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Smart contracts on the Ethereum blockchain greatly benefit from cutting-edge analysis techniques and pose significant challenges. A primary challenge is the extremely low-level representation of deployed contracts. We present Elipmoc, a decompiler for the next generation of smart contract analyses. Elipmoc is an evolution of Gigahorse, the top research decompiler, dramatically improving over it and over other state-of-the-art tools, by employing several high-precision techniques and making them scalable. Among these techniques are a new kind of context sensitivity (termed “transactional sensitivity”) that provides a more effective static abstraction of distinct dynamic executions; a path-sensitive (yet scalable, through path merging) algorithm for inference of function arguments and returns; and a fully context sensitive private function reconstruction process. As a result, smart contract security analyses and reverse-engineering tools built on top of Elipmoc achieve high scalability, precision and completeness. Elipmoc improves over all notable past decompilers, including its predecessor, Gigahorse, and the state-of-the-art industrial tool, Panoramix, integrated into the primary Ethereum blockchain explorer, Etherscan. Elipmoc produces decompiled contracts with fully resolved operands at a rate of 99.5% (compared to 62.8% for Gigahorse), and achieves much higher completeness in code decompilation than Panoramix—e.g., up to 67% more coverage of external call statements—while being over 5x faster. Elipmoc has been the enabler for recent (independent) discoveries of several exploitable vulnerabilities on popular protocols, over funds in the many millions of dollars.

Corporate social responsibility (CSR) is referred and analyzed as a phenomenon with an economic, cultural, and entrepreneurial dimension. Therefore, it is also considered as a social phenomenon which affects society in many different ways, as it attracts both organizations and governments around the world. The present study analyses the impact of CSR on customers' preference and loyalty in the hotel sector. The authors investigated the main concepts associated with CSR as well as the CSR's policies implemented in the tourism sector. At the same time, specific factors were studied such as customer's satisfaction and how CSR influences consumer's loyalty in a hotel. A semi-structured interview was conducted in 204 visitors in 30 Greek hotels. Findings showed that the average Greek citizen is not particularly familiar with the term of CSR and its activities, and that CSR does not impact on their intention to visit a hotel and then be loyal.

Living in Japan means having a close relationship with nature. The Japanese way of thinking about nature is that "without plants and animals there would be no food, and the rain that falls from the sky accumulates in the mountains and forests, and people eat it." The Japanese view of nature is that "without plants and animals, there would be no food. The Japanese have the phrase "Itadakimasu" (Thank you for the food) as a basis for this belief. The Christian view, however, is quite different. The idea is that God is the creator of all things, including man, and that man is a special being who looks most like God. Therefore, they believe that he is the administrator of the rest. And in the West, especially Christianity, man is considered to have the right to dominate and conquer nature. In fact, the basis for the Western view of nature as an object of conquest is mountain climbing. The expression "I have conquered the mountain" is often used in the West when the summit is reached. This background led people to believe that they could use nature freely, and the idea of "conquering" nature was born. At the same time, the opposite idea to that of Japan, that nature worship is uncivilized and socially unacceptable, was born.

We present a static analysis approach that combines concrete values and symbolic expressions. This symbolic value-flow (“symvalic”) analysis models program behavior with high precision, e.g., full path sensitivity. To achieve deep modeling of program semantics, the analysis relies on a symbiotic relationship between a traditional static analysis fixpoint computation and a symbolic solver: the solver does not merely receive a complex “path condition” to solve, but is instead invoked repeatedly (often tens or hundreds of thousands of times), in close cooperation with the flow computation of the analysis. The result of the symvalic analysis architecture is a static modeling of program behavior that is much more complete than symbolic execution, much more precise than conventional static analysis, and domain-agnostic: no special-purpose definition of anti-patterns is necessary in order to compute violations of safety conditions with high precision. We apply the analysis to the domain of Ethereum smart contracts. This domain represents a fundamental challenge for program analysis approaches: despite numerous publications, research work has not been effective at uncovering vulnerabilities of high real-world value. In systematic comparison of symvalic analysis with past tools, we find significantly increased completeness (shown as 83-96% statement coverage and more true error reports) combined with much higher precision, as measured by rate of true positive reports. In terms of real-world impact, since the beginning of 2021, the analysis has resulted in the discovery and disclosure of several critical vulnerabilities, over funds in the many millions of dollars. Six separate bug bounties totaling over $350K have been awarded for these disclosures.

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data.

The scope of the chapter is to analyze the relationship between the performance and the competitiveness of the public sector in Greece and the image of the country abroad, focusing on the critical importance for the Greek economy sector of investments attraction. Through the comparative evaluation of bureaucracy's and administrative burdens' impact on national entrepreneurship's competitiveness and the analysis of the administrative barriers and obstacles facing the investments' attraction, the performance of the existing investment policy in Greece is analyzed. Based on the evaluation of the administrative obstacles and pitfalls on national entrepreneurship and according to international experience's recent development on administrative simplification, a comprehensive and specialized action plan for the reform of the national investment policy and the emergence of a new international brand name for Greece were realized.

Smart contracts on permissionless blockchains are exposed to inherent security risks due to interactions with untrusted entities. Static analyzers are essential for identifying security risks and avoiding millions of dollars worth of damage. We introduce Ethainter, a security analyzer checking information flow with data sanitization in smart contracts. Ethainter identifies composite attacks that involve an escalation of tainted information, through multiple transactions, leading to severe violations. The analysis scales to the entire blockchain, consisting of hundreds of thousands of unique smart contracts, deployed over millions of accounts. Ethainter is more precise than previous approaches, as we confirm by automatic exploit generation (e.g., destroying over 800 contracts on the Ropsten network) and by manual inspection, showing a very high precision of 82.5% valid warnings for end-to-end vulnerabilities. Ethainter’s balance of precision and completeness offers significant advantages over other tools such as Securify, Securify2, and teEther.

The objective of the study is to find the key organizational factors that impact on digital transformation projects, creating thus a social value especially during the current pandemic crisis where many changes have been made in the ways that organizations operate. An interdisciplinary approach was used and comparison among selected European Union countries where digital governance has been applied to public-to-citizen transactions. A quantitative survey was also conducted by distributing online questionnaires to middle and senior executives of public organizations in Greece. A total of 151 questionnaires were answered and their analysis was performed using the SPSS statistical tool. The results showed that the internal factors that influence the adoption of digital governance and its successful implementation include the technological factors that compose the quality of services and mainly the organizational ones such as training and evaluation of human resources, leadership, organizational strategy, and the creation of digital culture.

The specific domain model for math disabilities postulates a core number deficit which presents a prime target for remedial interventions. This longitudinal study identified two groups of Grade 3 students based on their basic calculation abilities: students with persistent difficulties through Grade 4 (PD group) and students whose performance improved into the average range (IP group). Baseline data revealed a distinct cognitive profile for students in the PD group featuring predominant deficits in symbolic number processing. A conceptual intervention based on explicit teaching of basic arithmetic procedures was implemented when students attended Grade 5 or 6. Students in the PD group benefited more from the programme, especially in performing written calculations and in multiplication speed. Statement of contribution What is already known on this subject? Most interventions focus on young students' basic arithmetical skills to prevent serious math problems in future. Few interventions target older students who often face persistent math difficulties. These interventions are usually procedural and focus on age-appropriate math skills. What does this study add? A conceptual intervention was implemented to remediate basic calculation deficits at the end of elementary school. The aim was to help students compensate for their gaps in knowledge and motivate them to engage in math activities. Neuropsychological testing of arithmetic abilities revealed difficulties in symbolic number processing.

This chapter presents empirical research analyses and evaluates according to primary empirical data the viability and effectiveness of the alternative institutional arrangement for municipal services provision in the distinctive environment of Greek local government. The alternative provision appears to lead to cost reduction and quality improvement in most case studies analyzed, assuming, however, the appearance of a series of determinant factors of services performance, relating to the level of the competition and mainly the establishment of relationships of trust, mutual collaboration, engagement, and common culture among the partners and the stakeholders. Factors that transform the alternative provision from a simple cost rationalization tool into a promotion catalyst of a new governance model in Greek local government.

The proliferation of smartphones with video recording and action cameras has empowered users to effortlessly capture everyday moments and activities in video format. This process often results into long video sequences that need to be edited down and summarized in order to be properly archived, shared within social media, or simply projected to family and friends. For those who do not possess video-editing skills, isolating the most appropriate moments is a difficult and time-consuming task. This work presents "ActionTrip", a system that provides gestural user interaction during video-capture, in order to seamlessly tag personal points of interest. In addition, the system captures contextual information about nearby landmarks and information from social networking systems. This combination of personal with social preferences has enabled a single-step final editing process, which presents to the user a small list of points interest (public landmarks and personal preferences), instead of the actual recorded video that might be very long to comprehend. The benefits of this system have been demonstrated through a small-scale case study during the visit and tour in a European city.

This corrects the article on p. 189 in vol. 11, PMID: 28491028. In the original article, there was an error. We used the crystallized IQ term instead of the term fluid intelligence. This mistake does not affect the overall interpretation of our results since both of these terms are the same as far as the education is concerned. We have simply replaced the “crystallized” term with the “fluid” term within the original article.

Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface EEG measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying switching nodes that consistently change module during music listening. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the {\delta} and {\beta}H rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real-life applications., Comment: Pre-print version of the paper published in Journal of Neural Engineering (2018)

Almost four years after Greece's entry to a financial support programme, the Greek economy is still facing recession. The aim of this article is to analyse the interconnections between five vicious cycles of recession and highlight the country's development prospects. The five vicious cycles of the Greek economy are as follows: a) fiscal, b) internal devaluation, c) financial, d) psychological, and e) sociopolitical. These five vicious circles of recession must be seen in parallel with the adjustment policy applied. The results of this policy have created the preconditions for the maintenance of the recession. Also, other deficits of the adjustment policy and structural weaknesses of the Greek economy continue to limit the prospects of recovery for the Greek economy. We conclude with certain suggestions that might enable Greece to overcome the major obstacles to economic development.

This paper analyzes the relationship between tax evasion and the two main policy instruments affecting tax compliance, namely, the announced tax rate and the share of tax revenues allocated to tax monitoring mechanisms. For doing so, we adopt a simple one-sector endogenous growth model modified under tax evasion following the Roubini and Sala-i-Martin (1995) analysis on income taxes and tax compliance. Our model confirms Barro׳s (1990) theoretical finding stating that the optimal tax rate is equal to the elasticity of public capital. However, introducing a welfare function where governments care also about the degree of fiscal corruption in the economy, the effective tax rate is lower than the output elasticity of public capital in line with Futagami et al. (1993) and Turnovsky (1997) theoretical results. Finally, our model is calibrated using data on tax evasion from 35 OECD and 110 non-OECD countries for 2011. Simulation results suggest that both tax evasion and output growth are decreasing with the share of tax revenues allocated to monitoring expenses, while government׳s utility maximization imply an announced tax rate lower from the elasticity of public capital for both groups of countries.

Sensor-level network characteristics associated with arithmetic tasks varying in complexity were estimated using tools from modern network theory. EEG signals from children with math difficulties (MD) and typically achieving controls (NI) were analyzed using minimum spanning tree (MST) indices derived from Phase Lag Index values – a graph method that corrects for comparison bias. Results demonstrated progressive modulation of certain MST parameters with increased task difficulty. These findings were consistent with more distributed network activation in the theta band, and greater network integration (i.e., tighter communication between involved regions) in the alpha band as task demands increased. There was also evidence of stronger intraregional signal inter-dependencies in the higher frequency bands during the complex math task. Although these findings did not differ between groups, several MST parameters were positively correlated with individual performance on psychometric math tasks involving similar operations, especially in the NI group. The findings support the potential utility of MST analyses to evaluate function-related electrocortical reactivity over a wide range of EEG frequencies in children.

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid [corrected] Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

Multichannel EEG traces from healthy subjects are used to investigate the brain’s self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity. The interactions are quantified at the level of EEG sensors through descriptors that differ over the nature of functional dependencies sought (linear vs. nonlinear) and over the specific form of the measures employed (amplitude/phase covariation). Functional connectivity graphs (FCGs) are analysed with a novel clustering algorithm, and the resulting segregations enter an appropriate discriminant function. The magnitude of the contrast function depends on the frequency-band ( θ , α 1 , α 2 , β and γ ) and the neural synchrony descriptor. We first show that the maximal-contrast corresponds to a phase coupling descriptor and then identify the corresponding spatial patterns that represent best the task-induced changes for each frequency band. The principal finding of this study is that, during mental calculations, phase synchrony plays a crucial role in the segregation into distinct functional domains, and this segregation is the most prominent feature of the brain’s self-organisation as this is reflected in sensor space.

Notre etude a pour projet d’analyser le type de revendications prioritaires que le syndicat des instituteurs grecs (DOE) a pu deployer dans une periode historique particuliere (avant l’arrivee au pouvoir des colonels), en mettant l’accent sur la relation entre les demandes a caractere professionnel et celles a caractere proprement educatif. L’analyse du discours du DOE s’appuie sur un ensemble de sources primaires constituees par le bulletin « Didaskaliko Vima », organe de ce syndicat, et par les transcriptions des assemblees generales et des conseils d’administration de cette meme organisation. Notre approche se fonde sur une analyse de contenu quantitative et qualitative. Les conclusions et leur interpretation sont basees sur la notion de representation d’interets, denommee corporatisme d’Etat, qui a domine la periode examinee au sein de la societe grecque. Notre analyse conclut que les priorites de la DOE se sont concentrees sur ses revendications professionnelles. Deux facteurs ont contribue a cela : premierement le systeme de representation des interets existant alors et deuxiemement le bas niveau de vie des professeurs.

Symbolic dynamics is a powerful tool for studying complex dynamical systems. So far many techniques of this kind have been proposed as a means to analyze brain dynamics, but most of them are restricted to single-sensor measurements. Analyzing the dynamics in a channel-wise fashion is an invalid approach for multisite encephalographic recordings, since it ignores any pattern of coordinated activity that might emerge from the coherent activation of distinct brain areas. We suggest, here, the use of neural-gas algorithm (Martinez et al. in IEEE Trans Neural Netw 4:558-569, 1993) for encoding brain activity spatiotemporal dynamics in the form of a symbolic timeseries. A codebook of k prototypes, best representing the instantaneous multichannel data, is first designed. Each pattern of activity is then assigned to the most similar code vector. The symbolic timeseries derived in this way is mapped to a network, the topology of which encapsulates the most important phase transitions of the underlying dynamical system. Finally, global efficiency is used to characterize the obtained topology. We demonstrate the approach by applying it to EEG-data recorded from subjects while performing mental calculations. By working in a contrastive-fashion, and focusing in the phase aspects of the signals, we show that the underlying dynamics differ significantly in their symbolic representations.The online version of this article (doi:10.1007/s11571-011-9186-5) contains supplementary material, which is available to authorized users.

Multichannel EEG recordings from 18 healthy subjects were used to investigate brain activity in four delta subbands during two mental arithmetic tasks (number comparison and two-digit multiplication) and a control condition. The spatial redistribution of signal-power (SP) was explored based on four consecutives subbands of the delta rhythm. Additionally, network analysis was performed, independently for each subband, and the related graphs reflecting functional connectivity were characterized in terms of local structure (i.e. the clustering coefficient), overall integration (i.e. the path length) and the optimality of network organization (i.e. the "small-worldness"). EEG delta activity showed a widespread increase in all subbands during the performance of both arithmetic tasks. The inter-task comparison of the two arithmetic tasks revealed significant differences, in terms of signal-power, for the two subbands of higher frequency over left hemisphere (frontal, temporal, parietal and occipital) regions. The estimated brain networks exhibited small-world characteristics in the case of all subbands. On the contrary, lower frequency subbands were found to operate differently than the higher frequency subbands, with the latter featuring nodal organization and poor remote interconnectivity. These findings possibly reflect the deactivation of default mode network and could be attributed to inhibitory mechanisms activated during mental tasks.

Complex network analysis is currently employed in neuroscience research to describe the neuron pathways in the brain with a small number of computable measures that have neurobiological meaning. Connections in biological neural networks might fluctuate over time; therefore, surveillance can provide a more useful picture of brain dynamics than the standard approach that relies on a static graph to represent functional connectivity. Using the application of well-known measures of neural synchrony over short segments of brain activity in a time series, we attempted a time-dependent characterization of brain connectivity by investigating functional segregation and integration. In our implementation, a frequency-dependent time window was employed and regularly spaced (defined as overlapping segments), and a novel, parameter-free method was introduced to derive the required adjacency matrices. The resulting characterization was compared against conventional approaches that rely on static and time-evolving graphs, which are constructed from non-overlapping segments of arbitrarily defined durations. Our approach is demonstrated using EEG recordings during mental calculations. The derived consecutive values of network metrics were then compared with values from randomized networks. The results revealed the dynamic small-world character of the brain's functional connectivity, which otherwise can be hidden from estimators that rely on either long or stringent time-windows. Moreover, by involving a network-metric time series (NMTS) in a summarizing procedure that was based on replicator dynamics, consistent hubs that facilitated communication in the underlying networks were identified. Finally, the scale-free character of brain networks was also demonstrated based on the significant edges selected with the introduced approach.

Summarization: Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests. Presented on: IEEE Transactions on Information Technology in Biomedicine

A disturbance in the interactions between distributed cortical regions may underlie the cogni- tive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia ''disconnection hypothesis.'' This is achieved by analyzing the spatiotemporal struc- ture of resting state scalp EEG data previously acquired from 40 young subjects with a recent first epi- sode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the to- pography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of ''cliqu- ishness''), and hub structure of these graphs were used as metrics of the underlying brain network ac- tivity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths—features consistent with a ''small-world'' topology—as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still ''small-world,'' these effects are consistent with a subtle randomization in the underlying network architecture—likely associated with a greater number of links connecting disparate clusters. This randomization may underlie the cognitive distur- bances characteristic of schizophrenia. Hum Brain Mapp 30:403-416, 2009. V C 2007 Wiley-Liss, Inc.

Objective: To determine the functional connectivity of different EEG bands at the “baseline” situation (rest) and during mathematical thinking in children and young adults to study the maturation effect on brain networks at rest and during a cognitive task. Methods: Twenty children (8–12 years) and twenty students (21–26 years) were studied. The synchronization likelihood was used to evaluate the interregional synchronization of different EEG frequency bands in children and adults, at rest and during math. Then, graphs were constructed and characterized in terms of local structure (clustering coefficient) and overall integration (path length) and the “optimal” organization of the connectivity i.e., the small world network (SWN). Results: The main findings were: (i) Enhanced synchronization for theta band during math more prominent in adults. (ii) Decrease of the optimal SWN organization of the alpha2 band during math. (iii) The beta and especially gamma bands showed lower synchronization and signs of lower SWN organization in both situations in adults. Conclusion: There are interesting findings related to the two age groups and the two situations. The theta band showed higher synchronization during math in adults as a result of higher capacity of the working memory in this age group. The alpha2 band showed some SWN disorganization during math, a process analog to the known desynchronization. In adults, a dramatic reduction of the connections in gray matter occurs. Although this maturation process is probably related to higher efficiency, reduced connectivity is expressed by lower synchronization and lower mean values of the graph parameters in adults. Hum Brain Mapp 2009. © 2007 Wiley-Liss, Inc.

This study examined regional cortical activations and cortico-cortical connectivity in a group of 20 high-functioning patients with schizophrenia and 20 healthy controls matched for age and sex during a 0- and a 2-back working memory (WM) task. An earlier study comparing schizophrenia patients with education level-matched healthy controls revealed less "optimally" organized network during the 2-back task, whereas a second study with healthy volunteers had suggested that the degree of cortical organization may be inversely proportional to educational level (less optimal functional connectivity in better educated individuals interpreted as the result of higher efficiency). In the present study, both groups succeeded in the 2-back WM task although healthy individuals had generally attained a higher level of education. First absolute power spectrum of the different frequency bands corresponding to the electrodes of each lobe was calculated. Then the mean values of coherence were calculated as an index of the average synchronization to construct graphs in order to characterize local and large scale topological patterns of cortico-cortical connectivity. The power spectra analyses showed signs of hypofrontality in schizophrenics with an asymmetry. Additionally, differences between the groups with greater changes during WM in healthy individuals were visible in all lobes more on the left side. The graph parameter results indicated decreased small-world architecture i.e. less optimal cortico-cortical functional organization in patients as compared to controls. These findings are consistent with the notion of aberrant neural organization in schizophrenics which is nevertheless sufficient in supporting adequate task performance. © 2008 Springer Science+Business Media, LLC.

The network of cerebellum was analyzed in order to investigate its overall organization in individuals with high and low crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were collected from 150 subjects in resting-state from the Human Connectome Project database [15, 16] and further separated into two categorical groups based on their IQ scores, resulting to 76 low-IQ and 74 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas [2, 3] (lobules I-IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, X) as shown in Fig. 1 (cerebellum’s flat surface representation [3] is also provided where color coding has been applied based on each lobule’s volumetric size). These lobules serve different functions e.g. cognition [4, 5, 10, 11], emotion [4, 10, 12]. Lobule Vermis Crus I was found to contain less than 0.005% of the total cerebellar volume and thus was removed from further analysis. Thereafter, correlation matrices were constructed by computing the zero-lag temporal Pearson correlation coefficients between the average BOLD time-series for each pair of ROIs. By computing conventional graph metrics, small-world network properties [6, 7, 8, 18] were discovered using the weighted clustering coefficient (averaged over all nodes to define its global version) and characteristic path length (Supplementary Table 1) for estimating the trade-off between segregation and integration. In addition, the connectivity metric was computed for extracting the average cost per network (Supplementary Table 1). In this study, the concept of Minimum Spanning Tree (MST) was adapted and further implemented in order to avoid methodological biases and enable graph comparisons as well as retain only the strongest connections per network (average weighted and undirected graphs per IQ group are illustrated in Fig. 2 with their corresponding MSTs, where the size of each node in the MST linearly depends on its betweenness centrality value) [9, 13, 14]. Subsequently, six global (degree correlation, diameter, leaf fraction, kappa, radius, tree hierarchy) and three local (betweenness centrality, degree, eccentricity) MST metrics were calculated in order to retrieve useful information concerning the functional and structural characteristics of each MST (Supplementary Table 1) [14]. Moreover, the local metrics of degree (DEG) and betweenness centrality (BC) were used to detect hubs, i.e. nodes with high importance, for both IQ groups, as presented in Fig. 3 (where each node’s size linearly depends on the corresponding BC (A), DEG (B) values, according to the bar plots which represent the percentage of subjects with the highest BC (C), DEG (D) values per ROI). Additionally, the correlations between the median response time (MRT) and the corresponding lobule metrics per IQ group and gender were calculated. Studies in cerebrum have shown that efficiency of networks at rest is higher in more intelligent individuals [1, 15]. Cerebellar lobules have specific (some of them are reciprocal, some not) connections to cerebral sites (mainly frontal and parietal areas) so as to serve cognitive functions in cognitive function [4, 5, 10, 11]. Given the connection of the cerebellum to areas which at rest show high efficiency in high IQ, we are interested to examine whether a similar hypothesis stands for cerebellum. Our findings (Supplementary Table 2) show that: (i) Small-world network organization characterizes cerebellar networks of both men and women at rest state and for low and high-IQ subjects without significant differences. (ii) Maximum correlation between MRT and DEG (r = 0.48, p = 0.001) and BC (r = 0.41, p = 0.005) showed a positive correlation as well as a significant dominance of the Left X lobule in low IQ individuals in the women population. (iii) Higher values in DEG and BC were identified in lobules Left VI, Left Crus I, Right VI, Right Crus I. (iv) There were several differences in local network parameters: lobules Left VI, Left Crus I and Right VI had high local values (DEG & BC) and are hubs, i.e. nodes with high importance, with higher values in individuals with high IQ. (v) More importantly, there are interesting differences between men (low-IQ: 30, high-IQ: 36) and women (low-IQ: 46, high-IQ: 38) as follows. (a) In women there are differences in network parameters among high and low IQ individuals indicative of more robust network organization in cases of higher IQ. (b) Among low-IQ individuals, men network parameters showed more effective organization. Attempting to explain the findings for the lobules with higher correlation with the median response time, we refer to published studies. Left VI lobule is related to motor control, lobules Left Crus I, Right Crus I, Right VI and Right X are related to cognitive and limbic areas of the cerebral hemispheres [4, 5, 10, 11]. It is worth noticing that there are overlapping regions. Nevertheless, the aforementioned differences between men and women, as well as high and low IQ individuals, show more effective organization in high IQ females in relation to low IQ females and more effective organization in males. In summary, to our knowledge this is the first network study of the cerebellum that attempts to assess local and widespread brain-connectivity characteristics in relation to low and high-IQ men and women. Several studies have discovered differences between low and high-IQ subjects in cerebral organization and network metrics, such as differences in several brain regions or connections, small-world network organizations, as well as differences in network parameters and neural efficiency [1, 15]. Nonetheless, future studies are essential in order to explain cerebrum and cerebellar local and widespread findings.

Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.

Several neuroimaging studies have suggested that functional brain connectivity networks exhibit "small-world" characteristics, whereas recent studies based on structural data have proposed a "rich-club" organization of brain networks, whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density. In this study, we adopted an "attack strategy" to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model's hubs would reveal the organization that better describes the topology of the underlying brain networks. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross-frequency coupling between the δ and β frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.

Disturbances in "functional connectivity" have been proposed as a major pathophysiological mechanism for schizophrenia, and in particular, for cognitive disorganization. Detection and estimation of these disturbances would be of clinical interest. Here we characterize the spatial pattern of functional connectivity by computing the "synchronization likelihood" (SL) of EEG at rest and during performance of a 2Back working memory task using letters of the alphabet presented on a PC screen in subjects with schizophrenia and healthy controls. The spatial patterns of functional connectivity were then characterized with graph theoretical measures to test whether a disruption of an optimal spatial pattern ("small-world") of the functional connectivity network underlies schizophrenia. Twenty stabilized patients with schizophrenia, who were able to work, and 20 healthy controls participated in the study. During the working memory (WM) task healthy subjects exhibited small-world properties (a combination of local clustering and high overall integration of the functional networks) in the alpha, beta and gamma bands. These properties were not present in the schizophrenia group. These findings are in accordance with a partially inadequate organization of neuronal networks in subjects with schizophrenia. This method could be helpful for diagnosis and evaluation of the severity of the disease, as well as understanding the pathophysiologic mechanisms underlying cognitive dysfunction in schizophrenia.

Using electroencephalographic (EEG) signals and a novel methodology based on wavelet measures in the time-scale domain, we evaluated cortex reactions during mathematical thinking. Our purpose was to extract more precise information from the cortex reactions during this cognitive task. Initially, the brain areas (lobes) of significant activation during the task are extracted using time-averaged wavelet power spectrum estimation. Then, a refinement step makes use of statistical significance-based criteria for comparing wavelet power spectra between the task and the rest condition. EEG signals are recorded from 15 young normal volunteers using 30 scalp electrodes as participants performed one difficult arithmetic task and the results are compared with a rest situation. The results are in accordance with similar previous studies, showing activations of frontal and central regions. Compared with the alternative spectral-based techniques, the method we propose achieves higher task discrimination on the same dataset and provides additional detail-signal information to evaluate cortical reactivity during local cortical activation.

Previous studies demonstrated that intelligence is significantly related to an impressive array of psychological, social, biological and genetic factors and that working memory (WM) can be considered as a general cognitive resource strongly related with a wide variety of higher order cognitive competencies and intelligence. Also, evaluating the WM of subjects might allow one to test the neural efficiency hypothesis (NEH). WM typically involves functional interactions between frontal and parietal cortices. We recorded EEG signals to study neuronal interactions during one WM test in individuals who had few years of formal education (LE) as compared to individuals with university degrees (UE). The two groups of individuals differed in the scores they obtained in psychological tests. To quantify the synchronization between EEG channels in several frequency bands, we evaluated the "synchronization likelihood" (SL), which takes into consideration nonlinear processes as well as linear ones. SL was then converted into graphs to estimate the distance from "small-world network" (SWN) organization, i.e., an optimally organized network that would give rise to the data. In comparison to LE subjects, those with university degrees exhibited less prominent SWN properties in most frequency bands during the WM task. This finding supports the NEH and suggests that the connections between brain areas of well-educated subjects engaged in WM tasks are not as well-organized in the sense of SWN.

The purpose of the present study was threefold: First, to replicate previous findings of changes in local gamma band power as a function of the complexity of a visuo-semantic processing task, second, to extend these findings in tasks delivered in the auditory modality, and third to explore the use of non-linear algorithms as indices of complexity and distant synchronization in the EEG signal. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. Two auditory processing tasks involving the same stimuli, but requiring no semantic processing, served as controls. The degree of complexity of the semantic decision was associated with the predicted changes in local gamma power, as well as with broadband changes in the non-linear predictability of the signal (an index derived using an artificial neural network algorithm). These changes were observed at all scalp regions, a finding consistent with the wide cortical distribution of component processes involved in the tasks. In addition, the synchronization between temporal and parieto-occipital electrodes and the remaining recording sites was highest in the gamma bands and lowest in the alpha bands for the task that required the most complex visuo-semantic decision. This trend reversed with reduced task complexity, consistent with the view that multidimensional semantic decisions require the involvement of distributed cortical networks in auditory and visual association areas and in the frontal lobes.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral activation during performance of visuo-semantic analysis tasks in neurologically intact adult volunteers. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. In addition, data from a control task involving the same stimuli, but requiring no cognitive decision or response, was included. EEG records were quantified using power spectrum measures in five frequency bands (delta, theta, alpha, beta, and gamma). Results showed a significant linear increase in absolute power in the gamma band with increasing task complexity over left hemisphere frontal and occipital regions, and over right temporoparietal regions.

Human brain is the most complicated network and its functional mechanism is a demanding concept in neuroscience research. Graph theory and forms an interesting tool for modeling the brain interactions and estimated brain parameters. In this paper, we consider synchronization features for modeling brain operations in electro-encephalogram (EEG) responses to kanizsa and fractal stimuli, using minimal spanning tree (MST) on a network of phase synchronization EEG channels. Graphs of phase-synchronization activity and MST structures are computed using these graphs. The proposed approach yields evidence that the fractal stimuli generate stronger energy response and synchronization of theta band in occipital lobe.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral engagement activation during reading in neurologically intact adult volunteers. EEG was recorded from 16 scalp locations as participants performed four visual detection tasks designed to tap into increasingly more complex operations regularly involved in reading, namely visual-spatial, orthographic, phonological, and semantic. EEG records were quantified using power spectrum measures in four frequency bands (delta, theta, alpha, beta1, and beta2), in addition to a non-linear estimate of signal complexity (prediction error). Results showed that (1) changes in spectral power between pairs of reading tasks, and (2) regional variations in EEG measures for each task, were restricted to signals recorded over the left hemisphere. These findings are in agreement with knowledge regarding left hemisphere involvement in higher level component processes of reading.

Brain function during mathematical thinking is a common concern of scientists from different research fields. The study of functional brain networks extracted from electroencephalographic (EEG) signals using graph theory seems to meet the challenge of neuroscience to understand brain functioning in terms of dynamic flow of information among brain regions. Some studies have found differences between the basic arithmetic operations among brain regions; however, they have not explained the brain function during difficult mathematical tasks and complex mathematical processing. This study investigates the changes of the functional networks’ organization among different mathematical tasks. To this end, EEG data from 10 subjects were recorded during three different tasks, Number Looking which was served as the control situation, Simple Addition which refers to the addition of single-digit numbers and Difficult Multiplication with trials of two-digit multiplications. We analyzed weighted graphs so as to provide a more realistic representation of functional brain networks. Mutual information was employed to form the weights among the different channels, while various global and local graph indices were further examined. The results suggest that there are some statistically significant differences between graph theoretical indices among the different tasks and their range of values depend on the particular task.

The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy over other modalities. In this paper we consider synchronization features for identifying brain operations. Independent Component Analysis (ICA) is applied on MEG surface signals in controls and children with reading difficulties and are clustered to representative components. Then, coupling measures of mutual information and partial directed coherence are estimated in order to reveal dysfunction of cerebral networks and its related coordination.

In this study we investigate systematic patterns of rapidly changing sensor-level interdependencies in resting MEG data obtained from 23 children experiencing reading difficulties (RD) and 27 non-impaired readers (NI). Three-minute MEG time series were band-passed and subjected to blind source separation (BSS) prior to estimating sensor interdependencies using the weighted phase synchronization measure (wPLI). Dynamic sensor-level network properties were then derived for two network metrics (global and local efficiency). The temporal decay of long-range temporal correlations in network metrics (LRTC) was quantified using the scaling exponent (SE) in detrended fluctuation analysis (DFA) plots. Having established the reliability of SE estimates as robust descriptors of network dynamics, we found that RD students displayed significantly reduced (a) overall sensor-level network organization across all frequency bands (global efficiency), and (b) temporal correlations between sensors covering the left temporoparietal region and the remaining sensors in the β3 band (local efficiency). Importantly, both groups displayed scale-free global network connectivity dynamics. The direct application of DFA to MEG signals failed to reveal significant group differences. Results are discussed in relation to prior evidence for disrupted temporoparietal functional circuits for reading in developmental reading disability.

The estimation of brain function using neurophysiologic methods is of importance because, besides neuropsychiatric diseases, a variety of systemic diseases can influence brain function. Several types of brain dysfunction, resulting from many kinds of influences on the central nervous system, can cause poor school performance. Besides these dysfunctions of organic origin, there may be others that do not have an organic basis but may reflect the adverse effects of the environment. Often, the differential diagnosis between them is difficult. We performed a neurologic examination and an evaluation using the Illinois Test of Psycholinguistic Abilities and questionnaires (Eysenck and Rutter) to assess the personality of 22 poor school performers aged 10 to 12 years and 22 controls with good school performance of similar age. In order to assess the possibility of using quantitative electroencephalographic methods to evaluate nonorganic poor school performers, we estimated the power spectrum and coherence of the electroencephalographic signals at rest and their changes during motor tasks and auditory stimulation. Our results indicate that there were no signs of organicity in the group of poor school performers. (J Child Neurol 1996;11:309-312).

The application of two unsupervised Artificial Neural Networks (ART2 and Kohonen) for the classification of patients in three medical fields is considered. In the first case, data obtained from spectral analysis of the EEG signals are used in order to detect brain disfunction of persons exposed to organic solvents. The Contingent Negative variation which is an Evoked Response is utilized in the last two cases for the diagnosis of Huntingdon's Disease and for the separation of schizophrenic subtypes.

Gender differences in mathematical thinking is a common concern of scientists from different research fields. Both parents and teachers report that males seem to perform better in complex mathematics compared to females. This study comes to shed light in the different organization of the underlying functional networks, in order to investigate the aforementioned observation, without supporting or rejecting this statement. In this sense, it is generally accepted that females use their both hemispheres to accomplish a certain task, while males use mostly the hemisphere which is properly suited. For the purposes of the current analysis, electroencephalographic recordings were collected from 11 males and 11 females, during a difficult mathematical task. Then a previously proposed model was used in order to pass from the sensor level to the cortical one, in order to examine the networks formed among the cortical dipoles. Mutual information was employed to form the graphs represeting the functional connectivity among the different dipoles, while the density, the global and the local efficiencies were further examined. The results suggest that females use their both hemisphere to solve the complex mathematical task while males use mostly their left hemisphere which is the responsible one for the mathematical thinking.

We investigated the dynamical behavior of resting state functional connectivity using EEG signals. Employing a recently introduced methodology that considers the time variations of phase coupling among signals from different channels, a sequence of functional connectivity graphs (FCGs) was constructed for different frequency bands and analyzed based on graph theoretic tools. In the first stage of analysis, hubs were detected in the FCGs based on local and global efficiency. The probability of each node to be identified as a hub was estimated. This defined a topographic function that showed widespread distribution with prominence over the frontal brain regions for both local and global efficiency. Hubs consistent across time were identified via a summarization technique and found to locate over forehead. In the second stage of analysis, the modular structure of each single FCG was delineated. The derived time-dependent signatures of functional structure were compared in a systematic way revealing fluctuations modulated by frequency. Interestingly, the evolution of functional connectivity can be described via abrupt transitions between states, best described as short-lasting bimodal functional segregations. Based on a distance function that compares clusterings, we discovered that these segregations are recurrent. Entropic measures further revealed that the apparent fluctuations are subject to intrinsic constraints and that order emerges from spatially extended interactions.

Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli.

This work was supported in part by the EC-IST project Biopattern, contract no: 508803, by the EC ICT project TUMOR, contract no: 247754, by the University of Malta grant LBA-73-695, by an internal grant from the Technical University of Crete, ELKE# 80037 and by the Academy of Finland, project nos: 113572, 118355, 134767 and 213462., Background: In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed. Methods: We compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques. Results: Differences could be detected during the control (rest) task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion) of both is needed for efficient classification of subjects. Conclusions: Based on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task., peer-reviewed

The study examines acute, material-specific secondary memory performance in 26 patients with mild traumatic brain injury (MTBI) and 26 healthy controls, matched on demographic variables and indexes of crystallized intelligence. Neuropsychological tests were used to evaluate primary and secondary memory, executive functions, and verbal fluency. Participants were also tested on episodic memory tasks involving words, pseudowords, pictures of common objects, and abstract kaleidoscopic images. Patients showed reduced performance on episodic memory measures, and on tasks associated with visuospatial processing and executive function (Trail Making Test part B, semantic fluency). Significant differences between groups were also noted for correct rejections and response bias on the kaleidoscope task. MTBI patients' reduced performance on memory tasks for complex, abstract stimuli can be attributed to a dysfunction in the strategic component of memory process.

EEG signal analysis could be very helpful in the detection of signs of adverse reaction to the brain by a systemic disease. We performed an EEG spectral analysis in 12 young patients with thalassemia before and after their regular blood transfusion, and in 10 volunteer students. Our aim was to test if the EEG analysis could detect signs of brain dysfunction due to the cerebral hypoxia as a result of the anemia. We analyzed the EEG signals on line using a dedicated computer system estimating the power of the delta, theta, alpha, sigma and beta bands of the EEG. After transfusion, the power spectral density of the alpha band showed a significant enhancement in most areas of the brain, in the group of the thalassemia patients as compared to the normals. These differences correlated with the levels of hemoglobin of the patients, and possibly reflect the degree of oxygenation of the brain. Since the visual interpretation of the EEG is not efficient for estimating the brain dysfunction in the anemic state in thalassemia, the signal analysis of the EEG may provide a more sensitive alternative to study the effects of hypoxia on brain function.

We describe a modified setup for combined in vivo FMT and 3-D rendering of oxygen distribution used to measure change in hypoxic burden during tumour growth whilst simultaneously probing for fluorescence activity.