Evaluating functional connectivity in alcoholics based on maximal weight matching

EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimul

Analysis of EEG signals using complex brain networks

The human brain is so complex that two mega projects, the Human Brain Project and the BRAIN Initiative project, are under way in the hope of answering important questions for peoples' health and wellbeing. Complex networks become powerful tools for studying brain function due to the fact that network topologies on real-world systems share small world properties. Examples of these networks are the Internet, biological networks, social networks, climate networks and complex brain networks. Complex brain networks in real time biomedical signal processing applications are limited because some graph algorithms (such as graph isomorphism), cannot be solved in polynomial time. In addition, they are hard to use in single-channel EEG applications, such as clinic applications in sleep scoring and depth of anaesthesia monitoring. The first contribution of this research is to present two novel algorithms and two graph models. A fast weighted horizontal visibility algorithm (FWHVA) overcoming the speed limitations for constructing a graph from a time series is presented. Experimental results show that the FWHVA can be 3.8 times faster than the Fast Fourier Transfer (FFT) algorithm when input signals exceed 4000 data points. A linear time graph isomorphism algorithm (HVGI) can determine the isomorphism of two horizontal visibility graphs (HVGs) in a linear time domain. This is an efficient way to measure the synchronized index between two time series. Difference visibility graphs (DVGs) inherit the advantages of horizontal visibility graphs. They are noise-robust, and they overcome a pitfall of visibility graphs (VG): that the degree distribution (DD) doesn't satisfy a pure power-law. Jump visibility graphs (JVGs) enhance brain graphs allowing the processing of non-stationary biomedical signals. This research shows that the DD of JVGs always satisfies a power-lower if the input signals are purely non-stationary. The second highlight of this work is the study of three clinical biomedical signals: alcoholic, epileptic and sleep EEGs. Based on a synchronization likelihood and maximal weighted matching method, this work finds that the processing repeated stimuli and unrepeated stimuli in the controlled drinkers is larger than that in the alcoholics. Seizure detections based on epileptic EEGs have also been investigated with three graph features: graph entropy of VGs, mean strength of HVGs, and mean degrees of JVGs. All of these features can achieve 100% accuracy in seizure identification and differentiation from healthy EEG signals. Sleep EEGs are evaluated based on VG and DVG methods. It is shown that the complex brain networks exhibit more small world structure during deep sleep. Based on DVG methods, the accuracy peaks at 88:9% in a 5-state sleep stage classification from 14; 943 segments from single-channel EEGs. This study also introduces two weighted complex network approaches to analyse the nonlinear EEG signals. A weighted horizontal visibility graph (WHVG) is proposed to enhance noise-robustness properties. Tested with two Chaos signals and an epileptic EEG database, the research shows that the mean strength of the WHVG is more stable and noise-robust than those features from FFT and entropy. Maximal weighted matching algorithms have been applied to evaluate the difference in complex brain networks of alcoholics and controlled drinkers. The last contribution of this dissertation is to develop an unsupervised classifier for biomedical signal pattern recognition. A Multi-Scale Means (MSK-Means) algorithm is proposed for solving the subject-dependent biomedical signals classification issue. Using JVG features from the epileptic EEG database, the MSK-Means algorithm is 4:7% higher in identifying seizures than those by the K-means algorithm and achieves 92:3% accuracy for localizing the epileptogenic zone. The findings suggest that the outcome of this thesis can improve the performance of complex brain networks for biomedical signal processing and nonlinear time series analysis

Data selection in EEG signals classification

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design

Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523TESI

Effects of adolescent alcohol binge drinking on prefrontal myelin

Alcohol binge drinking is highly prevalent in teenagers and is associated with various harmful health effects and social problems. During adolescence, brain regions such as the prefrontal cortex (PFC) are still undergoing active development, characterized by increases in white matter volume. While the morphological details and the cellular and molecular sequences governing adolescent white matter development are not fully known, it is known that this development process is sensitive and can be disrupted. Although consumption of alcohol in a binge drinking pattern has been linked to lower white matter integrity in humans, it is important to determine if alcohol is causing this change or if predisposing factors can influence drinking. A rodent model of voluntary binge drinking was used to elicit high alcohol intake during a short developmental window in adolescence. Myelin was then assessed using several histochemical measures. Results showed that adolescent development is marked by an increase in myelinated fibers in the PFC that accompanied an increase in conduction velocity, and alcohol reduces prefrontal white matter and myelinated fiber density. In addition, heavy drinking was associated with long-term cognitive deficits. I also investigated sex differences in the effects of adolescent alcohol consumption on PFC myelination, showing that males appear to be more vulnerable than females. The findings altogether increase our understanding of the developmental process of prefrontal myelination in adolescence and the maladaptive effects alcohol can have on this critical process

Age-related network topological difference based on the sleep ECG signal

Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on to explore the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful(W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages. The subjects are divided into two age groups: younger (age<=40, n=11) group and older group (age>40, n=25). For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between two age groups, including the mean degree ( ), the clustering coefficient ( ), and the path length ( )features, such as the slope distribution of in younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. But the small-world index in the LS and REM stages significantly decreased with age (p=0.0006 and p=0.05 respectively). The comparison analysis result indicates that the network topology variations of the sleep ECG signals prone to show age-relevant differences which could be used for sleep stage classification and sleep disorder diagnosis

PARALLEL INDEPENDENT COMPONENT ANALYSIS WITH REFERENCE FOR IMAGING GENETICS: A SEMI-BLIND MULTIVARIATE APPROACH

Imaging genetics is an emerging field dedicated to the study of genetic underpinnings of brain structure and function. Over the last decade, brain imaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure morphometry, task-based function and connectivity in living brains. Meanwhile, high-throughput genotyping employing genome-wide techniques has made it feasible to sample the entire genome of a substantial number of individuals. While there is growing interest in image-wide and genome-wide approaches which allow unbiased searches over a large range of variants, one of the most challenging problems is the correction for the huge number of statistical tests used in univariate models. In contrast, a reference-guided multivariate approach shows specific advantage for simultaneously assessing many variables for aggregate effects while leveraging prior information. It can improve the robustness of the results compared to a fully blind approach. In this dissertation we present a semi-blind multivariate approach, parallel independent component analysis with reference (pICA-R), to better reveal relationships between hidden factors of particular attributes. First, a consistency-based order estimation approach is introduced to advance the application of ICA to genotype data. The pICA-R approach is then presented, where independent components are extracted from two modalities in parallel and inter-modality associations are subsequently optimized for pairs of components. In particular, prior information is incorporated to elicit components of particular interests, which helps identify factors carrying small amounts of variance in large complex datasets. The pICA-R approach is further extended to accommodate multiple references whose interrelationships are unknown, allowing the investigation of functional influence on neurobiological traits of potentially related genetic variants implicated in biology. Applied to a schizophrenia study, pICA-R reveals that a complex genetic factor involving multiple pathways underlies schizophrenia-related gray matter deficits in prefrontal and temporal regions. The extended multi-reference approach, when employed to study alcohol dependence, delineates a complex genetic architecture, where the CREB-BDNF pathway plays a key role in the genetic factor underlying a proportion of variation in cue-elicited brain activations, which plays a role in phenotypic symptoms of alcohol dependence. In summary, our work makes several important contributions to advance the application of ICA to imaging genetics studies, which holds the promise to improve our understating of genetics underlying brain structure and function in healthy and disease

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others