1,322 research outputs found

    EXplainable Artificial Intelligence: enabling AI in neurosciences and beyond

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    The adoption of AI models in medicine and neurosciences has the potential to play a significant role not only in bringing scientific advancements but also in clinical decision-making. However, concerns mounts due to the eventual biases AI could have which could result in far-reaching consequences particularly in a critical field like biomedicine. It is challenging to achieve usable intelligence because not only it is fundamental to learn from prior data, extract knowledge and guarantee generalization capabilities, but also to disentangle the underlying explanatory factors in order to deeply understand the variables leading to the final decisions. There hence has been a call for approaches to open the AI `black box' to increase trust and reliability on the decision-making capabilities of AI algorithms. Such approaches are commonly referred to as XAI and are starting to be applied in medical fields even if not yet fully exploited. With this thesis we aim at contributing to enabling the use of AI in medicine and neurosciences by taking two fundamental steps: (i) practically pervade AI models with XAI (ii) Strongly validate XAI models. The first step was achieved on one hand by focusing on XAI taxonomy and proposing some guidelines specific for the AI and XAI applications in the neuroscience domain. On the other hand, we faced concrete issues proposing XAI solutions to decode the brain modulations in neurodegeneration relying on the morphological, microstructural and functional changes occurring at different disease stages as well as their connections with the genotype substrate. The second step was as well achieved by firstly defining four attributes related to XAI validation, namely stability, consistency, understandability and plausibility. Each attribute refers to a different aspect of XAI ranging from the assessment of explanations stability across different XAI methods, or highly collinear inputs, to the alignment of the obtained explanations with the state-of-the-art literature. We then proposed different validation techniques aiming at practically fulfilling such requirements. With this thesis, we contributed to the advancement of the research into XAI aiming at increasing awareness and critical use of AI methods opening the way to real-life applications enabling the development of personalized medicine and treatment by taking a data-driven and objective approach to healthcare

    The effect of using multiple connectivity metrics in brain Functional Connectivity studies

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    Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2022Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to assist as a diagnostic or prognostic tool for a diverse set of neurological and neuropsychiatric disorders, which are often difficult to differentiate. fMRI focuses on the study of the brain functional Connectome, which is characterized by the functional connections and neuronal activity among different brain regions, also interpreted as communications between pairs of regions. This Functional Connectivity (FC) is quantified through the statistical dependences between brain regions’ blood-oxygen-level-dependent (BOLD) signals time-series, being traditionally evaluated by correlation coefficient metrics and represented as FC matrices. However, several studies underlined limitations regarding the use of correlation metrics to fully capture information from these signals, leading investigators towards different statistical metrics that would fill those shortcomings. Recently, investigators have turned their attention to Deep Learning (DL) models, outperforming traditional Machine Learning (ML) techniques due to their ability to automatically extract relevant information from high-dimensional data, like FC data, using these models with rs-fMRI data to improve diagnostic predictions, as well as to understand pathological patterns in functional Connectome, that can lead to the discovery of new biomarkers. In spite of very encouraging performances, the black-box nature of DL algorithms makes difficult to know which input information led the model to a certain prediction, restricting its use in clinical settings. The objective of this dissertation is to exploit the power of DL models, understanding how FC matrices created from different statistical metrics can provide information about the brain FC, beyond the conventionally used correlation family. Two publicly available datasets where studied, the ABIDE I dataset, composed by healthy and autism spectrum disease (ASD) individuals, and the ADHD-200 dataset, with typically developed controls and individuals with attention-deficit/hyperactive disorder (ADHD). The computation of the FC matrices of both datasets, using different statistical metrics, was performed in MATLAB using MULAN’s toolbox functions, encompassing the correlation coefficient, non-linear correlation coefficient, mutual information, coherence and transfer entropy. The classification of FC data was performed using two DL models, the improved ConnectomeCNN model and the innovative ConnectomeCNN-Autoencoder model. Moreover, another goal is to study the effect of a multi-metric approach in classification performances, combining multiple FC matrices computed from the different statistical metrics used, as well as to study the use of Explainable Artificial Intelligence (XAI) techniques, namely Layer-wise Relevance Propagation method (LRP), to surpass the black-box problem of DL models used, in order to reveal the most important brain regions in ADHD. The results show that the use of other statistical metrics to compute FC matrices can be a useful complement to the traditional correlation metric methods for the classification between healthy subjects and subjects diagnosed with ADHD and ASD. Namely, non-linear metrics like h2 and mutual information, achieved similar and, in some cases, even slightly better performances than correlation methods. The use of FC multi-metric, despite not showing improvements in classification performance compared to the best individual method, presented promising results, namely the ability of this approach to select the best features from all the FC matrices combined, achieving a similar performance in relation to the best individual metric in each of the evaluation measures of the model, leading to a more complete classification. The LRP analysis applied to ADHD-200 dataset proved to be promising, identifying brain regions related to the pathophysiology of ADHD, which are in broad accordance with FC and structural study’s findings.A ressonância magnética funcional em estado de repouso (rs-fMRI) tem o potencial de ser uma ferramenta auxiliar de diagnóstico ou prognóstico para um conjunto diversificado de distúrbios neurológicos e neuropsiquiátricos, que muitas vezes são difíceis de diferenciar. A análise de dados de rs-fMRI recorre muitas vezes ao conceito de conectoma funcional do cérebro, que se caracteriza pelas conexões funcionais entre as diferentes regiões do cérebro, sendo estas conexões interpretadas como comunicações entre diferentes pares de regiões cerebrais. Esta conectividade funcional é quantificada através de dependências estatísticas entre os sinais fMRI das regiões cerebrais, sendo estas tradicionalmente calculadas através da métrica coeficiente de correlação, e representadas através de matrizes de conectividade funcional. No entanto, vários estudos demonstraram limitações em relação ao uso de métricas de correlação, em que estas não conseguem capturar por completo todas as informações presentes nesses sinais, levando os investigadores à procura de diferentes métricas estatísticas que pudessem preencher essas lacunas na obtenção de informações mais completas desses sinais. O estudo destes distúrbios neurológicos e neuropsiquiátricos começou por se basear em técnicas como mapeamento paramétrico estatístico, no contexto de estudos de fMRI baseados em tarefas. Porém, essas técnicas apresentam certas limitações, nomeadamente a suposição de que cada região cerebral atua de forma independente, o que não corresponde ao conhecimento atual sobre o funcionamento do cérebro. O surgimento da rs-fMRI permitiu obter uma perspetiva mais global e deu origem a uma vasta literatura sobre o efeito de patologias nos padrões de conetividade em repouso, incluindo tentativas de diagnóstico automatizado com base em biomarcadores extraídos dos conectomas. Nos últimos anos, os investigadores voltaram a sua atenção para técnicas de diferentes ramos de Inteligência Artificial, mais propriamente para os algoritmos de Deep Learning (DL), uma vez que são capazes de superar os algoritmos tradicionais de Machine Learning (ML), que foram aplicados a estes estudos numa fase inicial, devido à sua capacidade de extrair automaticamente informações relevantes de dados de alta dimensão, como é o caso dos dados de conectividade funcional. Esses modelos utilizam os dados obtidos da rs-fMRI para melhorar as previsões de diagnóstico em relação às técnicas usadas atualmente em termos de precisão e rapidez, bem como para compreender melhor os padrões patológicos nas conexões funcionais destes distúrbios, podendo levar à descoberta de novos biomarcadores. Apesar do notável desempenho destes modelos, a arquitetura natural em caixa-preta dos algoritmos de DL, torna difícil saber quais as informações dos dados de entrada que levaram o modelo a executar uma determinada previsão, podendo este utilizar informações erradas dos dados para alcançar uma dada inferência, restringindo o seu uso em ambientes clínicos. O objetivo desta dissertação, desenvolvida no Instituto de Biofísica e Engenharia Biomédica, é explorar o poder dos modelos DL, de forma a avaliar até que ponto matrizes de conectividade funcional criadas a partir de diferentes métricas estatísticas podem fornecer mais informações sobre a conectividade funcional do cérebro, para além das métricas de correlação convencionalmente usadas neste tipo de estudos. Foram estudados dois conjuntos de dados bastante utilizados em estudos de Neurociência e que estão disponíveis publicamente: o conjunto de dados ABIDE-I, composto por indivíduos saudáveis e indivíduos com doenças do espectro do autismo (ASD), e o conjunto de dados ADHD-200, com controlos tipicamente desenvolvidos e indivíduos com transtorno do défice de atenção e hiperatividade (ADHD). Numa primeira fase foi realizada a computação das matrizes de conetividade funcional de ambos os conjuntos de dados, usando as diferentes métricas estatísticas. Para isso, foi desenvolvido código de MATLAB, onde se utilizam as séries temporais dos sinais BOLD obtidas dos dois conjuntos de dados para criar essas mesmas matrizes de conectividade funcional, incorporando funções de diferentes métricas estatísticas da caixa de ferramentas MULAN, compreendendo o coeficiente de correlação, o coeficiente de correlação não linear, a informação mútua, a coerência e a entropia de transferência. De seguida, a classificação dos dados de conectividade funcional, de forma a avaliar o efeito do uso de diferentes métricas estatísticas para a criação de matrizes de conectividade funcional na discriminação de sujeitos saudáveis e patológicos, foi realizada usando dois modelos de DL. O modelo ConnectomeCNN melhorado e o modelo inovador ConnectomeCNN-Autoencoder foram desenvolvidos com recurso à biblioteca de Redes Neuronais Keras, juntamente com o seu backend Tensorflow, ambos em Python. Estes modelos, desenvolvidos previamente no Instituto de Biofísica e Engenharia Biomédica, tiveram de ser otimizados de forma a obter a melhor performance, onde vários parâmetros dos modelos e do respetivo treino dos mesmos foram testados para os dados a estudar. Pretendeu-se também estudar o efeito de uma abordagem multi-métrica nas tarefas de classificação dos sujeitos de ambos os conjuntos de dados, sendo que, para estudar essa abordagem as diferentes matrizes calculadas a partir das diferentes métricas estatísticas utilizadas, foram combinadas, sendo usados os mesmos modelos que foram aplicados às matrizes de conectividade funcional de cada métrica estatística individualmente. É importante realçar que na abordagem multi-métrica também foi realizada a otimização dos parâmetros dos modelos utilizados e do respetivo treino, de modo a conseguir a melhor performance dos mesmos para estes dados. Para além destes dois objetivos, estudou-se o uso de técnicas de Inteligência Artificial Explicável (XAI), mais especificamente o método Layer-wise Relevance Propagation (LRP), com vista a superar o problema da caixa-preta dos modelos de DL, com a finalidade de explicar como é que os modelos estão a utilizar os dados de entrada para realizar uma dada previsão. O método LRP foi aplicado aos dois modelos utilizados anteriormente, usando como dados de entrada o conjunto de dados ADHD-200, permitindo assim revelar quais as regiões cerebrais mais importantes no que toca a um diagnóstico relacionado com o ADHD. Os resultados obtidos mostram que o uso de outras métricas estatísticas para criar as matrizes de Conectividade Funcional podem ser um complemento bastante útil às métricas estatísticas tradicionalmente utilizadas para a classificação entre indivíduos saudáveis e indivíduos como ASD e ADHD. Nomeadamente métricas estatísticas não lineares como o h2 e a informação mútua, obtiveram desempenhos semelhantes e, em alguns casos, desempenhos ligeiramente melhores em relação aos desempenhos obtidos por métodos de correlação, convencionalmente usados nestes estudos de conectividade funcional. A utilização da multi-métrica de conectividade funcional, apesar de não apresentar melhorias no desempenho geral da classificação em relação ao melhor método das matrizes de conectividade funcional individuais do conjunto de métricas estatísticas abordadas, apresenta resultados que justificam a exploração mais aprofundada deste tipo de abordagem, de forma a compreender melhor a complementaridade das métricas e a melhor maneira de as utilizar. O uso do método LRP aplicado ao conjunto de dados do ADHD-200 mostrou a sua aplicabilidade a este tipo de estudos e a modelos de DL, identificando as regiões cerebrais mais relacionadas à fisiopatologia do diagnóstico do ADHD que são compatíveis com o que é reportado por diversos estudos de conectividade funcional e estudos de alterações estruturais associados a esta doença. O facto destas técnicas de XAI demonstrarem como é que os modelos de DL estão a usar os dados de entrada para efetuar as previsões, pode significar uma mais rápida e aceite adoção destes algoritmos em ambientes clínicos. Estas técnicas podem auxiliar o diagnóstico e prognóstico destes distúrbios neurológicos e neuropsiquiátricos, que são na maioria das vezes difíceis de diferenciar, permitindo aos médicos adquirirem um conhecimento em relação à previsão realizada e poder explicar a mesma aos seus pacientes

    Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis

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    Multiple sclerosis (MS) is a chronic and severe disease of the central nervous system characterized by complex pathology including inflammatory demyelination and neurodegeneration. MS impacts >2.8 million people worldwide, with most starting with a relapsing-remitting form (RRMS) in young adulthood, and many of them worsening to a secondary-progressive course (SPMS) despite treatment. So, there is a clear need for improved disease characterization. MRI is an ideal tool for non-invasive assessment of MS pathology, but there is still no established measure of disease activity and functional consequences. This project aims to overcome the challenge by developing novel imaging measures based on brain diffusion MRI and phase congruency texture analysis of conventional MRI. Through advanced modeling and analysis of clinically feasible brain MRI, this thesis investigates whether and how the derived measures differentiate MS pathology types and disease severity and predict functional outcomes in MS. The overall process has led to important technical innovations in several aspects. These include: innovative modeling of simple diffusion acquisitions to generate high angular resolution diffusion imaging (HARDI) measures; new optimization and harmonization techniques for diffusion MRI; innovative neural network models to create new diffusion data for comprehensive HARDI modeling; and novel methods and a graphic user interface for optimizing phase congruency analyses. Assisted by different machine learning methods, collective findings show that advanced measures from both diffusion MRI and phase congruency are highly sensitive to subtle differences in MS pathology, which differentiate disease severity between RRMS and SPMS through multi-dimensional analyses including chronic active lesions, and predict functional outcomes especially in physical and neurocognitive domains. These results are clinically translational and the new measures and techniques can help improve the evaluation and management of both MS and similar diseases

    Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

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    The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

    Neural correlates of visual-motor disorders in children with developmental coordination disorder

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    Functional network correlates of language and semiology in epilepsy

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    Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs
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