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

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.Qatar National Librar

Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare

Real-time fMRI neurofeedback and smartphone-based interventions to modulate mental functions

Our brains are constantly changing on a molecular level depending on the demands thrown at them by our environments, behavior, and thoughts. This neuronal plasticity allows us to voluntarily influence mental functions. Taking conscious control over mental functions goes potentially back millenia, but it was psychotherapy since the early 20th century which moulded this concept into a concrete form to target specific mental disorders. Mental disorders constitute a large burden on modern societies. Stress-related disorders like anxiety and depression particularly make up a large part of this burden and new ways to treat or prevent them are highly desirable, since traditional approaches are not equally helpful to every person affected. This might be because the infrastructure is not available where the person lives, their schedules and obligations or financial means do not enable them to seek help or they simply do not respond to traditional forms of treatment. Technological advances bring forth new potential approaches to modulate mental functions and allow using additional information to tailor an intervention better to an individual patient. The focus of this dissertation lies on two promising approaches to cognitively intervene and modulate mental functions: real-time functional magnetic resonance imaging neurofeedback (rtfMRInf) on one hand and smartphone-based interventions (SBIs) on the other. To investigate various aspects of both these methods in the context of stress and in relation to personalized interventions, we designed and conducted two experiments with a main rtfMRInf intervention, and also with ambulatory training of mental strategies, which participants accessed on their mobile phones. The four publication this thesis entails, are related to this topic as follows: The first publication focuses on rtfMRInf effects on the physiological stress response, exploring whether neurofeedback could reduce stress-related changes in brain activity and blood pressure. The second publication focuses on rtfMRInf effects on psychological measures related to the stress response, namely on arousal and mood, based on data from self-report by the participants. The third publication focuses on rtfMRInf methodology itself, looking at complex connectivity data between major neural networks. Finally, the fourth publication focuses on personalized prediction of intervention success of an SBI using data from previous training days

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety