cross-sectional study involved 58 patients diagnosed with OCD and 38 healthy control subjects, totaling 96 participants. Resting-state functional MRI (fMRI) data were acquired and analyzed using the CONN toolbox to examine functional connectivity within intrinsic resting-state networks. Graph theory metrics were applied to evaluate node connections and the overall network topology. Clinical symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and their correlations with connectivity patterns and graph-theory parameters were analyzed. Results: The OCD patients and healthy controls were matched in terms of age, gender, marital status, socioeconomic status, and handedness. However, OCD patients had significantly worse general health, quality of life, and higher levels of depression and anxiety. Network analyses revealed altered whole-brain connectivity in OCD patients, particularly within the DMN and the frontoparietal network. The most significant between-group connectivity differences were observed between the posterior parietal cortex (PPC) and the precuneus. Disruptions in the DMN, specifically altered connectivity between the medial prefrontal cortex and posterior cingulate cortex, and changes in the SN involving the anterior insula and anterior cingulate cortex, were significantly correlated with the severity of OCD symptoms. Conclusions: The findings suggest that OCD is associated with distinct alterations in DMN connectivity, which may play a critical role in the disorder's pathophysiology. These disruptions offer potential targets for therapeutic intervention. Further research is needed to explore these connectivity changes in larger cohorts and at various stages of OCD to better understand their clinical significance. [ABSTRACT FROM AUTHOR]

IntroductionMajor depressive disorder with suicidal ideation (MDD/SI+) is characterized by high prevalence, high recurrence rate, high disability rate and low response rate. There is an urgent need for clarifying the pathogenesis and developing novel treatment methods.MethodsSubjects were recruited for the collection of Magnetic Resonance Imaging data and clinical scales. Individual target-transcranial magnetic stimulation (IT-TMS) using Stanford Neuromodulation Therapy over individualized left dorsolateral prefrontal cortex was performed to treat MDD/SI+ with the ethical approval (KY20212218-C-1). GRETNA software was used to analyze brain network characteristics according to graph theory.ResultsA total of 32 patients (aged 18-55) and 28 healthy controls (aged 20-51) had been recruited. Patients after IT-TMS treatment had significant reduction in suicidal ideation and depressive symptom. The functional network of all three groups conformed to small-world topology. There was a renormalization in topology structure after IT-TMS treatment. Decreased functional connectivity between right insula and left anterior cingulate gyrus correlated with improvement in Beck Scale for Suicide Ideation scores.DiscussionThe current study highlights that MDD/SI+ patients in this cohort showed abnormal brain network connections compared to healthy controls, and that IT-TMS may exert its treatment effects by reducing spontaneous hyper-connectivity in the salience network and insula.

The rapid growth of quantum computing is due to its ability to solve problems that cannot be solved using traditional computers. However, the inherent noise and other errors of quantum systems make them prone to failures, so fault tolerance is crucial. The rapid accumulation of errors can seriously affect the robustness of complex calculations. Techniques need to be developed to reduce these effects and enable reliable quantum computing. In this article, we adapt and extend the reliability polynomial, originally introduced by Moore and Shannon, for quantum circuits and gates. We introduce the Quantum Reliability Polynomial (QRP), a novel metric for evaluating the reliability of quantum gates and reversible circuits based on their complex properties. The QRP offers a powerful method for quantifying the fault tolerance of reversible gates under various random failures and stochastic fault models. We exploit the sensitivity of the QRP by carefully calculating its coefficients and associated probabilities for different fault models. This comprehensive analysis covers different circuit topologies and enables a comparative assessment of their reliability and effectiveness. Our results demonstrate the QRP potential as an effective tool for assessing the reliability of reversible quantum circuits and gates. Furthermore, they highlight the ongoing need to further develop this area of research to develop more reliable and efficient quantum computing systems. [ABSTRACT FROM AUTHOR]

This functional magnetic resonance imaging (fMRI) study examined resting-state (RS) connectivity in adolescent and adult patients with anorexia nervosa (AN) using symptom provocation paradigms. Differential food reward mechanisms were investigated through separate assessments of responses to food images and low-caloric/high-caloric food consumption. Thirteen young (≤ 21 years) and seventeen adult (> 21 years) patients with AN and age-matched controls underwent two stimulus-driven fMRI sessions involving RS scans before and after the presentation of food-related stimuli and food consumption. Graph theory and machine learning were used for analyzing the fMRI and clinical data. Healthy controls (HCs) showed widespread developmental changes, while young participants with AN exhibited cerebellum differences for high-calorie food. Young individuals with AN displayed increased connectivity during the consumption of potato chips compared to zucchini, with no differences in adults with AN. Multiparametric machine learning accurately distinguished young individuals with AN from healthy controls based on RS connectivity following food visual stimulation (“anticipatory”) and consumption (“consummatory”). This study highlights the differential food reward mechanisms and minimal developmental changes in RS connectivity from youth to adulthood in individuals with AN compared to healthy controls. Young individuals with AN demonstrated heightened reactivity to high-caloric foods, while adults showed decreased responsiveness, potentially due to desensitization. These findings shed light on aberrant eating behaviors in individuals with AN and contribute to our understanding of the chronicity of the disease.

Impulsive traits (i.e., the tendency to act without forethought regardless of negative outcomes) are frequently found in healthy populations. When exposed to risk factors, individuals may develop debilitating disorders of impulse control (addiction, substance abuse, gambling) characterized by behavioral and cognitive deficits, eventually leading to huge socioeconomic costs. With the far-reaching aim of preventing the onset of impulsive disorders, it is relevant to investigate the topological organization of functional brain networks associated with impulsivity in sub-clinical populations. Taking advantage of the open-source LEMON dataset, we investigated the topological features of resting-state functional brain networks associated with impulsivity in younger (n = 146, age: 20–35) and older (n = 61, age: 59–77) individuals, using a graph-theoretical approach. Specifically, we computed indices of segregation and integration at the level of specific circuits and nodes known to be involved in impulsivity (frontal, limbic, and striatal networks). In younger individuals, results revealed that impulsivity was associated with a more widespread, less clustered and less efficient functional organization, at all levels of analyses and in all selected networks. Conversely, impulsivity in older individuals was associated with reduced integration and increased segregation of striatal regions. Speculatively, such alterations of functional brain networks might underlie behavioral and cognitive abnormalities associated with impulsivity, a working hypothesis worth being tested in future research. Lastly, differences between younger and older individuals might reflect the implementation of age-specific adaptive strategies, possibly accounting for observed differences in behavioral manifestations. Potential interpretations, limitations and implications are discussed. [ABSTRACT FROM AUTHOR]

Pain represents a common symptom of several diseases and is often associated with a reduction in rehabilitation outcomes and recovery. The effectiveness of pain alleviation by botulinum toxin has been recently demonstrated. We searched in PubMed the papers about this topic published in the last ten years, and we selected clinical trials, guidelines, meta-analyses, reviews, and systematic reviews. We used different approaches: multiperspective presentation, lexical evaluation, and systematic review. The systematic review was only performed for the randomized controlled trials. We predominantly found reviews and trials about the rehabilitation of stroke/brain injury and epicondylitis. The most common outcome measures were pain, function, and spasticity. Among the common words, pain was the most frequent and the terms were grouped into different families, especially concerning the outcomes. Rehabilitation showed a relatively low frequency. Finally, the systematic review showed moderate-low levels of bias which confirms the effectiveness of botulinum toxin for pain treatment. The current literature about botulinum toxin is wide and globally diffuse but with some limitations in study strategies and clearness in the formal presentation. The evidence justifies the use of botulinum toxin in treating pain in different diseases. [ABSTRACT FROM AUTHOR]

In this work we present a novel approach for modeling complex industrial plants, employing directed graphs to the simulation and automatic reconfiguration after failures. The framework offers the possibility to model the failure propagation, estimating the overall condition of the system before and after the damage and exploit such a health index for dynamic recalibration. To model the typical operation of industrial plants, we propose several additions with respect to the standard graphs: i) a quantitative measure to control the overall condition of the system ii) nodes of different categories–and then different behaviors–and iii) a fault propagation procedure based on the predecessors and the redundancy of the system. The obtained graph is able to mimic the behavior of the real target plant when one or more faults occur. Additionally, we also implement a generative approach capable of activating a particular category of nodes in order to contain the issue propagation, equipping the network with the capability of reconfiguring itself and resulting in a mathematical tool useful not only for simulating and monitoring but also to design and optimize complex plants. The final asset of the system is provided in the output with its complete diagnostics and a detailed description of the steps that have been carried out to obtain the final realization.

Despite the therapeutical advancements in the surgical treatment of primary intra-axial neoplasms, which determined both a significative improvement in OS and QoL and a reduction in the incidence of surgery-induced major neurological deficits, nowadays patients continue to manifest subtle post-operative neurocognitive impairments, preventing them from a full reintegration back into social life and into the workforce. The birth of connectomics paved the way for a profound reappraisal of the traditional conception of brain architecture, in favour of a model based on large-scale structural and functional interactions of a complex mosaic of cortical areas organized in a fluid network interconnected by subcortical bundles. Thanks to these advancements, neurosurgery is facing a new era of connectome-based resections, in which the core principle is still represented by the achievement of an ideal onco-functional balance, but with a closer eye on whole-brain circuitry, which constitutes the foundations of both major neurological functions, to be intended as motricity; language and visuospatial function; and higher-order cognitive functions such as cognition, conation, emotion and adaptive behaviour. Indeed, the achievement of an ideal balance between the radicality of tumoral resection and the preservation, as far as possible, of the integrity of local and global brain networks stands as a mandatory goal to be fulfilled to allow patients to resume their previous life and to make neurosurgery tailored and gentler to their individual needs. [ABSTRACT FROM AUTHOR]

In this work we present a novel approach for modeling complex industrial plants, employing directed graphs to the simulation and automatic reconfiguration after failures. The framework offers the possibility to model the failure propagation, estimating the overall condition of the system before and after the damage and exploit such a health index for dynamic recalibration. To model the typical operation of industrial plants, we propose several additions with respect to the standard graphs: i) a quantitative measure to control the overall condition of the system ii) nodes of different categories--and then different behaviors--and iii) a fault propagation procedure based on the predecessors and the redundancy of the system. The obtained graph is able to mimic the behavior of the real target plant when one or more faults occur. Additionally, we also implement a generative approach capable of activating a particular category of nodes in order to contain the issue propagation, equipping the network with the capability of reconfiguring itself and resulting in a mathematical tool useful not only for simulating and monitoring but also to design and optimize complex plants. The final asset of the system is provided in the output with its complete diagnostics and a detailed description of the steps that have been carried out to obtain the final realization. [ABSTRACT FROM AUTHOR]

Although several previous studies have used resting-state functional magnetic resonance imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it remains unclear whether the individual structural covariance network (SCN) changes in epilepsy, especially in pediatric epilepsy with visual cortex resection but with normal functions. Herein, individual SCNs were mapped and analyzed for seven pediatric patients with epilepsy after surgery and 15 age-matched healthy controls. A whole-brain individual SCN was constructed based on an automated anatomical labeling template, and global and nodal network metrics were calculated for statistical analyses. Small-world properties were exhibited by pediatric patients after brain surgery and by healthy controls. After brain surgery, pediatric patients with epilepsy exhibited a higher shortest path length, lower global efficiency, and higher nodal efficiency in the cuneus than those in healthy controls. These results revealed that pediatric epilepsy after brain surgery, even with normal functions, showed altered topological organization of the individual SCNs, which revealed residual network topological abnormalities and may provide initial evidence for the underlying functional impairments in the brain of pediatric patients with epilepsy after surgery that can occur in the future.

Background: Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear. Objectives: To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG). Methods: Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph‐theory approach that included measures of cluster‐coefficient, degree, and global‐efficiency. A mixed‐model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls. Results: PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and β bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph‐theory analysis. Differences between groups in graph‐theory measures were found only in cluster‐coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001). Conclusions: Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and β activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Peripheral nervous system diseases are a complex and heterogenous group of diseases affecting the different nerves with various severity and impact on quality of life. The current literature does not provide a structured guide for the rehabilitation of these conditions. We performed a lexical literature evaluation based on graph theory to clarify this topic. We performed a search on PubMed and calculated the frequencies of the words indicating rehabilitation approaches, nerves, and diseases. We found the usefulness of exercises and different physical agents, like laser and ultrasound therapy and neuromuscular stimulation vibration therapy. Orthoses are useful for entrapment, trauma, and hereditary diseases. Correct knowledge and assessment of the neuropathies are fundamental for the therapeutic decision and to guide rehabilitation. Despite the usefulness shown by the different approaches to modulating pain, improving muscle strength and endurance, and ameliorating balance and the sensory system, further studies are needed to define the best-personalized protocols. [ABSTRACT FROM AUTHOR]

Aberrant responses to frustration are central mechanisms of pediatric irritability, which is a common reason for psychiatric consultation and a risk factor for affective disorders and suicidality. This pilot study aimed to characterize brain network configuration during and after frustration and test whether characteristics of networks formed during or after frustration relate to irritability. During functional magnetic resonance imaging, a transdiagnostic sample enriched for irritability (N = 66, mean age = 14.0 years, 50% female participants) completed a frustration-induction task flanked by pretask and posttask resting-state scans. We first tested whether and how the organization of brain regions (ie, nodes) into networks (ie, modules) changes during and after frustration. Then, using a train/test/held-out procedure, we aimed to predict past-week irritability from global efficiency (E glob) (ie, capacity for parallel information processing) of these modules. Two modules present in the baseline pretask resting-state scan (one encompassing anterior default mode and temporolimbic regions and one consisting of frontoparietal regions) contributed most to brain circuit reorganization during and after frustration. Only E glob of modules in the posttask resting-state scans (ie, after frustration) predicted irritability symptoms. Self-reported irritability was predicted by E glob of a frontotemporal-limbic module. Parent-reported irritability was predicted by E glob of ventral-prefrontal-subcortical and somatomotor-parietal modules. These pilot results suggest the importance of the postfrustration recovery period in the pathophysiology of irritability. E glob in 3 specific posttask modules, involved in emotion processing, reward processing, or motor function, predicted irritability. These findings, if replicated, could represent specific intervention targets for irritability. [ABSTRACT FROM AUTHOR]

This paper studies the stability, equilibrium and effi ciency of directed networks of trade intermediaries under a rule of equi-repartition of profi ts. This equi-repartition rule, although simple and natural, introduces complex topologic considerations from players. Core-periphery structures can arise endogenously and result in stable networks even assuming complete homogeneity of all agents in the network. The concept of network partition is introduced to provide examples of networks that could be seen as arising from the linking up of a set of disjoint networks in an endogenous manner. From an aggregate welfare perspective as well as sometimes from an individual perspective, leaving the linking up of those individually effi cient networks to each individual agent can result in an ineffi cient aggregate network, even if taken in isolation each pre-existing network is effi cient.

Simple Summary: This longitudinal study demonstrates the development of language plasticity in right-handed patients undergoing surgery for left-hemispheric low-grade glioma, characterized by the gradually increased involvement of the right hemisphere in language function. Two patterns of language reorganization were identified: type 1 changes may in part be treatment-related; type 2 may be tumor-induced, since atypical language organization was already present at baseline. Increased inter-hemispheric connectivity may represent the initial step in the development of plastic phenomena. This change could be partially compensatory towards clinical deficits and show prognostic value. Language reorganization may represent an adaptive phenomenon to compensate tumor invasion of the dominant hemisphere. However, the functional changes over time underlying language plasticity remain unknown. We evaluated language function in patients with low-grade glioma (LGG), using task-based functional MRI (tb-fMRI), graph-theory and standardized language assessment. We hypothesized that functional networks obtained from tb-fMRI would show connectivity changes over time, with increased right-hemispheric participation. We recruited five right-handed patients (4M, mean age 47.6Y) with left-hemispheric LGG. Tb-fMRI and language assessment were conducted pre-operatively (pre-op), and post-operatively: post-op1 (4–8 months), post-op2 (10–14 months) and post-op3 (16–23 months). We computed the individual functional networks applying optimal percolation thresholding. Language dominance and hemispheric connectivity were quantified by laterality indices (LI) on fMRI maps and connectivity matrices. A fixed linear mixed model was used to assess the intra-patient correlation trend of LI values over time and their correlation with language performance. Individual networks showed increased inter-hemispheric and right-sided connectivity involving language areas homologues. Two patterns of language reorganization emerged: Three/five patients demonstrated a left-to-codominant shift from pre-op to post-op3 (type 1). Two/five patients started as atypical dominant at pre-op, and remained unchanged at post-op3 (type 2). LI obtained from tb-fMRI showed a significant left-to-right trend in all patients across timepoints. There were no significant changes in language performance over time. Type 1 language reorganization may be related to the treatment, while type 2 may be tumor-induced, since it was already present at pre-op. Increased inter-hemispheric and right-side connectivity may represent the initial step to develop functional plasticity. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social‐communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph‐theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting‐state fMRI data collected from 5 to 10‐year‐old children—34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children. Lay Summary: Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Altered global modularity demonstrates discrepancies in prior studies of functional brain modular organization in autism spectrum disorder (ASD). These discrepancies result from the examination of mixed age groups. In the present study, we examined how such discrepancies and altered global modular organization in ASD originates from atypicality at the systematic nodal level, focusing on children with ASD. We demonstrate that many of the previous discrepancies of altered functional connectivity in ASD could be reconciled based on nodal role identification and functional cartography of nodes. Our findings suggest that alterations in nodal topology may start occurring at early ages in children with ASD. [ABSTRACT FROM AUTHOR]

Background: Several epidemiological studies and psychological models have suggested that major depressive disorder (MDD) with anhedonia is associated with suicidal ideation (SI). However, little is known about whether the functional network pattern and intrinsic topologically disrupted in patients with anhedonia are related to SI. Methods: The resting-fMRI by applying network-based statistic (NBS) and graph-theory analyses was estimated in 273 patients with MDD (144 high anhedonia [HA], 129 low anhedonia [LA]) and 150 healthy controls. In addition, we quantified the SI scores of each patient. Finally, the mediation analysis assessed whether anhedonia symptoms could mediate the relationship between anhedonia-related network metrics and SI. Result: The NBS analysis demonstrated that individuals with HA have a single abnormally increased functional connectivity component in a frontal-limbic circuit (termed the “anhedonia-related network”, including the frontal cortex, striatum, anterior cingulate cortex and amygdala). The graph-theory analysis demonstrated that the anhedonia-related network showed a significantly disrupted topological organization (lower gamma and lambda), which the small-world property trend randomized. Furthermore, the anhedonia symptoms could mediate the relationship between the anhedonia-related network metrics (the mean functional connectivity values, the area under the curves values of gamma and nodal local efficiency in nucleus accumbens) and SI. Conclusions: We found that disruption of the reward-related network in MDD leads to SI through anhedonia symptoms. These findings show the abnormal topological construction of functional brain network organization in anhedonia, shedding light on the neurological processes underlying SI in MDD patients with anhedonia symptoms.

Prior studies suggest that methylphenidate, the primary pharmacological treatment for attention‐deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant‐treatment naïve children and adults with ADHD. We obtained resting‐state functional MRI from 50 boys (10–12 years of age) and 49 men (23–40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre‐methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age‐dependent in stimulant‐treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment. [ABSTRACT FROM AUTHOR]

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness. [ABSTRACT FROM AUTHOR]

The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry. Because of its simplicity in design, it lacks most of the requirements needed for a security-proven communication protocol. However, a safe and secured environment is imperative for autonomous as well as connected vehicles. Therefore CAN security is considered one of the important topics in the automotive research community. In this article, we propose a four-stage intrusion detection system that uses the chi-squared method and can detect any kind of strong and weak cyber attacks in a CAN. This work is the first-ever graph-based defense system proposed for the CAN. Our experimental results show that we have a very low 5.26% misclassification for denial of service (DoS) attack, 10% misclassification for fuzzy attack, 4.76% misclassification for replay attack, and no misclassification for spoofing attack. In addition, the proposed methodology exhibits up to 13.73% better accuracy compared to existing ID sequence-based methods. [ABSTRACT FROM AUTHOR]

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD. [ABSTRACT FROM AUTHOR]

Background and Purpose: To investigate the reorganization of the central nervous system provided by resting state‐functional MRI (rs‐fMRI), graph‐theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection. Methods: Forty HIV‐positive patients without neurological impairment and 20 age‐ and sex‐matched healthy controls underwent rs‐fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T‐lymphocytes value and plasmatic HIV‐RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph‐theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART). Results: Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV‐RNA and local graph‐theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups. Conclusion: Our study demonstrates that rs‐fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients. [ABSTRACT FROM AUTHOR]

Prematurity disrupts brain development during a critical period of brain growth and organization and is known to be associated with an increased risk of neurodevelopmental impairments. Investigating whole-brain structural connectivity alterations accompanying preterm birth may provide a better comprehension of the neurobiological mechanisms related to the later neurocognitive deficits observed in this population.Using a connectome approach, we aimed to study the impact of prematurity on neonatal whole-brain structural network organization at term-equivalent age. In this cohort study, twenty-four very preterm infants at term-equivalent age (VPT-TEA) and fourteen full-term (FT) newborns underwent a brain MRI exam at term age, comprising T2-weighted imaging and diffusion MRI, used to reconstruct brain connectomes by applying probabilistic constrained spherical deconvolution whole-brain tractography. The topological properties of brain networks were quantified through a graph-theoretical approach. Furthermore, edge-wise connectivity strength was compared between groups.Overall, VPT-TEA infants’ brain networks evidenced increased segregation and decreased integration capacity, revealed by an increased clustering coefficient, increased modularity, increased characteristic path length, decreased global efficiency and diminished rich-club coefficient. Furthermore, in comparison to FT, VPT-TEA infants had decreased connectivity strength in various cortico-cortical, cortico-subcortical and intra-subcortical networks, the majority of them being intra-hemispheric fronto-paralimbic and fronto-limbic. Inter-hemispheric connectivity was also decreased in VPT-TEA infants, namely through connections linking to the left precuneus or left dorsal cingulate gyrus – two regions that were found to be hubs in FT but not in VPT-TEA infants. Moreover, posterior regions from Default-Mode-Network (DMN), namely precuneus and posterior cingulate gyrus, had decreased structural connectivity in VPT-TEA group.Our finding that VPT-TEA infants’ brain networks displayed increased modularity, weakened rich-club connectivity and diminished global efficiency compared to FT infants suggests a delayed transition from a local architecture, focused on short-range connections, to a more distributed architecture with efficient long-range connections in those infants. The disruption of connectivity in fronto-paralimbic/limbic and posterior DMN regions might underlie the behavioral and social cognition difficulties previously reported in the preterm population.

Cognitive impairment is a key clinical feature of ischemic leukoaraiosis (ILA); however, the underlying neurobiological mechanism is still unclear. ILA has been associated with widespread gray and white matter (WM) damage mainly located in cortical-cortical and cortico-subcortical pathways. A total of 36 patients with ILA (Fazekas rating score ≥2) and 31 healthy controls (HCs) underwent comprehensive neuropsychological assessments (covering four cognitive domains, i.e., information processing speed, episodic memory, executive and visuospatial function) and resting-state functional MRI scans. Graph theory-based analyses were employed to explore the topological organization of the brain connectome in ILA patients, and we further sought to explore the associations of connectome-based metrics and neuropsychological performances. An efficient small-world architecture in the functional brain connectome was observed in the ILA and control groups. Moreover, compared with the HCs, the ILA patients showed increased path length and decreased network efficiency (i.e., global and local efficiency) in their functional brain networks. Further network-based statistic (NBS) analysis revealed a functional-disconnected network in ILA, which is comprised of functional connections linking different brain modules (i.e., default mode, frontoparietal, ventral attention and limbic systems) and connections within single modules (i.e., ventral attention and limbic systems). Intriguingly, the abnormal network metrics correlated with cognitive deficits in ILA patients. Therefore, our findings provide further evidence to support the concept that ILA pathologies could disrupt brain connections, impairing network functioning, and cognition via a “disconnection syndrome.”

• Analytical time-depended travel patterns information extraction from multi-period origin-destination (OD) matrices. • Optimal identification of tours into ODs and estimation of activity schedules type and volume. • Duration of activities estimation based on OD synthesis. • Data-driven and optimization-based mobility demand modelling. Over the last decades, technological advances have allowed the capturing of travel behaviour at large-scale. Despite the unprecedented volume and the variety of personal mobility data, aggregate Origin-Destination (OD) matrices are still the most widespread means to organise and represent travel demand. Nonetheless, standard ODs cannot adequately capture significant elements affecting travel behaviour such as trip-interdependency and trip-chaining, therefore they are not particularly suitable for travel behaviour analysis at person-level. The currently presented modelling framework enables the in-depth study of personal mobility by firstly combining the trips present in OD matrices into home-based trip-chains (i.e. tours) and subsequently into sequences of activities (activity schedules). The above-mentioned process is completed based on advanced graph-theoretical and combinatorial optimisation concepts. The applicability of the methodology is meticulously verified through a large-scale test case where a set of multi-period, purpose dependant ODs is converted into realistic activity schedules able to incorporate more than 99% of the inputted travel demand. The accurate and highly detailed results showcase the significant potential of the proposed methodology to support the comprehensive analysis of travel behaviour at person level. [ABSTRACT FROM AUTHOR]

Cognitive impairment is a key clinical feature of ischemic leukoaraiosis (ILA); however, the underlying neurobiological mechanism is still unclear. ILA has been associated with widespread gray and white matter (WM) damage mainly located in cortical-cortical and cortico-subcortical pathways. A total of 36 patients with ILA (Fazekas rating score ≥2) and 31 healthy controls (HCs) underwent comprehensive neuropsychological assessments (covering four cognitive domains, i.e., information processing speed, episodic memory, executive and visuospatial function) and resting-state functional MRI scans. Graph theory-based analyses were employed to explore the topological organization of the brain connectome in ILA patients, and we further sought to explore the associations of connectome-based metrics and neuropsychological performances. An efficient small-world architecture in the functional brain connectome was observed in the ILA and control groups. Moreover, compared with the HCs, the ILA patients showed increased path length and decreased network efficiency (i.e., global and local efficiency) in their functional brain networks. Further network-based statistic (NBS) analysis revealed a functional-disconnected network in ILA, which is comprised of functional connections linking different brain modules (i.e., default mode, frontoparietal, ventral attention and limbic systems) and connections within single modules (i.e., ventral attention and limbic systems). Intriguingly, the abnormal network metrics correlated with cognitive deficits in ILA patients. Therefore, our findings provide further evidence to support the concept that ILA pathologies could disrupt brain connections, impairing network functioning, and cognition via a "disconnection syndrome." [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects. • Brain connectivity in AD is a complex scenario of multiple disrupted pathways. • Topological distance metrics bring novel methods for AD network analysis. • Changes in topological distance define the connectivity breakdown of AD and MCI. • Symptom severity associates to changes in connectivity distance in the human cortex. [ABSTRACT FROM AUTHOR]

• Food availability and mesoscale turbulence in the Marquesas jointly constrained larval dispersal, reducing its potential connectivity with other archipelagos in French Polynesia. • Settlement success is influenced by climatic ENSO cycles that drive ocean currents in the Pacific region. • Biophysical modelling refines management unit's definition in extremely fragmented archipelagos. Larval dispersal and connectivity are key processes that drive marine metapopulation dynamics, and therefore should be well characterized when designing effective management strategies. While temperature and food availability can structure marine species connectivity patterns, their contribution has not been thoroughly investigated in highly fragmented archipelagos. We used biophysical modeling of larval dispersal to explore the connectivity patterns of species with complex life-cycles across French Polynesia (FP), a territory formed by more than a hundred small, geographically isolated islands covering an area as large as Europe. We first simulated ten years of larval dispersal to investigate the spatial and temporal (seasonal and interannual) variability in larval dispersal pathways for different hypothetical species exhibiting a range of Larval Precompetency Period (LPP) values. Then, using the black-lip pearl oyster (Pinctada margaritifera) as a model species, we accounted for variability in the LPP induced by temperature and food availability, as derived from a Dynamic Energy Budget (DEB) model. The model showed that food availability and mesoscale turbulence (eddies) in the Marquesas jointly constrained larval dispersal, reducing its potential connectivity with other archipelagos in FP. The DEB simulations also revealed seasonal and interannual variability in connectivity driven by environmental conditions. However, accounting for food and temperature effects on larval development, barely changed the connectivity pattern at regional scale due to the remoteness of this archipelago. Our study thus provides appropriate management units definition at regional scale for the species across FP. [ABSTRACT FROM AUTHOR]

Among the tools useful for the management of neuropathic pain, ultrasound presents several advantages, shown by the literature. We assessed the scientific production about neuropathic pain and ultrasound from different points of view: general topics, journal categories, geographical origin and lexical analysis. We searched papers on PubMed using the Medical Subject Headings “neuropathic pain” AND “ultrasound”. We collected data about the journals where the papers were published, the country of the affiliation of the first author. For the lexical analysis, we evaluated the presence of selected words in the papers, and we built a graph representing the connections among words and papers. The papers were focused on the use of ultrasound as a diagnostic tool and guide for the therapy, assessing its application in different diseases such as Morton’s neuroma and piriformis syndrome. The most represented journal category was anesthesia while the most common country the United States of America. The lexical analysis confirmed the importance of ultrasound for diagnosis of specific disease and treatment of pain. The described approaches provide a multiperspective evaluation of the literature and may support the interpretation of the information contained by the papers.

Cocaine use disorder (CUD) is a major public health concern with devastating social, economic, and mental health implications. A better understanding of the underlying neurobiology and phenotypic variations in individuals with CUD is necessary for the development of effective and targeted treatments. In this study, 39 women and 54 men with CUD completed a 6-min resting-state functional magnetic resonance imaging scan after intranasal oxytocin (OXY) or placebo administration. Graph-theory network analysis was used to quantify functional connectivity changes caused by OXY in striatum, anterior cingulate cortex (ACC), insula, and amygdala nodes of interest. OXY increased connectivity in the right ACC and left amygdala in males, whereas OXY increased connectivity in the right ACC and right accumbens in females. Machine learning was then used to associate treatment response (placebo minus OXY) in nodes of interest with years of cocaine use and severity of childhood trauma separately for males and females. Childhood trauma and years of cocaine use were associated with OXY-induced changes in ACC connectivity for both men and women, but connectivity changes in the amygdala were associated with years of cocaine use in men and connectivity changes in the right insula were associated with years of cocaine use in women. These findings suggest that salience network nodes (ACC and insula) are potential OXY treatment targets in CUD, with the amygdala as a treatment target for men and the accumbens as a treatment target for women.

Brain plasticity occurs over the course of the human lifetime. Learning and training modify our neuronal synapses and adapt our brain activity, from priming effects in modal areas to higher-order changes in the association cortex. The current state of the art suggests that learning and training effects might induce large-scale brain connectivity changes. Here, we used task-fMRI data and graph-based approaches to study the immediate brain changes in functional connections associated with training on a visual search task, and the individual differences in learning were studied by means of brain-behavior correlations. In a previous work, we found that trained participants improved their response speed on a visual search task by 31%, whereas the control group hardly changed. In the present study, we showed that trained individuals changed regional connections (local links) in cortical areas devoted to the specific visual search processes and to areas that support information integration, and largely modified distributed connections (distant links) linking primary visual areas to specific attentional and cognitive control areas. In addition, we found that the individuals with the most enhanced connectivity in the dorsolateral prefrontal cortex performed the task faster after training. The observed behavioral and brain connectivity findings expand our understanding of large-scale dynamic readjustment of the human brain after learning experiences. [ABSTRACT FROM AUTHOR]

This paper investigates a class of stochastic coupled systems on networks with time-varying delay driven by G-Brownian motion (G-SCSNTVD, in short). By means of inequality technique, kth vertex-Lyapunov functions and graph-theory, we obtain asymptotical boundedness for G-SCSNTVD. As an application, stochastic coupled oscillator networks with time-varying delay driven by G-Brownian motion are discussed. Moreover, an example is given to illustrate the theory obtained. [ABSTRACT FROM AUTHOR]

Ballast water is a leading vector for the introduction of aquatic invasive species worldwide and, once a novel species is established, regional ballast water exchange between ports can accelerate secondary spread. The importance of shipping induced invasions in the Laurentian Great Lakes has resulted in policies that require more stringent ballast water treatment standards for transoceanic shipping than is required of ships operating regionally within the Great Lakes. As a result, ballast water discharges within the Great Lakes are not well regulated, primarily because of the challenge of treating the high volumes of water carried by vessels that are confined to the waters of the Great Lakes. We used a discrete-time Markov chain model on a network with annual time-steps to simulate ballast water management scenarios at high-priority ports in the Great Lakes shipping network for two potential invaders, golden mussel (Limnoperna fortunei) and monkey goby (Neogobius fluviatilis). We chose high-priority ports by using graph-theoretic network analysis techniques to calculate six network centrality metrics for 151 ports in the network. Ports scoring high in network centrality scores have more ties with other ports or are positioned within the network such that they potentially have greater influence over the secondary spread of aquatic invasive species than other ports. We simulated secondary spread scenarios where hypothetical ballast water treatment was implemented at the top twenty ranked ports in each network centrality metric, as well as the top twenty busiest ports by ship arrivals. The results of each scenario were compared to a scenario where no management action was taken. Simulated secondary spread for both golden mussel and monkey goby resulted in significantly reduced infestation probabilities (p < 0.001) under all management scenarios when compared to unmanaged spread scenarios. Management at ports with inwardly directed ties to other ports reduced infestations by the greatest amount compared to other management scenarios; 65.4% for golden mussel and 74.6% for monkey goby. The indegree centrality of ports in the Great Lakes was found to be an important factor in governing secondary spread. Here we show that prioritized management, like high volume shore based treatment systems based on network centrality, is a potentially effective strategy for impeding the secondary spread of new or localized invasive species in the Great Lakes. [ABSTRACT FROM AUTHOR]

Abstract In this work, we discuss the stability of stochastic pantograph multi-group models with dispersal perturbed by G -Brownian motion (G -SPMGMs, for short) with general decay rate, which includes polynomial stability, exponential stability, and logarithmic stability as special cases. With the help of G -vertex-Lyapunov functions, G -stochastic analysis technique and graph-theory on multi-digraph, sufficient criteria are obtained for the stability of G -SPMGMs. An application to a class of stochastic coupled pantograph oscillators with G -Brownian motion perturbation and an example are given to demonstrate the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stochastic Cohen–Grossberg neural networks driven by G -Brownian motion (G -SCGNNs, in short). We establish the exponential synchronization of G -SCGNNs by applying inequality technique, k th vertex- G -Lyapunov functions, graph-theory and state feedback control technique. A concrete example is given to verify the obtained theory. [ABSTRACT FROM AUTHOR]

Cocaine use disorder (CUD) is a major public health concern with devastating social, economic, and mental health implications. A better understanding of the underlying neurobiology and phenotypic variations in individuals with CUD is necessary for the development of effective and targeted treatments. In this study, 39 women and 54 men with CUD completed a 6-min resting-state functional magnetic resonance imaging scan after intranasal oxytocin (OXY) or placebo administration. Graph-theory network analysis was used to quantify functional connectivity changes caused by OXY in striatum, anterior cingulate cortex (ACC), insula, and amygdala nodes of interest. OXY increased connectivity in the right ACC and left amygdala in males, whereas OXY increased connectivity in the right ACC and right accumbens in females. Machine learning was then used to associate treatment response (placebo minus OXY) in nodes of interest with years of cocaine use and severity of childhood trauma separately for males and females. Childhood trauma and years of cocaine use were associated with OXY-induced changes in ACC connectivity for both men and women, but connectivity changes in the amygdala were associated with years of cocaine use in men and connectivity changes in the right insula were associated with years of cocaine use in women. These findings suggest that salience network nodes (ACC and insula) are potential OXY treatment targets in CUD, with the amygdala as a treatment target for men and the accumbens as a treatment target for women. [ABSTRACT FROM AUTHOR]

This paper investigates a class of stochastic coupled systems on networks driven by G -Brownian motion ( G -SCSN, in short). By means of inequality techniques, k th vertex G -Lyapunov functions and graph-theory, we obtain the asymptotical boundedness for G -SCSN. Moreover, p th exponential stability is derived. As an application, a class of stochastic coupled Cohen–Grossberg neural networks driven by G -Brownian motion are discussed. Moreover, an example is given to illustrate the theory obtained. [ABSTRACT FROM AUTHOR]

Abstract: Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path‐length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd‐ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase‐locking values (functional). Both groups showed a significant electroencephalographic task‐related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task‐related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task‐related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task‐related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients. [ABSTRACT FROM AUTHOR]

One hallmark example of a link between global topological network properties of complex functional brain connectivity and cognitive performance is the finding that general intelligence may depend on the efficiency of the brain's intrinsic functional network architecture. However, although this association has been featured prominently over the course of the last decade, the empirical basis for this broad association of general intelligence and global functional network efficiency is quite limited. In the current study, we set out to replicate the previously reported association between general intelligence and global functional network efficiency using the large sample size and high quality data of the Human Connectome Project, and extended the original study by testing for separate association of crystallized and fluid intelligence with global efficiency, characteristic path length, and global clustering coefficient. We were unable to provide evidence for the proposed association between general intelligence and functional brain network efficiency, as was demonstrated by van den Heuvel et al. (2009), or for any other association with the global network measures employed. More specifically, across multiple network definition schemes, ranging from voxel-level networks to networks of only 100 nodes, no robust associations and only very weak non-significant effects with a maximal R 2 of 0.01 could be observed. Notably, the strongest (non-significant) effects were observed in voxel-level networks. We discuss the possibility that the low power of previous studies and publication bias may have led to false positive results fostering the widely accepted notion of general intelligence being associated to functional global network efficiency. [ABSTRACT FROM AUTHOR]

Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a "naive", though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method. [ABSTRACT FROM AUTHOR]

State observability of dynamic systems is a notion which determines how well the states can be inferred from input-output data. For small-scale systems, observability analysis can be done manually, while for large-scale systems an automated systematic approach is advantageous. Here we present an approach based on the concept of structural observability analysis, using graph theory. This approach can be automated and applied to large-scale, complex dynamic systems modeled using Modelica. Modelica models are imported into Python via the JModelica.org-CasADi interface, and the Python packages NetworkX (for graph-theoretic analysis) and PyGraphviz (for graph layout and visualization) are used to analyze the structural observability of the systems. The method is demonstrated with a Modelica model created for the Copper production plant at Glencore Nikkelverk, Kristiansand, Norway. The Copper plant model has 39 states, 11 disturbances and 5 uncertain parameters. The possibility of estimating disturbances and parameters in addition to estimating the states are also discussed from the graph-theory point of view. All the software tools used on the analysis are freely available.