PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Graph-Based Network Analysis of Resting-State Functional MRI

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future

Artificial Intelligence for the Detection of Focal Cortical Dysplasia: Challenges in Translating Algorithms into Clinical Practice

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment

Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings

L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les performances. Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (iEEG) de longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des algorithmes de prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces données durant la conception d’un algorithme de prédiction de crises était l’absence d’information concernant la zone exacte de début des crises (information non fournie par les gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins de longue durée. Les principales contributions à cet égard incluent une localisation quantitative de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %. La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre (Spectrum weighted adaptive directed transfer function - swADTF) a été introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux. Ces résultats démontrent la possibilité d’une identification précise des sources et récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une meilleure sélection d’électrodes de manière quantitative dans un contexte de développement d’algorithme de prédiction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche, l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des précisions respectives de 78.11 %, 72.64% et 73.26%. Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant l’intégration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While the first line of treatment consists of long-term drug therapy about one-third of patients are said to be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to persisting negative attitudes towards resective surgery, fear of complications and only moderate success rates. An important direction of research is to investigate the possibility of predicting seizures which, if achieved, can lead to novel interventional avenues. The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of ictal events, and the short duration of interictal periods have been important obstacles for an adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable NeuroVista ambulatory monitoring device have been made available on line for the benefit of researchers. Still, an important limitation of these recordings for seizure-prediction studies was that the seizure onset zone was not disclosed/available. The first objective of this thesis was to develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG recordings. Main contributions include a quantitative, directed transfer function (DTF)-based, localization of the seizure onset zone (electrode selection), a new fitness function for the proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed performance improvement compared to previous studies, achieving an average sensitivity of 84.82% and a time in warning of 10 %. The DTF has been previously validated for quantifying causal relations when quasistationarity requirements are met. Although such requirements can be fulfilled in the case of canine recordings due to the relatively low number of channels (objective 1), the identification of stationary segments would be more challenging in the case of high density iEEG recordings. To cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function (swADTF) was recently introduced as a time-varying version of the DTF. The second objective of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for patients with good post-surgical outcomes, whereas different or additional seizure foci were identified in patients with poor post-surgical outcomes. Results confirmed the possibility of accurate identification of seizure origin and propagation by means of swADTF paving the way for its use in seizure prediction algorithms by allowing a more tailored electrode selection. Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new precursor of seizure activity by combining higher order spectral analysis and artificial neural networks (objective 3). Results showed statistically significant differences (p<0.05) between preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and 73.26%, respectively. Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings; the transition into the ictal state is not random and consists of a “build-up”, leading to seizures. However, additional efforts in terms of electrode selection, feature extraction, hardware and deep learning implementation, are required before the translation of current approaches into commercial devices

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

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

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

Development of Advanced, Clinically Feasible Neuroimaging Methodology with Diffusional Kurtosis Imaging

Diffusion MRI (dMRI) is a powerful, non-invasive tool for probing the structural organization of the human brain. Quantitative dMRI analyses provide unique capabilities for the characterization of tissue microstructure as well as imaging contrast that is not available to other modalities. White matter tractography relies on dMRI and is currently the only non-invasive technique for mapping structural connections in the human brain. In this chapter, we will describe diffusional kurtosis imaging, an effective and versatile dMRI technique, and discuss a clinical problem in temporal lobe epilepsy (TLE) which is insurmountable with current diagnostic approaches. Subsequent chapters will further develop the capabilities of DKI and demonstrate how it may be particularly well suited to overcome current barriers to care in the clinical management of TLE