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Power line inspection is one important task performed by electricity distribution network operators worldwide. It is part of the equipment maintenance for such companies and forms a crucial procedure since it can provide diagnostics and prognostics about the condition of the power line network. Furthermore, it helps with effective decision making in the case of fault detection. Nowadays, the inspection of power lines is performed either using human operators that scan the network on foot and search for obvious faults, or using unmanned aerial vehicles (UAVs) and/or helicopters equipped with camera sensors capable of recording videos of the power line network equipment, which are then inspected by human operators offline. In this study, we propose an autonomous, intelligent inspection system for power lines, which is equipped with camera sensors operating in the visual (Red–Green–Blue (RGB) imaging) and infrared (thermal imaging) spectrums, capable of providing real-time alerts about the condition of power lines. The very first step in power line monitoring is identifying and segmenting them from the background, which constitutes the principal goal of the presented study. The identification of power lines is accomplished through an innovative hybrid approach that combines RGB and thermal data-processing methods under a custom-made drone platform, providing an automated tool for in situ analyses not only in offline mode. In this direction, the human operator role is limited to the flight-planning and control operations of the UAV. The benefits of using such an intelligent UAV system are many, mostly related to the timely and accurate detection of possible faults, along with the side benefits of personnel safety and reduced operational costs. [ABSTRACT FROM AUTHOR]

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. In this paper, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special user needs of this population. Furthermore, the proposed platform integrates capabilities for the automatic recognition of health-related emergencies, such as fever, oxygen saturation decline, and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing appropriate notifications to caregivers and automated assistance to people with ID through voice instructions and interaction with a virtual assistant. A short-scale pilot study has been carried out, where a group of end-users participated in the testing of the integrated platform, verifying its effectiveness concerning the recommended services. The experimental results indicate the potential value of the proposed system in providing routine health measurements, identifying and managing emergency cases, and supporting a creative and qualitative daily life for people with disabilities. [ABSTRACT FROM AUTHOR]

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC. [ABSTRACT FROM AUTHOR]

Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler problems, allowing the application of numerous well-established learning algorithms that may not apply directly to the original task. This work focuses on the efficiency of decomposition-based methods and proposes several improvements to the meta-learning level. In this paper, four methods for optimizing the ensemble phase of multiclass classification are introduced. The first demonstrates that employing a mixture of experts scheme can drastically reduce the number of operations in the training phase by eliminating redundant learning processes in decomposition-based techniques for multiclass problems. The second technique for combining learner-based outcomes relies on Bayes' theorem. Combining the Bayes rule with arbitrary decompositions reduces training complexity relative to the number of classifiers even further. Two additional methods are also proposed for increasing the final classification accuracy by decomposing the initial task into smaller ones and ensembling the output of the base learners along with that of a multiclass classifier. Finally, the proposed novel meta-learning techniques are evaluated on four distinct datasets of varying classification difficulty. In every case, the proposed methods present a substantial accuracy improvement over existing traditional image classification techniques. [ABSTRACT FROM AUTHOR]

In this review paper, we look into how a recommendation system can be adapted to and support people with intellectual disability (ID). We start by reviewing and comparing the main classes of techniques for general-purpose content recommendation. Then, centering on individuals with ID, we collect information on their special needs that may be relevant to or affected by content recommendation tasks. We review the few existing recommendation systems specifically designed or adapted to the needs of this population and finally, based on the reviewed literature sources, we catalog the traits that a future content recommendation system should have in order to respond well to the identified special needs. We hope this listing of desirable traits and future directions in our concluding sections will stimulate research towards opening the doors to the digital world for individuals with ID. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Background Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation. Results A computational study is performed related to the performance of several gene selection methodologies on publicly available microarray data. Three basic types of experimental scenarios are evaluated, i.e. the independent test-set and the 10-fold cross-validation (CV) using maximum and average performance measures. Feature selection methods behave differently under different validation strategies. The performance results from CV do not mach well those from the independent test-set, except for the support vector machines (SVM) and the least squares SVM methods. However, these wrapper methods achieve variable (often low) performance, whereas the hybrid methods attain consistently higher accuracies. The use of an independent test-set within CV is important for the evaluation of the predictive power of algorithms. The optimal size of the selected gene-set also appears to be dependent on the evaluation scheme. The consistency of selected genes over variation of the training-set is another aspect important in reducing uncertainty in the evaluation of the derived gene signature. In all cases the presence of outlier samples can seriously affect algorithmic performance. Conclusion Multiple parameters can influence the selection of a gene-signature and its predictive power, thus possible biases in validation methods must always be accounted for. This paper illustrates that independent test-set evaluation reduces the bias of CV, and case-specific measures reveal stability characteristics of the gene-signature over changes of the training set. Moreover, frequency measures on gene selection address the algorithmic consistency in selecting the same gene signature under different training conditions. These issues contribute to the development of an objective evaluation framework and aid the derivation of statistically consistent gene signatures that could eventually be correlated with biological relevance. The benefits of the proposed framework are supported by the evaluation results and methodological comparisons performed for several gene-selection algorithms on three publicly available datasets.

eMass project aims to digitalize the medical examination procedure of recruitment phase of conscripts in the Hellenic Navy. eMass integrates recruits' Electronic Health Record (EHR), while allows a pre-screening test, through portable telemedicine equipment. The data will be exploited to assess the individual's cardiovascular risk through appropriate digital tools and algorithms. The eMass digital platform, will be accessible to health experts involved in the recruitment procedure for further assessment and processing. Recruits' personal data is stored in the database encrypted using Advanced Encryption Standard (AES). eMass solution contributes to beneficial management and medical data analysis, preventing inessential physical or medical examinations minimizing danger of possible errors and reducing time-consuming processes. Moreover, eMass exploits Electronic Health Record data through a machine-learning based cardiovascular risk assessment tool. [ABSTRACT FROM AUTHOR]

Abstract In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

INTRODUCTION: Absence seizures are characterized by a typical generalized spike-and-wave electroencephalographic (EEG) pattern around 3Hz. The automatic identification of this pattern and consequently its corresponding seizure is a valuable information towards the reliable patient’s clinical image and treatment planning. In this paper, we propose a method for absence seizures detection based on EEG signals decomposition via the Matching Pursuit (MP) algorithm. METHODS: Based on the ictal EEG semiology, MP features were extracted able to track the ictal pattern. This analysis was performed in a clinical dataset of 8 pediatric patients (4 females, 4 males) suffering from active absence epilepsy, containing 123 absence seizures in total. Automatic classification schema based on Machine Learning techniques were employed to categorize the MP patterns into non-ictal and ictal states. RESULTS: The seizure detection system achieved a time window based discrimination accuracy of 97.3% by using a Support Vector Machine (SVM) classifier and 10-fold cross-validation, in that way accomplishing a good state of the art performance. DISCUSSION: Compared to other popular spectral analysis methods, Matching Pursuit appears to be a robust and efficient method regarding absence seizures detection on EEG signals and our results indicate that the MP features proposed in this work are features that can be used effectively in seizure detection procedure. [ABSTRACT FROM AUTHOR]

Featured Application: The generic applicability of the entropy-empowered rank product (RP) calculation score supports the utilization of this non-parametric, threshold-free methodology in different kinds of data. This is not restricted only in meta-analysis of different data sets, but could serve as a key methodology for data integration of different sources of information, in the quest for highly automated, systemic big data biological interpretation. Background: Here, we propose a threshold-free selection method for the identification of differentially expressed features based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques, based on fixed thresholds. This work aims to propose a methodology, which automates and standardizes the statistical selection, through the utilization of established measures like that of entropy, already used in information retrieval from large biomedical datasets, thus departing from classical fixed-threshold based methods, relying in arbitrary p-value and fold change values as selection criteria, whose efficacy also depends on degree of conformity to parametric distributions,. Methods: Our work extends the rank product (RP) methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP entropy of the distribution, to isolate the features of interest by their contribution to its information content. Goal is a methodology of threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under study. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterizations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and rich in information, indicating phenotypic aspects specific to the tissue and biological phenomenon investigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresh-holding, thus facilitating the full automation of the analysis. [ABSTRACT FROM AUTHOR]

Fish‐cage dysfunction in aquaculture installations can trigger significant negative consequences affecting the operational costs. Low oxygen levels, due to excessive fooling's, leads to decrease growth performance, and feed efficiency. Therefore, frequent periodic inspection of fish‐cage nets is required, but this task can become quite expensive with the traditional means of employing professional divers that perform visual inspections at regular time intervals. The modern trend in aquaculture is to take advantage of IT technologies with the use of a small‐sized, low‐cost autonomous underwater vehicle, permanently residing within a fish cage and performing regular video inspection of the infrastructure for the entire net surface. In this study, we explore specialised image processing schemes to detect net holes of multiple area size and shape. These techniques are designed with the vision to provide robust solutions that take advantage of either global or local image structures to provide the efficient inspection of multiple net holes. [ABSTRACT FROM AUTHOR]

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine. [ABSTRACT FROM AUTHOR]

Background: The chemokine receptor CXCR4 and the transcription factor JUNB, expressed on a variety of tumor cells, seem to play an important role in the metastatic process. Since disseminated tumor cells (DTCs) in the bone marrow (BM) have been associated with worse outcomes, we evaluated the expression of CXCR4 and JUNB in DTCs of primary, nonmetastatic breast cancer (BC) patients before the onset of any systemic treatment. Methods: Bilateral BM (10 ml) aspirations of 39 hormone receptor (HR)-positive, HER2-negative BC patients were assessed for the presence of DTCs using the following combination of antibodies: pan-cytokeratin (A45-B/B3)/CXCR4/JUNB. An expression pattern of the examined proteins was created using confocal laser scanning microscopy, Image J software and BC cell lines. Results: CXCR4 was overexpressed in cancer cells and DTCs, with the following hierarchy of expression: SKBR3 > MCF7 > DTCs > MDA-MB231. Accordingly, the expression pattern of JUNB was: DTCs > MDA-MB231 > SKBR3 > MCF7. The mean intensity of CXCR4 (6411 ± 334) and JUNB (27725.64 ± 470) in DTCs was statistically higher compared with BM hematopoietic cells (2009 ± 456, p = 0.001; and 11112.89 ± 545, p = 0.001, respectively). The (CXCR4+JUNB+CK+) phenotype was the most frequently detected [90% (35/39)], followed by the (CXCR4–JUNB+CK+) phenotype [36% (14/39)]. However, (CXCR4+JUNB–CK+) tumor cells were found in only 5% (3/39) of patients. Those patients harboring DTCs with the (CXCR4+JUNB+CK+) phenotype revealed lower overall survival (Cox regression: p = 0.023). Conclusions: (CXCR4+JUNB+CK+)-expressing DTCs, detected frequently in the BM of BC patients, seem to identify a subgroup of patients at higher risk for relapse that may be considered for close follow up. [ABSTRACT FROM AUTHOR]

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91–97%, sensitivity: 100%, and specificity: 77–93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data. That instability hinders the validity of research findings and raises skepticism about the reliability of such methods. In this study, a complete framework for the extraction of stable and reliable lists of candidate genes is presented. The proposed methodology enforces stability of results at the validation step and as a result, it is independent of the feature selection and classification methods used. Furthermore, two different statistical tests are performed in order to assess the statistical significance of the observed results. Moreover, the consistency of the signatures extracted by independent executions of the proposed method is also evaluated. The results of this study highlight the importance of stability issues in genomic signatures, beyond their prediction capabilities. [ABSTRACT FROM PUBLISHER]

Functional brain connectivity networks exhibit "small-world" characteristics and some of these networks follow a "rich-club" organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an "attack strategy" to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the "true" underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hypersynchronization among rich-club hubs compared to controls in the δ band and the δ-γ1, θ-γ1, and β-γ2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ1 frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ1, θ-β, and β-γ2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery frommTBI. Furthermore, the proposed approachmight be used as a validation tool to assess patient recovery. [ABSTRACT FROM AUTHOR]

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 crystallized 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. [ABSTRACT FROM AUTHOR]

We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials. [ABSTRACT FROM AUTHOR]