Eigenvector Centrality Values Research Articles

A Weibo local network growth model constructed from the perspective of following-followed

Abstract In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model's validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

COVID-19 related cognitive, structural and functional brain changes among Italian adolescents and young adults: a multimodal longitudinal case-control study

Coronavirus disease 2019 (COVID-19) has been associated with brain functional, structural, and cognitive changes that persist months after infection. Most studies of the neurologic outcomes related to COVID-19 focus on severe infection and aging populations. Here, we investigated the neural activities underlying COVID-19 related outcomes in a case-control study of mildly infected youth enrolled in a longitudinal study in Lombardy, Italy, a global hotspot of COVID-19. All participants (13 cases, 27 controls, mean age 24 years) completed resting-state functional (fMRI), structural MRI, cognitive assessments (CANTAB spatial working memory) at baseline (pre-COVID) and follow-up (post-COVID). Using graph theory eigenvector centrality (EC) and data-driven statistical methods, we examined differences in ECdelta (i.e., the difference in EC values pre- and post-COVID-19) and Volumetricdelta (i.e., the difference in cortical volume of cortical and subcortical areas pre- and post-COVID) between COVID-19 cases and controls. We found that ECdelta significantly between COVID-19 and healthy participants in five brain regions; right intracalcarine cortex, right lingual gyrus, left hippocampus, left amygdala, left frontal orbital cortex. The left hippocampus showed a significant decrease in Volumetricdelta between groups (p = 0.041). The reduced ECdelta in the left amygdala associated with COVID-19 status mediated the association between COVID-19 and disrupted spatial working memory. Our results show persistent structural, functional and cognitive brain changes in key brain areas associated with olfaction and cognition. These results may guide treatment efforts to assess the longevity, reversibility and impact of the observed brain and cognitive changes following COVID-19.

Research Trend Analysis of Community-based Occupational Therapy in Korea through Key Word Network Analysis

Objectives: For this study, we analyzed research trends of community-based occupational therapy in Korea using key word network analysis (KNA). Methods: Our sample was 155 studies in the Journal of the Korean Society of Community-Based Occupational Therapy in Korea. We used key word network analysis (KNA), a method used to examine the flow and trends of research by using key words that represent the core topics of the literature, allowing for the identification of relationships, influence, and importance of key topics in a specific field. The analysis was divided into 2 parts – network distribution and centrality. Indicators for network distribution include average degree, density, mean distance, inclusiveness, and the number of isolated nodes. Network centrality is assessed through degree centrality, betweenness centrality, and eigenvector centrality. Results: The analysis of network distribution indicated that the average degree was 7.064, density was .024 (2.4%), mean distance was 3.195, and there were no isolated nodes. Key words with the highest degree centrality values were occupational therapy, stroke, elderly, quality of life, activity of daily living, and occupational therapist. Key words with the highest betweenness centrality values were occupational therapy, stroke, quality of life, elderly, occupational therapist, and depression. Key words with the highest eigenvector centrality values were curriculum, service provider qualification, sensory integration therapy, sensory developmental rehabilitation, developmental rehabilitation service, and program development. Conclusions: These results have important academic implications regarding the research trend of community-based occupational therapy in Korea. These findings can enhance understanding of community-based occupational therapy and provide guidance for future studies. Particularly, the analysis results of degree centrality and cohesion structure offer insights into various aspects of occupational therapy, facilitating the development of more effective treatment approaches. It is anticipated that these results will contribute to practical value in clinical settings.

Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study

ObjectiveWe decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA). MethodsFunctional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected. ResultsThe MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia. ConclusionOur findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.

A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance

We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.

Identification of keystone taxa in root canals and periapical lesions of post-treatment endodontic infections: Next generation microbiome research.

The aim of this study was to analyse and compare the microbiome present in root canals and periapical lesions of teeth with post-treatment infections, and to identify the presence of keystone taxa in both habitats using next-generation sequencing analysis. Apices and periapical lesions of patients with post-treatment apical periodontitis were surgically extracted. Specimens were cryo-pulverized, bacterial DNA was extracted, and the V3-V4 hypervariable regions of the 16S rRNA gene were sequenced using the Illumina Miseq platform. Bioinformatic analysis was carried out with Mothur software, whilst diversity indices were obtained using operational taxonomic units (OTUs). The diversity indices were compared with the Kruskal-Wallis test, and community composition differences were explored with Permutational Multivariate Analysis of Variance (PERMANOVA). A bacterial functional study was performed with the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Co-occurrence network analyses were performed using the Sparse Correlations for Compositional data (SparCC). Eigencentrality, clr-based abundance and ubiquitousness were applied to infer keystone taxa. P values <.05 were considered statistically significant. Thirty-two apices and thirty-nine periapical lesions were sequenced and analysed. A similar alpha-diversity (p < .05) and community composition (p = .91) was observed for apices and lesion samples. The most abundant OTUs identified amongst all samples included Fusobacterium nucleatum, Prevotella loescheii, Streptococcus intermedius, Porphyromonas gingivalis, Parvimonas micra, Synergistetes bacterium, Tannerella forsythia and Peptostreptococcus stomatis. The metabolic pathways with >0.81% abundances included membrane transport, genetic information processing and metabolic pathways. F. nucleatum was identified as a keystone taxon as it showed ubiquitousness, an eigenvector centrality value of 0.83 and a clr-based abundance >4. The microbiome in apices and periapical lesions of post-treatment endodontic infections showed a similar diversity and taxonomic composition. Co-occurrence network analyses at OTU level identified F. nucleatum as a keystone taxon candidate in these infections.

Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline.

The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer's disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer's disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer's disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer's disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.

Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis.

Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients. We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping. Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients. Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients. The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.

Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks

Terabytes of data are now being handled by an increasing number of apps, and rapid user decision-making is hampered by data analysis. At the same time, there is a rise in interest in big data analysis for social networks at the moment. Thus, adopting distributed multi-agent-based technology in an optimum way is one of the solutions to effective big data analysis for social networks. Studying the development of a social network helps users gain an understanding of interactions and relationships and guides them in making decisions. In this study, a method called Hessian Distributed Ant Optimized and Perron–Frobenius Eigen Centrality (HDAO-PFEC) is developed to analyze large amounts of data (i.e., Big Data) in a computationally accurate and efficient manner. Designing an adaptable Multi-Agent System architecture for large data analysis is the primary goal of HDAO-PFEC. Initially, using a Hessian Mutual Distributed Ant Optimization MapReduce model, comparable user interest tweets are produced in a computationally efficient manner. Eigen Vector Centrality is a measure of a node’s importance in a network (i.e., a social network), which allows association with other significant nodes (i.e., users), allowing for a greater effect on social networks. With this goal in mind, a MapReduce methodology in the Hadoop platform using Big Data, which enables quick and ordered calculations, is used in a distributed computing method to estimate the Eigen Vector Centrality value for each social network member. Lastly, extensive investigative experimental learning demonstrates the HDAO-PFEC method’s use and accuracy as well as its time and overhead on the well-known sentiment 140 dataset.

Modeling venous bias in resting state functional MRI metrics.

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have a high prevalence of chronic WTC-related post-traumatic stress disorder (WTC-PTSD). We investigated neural mechanisms underlying WTC-PTSD by applying eigenvector centrality (EC) metrics and data-driven methods on resting state functional magnetic resonance (fMRI). We identified how EC differences relate to WTC-exposure and behavioral symptoms. We found that connectivity differentiated significantly between WTC-PTSD and non-PTSD responders in nine brain regions, as these differences allowed an effective discrimination of PTSD and non-PTSD responders based solely on analysis of resting state data. Further, we found that WTC exposure duration (months on site) moderates the association between PTSD and EC values in two of the nine brain regions; the right anterior parahippocampal gyrus and the left amygdala (p = 0.010; p = 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, a dimensional measure of symptom severity was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Functional neuroimaging can provide effective tools to identify neural correlates of diagnostic and dimensional indicators of PTSD.

Acupuncture treatment for functional gastrointestinal disorders: Identification of major acupoints using network analysis

BackgroundUsing network analysis, we sought to determine the acupoints most commonly used to treat functional gastrointestinal disorders (FGIDs), particularly functional dyspepsia (FD) and irritable bowel syndrome (IBS). MethodsTo explore the acupoint patterns used for FGID, data on acupoint combinations for FD and IBS were gathered from systematic reviews. Network analysis was used to determine the degree, closeness centrality, betweenness centrality, and eigenvector centrality of each acupoint. The most common acupoint combinations for FD and IBS were examined based on the eigenvector centrality. ResultsNetwork analysis revealed that CV12, ST25, ST36, CV10, and LR3, which had the highest eigenvector centrality values, were the main acupoints for treating FGID. CV12 was the main acupoint for treating FD, while ST25 was the hub acupoint for treating IBS in the abdomen. ST36, LR3, and PC6 were the key peripheral acupoints for FD and IBS. ConclusionsUsing network analysis, we provided data that will aid the selection of both general and specific acupoints for FD and IBS, along with spatial information (i.e., the positions of acupoints on a body map). These findings could be applied in future acupuncture research on therapy for gastrointestinal system dysfunction. They may also help bridge the gap between the traditional meridian theory, which assumes that there is a link between diseases/symptoms and the specific body region being treated, and real-world clinical evidence.

Variations in Kiwifruit Microbiota across Cultivars and Tissues during Developmental Stages.

The plant microbiota plays a crucial role in promoting plant health by facilitating the nutrient acquisition, abiotic stress tolerance, biotic stress resilience, and host immune regulation. Despite decades of research efforts, the precise relationship and function between plants and microorganisms remain unclear. Kiwifruit (Actinidia spp.) is a widely cultivated horticultural crop known for its high vitamin C, potassium, and phytochemical content. In this study, we investigated the microbial communities of kiwifruit across different cultivars (cvs. Deliwoong and Sweetgold) and tissues at various developmental stages. Our results showed that the microbiota community similarity was confirmed between the cultivars using principal coordinates analysis. Network analysis using both degree and eigenvector centrality indicated similar network forms between the cultivars. Furthermore, Streptomycetaceae was identified in the endosphere of cv. Deliwoong by analyzing amplicon sequence variants corresponding to tissues with an eigenvector centrality value of 0.6 or higher. Our findings provide a foundation for maintaining kiwifruit health through the analysis of its microbial community.

ANALISIS JARINGAN KOMUNIKASI OPINI PUBLIK PADA FENOMENA “PEMECATAN Dr TERAWAN” DI TWITTER

This study aims to determine the system-level communication network and actors on the “Terawan” link and to find out public opinion about this phenomenon and how the IDI organization reacts in responding to public opinion. By using the theory of Computer Mediated Communication (CMC). The method used in this research is a quantitative approach with the method of Social Network Analysis with Netlytic and Gephi and content analysis. The population used in this study are active Twitter users, with a maximum sample of 2500 obtained from Netlytic. The results showed that this network has 1969 Nodes and 1991 Edges, with 5 large clusters in this network. This network has a value of Diameter 19, Destiny 0.000604, Reciprocity 0.011160, Centralization 0.107200 and Modularity 0.810800. The most influential actor in this network is @heraloebs with a Degree Centrality value of 391 and an Eigenvector Centrality value of 1.0. Therefore, this network includes a network that is widespread in the distribution of information, with influential actors in the distribution of information such as @heraloebss. While the focus of communication carried out by the IDI organization in responding to public opinion regarding the phenomenon of “Dr Terawan’s dismissal”

Functional eigenvector centrality dynamics are related to amyloid deposition in preclinical Alzheimer’s Disease

AbstractBackgroundIn preclinical Alzheimer’s disease (AD), amyloid accumulates in highly‐functionally connected brain regions. This selective vulnerability is related to the high neuronal fluctuations, typical of these regions. Dynamic functional connectivity (FC) was introduced to investigate network organization over time, with high network variations indicating regional flexibility, hence promoting functional integration. The relation of early amyloid deposition with FC dynamics remains unclear. Eigenvector centrality (EC) evaluates a node's importance in functional networks, both for the whole functional MRI time series (“static” EC) or within sliding‐windows (“dynamic” EC). We studied the association of cerebrospinal fluid (CSF) amyloid load with static and dynamic EC in non‐demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.MethodsData for 701 non‐demented participants were available. CSF Aβ1‐42 levels &lt;1000 pg/mL were defined as amyloid positive (A+) (Elecsys assay). Both static and dynamic voxel‐wise EC were computed from rs‐fMRI time series. Static EC differences between A+ and A‐ participants were assessed using linear models. Standard deviation and range of dynamic EC were compared between A+ and A‐ participants within significant clusters of static EC differences, and within 10 resting‐state networks (RSN). Linear models were used to determine interaction between amyloid and cognitive performance on dynamic EC variability (Figure 1).ResultsDemographics and clinical characteristics are shown in Table 1. A+ participants showed lower static EC values in parietal and occipital clusters, and higher static EC values in a medio‐frontal cluster (Figure 2). The medio‐frontal cluster had also lower dynamic EC variability in A+ (Figure 3). The default mode (Figure 4) and the visual networks of A+ participants also showed lower dynamic EC variability (p &lt; 0.001). Dynamic properties in the DMN and visual networks were differentially associated with cognitive domains in the A‐ and A+ groups, with lower variability found in A+ participants with higher cognitive scores (Figure 5).ConclusionWe demonstrate that early amyloid deposition affects static and dynamic EC, possibly by reducing involvement of functional hubs in different network dynamics and functional integration, thus relating to cognitive dysfunctions. Our data suggest dynamic EC as an early biomarker of the interplay between early amyloid deposition and cognitive decline.

Functional eigenvector centrality dynamics are related to amyloid deposition in preclinical Alzheimer’s Disease

AbstractBackgroundIn preclinical Alzheimer’s disease (AD), amyloid accumulates in highly‐functionally connected brain regions. This selective vulnerability is related to the high neuronal fluctuations, typical of these regions. Dynamic functional connectivity (FC) was introduced to investigate network organization over time, with high network variations indicating regional flexibility, hence promoting functional integration. The relation of early amyloid deposition with FC dynamics remains unclear. Eigenvector centrality (EC) evaluates a node's importance in functional networks, both for the whole functional MRI time series (“static” EC) or within sliding‐windows (“dynamic” EC). We studied the association of cerebrospinal fluid (CSF) amyloid load with static and dynamic EC in non‐demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.MethodData for 701 non‐demented participants were available. CSF Aβ1‐42 levels &lt;1000 pg/mL were defined as amyloid positive (A+) (Elecsys assay). Both static and dynamic voxel‐wise EC were computed from rs‐fMRI time series. Static EC differences between A+ and A‐ participants were assessed using linear models. Standard deviation and range of dynamic EC were compared between A+ and A‐ participants within significant clusters of static EC differences, and within 10 resting‐state networks (RSN). Linear models were used to determine interaction between amyloid and cognitive performance on dynamic EC variability (Figure 1).ResultDemographics and clinical characteristics are shown in Table 1. A+ participants showed lower static EC values in parietal and occipital clusters, and higher static EC values in a medio‐frontal cluster (Figure 2). The medio‐frontal cluster had also lower dynamic EC variability in A+ (Figure 3). The default mode (Figure 4) and the visual networks of A+ participants also showed lower dynamic EC variability (p &lt; 0.001). Dynamic properties in the DMN and visual networks were differentially associated with cognitive domains in the A‐ and A+ groups, with lower variability found in A+ participants with higher cognitive scores (Figure 5).ConclusionWe demonstrate that early amyloid deposition affects static and dynamic EC, possibly by reducing involvement of functional hubs in different network dynamics and functional integration, thus relating to cognitive dysfunctions. Our data suggest dynamic EC as an early biomarker of the interplay between early amyloid deposition and cognitive decline.

SARS-CoV-2 infection is associated with functional changes in resting-state neural mechanisms among Italian adolescents and young adults: a longitudinal case control study

Background: There is strong evidence for brain-related abnormalities COVID-19. Neurologic, cognitive, and olfactory deficits demonstrate the potential neurotoxic impact of the virus. Recent studies of covid-related brain changes focus on severe infection or older populations. To our knowledge, no studies have focused on brain changes in adolescents and young adults impacted mildly by COVID-19. We took a data-driven approach to identify functional brain changes in COVID-19 cases and controls from Lombardy, Italy, a global hotspot of COVID-19. Methods: From our ongoing longitudinal neuroimaging study of adolescents in Northern Italy (the Public Health Impact of Metals Exposure (PHIME) study), we selected 10 participants (age 16-25y) who tested positive for SARS-CoV-2 infection (confirmed via real-time reverse transcription polymerase chain reaction (RT-PCR) tests) and 11 age- and sex-matched non-infected controls (RT-PCR negative; no close-contact or self-reported symptoms). None of the 10 positive subjects were hospitalized or suffered from pneumonia. All participants completed resting state functional magnetic resonance imaging (rs-fMRI) studies prior to and after infection. Using graph theory metrics, we computed eigenvector centrality (EC) in 111 brain areas (Harvard Oxford Atlas) and computed ECdelta (i.e., the difference in EC values pre- and post-COVID-19). We used family wise error (FWE) corrected permutation statistics to quantify ECdelta differences between cases and controls. Results: ECdelta in four brain regions (right posterior temporal gyrus; right planum temporale; left insular cortex and left central opercular cortex; p= 0.034, 0.047, 0.016, 0.038, respectively) differed significantly between COVID-cases and matched controls. Conclusions: To our knowledge, this is the first longitudinal imaging study of COVID-19 in adolescence where participants were scanned before and after SARS-CoV-2 infection. Our results show that key brain areas associated with the primary gustatory cortex (insula and opercular cortex) and the ‘brain-fog’ (temporal gyrus and planum temporale) have altered functional connectivity patterns in adolescents infected by SARS-CoV-2.

Clinical validation of magnetoencephalography network analysis for presurgical epilepsy evaluation

ObjectiveTo clinically validate the connectivity-based magnetoencephalography (MEG) analyses to identify seizure onset zone (SOZ) with comparing to equivalent current dipole (ECD). MethodsThe ECD cluster was quantitatively analyzed by calculating the centroid of the cluster and maximum distance (the largest distance between all dipoles). The “primary hub” was determined by the highest eigencentrality. The distribution of nodes in the top 5% of eigenvector centrality values was quantified by generating the convex hull between each node. ResultsThirty-one patients who underwent MEG, stereotactic-EEG, and focal surgery were included. The primary hub was significantly closer to the sEEG-defined SOZ compared to ECD (p = 0.009). The seizure freedom positive and negative predictive values of complete ECD cluster and primary hub resections did not significantly differ, although complete resection of the primary hub showed slightly better negative predictive value (ECD: 50.0% NPV, hub: 64.7% NPV). Both quantitative ECD and functional connectivity analyses suggested that spatially restricted dipole distributions and higher connectivity in a smaller region correlate with better seizure outcomes. ConclusionsOur findings suggest that MEG network analysis could be a valuable complement to the ECD methods. SignificanceThe results of this study are an important step towards using non-invasive neurophysiologic recordings to accurately define the epileptic network.

Sequence and efficacy of game complexes in high-level women’s volleyball: A novel perspective through Social Network Analysis

The stereotyped sequence of volleyball actions allows organising them into six game complexes (K), each with specific dynamics. The purpose of this study was to verify the game flow in high-level women's volleyball through the sequencing of game complexes, their relative importance within the game and their relationship to attack efficacy. Seven matches of the Women's Brazil Cup’2020, knockout competition played by the 8 best teams of the 2019/2020 Superliga, were analysed (31 sets, 1268 rallies). The matches were obtained through the online video platform www.youtube.com . Pearson's chi-squared test (χ2) with Monte Carlo correction was used to assess the association between the variables, and Social Network Analysis with Eigenvector Centrality for the understanding of the nodes interactions their relative importance in a global context. The highest frequencies occurred for K0 (28.75%), KI (25.54%), and KII (23.39%), followed by KIII (15.60%), KV (3.73%), and KIV (2.99%). Positive associations occurred between KI and error; K0 and first KIV and continuity; and between KI, KII, first KIII, second KIII, first KV or second KV and point. The highest Eigenvector Centrality values occurred in first KVE1 and first KIVE1, and the lowest in K0E0, K0E2 and seventh KIIIE0. The results showed that the game complexes unfold in sequences with different frequencies; the sequences K0E1-KIE2, K0E1-KIE1-KIIE2; K0E1-KIE0 and K0E1-KIE1-KIIE1-KIII(1)E2 are the most frequent, which is informative for approximating the regularities of the training sessions to those of the matches.

Binocular Integrated Visual Field Deficits Are Associated With Changes in Local Network Function in Primary Open-Angle Glaucoma: A Resting-State fMRI Study.

In glaucoma participants, both structural and functional brain changes have been observed, but we still have insufficient understanding of how these changes also affect the integrity of cortical functional networks, and how these changes relate to visual function. This is relevant, as functional network integrity may affect the applicability of future treatments, as well as the options for rehabilitation or training. Here, we compare global and local functional connectivity in local and global brain networks between glaucoma and control participants. Moreover, we study the relationship between functional connectivity and visual field (VF) loss. For our study, 20 subjects with primary open-angle glaucoma (POAG) and 24 age-similar healthy participants were recruited to undergo an ophthalmic assessment followed by two resting-state (RS) (f)MRI scans. For each scan and for each group, the ROIs with eigenvector centrality (EC) values higher than the 95th percentile were considered the most central brain regions (“hubs”). Hubs for which we found a significant difference in EC in both scans between glaucoma and healthy participants were considered to provide evidence for network changes. In addition, we tested the notion that a brain region's hub function in POAG might relate to the severity of a participant's VF defect, irrespective of which eye contributed mostly to this. To determine this, for each participant, eye-independent scores were derived for: (1) sensitivity of the worse eye – indicating disease severity, (2) sensitivity of both eyes combined – with one eye potentially compensating for loss in the other, or (3) difference in eye sensitivity – potentially requiring additional network interactions. By correlating each of these VF scores and the EC values, we assessed whether VF defects could be associated with centrality alterations in POAG. Our results show that no functional connectivity disruptions were found at the global brain level in POAG participants. This indicates that in glaucoma global brain network communication is preserved. Furthermore, for the Lingual Gyrus, identified as a brain hub, we found a positive correlation between the EC value and the VF sensitivity of both eyes combined. The fact that reduced local network functioning is associated with reduced binocular VF sensitivity suggests the presence of local brain reorganization that has a bearing on functional visual abilities.