Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the information. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic subjects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).Spanish Government PGC2018-098813-B-C32Junta de Andalucia UMA20-FEDERJA-086European CommissionNVIDIA CorporationMinistry of Science and Innovation, Spain (MICINN) Spanish GovernmentEuropean CommissionUniversidad de Malaga/CBU

Computer-Aided Diagnosis in Neuroimaging

This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments

A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).TIC218, MINECO TEC2008-02113 and TEC2012-34306 projects, Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía P09-TIC-4530 and P11-TIC-71

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson’s Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson’s Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson’s Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson’s ProgressionMarkers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of definedMorphological Features) fromPPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.This work was supported by the MINECO/FEDER under the TEC2015-64718-R project and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103

Agricultural intensification erodes taxonomic and functional diversity in Mediterranean olive groves by filtering out rare species

Agri-Environmental Schemes (AES) have been proposed to mitigate the impact of agriculture on both taxonomic and functional biodiversity. However, a better knowledge of the mechanisms involved in the loss of agrobiodiversity is needed to implement efficient AES. An unbalanced effort on research towards arable lands compared to permanent crops, and on fauna relative to plants, is patent, which limits the generalization of AES effectiveness. We evaluated the effects of agricultural management and landscape simplification on taxonomic and functional diversity of the ground herb cover of 40 olive groves. We use a recently developed approach based on Hill numbers (rare, common and dominant species based) to analyse taxonomic and functional dissimilarity between farms with contrasting agricultural practices, and its potential attenuation by landscape complexity. We further explore the filtering effect of agricultural intensification on functional traits, and the relationship between functional and species richness across landscapes. We found that taxonomic and functional dissimilarity of herb assemblages between intensively and low-intensively managed fields was mainly due to rare species. Dissimilarity decreased as landscape complexity increased, evidencing that complex landscapes attenuate the impact of agriculture intensification on herb assemblage composition. Agricultural intensification favoured more functionally homogeneous assemblages and disfavoured the herbs pollinated by insects, while it did not seem to affect wind-pollinated species. Overall, functional richness increased exponentially with species richness across landscapes, but the latter was insufficient to drive any clear enhancement in functional richness in simple landscapes. In contrast, high species richness accelerated the enhancement in functional richness in intermediate and complex landscapes. These results highlight the functional filtering that intensive agriculture has generated for decades in homogeneous olive-dominated landscapes. Synthesis and applications. Herb cover is essential to support the fauna of permanent croplands and their sustainable production. Hence, Agri-Environmental Schemes (AES) in these croplands should promote management practices favouring the diversity and functionality of herb assemblages. Such AES should be particularly prioritized in homogeneous landscapes, where ground herb cover composition and function has long been homogenized to a great extent

Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.This work was supported by the MINECO/ FEDER under the TEC2015-64718-R project, the Ministry of Economy, Innovation, Science and Employment of the Junta de Andaluc´ıa under the P11-TIC-7103 Excellence Project and the Vicerectorate of Research and Knowledge Transfer of the University of Granada

Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images

The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns while providing information about the reliability of the classification. Specifically, each CCT scan is divided into cubic patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the pneumonia patterns regardless of their size or location. Decisions of each individual patch are then combined into a global one according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would result in a huge computational cost) evidence the applicability of our proposal in a real-world environment.Comment: 15 pages, 9 figure

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson’s Disease.

Finding new biomarkers to model Parkinson’s Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD, but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, the work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[123]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of 386 scans from Parkinson’s Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Na¨ıve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a 97.04% balanced accuracy when the performance was evaluated using a 10-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity; among others, but including both internal and external isosurfaces.This work was supported by the MINECO/FEDER under the RTI2018-098913-B-I00 and PGC2018- 098813-B-C32 projects and the General Secretariat of Universities, Research and Technology, Junta de Andalucía under the Excellence FEDER Project ATIC-117-UGR18

Granger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers.

In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. Wiener–Granger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.This work was partly supported by the MINECO/ FEDER under the RTI2018- 098913-B100 project. The authors would like to acknowledge the support of 370 CDTI (Centro para el Desarrollo Tecnologico Industrial, Ministerio de Cien cia, Innovacion y Universidades and FEDER, SPAIN) under the PASTORA project (Ref.: ITC-20181102). and to thank the companies within the PAS TORA consortium: Endesa, Ayesa, Ormaz´abal and Ingelectus. We would like to thank the reviewers for their thoughtful comments and efforts towards im 375 proving our manuscript. Finally, JM Gorriz would like to thank Dr G´omez Exp´osito for his helpful advice and comments