Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Non-Euclidean, convolutional learning on cortical brain surfaces

In recent years there have been many studies indicating that multiple cortical features, extracted at each surface vertex, are promising in the detection of various neurodevelopmental and neurodegenerative diseases. However, with limited datasets, it is challenging to train stable classifiers with such high-dimensional surface data. This necessitates a feature reduction that is commonly accomplished via regional volumetric morphometry from standard brain atlases. However, current regional summaries are not specific to the given age or pathology that is studied, which runs the risk of losing relevant information that can be critical in the classification process. To solve this issue, this paper proposes a novel data-driven approach by extending convolutional neural networks (CNN) for use on non-Euclidean manifolds such as cortical surfaces. The proposed network learns the most powerful features and brain regions from the extracted large dimensional feature space; thus creating a new feature space in which the dimensionality is reduced and feature distributions are better separated. We demonstrate the usability of the proposed surface-CNN framework in an example study classifying Alzheimers disease patients versus normal controls. The high performance in the cross-validation diagnostic results shows the potential of our proposed prediction system

The Role of the Lateral Geniculate Nucleus in Developmental Dyslexia: Evidence From Multi-Modal Magnetic Resonance Imaging

The ability to read proficiently is key to social participation and an important premise for individual well-being and vocational success. Individuals with developmental dyslexia, a highly prevalent neurodevelopmental disorder affecting hundreds of millions of children and adults worldwide, face severe and persistent difficulties in attaining adequate reading levels. Despite years of extensive research efforts to elucidate the neurobiological origin of this disorder, its exact etiology remains unclear to date. In this context, most neuroimaging research on dyslexia in humans has focused on the cerebral cortex and has identified alterations in a distributed left-lateralized cortical language network. However, pioneering post-mortem human studies and animal models suggest that dyslexia might also be associated with alterations in subcortical sensory thalami and early sensory pathways. The largely cortico-centric view of dyslexia is due in part to considerable technical challenges in assessing the human sensory thalami non-invasively using conventional magnetic resonance imaging (MRI). As a result, the role that sensory thalami may play in dyslexia has been largely unaddressed. In this dissertation, I leveraged recent advances in high-field MRI to investigate the role of the human lateral geniculate nucleus (LGN) of the visual thalamus in adults with dyslexia in-vivo. In three multi-modal high-field MRI studies, I show that (i) dyslexia is associated with structural alterations in the direct V1-bypassing white matter pathway connecting the LGN with cortical motion-sensitive area V5/MT in the left hemisphere; (ii) the connectivity strength of which predicts a core symptom of the disorder, i.e., rapid naming ability. I further demonstrate that (iii) the two major functional subdivisions of the LGN can be distinguished non-invasively based on differences in tissue microstructure; and that (iv) adults with dyslexia show functional response alterations specifically in the magnocellular subdivision of the LGN. I also demonstrate that this subdivision deficit (v) is more pronounced in male than female dyslexics; and (vi) predicts rapid naming ability in male dyslexics only. The results of this doctoral thesis are the first to confirm previous post-mortem evidence of LGN alterations in dyslexia in-vivo and point to their relevance to key symptoms of the disorder. In synergy, our research findings offer new perspectives on explanatory models of dyslexia and bear potential implications also for prospective treatment strategies.:Contribution Statement i Acknowledgments iii Abstract v Table of Contents vii 1 General Introduction 1 1.1 Developmental Dyslexia 1 1.1.1 Diagnostic Criteria 1 1.1.2 Prevalence and Etiology 2 1.1.3 Cognitive and Behavioral Symptoms 3 1.1.4 Explanatory Models in Cognitive Neuroscience 4 1.2 Lateral Geniculate Nucleus 7 1.2.1 Anatomy and Function 7 1.2.2 Technical Challenges in Conventional MRI 8 1.2.3 High-Field MRI 9 1.3 Research Aim and Chapter Outline 10 2 Altered Structural Connectivity of the Left Visual Thalamus in Developmental Dyslexia 13 2.1 Summary 14 2.2 Results and Discussion 15 2.3 Conclusions 22 2.4 Materials and Methods 23 2.4.1 Subject Details 23 2.4.2 High-Resolution MRI Acquisition and Preprocessing 23 2.4.3 Lateral Geniculate Nucleus Definition 24 2.4.4 Cortical Region of Interest Definition 26 2.4.5 Probabilistic Tractography 27 2.4.6 Quantification and Statistical Analysis 29 2.5 Supplementary Information 30 3 Mapping the Human Lateral Geniculate Nucleus and its Cytoarchitectonic Subdivisions Using Quantitative MRI 33 3.1 Abstract 34 3.2 Introduction 35 3.3 Materials and Methods 37 3.3.1 In-Vivo MRI 37 3.3.2 Post-Mortem MRI and Histology 41 3.4 Results 44 3.4.1 Lateral Geniculate Nucleus Subdivisions in In-Vivo MRI 44 3.4.2 Lateral Geniculate Nucleus Subdivisions in Post-Mortem MRI 46 3.5 Discussion 50 3.6 Supplementary Information 54 3.6.1 In-Vivo MRI 54 3.6.2 Post-Mortem MRI and Histology 58 3.6.3 Data and Code Availability 60 4 Dysfunction of the Visual Sensory Thalamus in Developmental Dyslexia 61 4.1 Abstract 62 4.2 Introduction 63 4.3 Materials and Methods 66 4.3.1 Subject Details 66 4.3.2 High-Resolution MRI Experiments 66 4.3.3 High-Resolution MRI Acquisition and Preprocessing 67 4.3.4 Lateral Geniculate Nucleus Definition 68 4.3.5 Quantification and Statistical Analysis 69 4.4 Results 70 4.5 Discussion 75 4.6 Supplementary Information 77 4.6.1 Supporting Methods 77 4.6.2 Supporting Results 81 4.6.3 Data and Code Availability 82 5 General Conclusion 83 5.1 Summary of Research Findings 83 5.2 Implications for Dyslexia Models 84 5.2.1 Phonological Deficit Hypothesis 84 5.2.2 Magnocellular Theory 84 5.2.3 Model According to Ramus 85 5.2.4 Need for Revised Model 86 5.3 Implications for Remediation 87 5.4 Research Prospects 88 5.5 Brief Concluding Remarks 90 6 Bibliography 91 7 List of Tables 113 8 List of Figures 115 9 Selbstständigkeitserklärung 11

Development and application of a human cortical brain atlas on MRI considering phylogeny = Développement et emploi d’un atlas du cortex cérébral humain réalisé sur IRM et tenant compte de la phylogénie

Le cortex cérébral est une structure en couches complexe qui remplit différents types de fonctions. Au cours de l’histoire des neurosciences, plusieurs atlas corticaux ont été développés pour classifier différentes régions du cortex en tant que zones aux caractéristiques structurelles ou fonctionnelles communes, afin d'étudier et de quantifier les changements aux états sain et pathologique. Cependant, il n'existe pas d'atlas suivant une approche phylogénétique, c'est-à-dire, basée sur les critères d'évolution communs. Ce mémoire présente les étapes de création d'un nouvel atlas dans un modèle d’imagerie par résonance magnétique (IRM) en espace standard (pseudo-Talairach) : le PAN-Atlas, basé sur l'origine phylogénétique commune de chaque zone corticale, et son application sur des scans d’IRM de dix individus pour évaluer sa performance. D’abord, nous avons regroupé les différentes régions corticales en cinq régions d'intérêt (RdI) d'origine phylogénétique connue (archicortex, paléocortex, périarchicortex, proïsocortex, isocortex ou néocortex) sur la base de protocoles de segmentation validés histologiquement par d'autres groupes de chercheurs. Puis, nous avons segmenté ces régions manuellement sur le modèle d’IRM cérébrale moyen MNI-ICBM 2009c, en formant des masques. Par la suite, on a utilisé un pipeline multi-étapes de traitement des images pour réaliser le recalage des masques de notre atlas aux scans pondérés T1 de dix participants sains, en obtenant ainsi des masques automatiques pour chaque RdI. Les masques automatiques ont été évalués après une correction manuelle par le biais de l’indice Dice-kappa, qui quantifie la colocalisation des voxels de chaque masque automatique vs. le masque corrigé manuellement. L’indice a montré une très bonne à excellente performance de notre atlas. Cela a permis l’évaluation et comparaison des volumes corticales de chaque région et la quantification des valeurs de transfert de magnétisation (ITM), qui sont sensibles à la quantité de myéline présente dans le tissu. Ce travail montre que la division régionale du cortex en IRM avec une approche phylogénétique est réalisable à l'aide de notre PAN-Atlas en espace standard et que les masques peuvent être utilisés pour différents types de quantifications, comme les volumes corticaux, ou l’estimation des valeurs de ITM. Notre atlas pourrait éventuellement servir à évaluer les différences entre personnes saines et celles atteintes par des maladies neurodégénératives ou d’autres maladies neurologiques.The cerebral cortex is a complex layered structure that performs different types of functions. Throughout the history of neuroscience, several cortical atlases have been developed to classify/divide different regions of the cortex into areas with common structural or functional characteristics, to then study and quantify changes in healthy and pathological states. However, to date, there is no atlas following a phylogenetic approach, i.e. based on the common evolution criteria. This thesis presents the steps of creation of a new atlas corresponding to a standard MRI template: the PAN-Atlas, based on the common phylogenetic origin of each cortical zone, and its application on MRI scans of ten healthy participants to assess its performance. First, we grouped the different cortical regions into five regions of interest (ROI) of known phylogenetic origin (archicortex, paleocortex, periarchicortex, proisocortex, isocortex or neocortex) based on MRI protocols previously validated through histology by other groups of researchers. Then, we manually segmented these ROIs on the MNI-ICBM 2009c average brain MRI template, creating corresponding masks. We then used a multi-step image processing pipeline to register the atlas’ masks to T1 weighted images of ten healthy participants, generating automatic masks for each scan. The accuracy of these automatic atlas’ masks was assessed after manual correction using Dice-kappa similarity index, to quantify the colocalization of the automatic vs. the manually corrected masks. The Dice-kappa values showed a very good to excellent performance of the automatic atlas’ masks. This allowed the evaluation and comparison of cortical volumes of each ROI, as well as the quantification of magnetization transfer ratio (MTR) values, which are sensitive to myelin content. This work shows that the division of the cortex on MRI following a phylogenetic approach is feasible using our PAN Atlas, and that the masks of the atlas can be used to perform different types of quantifications, such as the ones presented here (cortical volume and MTR per ROI). Our atlas could similarly be used to assess differences between the cortex of healthy individuals and people affected by neurodegenerative diseases and other neurological disorders

A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Development of a tool for automatic segmentation of the cerebellum in MR images of children

The human cerebellar cortex is a highly foliated structure that supports both motor and complex cognitive functions in humans. Magnetic Resonance Imaging (MRI) is commonly used to explore structural alterations in patients with psychiatric and neurological diseases. The ability to detect regional structural differences in cerebellar lobules may provide valuable insights into disease biology, progression and response to treatment, but has been hampered by the lack of appropriate tools for performing automated structural cerebellar segmentation and morphometry. In this thesis, time intensive manual tracings by an expert neuroanatomist of 16 cerebellar regions on high-resolution T1-weighted MR images of 18 children aged 9-13 years were used to generate the Cape Town Pediatric Cerebellar Atlas (CAPCA18) in the age-appropriate National Institute of Health Pediatric Database (NIHPD) asymmetric template space. An automated pipeline was developed to process the MR images and generate lobule-wise segmentations, as well as a measure of the uncertainty of the label assignments. Validation in an independent group of children with ages similar to those of the children used in the construction of the atlas, yielded spatial overlaps with manual segmentations greater than 70% in all lobules, except lobules VIIb and X. Average spatial overlap of the whole cerebellar cortex was 86%, compared to 78% using the alternative Spatially Unbiased Infra-tentorial Template (SUIT), which was developed using adult images

Learning from Complex Neuroimaging Datasets

Advancements in Magnetic Resonance Imaging (MRI) allowed for the early diagnosis of neurodevelopmental disorders and neurodegenerative diseases. Neuroanatomical abnormalities in the cerebral cortex are often investigated by examining group-level differences of brain morphometric measures extracted from highly-sampled cortical surfaces. However, group-level differences do not allow for individual-level outcome prediction critical for the application to clinical practice. Despite the success of MRI-based deep learning frameworks, critical issues have been identified: (1) extracting accurate and reliable local features from the cortical surface, (2) determining a parsimonious subset of cortical features for correct disease diagnosis, (3) learning directly from a non-Euclidean high-dimensional feature space, (4) improving the robustness of multi-task multi-modal models, and (5) identifying anomalies in imbalanced and heterogeneous settings. This dissertation describes novel methodological contributions to tackle the challenges above. First, I introduce a Laplacian-based method for quantifying local Extra-Axial Cerebrospinal Fluid (EA-CSF) from structural MRI. Next, I describe a deep learning approach for combining local EA-CSF with other morphometric cortical measures for early disease detection. Then, I propose a data-driven approach for extending convolutional learning to non-Euclidean manifolds such as cortical surfaces. I also present a unified framework for robust multi-task learning from imaging and non-imaging information. Finally, I propose a semi-supervised generative approach for the detection of samples from untrained classes in imbalanced and heterogeneous developmental datasets. The proposed methodological contributions are evaluated by applying them to the early detection of Autism Spectrum Disorder (ASD) in the first year of the infant’s life. Also, the aging human brain is examined in the context of studying different stages of Alzheimer’s Disease (AD).Doctor of Philosoph