Weighted Degree Centrality Research Articles

Systematic Review of Housing Research Using Digital Network Analysis

The aim of this study is to conduct a systematic review of housing research trend using a digital network analysis. Keywords from articles released over the past three decades at the Journal of the Korean Housing Association were applied. The cross-section analysis was applied to examine the trends by decades. Then a longitudinal design employing time series analysis was implemented, comparing the trends of relevant research in terms of periods. The degree centrality and weighted degree centrality of keywords were investigated. The results of this study are shown as follows. Studies on multi-family housing and apartment accounted almost half of the total studies, and studies on elderly people are on the rise. Initial research on cities focused on development, and now transitions to urban regeneration. Emotional and psychological variations such as housing satisfaction are shown instead of physical areas such as residential environment. Similarly, interests in housing development is changing to housing maintenance and continuity. The flow of the housing studies is that the interest of the study has changed from the quantitative and physical growth of housing to qualitative growth. Also, the flow of the study has gone through a transition, mirroring sociodemographic variations and national policies comprehensively.

Spatial Diversity of COVID-19 Cased in Poland Explained by Mobility Patterns - Preliminary Results

Problem: There is a need for a spatio-temporal analysis of the spread of the SARS-CoV-2 virus in Poland in order to prepare detailed solutions enabling actions to be taken to minimize the risk to public health during the likely second wave of infections in Autumn 2020. Methodology: Our task is to examine the relationship between the SARS-CoV-2 arrival and the number of confirmed COVID-19 cases in the period from March 4 to May 22, 2020 (unofficial data), and socio-economic variables at the poviat/county level (NUTS-4) using simple statistical techniques such as data visualization, correlation analysis, spatial clustering and multiple linear regression. Results: Simple models taking into account only socio-economic data explain the variance of the number of cases in 45% and the arrival time in 25%. Mobility calculated as weighted degree centrality in the employee flow graph explains the most the variance of infection dynamics in Poland. Emigration is the best predictor of SARS-CoV-2 arrival times, and the logarithm of general mobility explains the most of the size of the epidemic in poviats. Economic emigration - in the terms of flow of employees living in Polish poviats and working abroad, statistically significantly accelerated the import of SARS-CoV-2 in regions with high numbers of emigrants. In addition to mobility / migration, employment fraction, industrialization and political orientation are the best predictors of infection dynamics to a much greater extent than previously postulated variables such as population density, income or the size of the senior population. Conclusions: Polish authorities could, having a reliable analysis of the spread of the virus, prepare for the second wave of infections and manage resources in an optimal way and we have provided proposition of optimal distribution of human resoures between poviats. The arrival times are statistically significant associated with the migration of employees (probably due to returns of Polish emigrants from more affected by COVID-19 regions of the World). On the other hand, arrival times have no statistical significant links with as previously postulated mobility aspects of tourist or business trips (as it was the case in other European countries). However, regional income actually increases (insignificantly) the risk of diagnosing COVID-19. The relationship between the number of the most susceptible to the symptomatic form of COVID-19 seniors and the number of diagnosed cases in the selective model has been eliminated by other socio-economic variables. Due to the fact that large outbreaks often occur in industrial and mining workplaces, we observe a correlation between industrial areas or with high employment rates, high mobility and high support for the ruling party with the number of confirmed cases of COVID-19, so it should be strengthened personally, IT and authorization Sanitary Inspection in high risk poviats before the next wave of infections.

Evolving Characteristics of High-Speed Railway Network Structure in Yangtze River Delta, China: the Perspective of Passenger Flows

China’s high-speed railway (HSR) network is the largest planned network operating globally. Compared to other HSR networks, it has a unique and complicated structure. Multiple studies explored the supply side network structure of HSR services through train frequency data. However, due to data collection difficulties, there is limited research that studies the evolving characteristics of HSR development by using passenger flow data on the demand side network. This paper explores the evolving characteristics of the HSR network in the Yangtze River Delta (YRD), one of the most developed megaregions of China. HSR passenger flow data from the timeframe of 2014 to 2018 are used to examine the evolution of topological structure, hierarchical structure, and the spatial structure of the HSR network. Results indicate that the topological structure of the HSR network has been relatively stable, and has a roughly normal distribution. Although it is a relatively stable HSR network, a hierarchical structure exists, and there is a certain degree of variability among the third-tier cities. Based on the weighted degree centrality, and structural holes of passenger flow network in the YRD, Shanghai, Hangzhou, Nanjing, and Hefei, were identified. Correspondingly, four network communities were formed, and they varied across the evolutionary characteristics over the study period. The study proposes that in the planning of the HSR network, more focus should be given to connections between sub-regional nodes, in addition to connecting the core nodes. Additional relevant planning strategies are proposed based on the conclusions reached.

Multi-Modal Signatures of Tau Pathology, Neuronal Fiber Integrity, and Functional Connectivity in Traumatic Brain Injury.

[18F]AV-1451 (aka 18F-Flortaucipir, [18F]T807) was developed for positron-emission tomography (PET) imaging of paired helical filaments of hyperphosphorylated tau, which are of interest in a range of neuropathologies, including traumatic brain injury (TBI). Magnetic resonance imaging (MRI) techniques like diffusion tensor imaging (DTI) and resting state functional connectivity assess structural and functional characteristics of the brain, complementing the molecular information that can be obtained by PET. The goal herein was to explore the utility of such multi-modal imaging in a case series based on a population of TBI subjects. This study probes the interrelationship between tau deposition, white matter integrity, and gray matter functional connectivity across the spectrum of TBI. Nineteen subjects (11 controls, five former contact sports athletes, one automotive accident, and two with military-related injury) underwent [18F]AV-1451 PET and magnetic resonance scanning procedures. [18F]AV-1451 distribution volume ratio (DVR) was estimated using the Logan method and the cerebellum as a reference region. Diffusion tractography images and fractional anisotropy (FA) images were generated using diffusion toolkit and FSL. Resting-state functional MRI (fMRI) analysis was based on a graph theory metric, namely weighted degree centrality. TBI subjects showed greater heterogeneity in [18F]AV-1451 DVR when compared with control subjects. In a subset of TBI subjects, areas with high [18F]AV-1451 binding corresponded with increased FA and diminished white matter tract density in DTI. Functional MRI results exhibited an increase in functional connectivity, particularly among local connections, in the areas where tau aggregates were more prevalent. In a case series of a diverse group of TBI subjects, brain regions with elevated tau burden exhibited increased functional connectivity as well as decreased white matter integrity. These findings portray molecular, microstructural, and functional corollaries of TBI that spatially coincide and can be measured in the living human brain using noninvasive neuroimaging techniques.

Eyes Closed Elevates Brain Intrinsic Activity of Sensory Dominance Networks: A Classifier Discrimination Analysis.

Brain neocortex is usually dominated by visual input (with eyes open [EO]), whereas this visual predominance could be reduced by closing eyes. Cutting off visual input from the eyes (with eyes closed [EC]) would also benefit other sensory performance; however, the neural basis underlying the state-switching remains unclear. In this study, we investigated the brain intrinsic activity of either the EO or EC states by using the resting-state functional magnetic resonance imaging data from 22 healthy participants. The 10 resting-state networks (RSNs) of these participants were explored by the independent component analysis method. Within each RSN, various network parameters (i.e., the amplitude of low-frequency fluctuation, the voxel-wise weighted degree centrality, and the RSN-wise functional connectivity) were measured to depict the brain intrinsic activity properties underlying the EO and EC states. Taking these brain intrinsic activity properties as discriminative features in a linear classifier, we found that the EO and EC states could be effectively classified using the intrinsic properties of the sensory dominance networks and the salience network (SN). Further analysis showed that the brain intrinsic activity within the sensory dominance networks was constantly overwhelmed during the EC state relative to that in the EO state. The SN might play a key role as a switcher between state-switching. Therefore, this study indicated that the brain intrinsic activity in the sensory dominance networks would be enhanced with EC, which might improve other sensory-relative task performance.

A voting approach to uncover multiple influential spreaders on weighted networks

The identification of multiple spreaders on weighted complex networks is a crucial step towards efficient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to select multiple spreaders on unweighted networks while various real networks are weighted networks such as trade networks, traffic flow networks and etc. Thus our approach WVoteRank is generalized to deal with both unweighted and weighted networks by considering both degree and weight in voting process. Experimental studies on LFR synthetic networks and real networks show that in the context of Susceptible–Infected–Recovered (SIR) propagation, WVoteRank outperforms existing states of arts methods such as weighted H-index, weighted K-shell, weighted degree centrality and weighted betweeness centrality on final affected scale. It obtains an improvement of final affected scale as much as 8.96%. Linear time complexity enables it to be applied on large networks effectively.

The relationship of anxious and depressive symptoms in Parkinson's disease with voxel-based neuroanatomical and functional connectivity measures

BackgroundAnxiety and depression are two frequent comorbidities of Parkinson's disease (PD). However, the underlying neural mechanism is still unclear and the studies on their neural correlates were insufficient. MethodsUsing voxel-based neuroanatomical and functional connectivity (FC) measures, i.e. grey matter volume, fractional anisotropy, and weighted degree centrality (WD), we examined their correlations with the severity levels of anxious and depressive symptoms in 36 PD patients. ResultsPositive correlations were shown between anxiety and the WDs in the left amygdala, and between depression and short-ranged WDs in the left parahippocampal gyrus. Using these two regions as the seeds, we found that the severity levels of anxiety and depression were positively correlated with the FCs between the two seeds and the areas in the default mode network (DMN), while negatively correlated with the FCs between the two seeds and the prefrontal and superior temporal cortices. Anxiety was also positively correlated with the FC between the amygdala and the superior parietal lobule. LimitationsThe severity levels of anxious and depressive symptoms of our participants is relatively mild than some previous studies. The cross-sectional design of this study cannot clarify the etiological relationship between PD and two comorbidities. ConclusionsOur results were in line with the key roles of the amygdala and parahippocampal gyrus in anxiety and depression, and reflected the distinct effects of the DMN, prefrontal and superior temporal cortices, and sensory-motor regions on emotional regulation. The identification of these neural substrates might assist clinical monitoring mood disturbances in PD.

Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition

Ranking the spreading influence of nodes is crucial for developing strategies to control the spreading process on complex networks. In this letter, we define, for the first time, a remaining minimum degree (RMD) decomposition by removing the node(s) with the minimum degree iteratively. Based on the RMD decomposition, a weighted degree (WD) is presented by utilizing the RMD indices of the nearest neighbors of a node. WD assigns a weight to each degree of this node, which can distinguish the contribution of each degree to the spreading influence. Further, an extended weighted degree (EWD) centrality is proposed by extending the WD of the nearest neighbors of a node. Assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive experiments on a series of synthetic and real networks to comprehensively evaluate the performance of EWD and other eleven representative measures. The experimental results show that EWD is a relatively efficient measure in running efficiency, it exposes an advantage in accuracy in the networks with a relatively small degree heterogeneity, as well as exposes a competitive performance in resolution.

CDA: A Clustering Degree Based Influential Spreader Identification Algorithm in Weighted Complex Network

Identifying the most influential spreaders in a weighted complex network is vital for optimizing utilization of the network structure and promoting the information propagation. Most existing algorithms focus on node centrality, which consider more connectivity than clustering. In this paper, a novel algorithm based on clustering degree algorithm (CDA) is proposed to identify the most influential spreaders in a weighted network. First, the weighted degree of a node is defined according to the node degree and strength. Then, based on the node weighted degree, the clustering degree of a node is calculated in respect to the network topological structure. Finally, the propagation capability of a node is achieved by accounting the clustering degree of the node and the contribution from its neighbors. In order to evaluate the performance of the proposed CDA algorithm, the susceptible-infected-recovered model is adopted to simulate the propagation process in real-world networks. The experiment results have showed that CDA is the most effective algorithm in terms of Kendall’s tau coefficient and with the highest accuracy in influential spreader identification compared with other algorithms such as weighted degree centrality, weighted closeness centrality, evidential centrality, and evidential semilocal centrality.

Altered functional connectivity strength in informant-reported subjective cognitive decline: A resting-state functional magnetic resonance imaging study

IntroductionInformant-reported subjective cognitive decline (iSCD) has been associated with a higher risk of conversion to dementia, but the findings of whole brain functional connectivity strength (FCS) changes in iSCD are limited. MethodsThe sample comprised 39 participants with iSCD and 39 age- and sex- matched healthy controls. The global absolute (aFCS) and relative functional connectivity strengths were estimated using weighted degree centrality and the z-scores of the weighted degree centrality respectively. FreeSurfer was used for measuring cortical thickness. ResultsThe aFCS was lower in iSCD primarily in left medial superior frontal, left precuneus, left parietal, right cuneus, and bilateral calcarine; while relative functional connectivity strength was higher in posterior cingulate cortex/precuneus compared with healthy controls. No significant differences in cortical thickness were observed. DiscussionThere are detectable changes of FCS in iSCD, with the precuneus possibly playing a compensatory role. FCS could therefore have a potential role to serve as one of the earliest neuroimaging markers of neurodegenerative disease.

Biological activities of beta-glucans isolated from spent brewer's yeast

Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies.A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches.The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p = 0.53). However, the pipeline-based system had significantly better performance than the two baselines.The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.

Impacts of high-speed rail lines on the city network in China

China's large-scale HSR network has a significant influence on both accessibility and connectivity, but little attention has been paid to its impacts on connectivity. Changes in connectivity largely affect the external relations among cities, thus influencing the structure of the city network. This paper examines the impacts of China's HSR network on the overall connectivity and nodal centrality of the city network, as evaluated by passenger trains from 2003 to 2014. This paper employs the graph index, average path length, clustering coefficient and three indicators of centrality, namely weighted degree centrality (WDC), weighted closeness centrality (WCC) and weighted betweenness centrality (WBC). The results show that the HSR network largely increased overall connectivity according to the increasing Beta index and clustering coefficient, and decreasing average path length. The HSR lines also increased the average centrality of the city network and the inequality of nodal centrality according to the WCC indicator, but they decreased the inequality of nodal centrality with regard to the WDC and WBC indicators. Meanwhile, the growing HSR network led to the centrality tended to intensify in large cities in terms of the WCC indicator, but intensify in small cities according to the WDC and WBC indicators. Spatially, the cities with HSR stations, population over 3 million and GDP over 100 billion RMB saw higher increases in centrality than the others; the cities with high values of WDC and WCC, especially the top 20 cities, tended to concentrate in the populous areas with well-developed economies (e.g., Yangtze River Delta), whereas the cities with high WBC values in 2014 were coupled with the hub cities of regional railway management administration.

A New Method for Human Mental Fatigue Detection with Several EEG Channels

Previous researchers have applied EEG in human mental fatigue detection and concluded that the ratio of slow wave to fast wave EEG activities increased with increasing mental fatigue. But these methods failed to reflect functional interactions between different brain areas. The objective of this paper is to develop an indicator for detecting human mental fatigue with several electroencephalogram (EEG) channels considering functional interactions among the brain. To this end, we collected eighteen participants’ EEG data, and constructed the adjacency matrix on the basis of EEG electrode positions using mutual information (MI) to determine the functional connectivity between all pairwise EEG channels. The maximum eigenvalue of adjacency matrix was discussed during the mental fatigue process and considered as the reflections of the corresponding network features. The results indicated that significant statistical differences of the maximum eigenvalue were found only in alpha1 (8–10 Hz) band during task state. With increasing levels of mental fatigue, the maximum eigenvalue increased, indicating that the maximum eigenvalue can be used as an indicator for mental fatigue estimation taking brain function changes into account. For real-time engineering applications, we reduced the EEG channels by finding the central vertices in the brain functional network using weighted degree centrality. Finally, the channels of F3, F4, C3, C4, P3, P4, Fz, Cz, and Pz were obtained for further applications. We also provided an algorithm for automatic human mental fatigue evaluation with these 9 EEG channels. Our results have potential applications in several domains, including in flight, traffic and industrial human safety.

Adding the dimension of knowledge trading to source impact assessment: Approaches, indicators, and implications

The objective of this paper is to systematically assess sources' (e.g., journals and proceedings) impact in knowledge trading. While there have been efforts at evaluating different aspects of journal impact, the dimension of knowledge trading is largely absent. To fill the gap, this study employed a set of trading‐based indicators, including weighted degree centrality, Shannon entropy, and weighted betweenness centrality, to assess sources' trading impact. These indicators were applied to several time‐sliced source‐to‐source citation networks that comprise 33,634 sources indexed in the Scopus database. The results show that several interdisciplinary sources, such as Nature, PLoS One, Proceedings of the National Academy of Sciences, and Science, and several specialty sources, such as Lancet, Lecture Notes in Computer Science, Journal of the American Chemical Society, Journal of Biological Chemistry, and New England Journal of Medicine, have demonstrated their marked importance in knowledge trading. Furthermore, this study also reveals that, overall, sources have established more trading partners, increased their trading volumes, broadened their trading areas, and diversified their trading contents over the past 15 years from 1997 to 2011. These results inform the understanding of source‐level impact assessment and knowledge diffusion.

Pair Housing of Dairy Calves and Age at Pairing: Effects on Weaning Stress, Health, Production and Social Networks.

The early social environment can influence the health and behaviour of animals, with effects lasting into adulthood. In Europe, around 60% of dairy calves are reared individually during their first eight weeks of life, while others may be housed in pairs or small groups. This study assessed the effects of varying degrees of social contact on weaning stress, health and production during pen rearing, and on the social networks that calves later formed when grouped. Forty female Holstein-Friesian calves were allocated to one of three treatments: individually housed (I, n = 8), pair-housed from day five (P5, n = 8 pairs), and pair-housed from day 28 (P28, n = 8 pairs). From day 48, calves were weaned by gradual reduction of milk over three days, and vocalisations were recorded as a measure of stress for three days before, during and after weaning. Health and production (growth rate and concentrate intakes) were not affected by treatment during the weaning period or over the whole study. Vocalisations were highest post-weaning, and were significantly higher in I calves than pair-reared calves. Furthermore, P28 calves vocalised significantly more than P5 calves. The social network of calves was measured for one month after all calves were grouped in a barn, using association data from spatial proximity loggers. We tested for week-week stability, social differentiation and assortment in the calf network. Additionally, we tested for treatment differences in: coefficient of variation (CV) in association strength, percentage of time spent with ex-penmate (P5 and P28 calves only) and weighted degree centrality (the sum of the strength of an individual’s associations). The network was relatively stable from weeks one to four and was significantly differentiated, with individuals assorting based on prior familiarity. P5 calves had significantly higher CV in association strength than I calves in week one (indicating more heterogeneous social associations) but there were no significant treatment differences in week four. The mean percentage of time that individuals spent with their ex-penmate after regrouping decreased from weeks 1–4, though treatment did not affect this. There were also no significant differences in weighted degree centrality between calves in each rearing treatment. These results suggest that early pair-rearing can allow calves the stress buffering benefits of social support (and that this is more effective when calves are paired earlier) without compromising health or production, and sheds light on the early development of social behaviour in cattle.

Impact of Personality on Task Selection in Crowdsourcing Software Development: A Sorting Approach

Growing demand for software has attracted the attention of the software development industry. Crowdsourced software development has provided a new method for the software industry to produce quality software based on an open-call format. Selecting an appropriate task to develop (developer-end) or evaluate (platform-end) is one of the primary problems in this type of open-call format. Receiving or assigning an improper task to an improper crowdsource (CS) developer does not only decrease the quality of the software deliverables, but also causes overburden on both the platform and the developers. To solve this problem, sorting the tasks based on the developers’ human characteristics may increase task relevancy for developers, which can accelerate efficiency and lessen complexity. Thus, this paper has conducted an empirical experiment to measure the influence of personality on task selection based on the important characteristics of a task: money, time, and type. A total of 83 students from the University of Sindh voluntarily participated in four different short-duration rounds of task development using the developed CS platform. The personality types of the participants were measured based on the Myers-Briggs type indicator. In addition, a complex network technique called weighted degree centrality was applied to identify the most suitable personality for task sorting based on money or complexity attractions (i.e., time or type). Based on the results, it can be observed that personality has a significant relationship with task selection. For instance, developers with intuitive (N) and feeling (F) personality traits are primarily focused on the time duration of a project.

Generating disease-pertinent treatment vocabularies from MEDLINE citations

ObjectiveHealthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. MethodsA pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. ResultsThe pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. ConclusionsThe pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.

A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic.

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model’s results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node’s risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans.

Structural Investigation of Aluminum in the U.S. Economy using Network Analysis.

Metals are used in numerous products and are sourced via increasingly global and complex supply chains. Monetary input-output tables (MIOT) and network analysis can be applied to intersectoral supply chains and used to analyze structural aspects. We first provide a concise review of the literature related to network analysis applied to MIOTs. On the basis of a physical input-output table (PIOT) table of aluminum in the United States economy in 2007, we identify key sectors and discuss the overall topology of the aluminum network using tools of network analysis. Sectors highly dependent on metal product inputs or sales are identified using weighted degree centrality and their hierarchical organization is explored via clustering. Betweenness centrality and random walk centrality (page rank) are explored as means to identify network bottlenecks and relative sector importance. Aluminum, even though dominated by uses in the automobile, beverage and containers, and construction industries, finds application in a wide range of sectors. Motor vehicle parts manufacturing relies on a large number of upstream and downstream suppliers to function. We conclude by analyzing structural aspects of a subnetwork for automobile manufacturing and discuss how the use of network analysis relates to current criticality analyses of metal and mineral resources.

Abnormal Brain Areas Common to the Focal Epilepsies: Multivariate Pattern Analysis of fMRI.

Individuals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of amplitude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy.